diff --git a/notebooks/evaluation/prompt_experiments.ipynb b/notebooks/evaluation/prompt_experiments.ipynb new file mode 100644 index 0000000..9834119 --- /dev/null +++ b/notebooks/evaluation/prompt_experiments.ipynb @@ -0,0 +1,11695 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "45dbe487-8fe8-4028-bb31-bb96c23290fd", + "metadata": {}, + "source": [ + "# Prompt Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "42912850-80a8-4b7d-b1f9-12b62f15a648", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/app-root/src/prompt-pr/api-docs-generation/notebooks/evaluation/../../app/utils.py:2: DeprecationWarning: Deprecated import of TextGenerationParameters from module genai.text.generation. Please use `from genai.schema import TextGenerationParameters`.\n", + " from genai.text.generation import TextGenerationParameters\n", + "/opt/app-root/src/prompt-pr/api-docs-generation/notebooks/evaluation/../../app/utils.py:3: DeprecationWarning: Deprecated import of TextTokenizationParameters from module genai.text.tokenization. Please use `from genai.schema import TextTokenizationParameters`.\n", + " from genai.text.tokenization import (\n", + "/opt/app-root/src/prompt-pr/api-docs-generation/notebooks/evaluation/../../app/utils.py:3: DeprecationWarning: Deprecated import of TextTokenizationReturnOptions from module genai.text.tokenization. Please use `from genai.schema import TextTokenizationReturnOptions`.\n", + " from genai.text.tokenization import (\n", + "/opt/app-root/src/prompt-pr/api-docs-generation/notebooks/evaluation/../../app/utils.py:3: DeprecationWarning: Deprecated import of TextTokenizationCreateResults from module genai.text.tokenization. Please use `from genai.schema import TextTokenizationCreateResults`.\n", + " from genai.text.tokenization import (\n" + ] + } + ], + "source": [ + "import os\n", + "import json\n", + "import re\n", + "import pandas as pd\n", + "import sys\n", + "sys.path.append('../../app')\n", + "from utils import eval_using_model\n", + "from dotenv import load_dotenv\n", + "from ipynb.fs.defs.helper_functions import get_response, extract_scores, append_row_to_dataframe, langchain_scores" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "199b69f5-6ce5-416f-b3a4-4a75f66d9ae8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# make sure you have a .env file in the root folder with genaikey and genaiapi\n", + "load_dotenv()\n", + "api_key = os.getenv(\"GENAI_KEY\", None)\n", + "api_endpoint = os.getenv(\"GENAI_API\", None)\n", + "openai_key = os.getenv(\"OPENAI_API_KEY\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "46a00c72-5f94-4e56-acb0-c856645b30b6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/app-root/lib64/python3.9/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.openai.ChatOpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.\n", + " warn_deprecated(\n" + ] + } + ], + "source": [ + "llm = ChatOpenAI(model=\"gpt-4\", temperature=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6ca18b51-979e-4251-8a06-39e0d5abb39b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data = {\n", + " 'prompt': [],\n", + " 'response': [],\n", + " 'langchain_helpfulness': [],\n", + " 'langchain_correctness': [],\n", + " 'langchain_logical': [],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b4927244-81f5-40f8-a2de-cdc860c7bff1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def get_response(instruction, model_id, file, functions, classes, documentation, imports, other, functions_code, functions_doc, classes_code, classes_doc):\n", + "\n", + "\n", + " DATASET_PATH = \"../../data/raw/chunked_data.json\"\n", + "\n", + " with open(DATASET_PATH, \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + "\n", + " code = data[file][\"code_chunks\"]\n", + "\n", + " actual_doc = data[file][\"markdown\"]\n", + "\n", + " functions_text = code[\"functions\"]\n", + " classes_text = code[\"classes\"]\n", + " documentation_text = code[\"documentation\"]\n", + " imports_text = code[\"imports\"]\n", + " other_text = code[\"other\"]\n", + " functions_code_text = code[\"functions_code\"]\n", + " functions_doc_text = code[\"functions_docstrings\"]\n", + " classes_code_text = code[\"classes_code\"]\n", + " classes_doc_text = code[\"classes_docstrings\"]\n", + "\n", + "\n", + " prompt = generate_prompt(\n", + " instruction,\n", + " functions=functions,\n", + " functions_text=functions_text,\n", + " classes=classes,\n", + " classes_text=classes_text,\n", + " documentation=documentation,\n", + " documentation_text=documentation_text,\n", + " imports=imports,\n", + " imports_text=imports_text,\n", + " other=other,\n", + " other_text=other_text,\n", + " functions_code=functions_code,\n", + " functions_code_text=functions_code_text,\n", + " functions_doc=functions_doc,\n", + " functions_doc_text=functions_doc_text,\n", + " classes_code=classes_code,\n", + " classes_code_text=classes_code_text,\n", + " classes_doc=classes_doc,\n", + " classes_doc_text=classes_doc_text,\n", + " )\n", + "\n", + " if model_id == \"OpenAI/gpt3.5\":\n", + " result = generate_text_using_OpenAI(prompt, openai_key)\n", + "\n", + " else:\n", + " result = generate_text(model_id, prompt, decoding_method=\"sample\", max_new_tokens=1024, temperature=0.7, top_k=50, top_p=0.50, genai_key=api_key)\n", + " \n", + " return prompt, result, actual_doc" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "61274c33-8b65-4210-8f04-fbab650c72e0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df = pd.DataFrame(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6f5a8b11-7028-43da-b313-71bcf5eef2d7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def append_row_to_dataframe(df, prompt, generated_patch):\n", + "\n", + " evaluator = load_evaluator(\"criteria\", llm=llm, criteria=\"helpfulness\")\n", + " eval_result = evaluator.evaluate_strings(prediction=generated_patch, input=prompt)\n", + " print(eval_result)\n", + " langchain_helpfulness = eval_result['score']\n", + " \n", + " evaluator = load_evaluator(\"labeled_criteria\", llm=llm, criteria=\"correctness\")\n", + " eval_result = evaluator.evaluate_strings(prediction=generated_patch, input=prompt, reference=actual_doc)\n", + " print(eval_result)\n", + " langchain_correctness = eval_result['score']\n", + "\n", + " custom_criteria = {\n", + " \"logical\": \"Is the output complete? Does it capture all required fields\"\n", + " }\n", + " eval_chain = load_evaluator(\n", + " EvaluatorType.CRITERIA,\n", + " criteria=custom_criteria,\n", + " llm=llm\n", + " )\n", + " eval_result = eval_chain.evaluate_strings(prediction=generated_patch, input=prompt)\n", + " print(eval_result)\n", + " langchain_logical = eval_result['score']\n", + "\n", + " new_row = {\n", + " 'prompt': prompt,\n", + " 'response': generated_patch,\n", + " 'langchain_helpfulness' : langchain_helpfulness,\n", + " 'langchain_correctness' : langchain_correctness,\n", + " 'langchain_logical' : langchain_logical\n", + " }\n", + "\n", + " df = df.append(new_row, ignore_index=True)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "id": "17240da6-0592-44b2-ba3a-2c2997c73bd7", + "metadata": {}, + "source": [ + "### Prompt 1 " + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "840f8002-8bc9-487d-828b-7aacf9bbc64e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction = \"\"\"\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "3. Error Handling: Describe possible error responses and their meanings.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "bb5171e2-202d-4d2d-8d83-b9422eede62d", + "metadata": {}, + "source": [ + "#### Exp 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "39c454c0-f1d6-43cc-8af0-f37ee1f90325", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class Name:** LogInclusionProof\n", + "\n", + "**1. Introduction:**\n", + "The LogInclusionProof class represents an inclusion proof for a log entry in a Merkle Tree. It provides methods to validate the proof and retrieve information about the proof.\n", + "\n", + "**2. Properties:**\n", + "\n", + "- `checkpoint` (StrictStr): The checkpoint associated with the inclusion proof.\n", + "- `hashes` (List[StrictStr]): The list of hashes in the inclusion proof path.\n", + "- `log_index` (StrictInt): The index of the log entry in the Merkle Tree.\n", + "- `root_hash` (StrictStr): The root hash of the Merkle Tree.\n", + "- `tree_size` (StrictInt): The current size of the Merkle Tree.\n", + "\n", + "**3. Methods:**\n", + "\n", + "3.1. `__init__(self, checkpoint: StrictStr, hashes: List[StrictStr], log_index: StrictInt, root_hash: StrictStr, tree_size: StrictInt)`\n", + "- Description: Initializes a new instance of the LogInclusionProof class.\n", + "- Parameters:\n", + " - `checkpoint` (StrictStr): The checkpoint associated with the inclusion proof.\n", + " - `hashes` (List[StrictStr]): The list of hashes in the inclusion proof path.\n", + " - `log_index` (StrictInt): The index of the log entry in the Merkle Tree.\n", + " - `root_hash` (StrictStr): The root hash of the Merkle Tree.\n", + " - `tree_size` (StrictInt): The current size of the Merkle Tree.\n", + "\n", + "3.2. `validate(self, merkle_tree: MerkleTree) -> bool`\n", + "- Description: Validates the inclusion proof against a given Merkle Tree.\n", + "- Parameters:\n", + " - `merkle_tree` (MerkleTree): The Merkle Tree to validate the inclusion proof against.\n", + "- Return Value: \n", + " - (bool): True if the inclusion proof is valid, False otherwise.\n", + "\n", + "3.3. `get_proof_path(self) -> List[StrictStr]`\n", + "- Description: Retrieves the inclusion proof path.\n", + "- Return Value: \n", + " - (List[StrictStr]): The list of hashes in the inclusion proof path.\n", + "\n", + "3.4. `get_proof_root_hash(self) -> StrictStr`\n", + "- Description: Retrieves the root hash of the inclusion proof.\n", + "- Return Value:\n", + " - (StrictStr): The root hash of the inclusion proof.\n", + "\n", + "**4. Error Handling:**\n", + "\n", + "The LogInclusionProof class may raise the following exceptions:\n", + "\n", + "- `ValueError` - When the inclusion proof has invalid log index or tree size.\n", + "- `KeyError` - When the information required for validating the log index within the tree size is missing.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'transparency', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=False, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "69736205-179d-4681-9253-1d8e800605e1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "3. Error Handling: Describe possible error responses and their meanings.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "\n", + "\n", + " \n", + "Class code:\n", + "\n", + "class LogInclusionProof(BaseModel):\n", + " \n", + "\n", + " model_config = ConfigDict(populate_by_name=True)\n", + "\n", + " checkpoint: StrictStr = Field(..., alias=\"checkpoint\")\n", + " hashes: List[StrictStr] = Field(..., alias=\"hashes\")\n", + " log_index: StrictInt = Field(..., alias=\"logIndex\")\n", + " root_hash: StrictStr = Field(..., alias=\"rootHash\")\n", + " tree_size: StrictInt = Field(..., alias=\"treeSize\")\n", + "\n", + " @field_validator(\"log_index\")\n", + " def _log_index_positive(cls, v: int) -> int:\n", + " if v < 0:\n", + " raise ValueError(f\"Inclusion proof has invalid log index: {v} < 0\")\n", + " return v\n", + "\n", + " @field_validator(\"tree_size\")\n", + " def _tree_size_positive(cls, v: int) -> int:\n", + " if v < 0:\n", + " raise ValueError(f\"Inclusion proof has invalid tree size: {v} < 0\")\n", + " return v\n", + "\n", + " @field_validator(\"tree_size\")\n", + " def _log_index_within_tree_size(\n", + " cls, v: int, info: ValidationInfo, **kwargs: Any\n", + " ) -> int:\n", + " if \"log_index\" in info.data and v <= info.data[\"log_index\"]:\n", + " raise ValueError(\n", + " \"Inclusion proof has log index greater than or equal to tree size: \"\n", + " f\"{v} <= {info.data['log_index']}\"\n", + " )\n", + " return v\n", + "\n", + "Class Documentation:\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9d0a0d85-6e9c-4e4a-8c20-6fe74bcd23e2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed documentation of the Python class \"LogInclusionProof\". It includes an introduction that explains the purpose of the class, a list of properties with their data types and descriptions, and a list of methods with their descriptions, parameters, and return values. This is helpful for anyone who needs to understand what this class does and how to use it.\\n\\nThe submission also includes a section on error handling, which describes the exceptions that the class may raise. This is insightful as it helps users understand the potential errors they might encounter when using this class and how to handle them.\\n\\nThe submission is appropriate as it follows the output structure provided in the task input. It is clear, concise, accurate, and user-centric, avoiding speculative information and prioritizing accuracy and completeness.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria is to assess if the submission is correct, accurate, and factual.\\n\\nLooking at the submission, it provides a detailed documentation of the class `LogInclusionProof`. It includes an introduction, properties, methods, and error handling. \\n\\nThe introduction correctly describes the purpose of the class. The properties section accurately lists all the properties of the class and their data types. \\n\\nHowever, the methods section in the submission includes methods that are not present in the class code provided in the input. The methods `validate`, `get_proof_path`, and `get_proof_root_hash` are not part of the class `LogInclusionProof`. This makes the submission inaccurate and not factual.\\n\\nThe error handling section correctly describes the possible errors that can be raised by the class.\\n\\nComparing the submission with the reference, it is clear that the submission has added extra methods that are not present in the class `LogInclusionProof`. The reference does not mention any methods for the class `LogInclusionProof` other than the inherited ones.\\n\\nTherefore, the submission does not meet the criteria of being correct, accurate, and factual due to the inclusion of non-existent methods in the documentation.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission, it provides a detailed documentation for the class `LogInclusionProof`. \\n\\n1. Introduction: The submission provides a brief introduction about the class and its purpose. \\n\\n2. Properties: The submission lists all the properties of the class along with their data types and descriptions. \\n\\n3. Methods: The submission provides documentation for the `__init__` method, but it also includes documentation for methods `validate`, `get_proof_path`, and `get_proof_root_hash` which are not present in the provided class code. This is speculative information and does not meet the criteria of accuracy.\\n\\n4. Error Handling: The submission describes the possible errors that can be raised by the class. However, it mentions a `KeyError` which is not raised in the provided class code. This is also speculative and inaccurate.\\n\\nBased on the above analysis, the submission does not meet the criteria as it includes speculative information and is not completely accurate. \\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ef084b10-3725-4162-b894-8118b288b015", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
promptresponselangchain_helpfulnesslangchain_correctnesslangchain_logical
0\\nYou are an AI system specialized at generati...**Class Name:** LogInclusionProof\\n\\n**1. Intr...1.00.00.0
\n", + "
" + ], + "text/plain": [ + " prompt \\\n", + "0 \\nYou are an AI system specialized at generati... \n", + "\n", + " response langchain_helpfulness \\\n", + "0 **Class Name:** LogInclusionProof\\n\\n**1. Intr... 1.0 \n", + "\n", + " langchain_correctness langchain_logical \n", + "0 0.0 0.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "c4ff482c-cdaf-4f4e-8526-0cc1e633c408", + "metadata": {}, + "source": [ + "#### Exp 2" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "ea46eb4d-50c9-4278-917b-c72c57ae03f9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Introduction: \n", + "\n", + "The `detect_credential` function is used to detect the user's credential for a given audience in order to authenticate with an identity provider. It returns the detected credential or None if no credential is found.\n", + "\n", + "2. Function: detect_credential()\n", + "\n", + " Description:\n", + " \n", + " The `detect_credential` function detects the user's credential for a given audience. It internally calls the `id.detect_credential` function with a default audience to detect the credential. If a credential is found, it is returned; otherwise, None is returned.\n", + " \n", + " Parameters:\n", + " \n", + " This function does not accept any parameters.\n", + " \n", + " Return Value:\n", + " \n", + " - Returns the detected credential as a string if found.\n", + " - Returns None if no credential is found.\n", + " \n", + "3. Error Handling:\n", + "\n", + " - If an error occurs during the detection of the credential, an IdentityError is raised. This can happen if there is an issue with the identity provider or if the credential cannot be detected for any reason. The error is raised using the `raise_from_id` method of the IdentityError class.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'oidc', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "af8dcc0a-7cae-4836-be9a-bb250962043f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "3. Error Handling: Describe possible error responses and their meanings.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "\n", + "\n", + "Function Code:\n", + "\n", + "def detect_credential() -> Optional[str]:\n", + " \n", + " try:\n", + " return cast(Optional[str], id.detect_credential(_DEFAULT_AUDIENCE))\n", + " except id.IdentityError as exc:\n", + " IdentityError.raise_from_id(exc)\n", + "\n", + "Function Documentation:\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "eed99df1-cc91-4157-8616-3d6c942ad1c4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission:\\n\\n1. Introduction: The submission provides a clear and concise introduction to the `detect_credential` function. It explains what the function does and its intended use, which is helpful for users trying to understand the function.\\n\\n2. Function: The submission provides a detailed description of the `detect_credential` function. It explains what the function does, the parameters it takes (or in this case, doesn\\'t take), and the return value. This is insightful and helpful for users trying to understand how to use the function.\\n\\n3. Error Handling: The submission describes the possible error that can occur during the execution of the function and how it is handled. This is helpful for users trying to understand what might go wrong when using the function and how to handle such situations.\\n\\nOverall, the submission is helpful, insightful, and appropriate. It provides a clear and detailed explanation of the `detect_credential` function, its usage, and error handling. Therefore, the submission meets the criterion. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criteria is to assess the correctness, accuracy, and factualness of the submission.\\n\\nLooking at the submission, it provides a detailed explanation of the `detect_credential` function. It correctly identifies that the function does not accept any parameters and returns either a string (the detected credential) or None if no credential is found. This matches the function signature in the provided Python code.\\n\\nThe submission also correctly identifies that an IdentityError is raised if an error occurs during the detection of the credential. This is accurate as per the provided Python code where an IdentityError is raised in the except block.\\n\\nThe submission also correctly describes the purpose of the `detect_credential` function, which is to detect the user's credential for a given audience. This is in line with the reference documentation which states that the function calls `id.detect_credential`, but wraps exceptions with our own exception type.\\n\\nTherefore, the submission is correct, accurate, and factual as per the provided Python code and the reference documentation.\\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criterion for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides an introduction that describes the purpose of the API and its intended use. It explains that the `detect_credential` function is used to detect the user's credential for a given audience in order to authenticate with an identity provider.\\n\\n2. Functions: The submission documents the `detect_credential` function, including:\\n - Description: The submission provides a clear explanation of what the function does. It explains that the function detects the user's credential for a given audience by calling the `id.detect_credential` function with a default audience.\\n - Parameters: The submission correctly states that the function does not accept any parameters.\\n - Return Values: The submission specifies that the function returns the detected credential as a string if found, and None if no credential is found.\\n\\n3. Error Handling: The submission describes the possible error response and its meaning. It explains that an IdentityError is raised if an error occurs during the detection of the credential, which can happen if there is an issue with the identity provider or if the credential cannot be detected.\\n\\nBased on this analysis, the submission appears to be complete and captures all required fields.\\n\\nY\", 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "225bcc67-6894-491b-b7c8-78e6ddd83dd1", + "metadata": {}, + "source": [ + "### Prompt 2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2d3edbe1-6fe2-4683-a792-1d2e31755094", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction = \"\"\"\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and describe what it does.\n", + " - Class Attributes - List and describe each attribute, including data types and any constraints.\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "4. Error Handling: Describe each possible error response and the meaning of each error response.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "2e01f14f-81fa-4c8d-a25a-b81789bcaa36", + "metadata": {}, + "source": [ + "#### Exp 1" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8a2ea3b6-ed84-4992-be0f-c42cbc8c9f70", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class: LogInclusionProof**\n", + "\n", + "The LogInclusionProof class represents an inclusion proof in a hash tree data structure. It is used to prove the inclusion of a specific data item in the hash tree.\n", + "\n", + "**Class Attributes:**\n", + "\n", + "1. `model_config` - A dictionary containing the model configuration. This attribute uses the ConfigDict class.\n", + "2. `checkpoint` - The checkpoint of the hash tree.\n", + " - Data Type: StrictStr\n", + " - Constraints: This attribute is required.\n", + "3. `hashes` - A list of hashes that form the inclusion proof.\n", + " - Data Type: List[StrictStr]\n", + " - Constraints: This attribute is required.\n", + "4. `log_index` - The index of the data item in the hash tree.\n", + " - Data Type: StrictInt\n", + " - Constraints: This attribute is required and must be a positive integer.\n", + "5. `root_hash` - The root hash of the hash tree.\n", + " - Data Type: StrictStr\n", + " - Constraints: This attribute is required.\n", + "6. `tree_size` - The size of the hash tree.\n", + " - Data Type: StrictInt\n", + " - Constraints: This attribute is required and must be a positive integer.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `_log_index_positive(v: int) -> int`\n", + "\n", + " This function validates that the log index attribute is a positive integer.\n", + "\n", + " **Parameters:**\n", + "\n", + " - `v` - The log index to validate.\n", + " - Data Type: int\n", + "\n", + " **Return Value:**\n", + "\n", + " - Data Type: int\n", + " - Possible Values: The same value as the input if it is a positive integer.\n", + "\n", + "2. `_tree_size_positive(v: int) -> int`\n", + "\n", + " This function validates that the tree size attribute is a positive integer.\n", + "\n", + " **Parameters:**\n", + "\n", + " - `v` - The tree size to validate.\n", + " - Data Type: int\n", + "\n", + " **Return Value:**\n", + "\n", + " - Data Type: int\n", + " - Possible Values: The same value as the input if it is a positive integer.\n", + "\n", + "3. `_log_index_within_tree_size(v: int, info: ValidationInfo, **kwargs: Any) -> int`\n", + "\n", + " This function validates that the log index is within the tree size.\n", + "\n", + " **Parameters:**\n", + "\n", + " - `v` - The log index to validate.\n", + " - Data Type: int\n", + " - `info` - The validation information.\n", + " - Data Type: ValidationInfo\n", + " - `**kwargs` - Additional keyword arguments.\n", + "\n", + " **Return Value:**\n", + "\n", + " - Data Type: int\n", + " - Possible Values: The same value as the input if the log index is less than the tree size. \n", + "\n", + "Error Handling:\n", + "\n", + "- If the log index is less than 0, a `ValueError` with the message \"Inclusion proof has invalid log index: {v} < 0\" is raised.\n", + "- If the tree size is less than 0, a `ValueError` with the message \"Inclusion proof has invalid tree size: {v} < 0\" is raised.\n", + "- If the log index is greater than or equal to the tree size, a `ValueError` with the message \"Inclusion proof has log index greater than or equal to tree size: {v} <= {info.data['log_index']}\" is raised.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'transparency', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=False, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d8c97a07-75b1-4937-9fe2-bdd4249cc469", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and describe what it does.\n", + " - Class Attributes - List and describe each attribute, including data types and any constraints.\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "4. Error Handling: Describe each possible error response and the meaning of each error response.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n", + " \n", + "Class code:\n", + "\n", + "class LogInclusionProof(BaseModel):\n", + " \n", + "\n", + " model_config = ConfigDict(populate_by_name=True)\n", + "\n", + " checkpoint: StrictStr = Field(..., alias=\"checkpoint\")\n", + " hashes: List[StrictStr] = Field(..., alias=\"hashes\")\n", + " log_index: StrictInt = Field(..., alias=\"logIndex\")\n", + " root_hash: StrictStr = Field(..., alias=\"rootHash\")\n", + " tree_size: StrictInt = Field(..., alias=\"treeSize\")\n", + "\n", + " @field_validator(\"log_index\")\n", + " def _log_index_positive(cls, v: int) -> int:\n", + " if v < 0:\n", + " raise ValueError(f\"Inclusion proof has invalid log index: {v} < 0\")\n", + " return v\n", + "\n", + " @field_validator(\"tree_size\")\n", + " def _tree_size_positive(cls, v: int) -> int:\n", + " if v < 0:\n", + " raise ValueError(f\"Inclusion proof has invalid tree size: {v} < 0\")\n", + " return v\n", + "\n", + " @field_validator(\"tree_size\")\n", + " def _log_index_within_tree_size(\n", + " cls, v: int, info: ValidationInfo, **kwargs: Any\n", + " ) -> int:\n", + " if \"log_index\" in info.data and v <= info.data[\"log_index\"]:\n", + " raise ValueError(\n", + " \"Inclusion proof has log index greater than or equal to tree size: \"\n", + " f\"{v} <= {info.data['log_index']}\"\n", + " )\n", + " return v\n", + "\n", + "Class Documentation:\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c3afd8f1-cbac-4afd-8ab0-92ffc6be6c84", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed documentation for the given Python class code. It follows the structure provided in the input, including an introduction, class attributes, functions, and error handling.\\n\\nThe introduction gives a brief overview of the class and its purpose. The class attributes section lists all the attributes of the class, their data types, and constraints. The functions section provides a detailed explanation of each function, their parameters, and return values. The error handling section describes the possible errors that can occur and their meanings.\\n\\nThe submission is insightful as it provides a deep understanding of the class code. It explains the purpose of each attribute and function, and how they interact with each other. It also provides a clear explanation of the possible errors, which can be very helpful for users.\\n\\nThe submission is appropriate as it follows the instructions given in the input. It does not hallucinate variable names, function names, class names and the intended API usage. It only generates documentation for the code that is actually present.\\n\\nBased on the above reasoning, the submission meets the criterion of being helpful, insightful, and appropriate. Therefore, the answer is \"Y\". \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission is being evaluated for correctness, accuracy, and factualness. \\n\\n1. The submission correctly identifies the class name as \"LogInclusionProof\" and accurately describes its purpose as representing an inclusion proof in a hash tree data structure.\\n\\n2. The submission correctly lists and describes all the class attributes, including their data types and constraints. It correctly identifies `model_config`, `checkpoint`, `hashes`, `log_index`, `root_hash`, and `tree_size` as the class attributes.\\n\\n3. The submission correctly documents the functions in the class, including their descriptions, parameters, return values, and error handling. It correctly identifies `_log_index_positive`, `_tree_size_positive`, and `_log_index_within_tree_size` as the functions in the class.\\n\\n4. The submission accurately describes the error handling in the class. It correctly identifies the conditions under which `ValueError` is raised and the corresponding error messages.\\n\\n5. The submission does not hallucinate any variable names, function names, class names, or the intended API usage. It only generates documentation for the code that is actually present.\\n\\n6. The submission is factual and does not include any speculative information. It is based on the provided class code and does not make any assumptions or predictions.\\n\\nBased on the above evaluation, the submission meets the criteria of correctness, accuracy, and factualness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nLooking at the submission, the following points are observed:\\n\\n1. The class name and its purpose are correctly documented.\\n2. All class attributes are listed and described, including their data types and constraints.\\n3. All functions within the class are documented, including their descriptions, parameters, return values, data types, and constraints.\\n4. Error handling is also documented, describing each possible error response and its meaning.\\n\\nTherefore, the submission appears to meet all the criteria as it is complete and captures all required fields.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "19407fd7-bc46-4cf5-ac89-b639a5e07ae7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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promptresponselangchain_helpfulnesslangchain_correctnesslangchain_logical
0\\nYou are an AI system specialized at generati...**Class Name:** LogInclusionProof\\n\\n**1. Intr...1.00.00.0
1\\nYou are an AI system specialized at generati...**Class: LogInclusionProof**\\n\\nThe LogInclusi...1.01.01.0
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" + ], + "text/plain": [ + " prompt \\\n", + "0 \\nYou are an AI system specialized at generati... \n", + "1 \\nYou are an AI system specialized at generati... \n", + "\n", + " response langchain_helpfulness \\\n", + "0 **Class Name:** LogInclusionProof\\n\\n**1. Intr... 1.0 \n", + "1 **Class: LogInclusionProof**\\n\\nThe LogInclusi... 1.0 \n", + "\n", + " langchain_correctness langchain_logical \n", + "0 0.0 0.0 \n", + "1 1.0 1.0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "9b527df9-a531-4fbb-b384-52918c67d4f6", + "metadata": {}, + "source": [ + "#### Exp 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "d06dabbb-65ec-43a2-855c-f56ecd0d936f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "The API provides functions for manipulating and analyzing user data. It allows users to perform various operations such as calculating statistics, filtering data, and generating reports.\n", + "\n", + "## Class: UserDataAnalyzer\n", + "This class provides methods for analyzing user data.\n", + "\n", + "### Class Attributes\n", + "- `data` (list): A list of user data dictionaries. Each dictionary represents a user and has the following keys:\n", + " - `'name'` (str): The name of the user.\n", + " - `'age'` (int): The age of the user.\n", + " - `'gender'` (str): The gender of the user.\n", + " - `'income'` (float): The annual income of the user.\n", + "\n", + "### Methods\n", + "\n", + "#### `__init__(self, data: List[Dict[str, Union[str, int, float]]]) -> None`\n", + "Constructor method to initialize the UserDataAnalyzer object with the user data.\n", + "\n", + "##### Parameters\n", + "- `data` (list): A list of dictionaries representing the user data. Each dictionary has the following keys:\n", + " - `'name'` (str): The name of the user.\n", + " - `'age'` (int): The age of the user.\n", + " - `'gender'` (str): The gender of the user.\n", + " - `'income'` (float): The annual income of the user.\n", + "\n", + "#### `get_user_count(self) -> int`\n", + "Returns the total count of users in the data.\n", + "\n", + "##### Return Value\n", + "- `int`: The total count of users.\n", + "\n", + "#### `get_average_age(self) -> float`\n", + "Calculates and returns the average age of all the users in the data.\n", + "\n", + "##### Return Value\n", + "- `float`: The average age of all the users.\n", + "\n", + "#### `get_avg_income_by_gender(self) -> Dict[str, float]`\n", + "Calculates and returns the average income for each gender.\n", + "\n", + "##### Return Value\n", + "- `Dict[str, float]`: A dictionary where the keys are genders and the values are the corresponding average incomes.\n", + "\n", + "#### `get_users_above_age(self, age: int) -> List[str]`\n", + "Returns the names of the users who are above the specified age.\n", + "\n", + "##### Parameters\n", + "- `age` (int): The age threshold.\n", + "\n", + "##### Return Value\n", + "- `List[str]`: A list of names of users who are above the specified age.\n", + "\n", + "#### `get_users_with_income_range(self, min_income: float, max_income: float) -> List[str]`\n", + "Returns the names of the users whose income falls within the specified range.\n", + "\n", + "##### Parameters\n", + "- `min_income` (float): The minimum income threshold.\n", + "- `max_income` (float): The maximum income threshold.\n", + "\n", + "##### Return Value\n", + "- `List[str]`: A list of names of users whose income falls within the specified range.\n", + "\n", + "## Error Handling\n", + "- If the input data is not a list of dictionaries, a `ValueError` is raised with the error message \"Invalid data format. Expected a list of dictionaries.\"\n", + "- If any of the dictionaries in the input data do not have the required keys `'name'`, `'age'`, `'gender'`, and `'income'`, a `ValueError` is raised with the error message \"Invalid data format for user. Missing required keys.\"\n", + "- If any of the users have a non-string name, a `ValueError` is raised with the error message \"Invalid data format for user. Name must be a string.\"\n", + "- If any of the users have a non-integer age, a `ValueError` is raised with the error message \"Invalid data format for user. Age must be an integer.\"\n", + "- If any of the users have a non-string gender, a `ValueError` is raised with the error message \"Invalid data format for user. Gender must be a string.\"\n", + "- If any of the users have a non-float income, a `ValueError` is raised with the error message \"Invalid data format for user. Income must be a float.\"\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'transparency', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f98f4169-c469-48e9-bbeb-402a654ed229", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and describe what it does.\n", + " - Class Attributes - List and describe each attribute, including data types and any constraints.\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "4. Error Handling: Describe each possible error response and the meaning of each error response.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "markdown", + "id": "3f3056b3-6fdd-43f6-b4c4-6473f12717e5", + "metadata": { + "tags": [] + }, + "source": [ + "### Prompt 3" + ] + }, + { + "cell_type": "markdown", + "id": "5cdb496c-642e-40c7-a18b-2c425c6e0d3b", + "metadata": {}, + "source": [ + "#### Exp 1" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "9fc560f2-32d5-4493-a36f-1b6696951e73", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction = \"\"\"\n", + "You are an AI system specialized at generating API documentation for given Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and describe what it does.\n", + " - Class Attributes - List and describe each attribute, including data types and any constraints.\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "4. Error Handling: Describe each possible error response and the meaning of each error response.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate documentation, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "03cb1597-e918-4641-bb45-758eb83f1dd2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'transparency', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "f7ebf9d5-3328-4332-9461-e1dc9b0c6a38", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and describe what it does.\n", + " - Class Attributes - List and describe each attribute, including data types and any constraints.\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "4. Error Handling: Describe each possible error response and the meaning of each error response.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate documentation, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "markdown", + "id": "be18249d-f7dc-4d7d-af54-cb244fbe7b7d", + "metadata": { + "tags": [] + }, + "source": [ + "#### Exp 2" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "b3c42e06-339d-4e7a-91b8-a44cb638e034", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# **API Documentation**\n", + "\n", + "## Introduction\n", + "This API is designed to provide functionality related to user authentication and authorization. It allows users to create accounts, log in, and perform actions that require authentication.\n", + "\n", + "## Class\n", + "\n", + "### UserAuth\n", + "\n", + "The `UserAuth` class provides methods for user authentication.\n", + "\n", + "#### Attributes\n", + "\n", + "- `username` (str): The username of the user.\n", + "- `password` (str): The password of the user.\n", + "\n", + "#### Methods\n", + "\n", + "##### `__init__(self, username: str, password: str) -> None`\n", + "\n", + "Constructs a new `UserAuth` object with the provided username and password.\n", + "\n", + "###### Parameters\n", + "- `username` (str): The username of the user.\n", + "- `password` (str): The password of the user.\n", + "\n", + "##### `login(self) -> bool`\n", + "\n", + "Logs in the user with the provided username and password.\n", + "\n", + "###### Returns\n", + "- `bool`: True if the login was successful, False otherwise.\n", + "\n", + "##### `logout(self) -> None`\n", + "\n", + "Logs out the user.\n", + "\n", + "##### `change_password(self, new_password: str) -> None`\n", + "\n", + "Changes the password of the user to the new password provided.\n", + "\n", + "###### Parameters\n", + "- `new_password` (str): The new password for the user.\n", + "\n", + "## Functions\n", + "\n", + "### create_account(username: str, password: str) -> bool\n", + "\n", + "This function creates a new user account with the provided username and password.\n", + "\n", + "#### Parameters\n", + "- `username` (str): The username for the new account.\n", + "- `password` (str): The password for the new account.\n", + "\n", + "#### Returns\n", + "- `bool`: True if the account was successfully created, False otherwise.\n", + "\n", + "### reset_password(username: str) -> str\n", + "\n", + "This function generates a new random password for the specified username and sends it to the user's email.\n", + "\n", + "#### Parameters\n", + "- `username` (str): The username for which to reset the password.\n", + "\n", + "#### Returns\n", + "- `str`: A message indicating the result of the password reset operation.\n", + "\n", + "## Error Handling\n", + "\n", + "- `InvalidCredentialsError`: Raised if the provided username and password combination is invalid during the login operation. Meaning: The provided username and password combination is incorrect.\n", + "- `AccountCreationError`: Raised if there is an error while creating a new user account. Meaning: The user account could not be created due to an internal error.\n", + "- `PasswordResetError`: Raised if there is an error while resetting the user's password. Meaning: The user's password could not be reset due to an internal error.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'errors', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "0c27e5f5-f4f4-4db5-981c-322e126838c6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and describe what it does.\n", + " - Class Attributes - List and describe each attribute, including data types and any constraints.\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "4. Error Handling: Describe each possible error response and the meaning of each error response.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate documentation, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "faaa43f6-b0bc-4c3c-a3bf-a882cbd26bec", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that it is helpful. It provides a detailed documentation of the API, including an introduction, class details, function details, and error handling. This would be very useful for a developer trying to understand how to use this API.\\n\\nThe submission is also insightful. It provides a clear explanation of what each function does, what parameters it takes, and what it returns. It also explains what each error means, which would be very useful for debugging.\\n\\nFinally, the submission is appropriate. It follows the structure outlined in the input, and it does not include any speculative information or hallucinated variable names, function names, class names, or intended API usage.\\n\\nTherefore, the submission meets the criterion of being helpful, insightful, and appropriate.', 'value': 'Therefore, the submission meets the criterion of being helpful, insightful, and appropriate.', 'score': None}\n", + "{'reasoning': \"The criteria is to assess the correctness, accuracy, and factualness of the submission. \\n\\nThe submission is an API documentation for a hypothetical User Authentication system. It includes an introduction, class documentation, function documentation, and error handling. \\n\\nHowever, the reference provided is for a different API, the 'sigstore' API, which deals with exceptions and errors. The classes and functions documented in the submission do not match those in the reference. \\n\\nThe submission is well-structured and follows the guidelines for creating API documentation, but it does not accurately reflect the reference provided. \\n\\nTherefore, the submission does not meet the criteria of correctness and accuracy. \\n\\nN\", 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission includes an introduction that describes the purpose of the API and its intended use. \\n\\n2. Class: The submission includes a class named `UserAuth` with a description of what it does. It also includes the class attributes `username` and `password` with their data types and descriptions. The class methods `__init__`, `login`, `logout`, and `change_password` are also documented with their descriptions, parameters, and return values.\\n\\n3. Functions: The submission includes two functions `create_account` and `reset_password` with their descriptions, parameters, and return values.\\n\\n4. Error Handling: The submission includes three error responses `InvalidCredentialsError`, `AccountCreationError`, and `PasswordResetError` with their meanings.\\n\\nThe submission seems to have covered all the required fields as per the input instructions. Therefore, the output is complete. \\n\\nNow, I will print the single character corresponding to the correct answer of whether the submission meets all criteria.', 'value': 'Now, I will print the single character corresponding to the correct answer of whether the submission meets all criteria.', 'score': None}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "7ebd6506-c37e-494b-b5b0-6a11ffc1d83f", + "metadata": {}, + "source": [ + "### Prompt 4" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "9049acf0-640f-4d7c-939c-b432bf38b05a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction = \"\"\"\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "6a6dc519-6413-4fc8-986b-06f610dc5d42", + "metadata": { + "tags": [] + }, + "source": [ + "#### Exp 1" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "e474b802-c8e5-487b-9e82-099b2aa57cc1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'transparency', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "markdown", + "id": "673ab859-5f2e-4d43-b4e5-7e2e9ea33cdb", + "metadata": { + "tags": [] + }, + "source": [ + "#### Exp 2" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "06c73b3d-de7b-44be-9871-21560ea71113", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'errors', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e101777b-dc1d-457d-8eaf-caae2a8bc438", + "metadata": {}, + "source": [ + "#### Exp 3" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "7f05a8e5-fe54-4746-9110-259f8c480229", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Introduction:\n", + "This is the API documentation for a Python code.\n", + "\n", + "### No Code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'sign', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "markdown", + "id": "7e2922a9-7692-4d61-8681-085c95f59794", + "metadata": {}, + "source": [ + "#### Exp 4" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4e7a45f7-6c38-4026-97b2-5db22ae3c817", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Introduction:\n", + "This is a function called `detect_credential` that is used to detect credentials.\n", + "\n", + "2. Function: `detect_credential`\n", + "\n", + " Description:\n", + " This function is used to detect credentials. It attempts to detect the credentials by calling the `id.detect_credential()` function with a default audience. If an `id.IdentityError` is raised during the detection process, it is handled by raising another `IdentityError`.\n", + " \n", + " Parameters:\n", + " This function does not take any parameters.\n", + " \n", + " Return Value:\n", + " This function returns an optional string, which represents the detected credentials. If no credentials are detected, None is returned.\n", + " \n", + "3. Error Handling:\n", + " Possible error responses include an `IdentityError` being raised during the detection process. This error is handled by raising another `IdentityError`.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'oidc', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "26a650f0-1e04-4340-bf84-0474c9723d94", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n", + "Function Code:\n", + "\n", + "def detect_credential() -> Optional[str]:\n", + " \n", + " try:\n", + " return cast(Optional[str], id.detect_credential(_DEFAULT_AUDIENCE))\n", + " except id.IdentityError as exc:\n", + " IdentityError.raise_from_id(exc)\n", + "\n", + "Function Documentation:\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "9a1e32cb-e514-450a-a39d-391809e16924", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\n1. Helpfulness: The submission provides a detailed explanation of the function `detect_credential`. It explains what the function does, the parameters it takes, the return value, and how it handles errors. This information is helpful for someone trying to understand the function.\\n\\n2. Insightfulness: The submission provides insights into how the function works. It explains that the function attempts to detect credentials by calling another function and handles any errors that occur during this process. This information provides insights into the inner workings of the function.\\n\\n3. Appropriateness: The submission is appropriate. It follows the structure provided in the prompt and provides all the necessary information. It does not include any speculative information or hallucinate any details.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The submission is being evaluated on the criterion of correctness, which includes accuracy and factualness.\\n\\nLooking at the submission, the introduction correctly identifies the function name as `detect_credential` and its purpose to detect credentials. This is accurate as per the provided function code.\\n\\nThe description of the function in the submission is also accurate. It correctly explains that the function attempts to detect credentials by calling the `id.detect_credential()` function with a default audience. It also correctly mentions that if an `id.IdentityError` is raised during the detection process, it is handled by raising another `IdentityError`.\\n\\nThe submission correctly states that the function does not take any parameters. This is factual as per the provided function code.\\n\\nThe return value of the function is correctly identified in the submission as an optional string, which represents the detected credentials. If no credentials are detected, None is returned. This is accurate as per the function's return type annotation.\\n\\nThe error handling section of the submission correctly identifies that an `IdentityError` could be raised during the detection process and that this error is handled by raising another `IdentityError`. This is factual as per the provided function code.\\n\\nThe reference provided does not contradict any of the information in the submission. The reference provides additional context about the `detect_credential` function, but it does not provide any information that would make the submission incorrect.\\n\\nBased on the above analysis, the submission is correct, accurate, and factual. Therefore, it meets the criterion of correctness. \\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess if the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides an introduction that describes the function `detect_credential`. This meets the requirement.\\n\\n2. Function: The submission provides a description of the function, stating its purpose and how it works. This meets the requirement.\\n\\n3. Parameters: The submission correctly states that the function does not take any parameters. This meets the requirement.\\n\\n4. Return Value: The submission correctly describes the return value of the function. This meets the requirement.\\n\\n5. Error Handling: The submission describes the possible error responses and how they are handled. This meets the requirement.\\n\\nBased on the above analysis, the submission meets all the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "1f0421f1-c2ff-4319-89d5-632a5d51a350", + "metadata": { + "tags": [] + }, + "source": [ + "#### Exp 5" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "0ce85f1f-dfc7-4ad6-8701-390457bc05a7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'oidc', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=False, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "1283b268-2d27-4724-be83-0d691ec2d74a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n", + " \n", + "Class code:\n", + "\n", + "class _OpenIDConfiguration(BaseModel):\n", + " \n", + "\n", + " authorization_endpoint: StrictStr\n", + " token_endpoint: StrictStr\n", + "class ExpiredIdentity(Exception):\n", + " \n", + "class IdentityToken:\n", + " \n", + "\n", + " def __init__(self, raw_token: str) -> None:\n", + " \n", + "\n", + " self._raw_token = raw_token\n", + "\n", + " # NOTE: The lack of verification here is intentional, and is part of\n", + " # Sigstore's verification model: clients like sigstore-python are\n", + " # responsible only for forwarding the OIDC identity to Fulcio for\n", + " # certificate binding and issuance.\n", + " try:\n", + " self._unverified_claims = jwt.decode(\n", + " raw_token,\n", + " options={\n", + " \"verify_signature\": False,\n", + " \"verify_aud\": True,\n", + " \"verify_iat\": True,\n", + " \"verify_exp\": True,\n", + " # These claims are required by OpenID Connect, so\n", + " # we can strongly enforce their presence.\n", + " # See: https://openid.net/specs/openid-connect-basic-1_0.html#IDToken\n", + " \"require\": [\"aud\", \"sub\", \"iat\", \"exp\", \"iss\"],\n", + " },\n", + " audience=DEFAULT_AUDIENCE,\n", + " # NOTE: This leeway shouldn't be strictly necessary, but is\n", + " # included to preempt any (small) skew between the host\n", + " # and the originating IdP.\n", + " leeway=5,\n", + " )\n", + " except Exception as exc:\n", + " raise IdentityError(\n", + " \"Identity token is malformed or missing claims\"\n", + " ) from exc\n", + "\n", + " self._iss: str = self._unverified_claims[\"iss\"]\n", + " self._nbf: int | None = self._unverified_claims.get(\"nbf\")\n", + " self._exp: int = self._unverified_claims[\"exp\"]\n", + "\n", + " # Fail early if this token isn't within its validity period.\n", + " if not self.in_validity_period():\n", + " raise IdentityError(\"Identity token is not within its validity period\")\n", + "\n", + " # When verifying the private key possession proof, Fulcio uses\n", + " # different claims depending on the token's issuer.\n", + " # We currently special-case a handful of these, and fall back\n", + " # on signing the \"sub\" claim otherwise.\n", + " identity_claim = _KNOWN_OIDC_ISSUERS.get(self.issuer)\n", + " if identity_claim is not None:\n", + " if identity_claim not in self._unverified_claims:\n", + " raise IdentityError(\n", + " f\"Identity token is missing the required {identity_claim!r} claim\"\n", + " )\n", + "\n", + " self._identity = str(self._unverified_claims.get(identity_claim))\n", + " else:\n", + " try:\n", + " self._identity = str(self._unverified_claims[\"sub\"])\n", + " except KeyError:\n", + " raise IdentityError(\n", + " \"Identity token is missing the required 'sub' claim\"\n", + " )\n", + "\n", + " # This identity token might have been retrieved directly from\n", + " # an identity provider, or it might be a \"federated\" identity token\n", + " # retrieved from a federated IdP (e.g., Sigstore's own Dex instance).\n", + " # In the latter case, the claims will also include a `federated_claims`\n", + " # set, which in turn should include a `connector_id` that reflects\n", + " # the \"real\" token issuer. We retrieve this, despite technically\n", + " # being an implementation detail, because it has value to client\n", + " # users: a client might want to make sure that its user is identifying\n", + " # with a *particular* IdP, which means that they need to pierce the\n", + " # federation layer to check which IdP is actually being used.\n", + " self._federated_issuer: str | None = None\n", + " federated_claims = self._unverified_claims.get(\"federated_claims\")\n", + " if federated_claims is not None:\n", + " if not isinstance(federated_claims, dict):\n", + " raise IdentityError(\n", + " \"unexpected claim type: federated_claims is not a dict\"\n", + " )\n", + "\n", + " federated_issuer = federated_claims.get(\"connector_id\")\n", + " if federated_issuer is not None:\n", + " if not isinstance(federated_issuer, str):\n", + " raise IdentityError(\n", + " \"unexpected claim type: federated_claims.connector_id is not a string\"\n", + " )\n", + "\n", + " self._federated_issuer = federated_issuer\n", + "\n", + " def in_validity_period(self) -> bool:\n", + " \n", + "\n", + " now = datetime.now(timezone.utc).timestamp()\n", + "\n", + " if self._nbf is not None:\n", + " return self._nbf <= now < self._exp\n", + " else:\n", + " return now < self._exp\n", + "\n", + " @property\n", + " def identity(self) -> str:\n", + " \n", + " return self._identity\n", + "\n", + " @property\n", + " def issuer(self) -> str:\n", + " \n", + " return self._iss\n", + "\n", + " @property\n", + " def expected_certificate_subject(self) -> str:\n", + " \n", + " if self._federated_issuer is not None:\n", + " return self._federated_issuer\n", + "\n", + " return self.issuer\n", + "\n", + " def __str__(self) -> str:\n", + " \n", + " return self._raw_token\n", + "class IssuerError(Exception):\n", + " \n", + "\n", + " pass\n", + "class Issuer:\n", + " \n", + "\n", + " def __init__(self, base_url: str) -> None:\n", + " \n", + " oidc_config_url = urllib.parse.urljoin(\n", + " f\"{base_url}/\", \".well-known/openid-configuration\"\n", + " )\n", + "\n", + " try:\n", + " resp: requests.Response = requests.get(oidc_config_url, timeout=30)\n", + " except (requests.ConnectionError, requests.Timeout) as exc:\n", + " raise NetworkError from exc\n", + "\n", + " try:\n", + " resp.raise_for_status()\n", + " except requests.HTTPError as http_error:\n", + " raise IssuerError from http_error\n", + "\n", + " try:\n", + " # We don't generally expect this to fail (since the provider should\n", + " # return a non-success HTTP code which we catch above), but we\n", + " # check just in case we have a misbehaving OIDC issuer.\n", + " self.oidc_config = _OpenIDConfiguration.model_validate(resp.json())\n", + " except ValueError as exc:\n", + " raise IssuerError(f\"OIDC issuer returned invalid configuration: {exc}\")\n", + "\n", + " @classmethod\n", + " def production(cls) -> Issuer:\n", + " \n", + " return cls(DEFAULT_OAUTH_ISSUER_URL)\n", + "\n", + " @classmethod\n", + " def staging(cls) -> Issuer:\n", + " \n", + " return cls(STAGING_OAUTH_ISSUER_URL)\n", + "\n", + " def identity_token( # nosec: B107\n", + " self,\n", + " client_id: str = \"sigstore\",\n", + " client_secret: str = \"\",\n", + " force_oob: bool = False,\n", + " ) -> IdentityToken:\n", + " \n", + "\n", + " # This function and the components that it relies on are based off of:\n", + " # https://github.com/psteniusubi/python-sample\n", + "\n", + " from sigstore._internal.oidc.oauth import _OAuthFlow\n", + "\n", + " code: str\n", + " with _OAuthFlow(client_id, client_secret, self) as server:\n", + " # Launch web browser\n", + " if not force_oob and webbrowser.open(server.base_uri):\n", + " print(\"Waiting for browser interaction...\", file=sys.stderr)\n", + " else:\n", + " server.enable_oob()\n", + " print(\n", + " f\"Go to the following link in a browser:\\n\\n\\t{server.auth_endpoint}\",\n", + " file=sys.stderr,\n", + " )\n", + "\n", + " if not server.is_oob():\n", + " # Wait until the redirect server populates the response\n", + " while server.auth_response is None:\n", + " time.sleep(0.1)\n", + "\n", + " auth_error = server.auth_response.get(\"error\")\n", + " if auth_error is not None:\n", + " raise IdentityError(\n", + " f\"Error response from auth endpoint: {auth_error[0]}\"\n", + " )\n", + " code = server.auth_response[\"code\"][0]\n", + " else:\n", + " # In the out-of-band case, we wait until the user provides the code\n", + " code = input(\"Enter verification code: \")\n", + "\n", + " # Provide code to token endpoint\n", + " data = {\n", + " \"grant_type\": \"authorization_code\",\n", + " \"redirect_uri\": server.redirect_uri,\n", + " \"code\": code,\n", + " \"code_verifier\": server.oauth_session.code_verifier,\n", + " }\n", + " auth = (\n", + " client_id,\n", + " client_secret,\n", + " )\n", + " logging.debug(f\"PAYLOAD: data={data}\")\n", + " try:\n", + " resp: requests.Response = requests.post(\n", + " self.oidc_config.token_endpoint,\n", + " data=data,\n", + " auth=auth,\n", + " timeout=30,\n", + " )\n", + " except (requests.ConnectionError, requests.Timeout) as exc:\n", + " raise NetworkError from exc\n", + "\n", + " try:\n", + " resp.raise_for_status()\n", + " except requests.HTTPError as http_error:\n", + " raise IdentityError(\n", + " f\"Token request failed with {resp.status_code}\"\n", + " ) from http_error\n", + "\n", + " token_json = resp.json()\n", + " token_error = token_json.get(\"error\")\n", + " if token_error is not None:\n", + " raise IdentityError(f\"Error response from token endpoint: {token_error}\")\n", + "\n", + " return IdentityToken(token_json[\"access_token\"])\n", + "class IdentityError(Error):\n", + " \n", + "\n", + " @classmethod\n", + " def raise_from_id(cls, exc: id.IdentityError) -> NoReturn:\n", + " \n", + " raise cls(str(exc)) from exc\n", + "\n", + " def diagnostics(self) -> str:\n", + " \n", + " if isinstance(self.__cause__, id.GitHubOidcPermissionCredentialError):\n", + " return f\n", + " Insufficient permissions for GitHub Actions workflow.\n", + "\n", + " The most common reason for this is incorrect\n", + " configuration of the top-level `permissions` setting of the\n", + " workflow YAML file. It should be configured like so:\n", + "\n", + " permissions:\n", + " id-token: write\n", + "\n", + " Relevant documentation here:\n", + "\n", + " https://docs.github.com/en/actions/deployment/security-hardening-your-deployments/about-security-hardening-with-openid-connect#adding-permissions-settings\n", + "\n", + " Another possible reason is that the workflow run has been\n", + " triggered by a PR from a forked repository. PRs from forked\n", + " repositories typically cannot be granted write access.\n", + "\n", + " Relevant documentation here:\n", + "\n", + " https://docs.github.com/en/actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token\n", + "\n", + " Additional context:\n", + "\n", + " {self.__cause__}\n", + " \n", + " else:\n", + " return f\n", + " An issue occurred with ambient credential detection.\n", + "\n", + " Additional context:\n", + "\n", + " {self}\n", + " \n", + "\n", + "Class Documentation:\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "35038f60-53e1-4ffc-bb7f-ccc8aba401f0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate. \\n\\nLooking at the data, the input provided a Python code for which the AI was supposed to generate API documentation. The code includes several classes and functions. \\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect as there is clearly a Python code provided in the input. \\n\\nTherefore, the submission is not helpful or appropriate as it does not provide the required API documentation for the provided Python code. \\n\\nSo, the submission does not meet the criterion. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria is to assess if the submission is correct, accurate, and factual. \\n\\nThe task was to generate API documentation for the provided Python code. The code provided was a Python script with several classes and functions. \\n\\nThe submission, however, states \"No code has been provided in the prompt.\" This is incorrect as there is clearly Python code provided in the input. \\n\\nTherefore, the submission is not correct or accurate as it does not reflect the actual content of the input. \\n\\nThe reference provided is an example of how the API documentation should have been generated based on the provided Python code. The submission does not match this reference as it does not provide any documentation for the provided code. \\n\\nBased on this analysis, the submission does not meet the criteria. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria asks if the output is complete and captures all required fields. The task was to generate API documentation for the provided Python code. The code provided includes several classes and functions. The submission, however, states that no code has been provided in the prompt. This is incorrect as there is clearly code provided in the prompt. Therefore, the submission does not meet the criteria as it does not capture all required fields and is not complete.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "bc4cfc52-7595-42ff-9152-a87165642d6d", + "metadata": {}, + "source": [ + "#### Exp 6" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "a121ef21-e0b7-4f08-9e3b-92e0ef7004b5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class Signer**\n", + "\n", + "This class represents a signer that is responsible for signing artifacts. It takes an `identity_token`, `signing_ctx`, and an optional `cache` parameter. The `identity_token` is used to verify the identity of the signer, the `signing_ctx` provides the necessary context and dependencies for signing, and the `cache` parameter determines whether to cache the private key and signing certificate.\n", + "\n", + "Attributes:\n", + "- `_identity_token`: The identity token used for verifying the signer's identity.\n", + "- `_signing_ctx`: The signing context that provides the necessary dependencies for signing.\n", + "- `__cached_private_key`: An optional cached private key.\n", + "- `__cached_signing_certificate`: An optional cached signing certificate.\n", + "\n", + "Methods:\n", + "- `__init__(identity_token: IdentityToken, signing_ctx: SigningContext, cache: bool = True)`: Initializes the Signer instance with the provided `identity_token`, `signing_ctx`, and `cache` parameters. If `cache` is `True`, it generates an ephemeral private key and requests an ephemeral certificate.\n", + "- `_private_key() -> ec.EllipticCurvePrivateKey`: Returns the private key. If it is not already cached, it generates a new ephemeral private key.\n", + "- `_signing_cert(private_key: ec.EllipticCurvePrivateKey) -> FulcioCertificateSigningResponse`: Retrieves the signing certificate. If a cached signing certificate exists, it verifies if it has expired and returns it. Otherwise, it retrieves a signed certificate by building an X.509 Certificate Signing Request and sending it to the signing context's `fulcio` endpoint.\n", + "- `sign(input_: IO[bytes]) -> SigningResult`: Signs the provided input artifact. It verifies the validity period of the identity token, retrieves the signing certificate using the private key, verifies the SCT (Signed Certificate Timestamp), and signs the artifact using the private key. It then creates a transparency log entry, and returns a `SigningResult` instance with the input digest, certificate PEM, base64 signature, and log entry.\n", + "\n", + "**Class SigningContext**\n", + "\n", + "This class represents the signing context that provides the necessary dependencies for signing. It takes the `fulcio` and `rekor` clients as parameters.\n", + "\n", + "Attributes:\n", + "- `_fulcio`: The `fulcio` client that handles certificate-related operations.\n", + "- `_rekor`: The `rekor` client that handles transparency log operations.\n", + "\n", + "Methods:\n", + "- `__init__(fulcio: FulcioClient, rekor: RekorClient)`: Initializes the SigningContext with the provided `fulcio` and `rekor` clients.\n", + "- `production() -> SigningContext`: Returns a production instance of the SigningContext with production clients for `fulcio` and `rekor`.\n", + "- `staging() -> SigningContext`: Returns a staging instance of the SigningContext with staging clients for `fulcio` and `rekor`.\n", + "- `signer(identity_token: IdentityToken, cache: bool = True) -> Iterator[Signer]`: Context manager that yields a Signer instance with the provided `identity_token` and optional `cache` parameter. The Signer instance is created using the current SigningContext.\n", + "\n", + "**Class SigningResult**\n", + "\n", + "This class represents the result of a signing operation.\n", + "\n", + "Attributes:\n", + "- `input_digest`: The input digest of the signed artifact.\n", + "- `cert_pem`: The certificate in PEM format.\n", + "- `b64_signature`: The base64-encoded signature of the artifact.\n", + "- `log_entry`: The transparency log entry.\n", + "\n", + "Methods:\n", + "- `to_bundle() -> Bundle`: Converts the SigningResult to a Bundle object that contains the necessary information for verification and validation.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'sign', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=False, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "4afef178-7d3b-4f04-8802-a5f9018660a8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n", + " \n", + "Class code:\n", + "\n", + "class Signer:\n", + " \n", + "\n", + " def __init__(\n", + " self,\n", + " identity_token: IdentityToken,\n", + " signing_ctx: SigningContext,\n", + " cache: bool = True,\n", + " ) -> None:\n", + " \n", + " self._identity_token = identity_token\n", + " self._signing_ctx: SigningContext = signing_ctx\n", + " self.__cached_private_key: Optional[ec.EllipticCurvePrivateKey] = None\n", + " self.__cached_signing_certificate: Optional[\n", + " FulcioCertificateSigningResponse\n", + " ] = None\n", + " if cache:\n", + " logger.debug(\"Generating ephemeral keys...\")\n", + " self.__cached_private_key = ec.generate_private_key(ec.SECP256R1())\n", + " logger.debug(\"Requesting ephemeral certificate...\")\n", + " self.__cached_signing_certificate = self._signing_cert(self._private_key)\n", + "\n", + " @property\n", + " def _private_key(self) -> ec.EllipticCurvePrivateKey:\n", + " \n", + " if self.__cached_private_key is None:\n", + " logger.debug(\"no cached key; generating ephemeral key\")\n", + " return ec.generate_private_key(ec.SECP256R1())\n", + " return self.__cached_private_key\n", + "\n", + " def _signing_cert(\n", + " self,\n", + " private_key: ec.EllipticCurvePrivateKey,\n", + " ) -> FulcioCertificateSigningResponse:\n", + " \n", + " # If it exists, verify if the current certificate is expired\n", + " if self.__cached_signing_certificate:\n", + " not_valid_after = self.__cached_signing_certificate.cert.not_valid_after\n", + " not_valid_after_tzutc = not_valid_after.replace(tzinfo=timezone.utc)\n", + " if datetime.now(timezone.utc) > not_valid_after_tzutc:\n", + " raise ExpiredCertificate\n", + " return self.__cached_signing_certificate\n", + "\n", + " else:\n", + " logger.debug(\"Retrieving signed certificate...\")\n", + "\n", + " # Build an X.509 Certificiate Signing Request\n", + " builder = (\n", + " x509.CertificateSigningRequestBuilder()\n", + " .subject_name(\n", + " x509.Name(\n", + " [\n", + " x509.NameAttribute(\n", + " NameOID.EMAIL_ADDRESS, self._identity_token._identity\n", + " ),\n", + " ]\n", + " )\n", + " )\n", + " .add_extension(\n", + " x509.BasicConstraints(ca=False, path_length=None),\n", + " critical=True,\n", + " )\n", + " )\n", + " certificate_request = builder.sign(private_key, hashes.SHA256())\n", + "\n", + " certificate_response = self._signing_ctx._fulcio.signing_cert.post(\n", + " certificate_request, self._identity_token\n", + " )\n", + "\n", + " return certificate_response\n", + "\n", + " def sign(\n", + " self,\n", + " input_: IO[bytes],\n", + " ) -> SigningResult:\n", + " \n", + " input_digest = sha256_streaming(input_)\n", + " private_key = self._private_key\n", + "\n", + " if not self._identity_token.in_validity_period():\n", + " raise ExpiredIdentity\n", + "\n", + " try:\n", + " certificate_response = self._signing_cert(private_key)\n", + " except ExpiredCertificate as e:\n", + " raise e\n", + "\n", + " # TODO(alex): Retrieve the public key via TUF\n", + " #\n", + " # Verify the SCT\n", + " sct = certificate_response.sct # noqa\n", + " cert = certificate_response.cert # noqa\n", + " chain = certificate_response.chain\n", + "\n", + " verify_sct(sct, cert, chain, self._signing_ctx._rekor._ct_keyring)\n", + "\n", + " logger.debug(\"Successfully verified SCT...\")\n", + "\n", + " # Sign artifact\n", + " artifact_signature = private_key.sign(\n", + " input_digest, ec.ECDSA(Prehashed(hashes.SHA256()))\n", + " )\n", + " b64_artifact_signature = B64Str(base64.b64encode(artifact_signature).decode())\n", + "\n", + " # Prepare inputs\n", + " b64_cert = base64.b64encode(\n", + " cert.public_bytes(encoding=serialization.Encoding.PEM)\n", + " )\n", + "\n", + " # Create the transparency log entry\n", + " proposed_entry = sigstore_rekor_types.Hashedrekord(\n", + " kind=\"hashedrekord\",\n", + " api_version=\"0.0.1\",\n", + " spec=sigstore_rekor_types.HashedrekordV001Schema(\n", + " signature=sigstore_rekor_types.Signature1(\n", + " content=b64_artifact_signature,\n", + " public_key=sigstore_rekor_types.PublicKey1(\n", + " content=b64_cert.decode()\n", + " ),\n", + " ),\n", + " data=sigstore_rekor_types.Data(\n", + " hash=sigstore_rekor_types.Hash(\n", + " algorithm=sigstore_rekor_types.Algorithm.SHA256,\n", + " value=input_digest.hex(),\n", + " )\n", + " ),\n", + " ),\n", + " )\n", + " entry = self._signing_ctx._rekor.log.entries.post(proposed_entry)\n", + "\n", + " logger.debug(f\"Transparency log entry created with index: {entry.log_index}\")\n", + "\n", + " return SigningResult(\n", + " input_digest=HexStr(input_digest.hex()),\n", + " cert_pem=PEMCert(\n", + " cert.public_bytes(encoding=serialization.Encoding.PEM).decode()\n", + " ),\n", + " b64_signature=B64Str(b64_artifact_signature),\n", + " log_entry=entry,\n", + " )\n", + "class SigningContext:\n", + " \n", + "\n", + " def __init__(\n", + " self,\n", + " *,\n", + " fulcio: FulcioClient,\n", + " rekor: RekorClient,\n", + " ):\n", + " \n", + " self._fulcio = fulcio\n", + " self._rekor = rekor\n", + "\n", + " @classmethod\n", + " def production(cls) -> SigningContext:\n", + " \n", + " updater = TrustUpdater.production()\n", + " rekor = RekorClient.production(updater)\n", + " return cls(\n", + " fulcio=FulcioClient.production(),\n", + " rekor=rekor,\n", + " )\n", + "\n", + " @classmethod\n", + " def staging(cls) -> SigningContext:\n", + " \n", + " updater = TrustUpdater.staging()\n", + " rekor = RekorClient.staging(updater)\n", + " return cls(\n", + " fulcio=FulcioClient.staging(),\n", + " rekor=rekor,\n", + " )\n", + "\n", + " @contextmanager\n", + " def signer(\n", + " self, identity_token: IdentityToken, *, cache: bool = True\n", + " ) -> Iterator[Signer]:\n", + " \n", + " yield Signer(identity_token, self, cache)\n", + "class SigningResult(BaseModel):\n", + " \n", + "\n", + " input_digest: HexStr\n", + " \n", + "\n", + " cert_pem: PEMCert\n", + " \n", + "\n", + " b64_signature: B64Str\n", + " \n", + "\n", + " log_entry: LogEntry\n", + " \n", + "\n", + " def to_bundle(self) -> Bundle:\n", + " \n", + "\n", + " # NOTE: We explicitly only include the leaf certificate in the bundle's \"chain\"\n", + " # here: the specs explicitly forbid the inclusion of the root certificate,\n", + " # and discourage inclusion of any intermediates (since they're in the root of\n", + " # trust already).\n", + " cert = x509.load_pem_x509_certificate(self.cert_pem.encode())\n", + " cert_der = cert.public_bytes(encoding=serialization.Encoding.DER)\n", + " chain = X509CertificateChain(certificates=[X509Certificate(raw_bytes=cert_der)])\n", + "\n", + " inclusion_proof: InclusionProof | None = None\n", + " if self.log_entry.inclusion_proof is not None:\n", + " inclusion_proof = InclusionProof(\n", + " log_index=self.log_entry.inclusion_proof.log_index,\n", + " root_hash=bytes.fromhex(self.log_entry.inclusion_proof.root_hash),\n", + " tree_size=self.log_entry.inclusion_proof.tree_size,\n", + " hashes=[\n", + " bytes.fromhex(h) for h in self.log_entry.inclusion_proof.hashes\n", + " ],\n", + " checkpoint=Checkpoint(\n", + " envelope=self.log_entry.inclusion_proof.checkpoint\n", + " ),\n", + " )\n", + "\n", + " tlog_entry = TransparencyLogEntry(\n", + " log_index=self.log_entry.log_index,\n", + " log_id=LogId(key_id=bytes.fromhex(self.log_entry.log_id)),\n", + " kind_version=KindVersion(kind=\"hashedrekord\", version=\"0.0.1\"),\n", + " integrated_time=self.log_entry.integrated_time,\n", + " inclusion_promise=InclusionPromise(\n", + " signed_entry_timestamp=base64.b64decode(\n", + " self.log_entry.inclusion_promise\n", + " )\n", + " )\n", + " if self.log_entry.inclusion_promise\n", + " else None,\n", + " inclusion_proof=inclusion_proof,\n", + " canonicalized_body=base64.b64decode(self.log_entry.body),\n", + " )\n", + "\n", + " material = VerificationMaterial(\n", + " x509_certificate_chain=chain,\n", + " tlog_entries=[tlog_entry],\n", + " )\n", + "\n", + " bundle = Bundle(\n", + " media_type=\"application/vnd.dev.sigstore.bundle+json;version=0.2\",\n", + " verification_material=material,\n", + " message_signature=MessageSignature(\n", + " message_digest=HashOutput(\n", + " algorithm=HashAlgorithm.SHA2_256,\n", + " digest=bytes.fromhex(self.input_digest),\n", + " ),\n", + " signature=base64.b64decode(self.b64_signature),\n", + " ),\n", + " )\n", + "\n", + " return bundle\n", + "\n", + "Class Documentation:\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "1db498eb-1336-4d31-a0d6-2f8210f81464", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed explanation of the classes and their methods. It describes the purpose of each class, their attributes, and the functionality of their methods. The submission also provides the data types of the parameters and return values, which is very helpful for understanding the code.\\n\\nThe submission is insightful as it not only describes what each method does but also explains the context in which they are used. For example, it explains that the `sign` method in the `Signer` class is used to sign an input artifact and that it verifies the validity period of the identity token, retrieves the signing certificate, verifies the SCT, and signs the artifact.\\n\\nThe submission is appropriate as it follows the structure provided in the prompt. It provides an introduction for each class, documents the class attributes and data types, and documents each function in the class.\\n\\nTherefore, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission is being evaluated on the basis of correctness, accuracy, and factualness. \\n\\nThe submission provides a detailed explanation of the classes `Signer`, `SigningContext`, and `SigningResult`. It correctly identifies the purpose of each class, their attributes, and their methods. \\n\\nFor the `Signer` class, the submission accurately describes the purpose of the class, its attributes, and its methods. It correctly explains the purpose of the `__init__`, `_private_key`, `_signing_cert`, and `sign` methods. \\n\\nFor the `SigningContext` class, the submission correctly describes the purpose of the class, its attributes, and its methods. It accurately explains the purpose of the `__init__`, `production`, `staging`, and `signer` methods. \\n\\nFor the `SigningResult` class, the submission correctly describes the purpose of the class, its attributes, and its method `to_bundle`. \\n\\nThe submission does not contain any factual errors or inaccuracies. It provides a clear and concise explanation of the classes and their functionalities. \\n\\nTherefore, the submission meets the criteria of correctness, accuracy, and factualness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nLooking at the submission, it seems to have covered all the classes and their respective attributes and methods. \\n\\nFor the class `Signer`, the submission has documented the class description, attributes, and methods. It has also provided the data types for the attributes and the return types for the methods. \\n\\nFor the class `SigningContext`, the submission has documented the class description, attributes, and methods. It has also provided the data types for the attributes and the return types for the methods. \\n\\nFor the class `SigningResult`, the submission has documented the class description, attributes, and methods. It has also provided the data types for the attributes and the return types for the methods. \\n\\nTherefore, the submission seems to be complete and captures all required fields. \\n\\nLet's print the final answer.\", 'value': \"Let's print the final answer.\", 'score': None}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "30834f9e-2cf9-479d-b29e-03c7b3d1761b", + "metadata": {}, + "source": [ + "#### Exp 7" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "eb832818-5588-4b83-8c52-4a893be8bea3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class:** LogInclusionProof\n", + "\n", + "- **Description**: This class represents an inclusion proof in a log. It contains various attributes and methods to validate the proof.\n", + "\n", + "**Class Attributes:**\n", + "\n", + "- **model_config**: A configuration dictionary for the model. (Type: ConfigDict)\n", + "- **checkpoint**: The checkpoint of the inclusion proof. (Type: StrictStr)\n", + "- **hashes**: A list of hashes involved in the proof. (Type: List[StrictStr])\n", + "- **log_index**: The index of the log in the proof. (Type: StrictInt)\n", + "- **root_hash**: The root hash of the log. (Type: StrictStr)\n", + "- **tree_size**: The size of the tree. (Type: StrictInt)\n", + "\n", + "**Methods:**\n", + "\n", + "- **_log_index_positive(v: int) -> int**: A field validator method that checks if the log index is a positive integer. Raises a ValueError if the log index is less than 0. (Parameters: v - the log index to validate) (Return Type: int)\n", + "\n", + "- **_tree_size_positive(v: int) -> int**: A field validator method that checks if the tree size is a positive integer. Raises a ValueError if the tree size is less than 0. (Parameters: v - the tree size to validate) (Return Type: int)\n", + "\n", + "- **_log_index_within_tree_size(v: int, info: ValidationInfo, \\*\\*kwargs: Any) -> int**: A field validator method that checks if the log index is within the tree size. Raises a ValueError if the log index is greater than or equal to the tree size. (Parameters: v - the log index to validate, info - validation information, \\*\\*kwargs - additional arguments) (Return Type: int)\n", + "\n", + "\n", + "**Error Handling:**\n", + "\n", + "- ValueError: Raised when the log index is less than 0 in the `_log_index_positive` method.\n", + "- ValueError: Raised when the tree size is less than 0 in the `_tree_size_positive` method.\n", + "- ValueError: Raised when the log index is greater than or equal to the tree size in the `_log_index_within_tree_size` method.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'transparency', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=False, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "b697f0a7-105c-493d-9284-92fb0d0bd034", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n", + " \n", + "Class code:\n", + "\n", + "class LogInclusionProof(BaseModel):\n", + " \n", + "\n", + " model_config = ConfigDict(populate_by_name=True)\n", + "\n", + " checkpoint: StrictStr = Field(..., alias=\"checkpoint\")\n", + " hashes: List[StrictStr] = Field(..., alias=\"hashes\")\n", + " log_index: StrictInt = Field(..., alias=\"logIndex\")\n", + " root_hash: StrictStr = Field(..., alias=\"rootHash\")\n", + " tree_size: StrictInt = Field(..., alias=\"treeSize\")\n", + "\n", + " @field_validator(\"log_index\")\n", + " def _log_index_positive(cls, v: int) -> int:\n", + " if v < 0:\n", + " raise ValueError(f\"Inclusion proof has invalid log index: {v} < 0\")\n", + " return v\n", + "\n", + " @field_validator(\"tree_size\")\n", + " def _tree_size_positive(cls, v: int) -> int:\n", + " if v < 0:\n", + " raise ValueError(f\"Inclusion proof has invalid tree size: {v} < 0\")\n", + " return v\n", + "\n", + " @field_validator(\"tree_size\")\n", + " def _log_index_within_tree_size(\n", + " cls, v: int, info: ValidationInfo, **kwargs: Any\n", + " ) -> int:\n", + " if \"log_index\" in info.data and v <= info.data[\"log_index\"]:\n", + " raise ValueError(\n", + " \"Inclusion proof has log index greater than or equal to tree size: \"\n", + " f\"{v} <= {info.data['log_index']}\"\n", + " )\n", + " return v\n", + "\n", + "Class Documentation:\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "b1d33978-1491-4d03-9c94-aabf1b4c53bb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed documentation of the given Python class. It includes the class name and a brief description of what the class represents. This is helpful for users who want to understand the purpose of the class.\\n\\nThe submission also documents the class attributes, including their data types and what they represent. This is insightful as it provides users with a clear understanding of the data that the class handles.\\n\\nThe methods of the class are also well-documented. The submission provides the description, parameters, and return values for each method. This is appropriate as it gives users a clear understanding of how to use the methods and what to expect from them.\\n\\nThe submission also includes error handling, documenting the possible errors that can be raised when using the class. This is helpful as it informs users of the potential issues they might encounter and why they might occur.\\n\\nBased on the above analysis, the submission is helpful, insightful, and appropriate. It provides a comprehensive documentation of the Python class, which would be useful for users trying to understand and use the class.\\n\\nTherefore, the submission meets the criterion. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria is to assess the correctness, accuracy, and factualness of the submission.\\n\\n1. The submission correctly identifies the class name as \"LogInclusionProof\".\\n2. The description of the class in the submission is accurate and matches the reference.\\n3. The class attributes listed in the submission are correct and match the reference. The data types of the attributes are also correctly identified.\\n4. The methods listed in the submission are correct and match the reference. The descriptions of the methods, their parameters, and return types are also accurate.\\n5. The error handling section in the submission correctly identifies the errors that can be raised by the methods in the class.\\n\\nBased on the above points, the submission is correct, accurate, and factual. Therefore, it meets the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criteria is to assess if the output is complete and captures all required fields. \\n\\nLooking at the submission, it has provided the following:\\n\\n1. Introduction: The submission has provided a brief description of the class.\\n2. Class Name and Description: The class name 'LogInclusionProof' and its description are provided.\\n3. Class Attributes and Data types: All the class attributes 'model_config', 'checkpoint', 'hashes', 'log_index', 'root_hash', 'tree_size' and their data types are documented.\\n4. Document each function in the class: All the functions '_log_index_positive', '_tree_size_positive', '_log_index_within_tree_size' are documented with their descriptions, parameters, data types, and return values.\\n5. Error Handling: Possible error responses are documented for each function.\\n\\nThe submission has provided all the required fields and is complete. Therefore, the submission meets the criteria.\\n\\nY\", 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "7e22c86d-b434-4268-880d-b83cc673400a", + "metadata": {}, + "source": [ + "#### Exp 8" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "818b8fca-2544-4a68-846e-811a14344e3c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'oidc', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "f3a2d15f-6316-4d79-b623-18cd36c7d441", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n", + "Function Code:\n", + "\n", + "def detect_credential() -> Optional[str]:\n", + " \n", + " try:\n", + " return cast(Optional[str], id.detect_credential(_DEFAULT_AUDIENCE))\n", + " except id.IdentityError as exc:\n", + " IdentityError.raise_from_id(exc)\n", + "\n", + "Function Documentation:\n", + "\n", + "\n", + "\n", + " \n", + "Class code:\n", + "\n", + "class _OpenIDConfiguration(BaseModel):\n", + " \n", + "\n", + " authorization_endpoint: StrictStr\n", + " token_endpoint: StrictStr\n", + "class ExpiredIdentity(Exception):\n", + " \n", + "class IdentityToken:\n", + " \n", + "\n", + " def __init__(self, raw_token: str) -> None:\n", + " \n", + "\n", + " self._raw_token = raw_token\n", + "\n", + " # NOTE: The lack of verification here is intentional, and is part of\n", + " # Sigstore's verification model: clients like sigstore-python are\n", + " # responsible only for forwarding the OIDC identity to Fulcio for\n", + " # certificate binding and issuance.\n", + " try:\n", + " self._unverified_claims = jwt.decode(\n", + " raw_token,\n", + " options={\n", + " \"verify_signature\": False,\n", + " \"verify_aud\": True,\n", + " \"verify_iat\": True,\n", + " \"verify_exp\": True,\n", + " # These claims are required by OpenID Connect, so\n", + " # we can strongly enforce their presence.\n", + " # See: https://openid.net/specs/openid-connect-basic-1_0.html#IDToken\n", + " \"require\": [\"aud\", \"sub\", \"iat\", \"exp\", \"iss\"],\n", + " },\n", + " audience=DEFAULT_AUDIENCE,\n", + " # NOTE: This leeway shouldn't be strictly necessary, but is\n", + " # included to preempt any (small) skew between the host\n", + " # and the originating IdP.\n", + " leeway=5,\n", + " )\n", + " except Exception as exc:\n", + " raise IdentityError(\n", + " \"Identity token is malformed or missing claims\"\n", + " ) from exc\n", + "\n", + " self._iss: str = self._unverified_claims[\"iss\"]\n", + " self._nbf: int | None = self._unverified_claims.get(\"nbf\")\n", + " self._exp: int = self._unverified_claims[\"exp\"]\n", + "\n", + " # Fail early if this token isn't within its validity period.\n", + " if not self.in_validity_period():\n", + " raise IdentityError(\"Identity token is not within its validity period\")\n", + "\n", + " # When verifying the private key possession proof, Fulcio uses\n", + " # different claims depending on the token's issuer.\n", + " # We currently special-case a handful of these, and fall back\n", + " # on signing the \"sub\" claim otherwise.\n", + " identity_claim = _KNOWN_OIDC_ISSUERS.get(self.issuer)\n", + " if identity_claim is not None:\n", + " if identity_claim not in self._unverified_claims:\n", + " raise IdentityError(\n", + " f\"Identity token is missing the required {identity_claim!r} claim\"\n", + " )\n", + "\n", + " self._identity = str(self._unverified_claims.get(identity_claim))\n", + " else:\n", + " try:\n", + " self._identity = str(self._unverified_claims[\"sub\"])\n", + " except KeyError:\n", + " raise IdentityError(\n", + " \"Identity token is missing the required 'sub' claim\"\n", + " )\n", + "\n", + " # This identity token might have been retrieved directly from\n", + " # an identity provider, or it might be a \"federated\" identity token\n", + " # retrieved from a federated IdP (e.g., Sigstore's own Dex instance).\n", + " # In the latter case, the claims will also include a `federated_claims`\n", + " # set, which in turn should include a `connector_id` that reflects\n", + " # the \"real\" token issuer. We retrieve this, despite technically\n", + " # being an implementation detail, because it has value to client\n", + " # users: a client might want to make sure that its user is identifying\n", + " # with a *particular* IdP, which means that they need to pierce the\n", + " # federation layer to check which IdP is actually being used.\n", + " self._federated_issuer: str | None = None\n", + " federated_claims = self._unverified_claims.get(\"federated_claims\")\n", + " if federated_claims is not None:\n", + " if not isinstance(federated_claims, dict):\n", + " raise IdentityError(\n", + " \"unexpected claim type: federated_claims is not a dict\"\n", + " )\n", + "\n", + " federated_issuer = federated_claims.get(\"connector_id\")\n", + " if federated_issuer is not None:\n", + " if not isinstance(federated_issuer, str):\n", + " raise IdentityError(\n", + " \"unexpected claim type: federated_claims.connector_id is not a string\"\n", + " )\n", + "\n", + " self._federated_issuer = federated_issuer\n", + "\n", + " def in_validity_period(self) -> bool:\n", + " \n", + "\n", + " now = datetime.now(timezone.utc).timestamp()\n", + "\n", + " if self._nbf is not None:\n", + " return self._nbf <= now < self._exp\n", + " else:\n", + " return now < self._exp\n", + "\n", + " @property\n", + " def identity(self) -> str:\n", + " \n", + " return self._identity\n", + "\n", + " @property\n", + " def issuer(self) -> str:\n", + " \n", + " return self._iss\n", + "\n", + " @property\n", + " def expected_certificate_subject(self) -> str:\n", + " \n", + " if self._federated_issuer is not None:\n", + " return self._federated_issuer\n", + "\n", + " return self.issuer\n", + "\n", + " def __str__(self) -> str:\n", + " \n", + " return self._raw_token\n", + "class IssuerError(Exception):\n", + " \n", + "\n", + " pass\n", + "class Issuer:\n", + " \n", + "\n", + " def __init__(self, base_url: str) -> None:\n", + " \n", + " oidc_config_url = urllib.parse.urljoin(\n", + " f\"{base_url}/\", \".well-known/openid-configuration\"\n", + " )\n", + "\n", + " try:\n", + " resp: requests.Response = requests.get(oidc_config_url, timeout=30)\n", + " except (requests.ConnectionError, requests.Timeout) as exc:\n", + " raise NetworkError from exc\n", + "\n", + " try:\n", + " resp.raise_for_status()\n", + " except requests.HTTPError as http_error:\n", + " raise IssuerError from http_error\n", + "\n", + " try:\n", + " # We don't generally expect this to fail (since the provider should\n", + " # return a non-success HTTP code which we catch above), but we\n", + " # check just in case we have a misbehaving OIDC issuer.\n", + " self.oidc_config = _OpenIDConfiguration.model_validate(resp.json())\n", + " except ValueError as exc:\n", + " raise IssuerError(f\"OIDC issuer returned invalid configuration: {exc}\")\n", + "\n", + " @classmethod\n", + " def production(cls) -> Issuer:\n", + " \n", + " return cls(DEFAULT_OAUTH_ISSUER_URL)\n", + "\n", + " @classmethod\n", + " def staging(cls) -> Issuer:\n", + " \n", + " return cls(STAGING_OAUTH_ISSUER_URL)\n", + "\n", + " def identity_token( # nosec: B107\n", + " self,\n", + " client_id: str = \"sigstore\",\n", + " client_secret: str = \"\",\n", + " force_oob: bool = False,\n", + " ) -> IdentityToken:\n", + " \n", + "\n", + " # This function and the components that it relies on are based off of:\n", + " # https://github.com/psteniusubi/python-sample\n", + "\n", + " from sigstore._internal.oidc.oauth import _OAuthFlow\n", + "\n", + " code: str\n", + " with _OAuthFlow(client_id, client_secret, self) as server:\n", + " # Launch web browser\n", + " if not force_oob and webbrowser.open(server.base_uri):\n", + " print(\"Waiting for browser interaction...\", file=sys.stderr)\n", + " else:\n", + " server.enable_oob()\n", + " print(\n", + " f\"Go to the following link in a browser:\\n\\n\\t{server.auth_endpoint}\",\n", + " file=sys.stderr,\n", + " )\n", + "\n", + " if not server.is_oob():\n", + " # Wait until the redirect server populates the response\n", + " while server.auth_response is None:\n", + " time.sleep(0.1)\n", + "\n", + " auth_error = server.auth_response.get(\"error\")\n", + " if auth_error is not None:\n", + " raise IdentityError(\n", + " f\"Error response from auth endpoint: {auth_error[0]}\"\n", + " )\n", + " code = server.auth_response[\"code\"][0]\n", + " else:\n", + " # In the out-of-band case, we wait until the user provides the code\n", + " code = input(\"Enter verification code: \")\n", + "\n", + " # Provide code to token endpoint\n", + " data = {\n", + " \"grant_type\": \"authorization_code\",\n", + " \"redirect_uri\": server.redirect_uri,\n", + " \"code\": code,\n", + " \"code_verifier\": server.oauth_session.code_verifier,\n", + " }\n", + " auth = (\n", + " client_id,\n", + " client_secret,\n", + " )\n", + " logging.debug(f\"PAYLOAD: data={data}\")\n", + " try:\n", + " resp: requests.Response = requests.post(\n", + " self.oidc_config.token_endpoint,\n", + " data=data,\n", + " auth=auth,\n", + " timeout=30,\n", + " )\n", + " except (requests.ConnectionError, requests.Timeout) as exc:\n", + " raise NetworkError from exc\n", + "\n", + " try:\n", + " resp.raise_for_status()\n", + " except requests.HTTPError as http_error:\n", + " raise IdentityError(\n", + " f\"Token request failed with {resp.status_code}\"\n", + " ) from http_error\n", + "\n", + " token_json = resp.json()\n", + " token_error = token_json.get(\"error\")\n", + " if token_error is not None:\n", + " raise IdentityError(f\"Error response from token endpoint: {token_error}\")\n", + "\n", + " return IdentityToken(token_json[\"access_token\"])\n", + "class IdentityError(Error):\n", + " \n", + "\n", + " @classmethod\n", + " def raise_from_id(cls, exc: id.IdentityError) -> NoReturn:\n", + " \n", + " raise cls(str(exc)) from exc\n", + "\n", + " def diagnostics(self) -> str:\n", + " \n", + " if isinstance(self.__cause__, id.GitHubOidcPermissionCredentialError):\n", + " return f\n", + " Insufficient permissions for GitHub Actions workflow.\n", + "\n", + " The most common reason for this is incorrect\n", + " configuration of the top-level `permissions` setting of the\n", + " workflow YAML file. It should be configured like so:\n", + "\n", + " permissions:\n", + " id-token: write\n", + "\n", + " Relevant documentation here:\n", + "\n", + " https://docs.github.com/en/actions/deployment/security-hardening-your-deployments/about-security-hardening-with-openid-connect#adding-permissions-settings\n", + "\n", + " Another possible reason is that the workflow run has been\n", + " triggered by a PR from a forked repository. PRs from forked\n", + " repositories typically cannot be granted write access.\n", + "\n", + " Relevant documentation here:\n", + "\n", + " https://docs.github.com/en/actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token\n", + "\n", + " Additional context:\n", + "\n", + " {self.__cause__}\n", + " \n", + " else:\n", + " return f\n", + " An issue occurred with ambient credential detection.\n", + "\n", + " Additional context:\n", + "\n", + " {self}\n", + " \n", + "\n", + "Class Documentation:\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "7e1e7637-ad29-4bd2-97f4-7b9a308c71c0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate. \\n\\nLooking at the data, the task was to generate API documentation for the provided Python code. The code includes a function and several classes. The task also specifies that if no code is present in the prompt, the assistant should state \"No Code has been provided in the prompt\".\\n\\nIn the submission, the assistant states \"No code has been provided in the prompt\". However, this is incorrect as there is clearly code provided in the input. Therefore, the assistant\\'s response is not helpful or appropriate as it does not provide the requested API documentation for the provided code.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The task requires the AI to generate API documentation for the provided Python code. The code provided includes a function and several classes. The AI is supposed to generate documentation for these, following the structure provided in the prompt.\\n\\nThe submission, however, states \"No code has been provided in the prompt.\" This is incorrect, as there is clearly code provided in the prompt. The AI has failed to generate the required documentation for the provided code.\\n\\nTherefore, the submission does not meet the criteria of correctness, as it is not accurate or factual. The AI has failed to correctly interpret the task and generate the required output. \\n\\nThe reference provided is a detailed API documentation for the provided code, which the AI was supposed to generate. The AI\\'s submission does not match this reference at all, further confirming that it does not meet the criteria. \\n\\nBased on this analysis, the answer is \"N\" for No, the submission does not meet the criteria. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe task requires the AI to generate API documentation for the provided Python code. The Python code includes a function and several classes. The AI is supposed to document the function and classes, including their descriptions, parameters, return values, and possible error responses.\\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect as there is indeed Python code provided in the prompt. Therefore, the submission does not meet the criteria of being complete and capturing all required fields.\\n\\nSo, the answer is No, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "e789ca7b-bac0-4af2-a2c2-0b807e65dce8", + "metadata": { + "tags": [] + }, + "source": [ + "#### Exp 9" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "2889dd53-8af8-4f96-8cb6-8a809741d093", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**API Documentation**\n", + "\n", + "1. Introduction:\n", + "\n", + "No code has been provided in the prompt.\n", + "\n", + "No code has been provided in the prompt.\n", + "\n", + "\n", + "2. Class:\n", + "\n", + "No code has been provided in the prompt.\n", + "\n", + "\n", + "3. Functions:\n", + "\n", + "No code has been provided in the prompt.\n", + "\n", + "\n", + "4. Error Handling:\n", + "\n", + "No code has been provided in the prompt.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'verify_models', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=True, functions_doc=False, classes_code=False, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "4d17fd8b-7c0f-42e3-be35-b79ce06f7cec", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "c16ec9a2-6831-484e-9d8d-f29316bcec57", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to provide API documentation for a given Python code. However, the prompt does not provide any code. The submission correctly identifies this and states \"No code has been provided in the prompt\" for each section of the documentation. This is accurate and appropriate given the lack of code in the prompt. Therefore, the submission can be considered helpful as it correctly identifies the lack of code and does not attempt to generate documentation for non-existent code. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission is asked to generate API documentation for a given Python code. However, no code was provided in the input. The submission correctly states \"No code has been provided in the prompt\" for each section of the documentation, which is accurate and factual. Therefore, the submission meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria is to assess if the output is complete and captures all required fields. \\n\\nLooking at the submission, the AI has correctly identified that no code has been provided in the prompt. It has also correctly followed the structure of the documentation as provided in the input, covering all the required fields: Introduction, Class, Functions, and Error Handling. \\n\\nEven though there is no specific information in each of these sections, this is because no code was provided, and the AI correctly identified this. Therefore, the output is complete and captures all required fields given the context.\\n\\nSo, the submission meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1163/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "27536a30-c866-4d77-8481-b625735b0a46", + "metadata": {}, + "source": [ + "Note:\n", + "\n", + "The updated prompt is working well where we are not providing any code, that is it is able to detect clearly that no code has been given in thr prompt, but sometimes when a long chunk of code is present, it also says that \"No code has been provided\" probably becuase it fails to parse through big chunks,\n", + "\n", + "rather than updating prompts, a better solution would be to chunk better where we just give it single classes and single functions to document." + ] + }, + { + "cell_type": "markdown", + "id": "6ac95c27-510b-4a4f-baf0-2c1c3b8d6294", + "metadata": {}, + "source": [ + "### Prompt 5" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "eda4d733-2bcd-4bc2-94d4-cbd0f67224ff", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction = \"\"\"\n", + "Generate API documentation for Python code provided in the prompt. Ensure clarity, accuracy, and user-centricity.\n", + "If no code is provided, do not speculate or generate generic examples. Instead, leave this section blank or state \"No code provided\".\n", + "\n", + "If Python code is provided:\n", + "\n", + "1. Introduction: \n", + "2. Class Documentation:\n", + " - Document each class present in the code, including:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Documentation for each method within the class, following the instructions below.\n", + "3. Function Documentation:\n", + " - For each function in the code:\n", + " - Function Description\n", + " - Parameters, including names and data types.\n", + " - Return values, including data types.\n", + "4. Error Handling:\n", + "Describe possible error responses and how they are handled in the code.\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "72db106e-4fe4-406e-b31b-7adfb6e2102d", + "metadata": {}, + "source": [ + "#### Exp 1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "24f6a505-b6cb-4bbf-927d-8906cab39bf0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. VerificationResult:\n", + " - Description: This class represents the result of a verification process. It contains a boolean attribute \"success\" which indicates whether the verification was successful or not.\n", + " - Attributes:\n", + " - success (bool): Flag indicating the success of the verification.\n", + "\n", + " - Method Documentation:\n", + " - __bool__(self) -> bool:\n", + " - Description: This method allows the VerificationResult object to be used as a boolean value. It returns the value of the \"success\" attribute.\n", + " - Returns: bool - The value of the \"success\" attribute.\n", + "\n", + "2. VerificationSuccess:\n", + " - Description: This class represents a successful verification result. It is a subclass of VerificationResult and inherits its attributes and methods.\n", + " - Attributes:\n", + " - success (bool): Flag indicating the success of the verification. It is set to True.\n", + "\n", + "3. VerificationFailure:\n", + " - Description: This class represents a failed verification result. It is a subclass of VerificationResult and inherits its attributes and methods.\n", + " - Attributes:\n", + " - success (bool): Flag indicating the success of the verification. It is set to False.\n", + " - reason (str): A string indicating the reason for the verification failure.\n", + "\n", + "4. InvalidMaterials:\n", + " - Description: This class represents an error that occurs when parsing verification materials. It is a subclass of the Error class.\n", + " - Method Documentation:\n", + " - diagnostics(self) -> str:\n", + " - Description: This method returns a diagnostic message with details about the error. It provides information about the issue that occurred while parsing the verification materials.\n", + " - Returns: str - A diagnostic message with details about the error.\n", + "\n", + "5. RekorEntryMissing:\n", + " - Description: This class represents an exception that is raised when a Rekor entry is missing.\n", + " - Inherits: Exception\n", + "\n", + "6. InvalidRekorEntry:\n", + " - Description: This class represents an error that occurs when parsing invalid Rekor entries. It is a subclass of InvalidMaterials.\n" + ] + } + ], + "source": [ + "prompt, generated_text, actual_doc = get_response(instruction, \"OpenAI/gpt3.5\", 'verify_models', functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=False, functions_doc=False, classes_code=True, classes_doc=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e9db4023-e8d8-440c-b5f5-4ecef2f91149", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Generate API documentation for Python code provided in the prompt. Ensure clarity, accuracy, and user-centricity.\n", + "If no code is provided, do not speculate or generate generic examples. Instead, leave this section blank or state \"No code provided\".\n", + "\n", + "If Python code is provided:\n", + "\n", + "1. Introduction: \n", + "2. Class Documentation:\n", + " - Document each class present in the code, including:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Documentation for each method within the class, following the instructions below.\n", + "3. Function Documentation:\n", + " - For each function in the code:\n", + " - Function Description\n", + " - Parameters, including names and data types.\n", + " - Return values, including data types.\n", + "4. Error Handling:\n", + "Describe possible error responses and how they are handled in the code.\n", + "Class code:\n", + "class VerificationResult(BaseModel):\n", + " \n", + "\n", + " success: bool\n", + " \n", + "\n", + " def __bool__(self) -> bool:\n", + " \n", + " return self.success\n", + "class VerificationSuccess(VerificationResult):\n", + " \n", + "\n", + " success: bool = True\n", + " \n", + "class VerificationFailure(VerificationResult):\n", + " \n", + "\n", + " success: bool = False\n", + " \n", + "\n", + " reason: str\n", + " \n", + "class InvalidMaterials(Error):\n", + " \n", + "\n", + " def diagnostics(self) -> str:\n", + " \n", + "\n", + " return dedent(\n", + " f\\\n", + " An issue occurred while parsing the verification materials.\n", + "\n", + " The provided verification materials are malformed and may have been\n", + " modified maliciously.\n", + "\n", + " Additional context:\n", + "\n", + " {self}\n", + " \n", + " )\n", + "class RekorEntryMissing(Exception):\n", + " \n", + "\n", + " pass\n", + "class InvalidRekorEntry(InvalidMaterials):\n", + " \n", + "\n", + " pass\n", + "Class Documentation:\n", + "\n" + ] + } + ], + "source": [ + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c71f919b-a9d6-4309-8100-47cf2e1d354e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that the user has provided detailed documentation for each class present in the Python code. The documentation includes the class name, a description of the class, the class attributes and their data types, and documentation for each method within the class. This is in line with the instructions provided in the input.\\n\\nThe documentation is also user-centric, as it provides clear and concise descriptions of each class and method, making it easy for users to understand the purpose and functionality of each component of the code.\\n\\nThe submission is also accurate, as it correctly identifies and describes each class and method in the code. It correctly identifies the data types of the class attributes and return values of the methods.\\n\\nThe submission does not include a section on error handling, but this is not a requirement in the input, so it does not detract from the helpfulness of the submission.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness, which means the submission should be accurate and factual. \\n\\nLooking at the submission, it appears to have correctly documented the Python classes provided in the input. \\n\\n1. For the VerificationResult class, the submission correctly identifies the class name, description, attributes, and methods. It also correctly identifies the data types of the attributes and return values of the methods.\\n\\n2. For the VerificationSuccess class, the submission correctly identifies the class as a subclass of VerificationResult and correctly identifies the attributes. \\n\\n3. For the VerificationFailure class, the submission correctly identifies the class as a subclass of VerificationResult and correctly identifies the attributes. \\n\\n4. For the InvalidMaterials class, the submission correctly identifies the class as a subclass of Error and correctly identifies the methods and their return values.\\n\\n5. For the RekorEntryMissing class, the submission correctly identifies the class as an exception and correctly identifies it as inheriting from the Exception class.\\n\\n6. For the InvalidRekorEntry class, the submission correctly identifies the class as a subclass of InvalidMaterials.\\n\\nThe submission does not include any incorrect or inaccurate information, and it appears to be factual based on the provided Python code. Therefore, the submission meets the criterion of correctness.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission:\\n\\n1. The submission has documented all the classes provided in the input. \\n2. For each class, the name and a description are provided.\\n3. The attributes of each class, along with their data types, are documented.\\n4. The methods within each class are documented, including their descriptions and return values.\\n\\nTherefore, the submission appears to meet all the requirements of the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_4392/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "df = append_row_to_dataframe(df, prompt, generated_text)" + ] + }, + { + "cell_type": "markdown", + "id": "a35a6198-5abf-4bf7-8723-904eabaa6ee5", + "metadata": {}, + "source": [ + "## Run automated experiment\n", + "\n", + "For all models and chunks generate outputs and score them and see which prompt has higher average score" + ] + }, + { + "cell_type": "markdown", + "id": "f123997d-ec03-4bb4-a99f-b6053f9532a3", + "metadata": {}, + "source": [ + "### Experiment 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e25caab3-2790-46d0-bdcf-0fe68dfe09fd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction_1 = \"\"\"\n", + "You are an AI system specialized at generating API documentation for given Python code. Be as objective as possible. You will be provided functions, classes, or Python scripts. \n", + "\n", + "The documentation follow the structure below:\n", + "\n", + "1. Introduction: \n", + "2. Class: If a class code is passed, document the following:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Document each function in the class following the instructions below.\n", + "3. Functions: \n", + " - Description\n", + " - Parameters and Data types\n", + " - Return Values\n", + "\n", + "4. Error Handling: Possible error responses\n", + "\n", + "Create API documentation that is clear, concise, accurate, and user-centric. \n", + "\n", + "Special Caution:\n", + "\n", + "- If no code is present in the prompt, do not generate generic examples, simply state \"No Code has been provided in the prompt\".\n", + "- Avoid speculative information and prioritize accuracy and completeness.\n", + "- Do not hallucinate variable names, function names, class names and the intended API usage. Only generate documentation for the code that is actually present.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5976c917-412c-40eb-bd50-47f27525614c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction_2 = \"\"\"\n", + "Generate API documentation for Python code provided in the prompt. Ensure clarity, accuracy, and user-centricity.\n", + "If no code is provided, do not speculate or generate generic examples. Instead, leave this section blank or state \"No code provided\".\n", + "\n", + "If Python code is provided:\n", + "\n", + "1. Introduction: \n", + "2. Class Documentation:\n", + " - Document each class present in the code, including:\n", + " - Class Name and Description\n", + " - Class Attributes and Data types\n", + " - Documentation for each method within the class, following the instructions below.\n", + "3. Function Documentation:\n", + " - For each function in the code:\n", + " - Function Description\n", + " - Parameters, including names and data types.\n", + " - Return values, including data types.\n", + "4. Error Handling:\n", + "Describe possible error responses and how they are handled in the code.\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b6329b8f-5287-42eb-a1ec-eb3311b8bb9e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "instruction_old = \"\"\"\n", + "You are an AI system specialized at generating API documentation for the provided Python code. You will be provided functions, classes, or Python scripts. Your documentation should include:\n", + "\n", + "1. Introduction: Briefly describe the purpose of the API and its intended use.\n", + "2. Functions: Document each API function, including:\n", + " - Description: Clearly explain what the endpoint or function does.\n", + " - Parameters: List and describe each parameter, including data types and any constraints.\n", + " - Return Values: Specify the data type and possible values returned.\n", + "\n", + "3. Error Handling: Describe possible error responses and their meanings.\n", + "\n", + "Make sure to follow this output structure to create API documentation that is clear, concise, accurate, and user-centric. Avoid speculative information and prioritize accuracy and completeness.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fc7e41bc-e95f-4df9-a307-4012712b1b2d", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Introduction:\n", + "This API function is used to detect a credential. It returns an optional string value representing the detected credential.\n", + "\n", + "2. Function:\n", + "- Description:\n", + " - This function attempts to detect a credential.\n", + "- Parameters and Data Types:\n", + " - No parameters are required.\n", + "- Return Value:\n", + " - This function returns an optional string value representing the detected credential. If no credential is detected, it returns None.\n", + "\n", + "3. Error Handling:\n", + "- Possible error responses:\n", + " - If an error occurs during the detection process, an `IdentityError` is raised.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission:\\n\\n1. The introduction provides a brief overview of the function\\'s purpose, which is helpful for users to understand what the function does.\\n\\n2. The function description is clear and concise, providing insight into the function\\'s operation. It correctly states that the function does not require any parameters and returns an optional string value.\\n\\n3. The error handling section is also helpful as it informs users about the possible error that can occur during the function\\'s execution.\\n\\n4. The submission is appropriate as it follows the structure provided in the input and does not include any speculative or inaccurate information.\\n\\nBased on these points, the submission meets the criterion of being helpful, insightful, and appropriate. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nLet\\'s evaluate the submission based on this criterion:\\n\\n1. The submission correctly identifies the function name as \"detect_credential\".\\n2. The submission accurately describes the function\\'s purpose, which is to detect a credential.\\n3. The submission correctly states that the function does not require any parameters.\\n4. The submission accurately describes the return value of the function, which is an optional string representing the detected credential.\\n5. The submission correctly identifies that an `IdentityError` is raised if an error occurs during the detection process.\\n\\nBased on the above evaluation, the submission appears to be correct, accurate, and factual. It has correctly documented the function based on the provided Python code and the reference documentation.\\n\\nTherefore, the submission meets the criterion. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The introduction is present and describes the function's purpose. \\n\\n2. Function: \\n - Description: The description is present and explains what the function does.\\n - Parameters and Data Types: The function does not take any parameters, and this is correctly stated in the documentation.\\n - Return Value: The return value is described correctly as an optional string.\\n\\n3. Error Handling: The error handling section is present and correctly describes the error that can be raised.\\n\\nThe submission has met all the criteria as it is complete and captures all required fields.\\n\\nY\", 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No Code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate. \\n\\nLooking at the data, the input provided a Python code and asked for API documentation to be created for it. The code includes several classes and functions that need to be documented. \\n\\nHowever, the submission states \"No Code has been provided in the prompt.\" This is incorrect as there is clearly a Python code provided in the input. \\n\\nTherefore, the submission is not helpful or appropriate as it does not provide the requested API documentation for the provided Python code. It is also not insightful as it does not provide any useful information or analysis. \\n\\nBased on this reasoning, the submission does not meet the criterion.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria is to assess if the submission is correct, accurate, and factual. \\n\\nThe submission states \"No Code has been provided in the prompt.\" However, the input data clearly contains Python code for which API documentation is to be generated. The code includes several classes such as \"_OpenIDConfiguration\", \"ExpiredIdentity\", \"IdentityToken\", \"IssuerError\", \"Issuer\", and \"IdentityError\". \\n\\nTherefore, the submission is incorrect as it does not accurately reflect the input data. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria asks if the output is complete and captures all required fields. The task was to generate API documentation for the provided Python code. The code provided includes several classes and functions. \\n\\nThe submission, however, states \"No Code has been provided in the prompt.\" This is incorrect as there is clearly code provided in the prompt. Therefore, the submission does not meet the criteria as it does not capture all required fields and is not complete. \\n\\nThe correct answer is \"N\" as the submission does not meet the criteria. \\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**1. Introduction:**\n", + "\n", + "The Python script provided includes classes and functions that perform specific tasks. This documentation will guide you through the usage and functionality of the API.\n", + "\n", + "**2. Class:**\n", + "\n", + "\n", + "2.1 MyClass\n", + "\n", + " This class represents a sample class and provides some useful functions to perform certain tasks.\n", + "\n", + " **Attributes:**\n", + "\n", + " - `attribute1` (int): Represents the first attribute of MyClass.\n", + " - `attribute2` (str): Represents the second attribute of MyClass.\n", + "\n", + " **Methods:**\n", + "\n", + " - `__init__(self, attribute1: int, attribute2: str) -> None`: Initializes the MyClass object with the provided attribute values.\n", + "\n", + " - `method1(self, param1: str, param2: int) -> str`: Represents the first method of MyClass. It takes in two parameters, `param1` of type str and `param2` of type int, and returns a string.\n", + "\n", + " - `method2(self) -> None`: Represents the second method of MyClass. It doesn't take any parameters and doesn't return anything.\n", + "\n", + "\n", + "**3. Functions:**\n", + "\n", + "\n", + "3.1 `function1(param1: int, param2: str) -> bool`\n", + "\n", + " This function performs a specific task and returns a boolean value. It takes in two parameters: `param1` of type int and `param2` of type str.\n", + "\n", + " **Parameters:**\n", + "\n", + " - `param1` (int): Represents the first parameter required by function1.\n", + " - `param2` (str): Represents the second parameter required by function1.\n", + "\n", + " **Return Value:**\n", + "\n", + " - bool: Returns a boolean value based on the result of the task performed.\n", + "\n", + "3.2 `function2() -> int`\n", + "\n", + " This function performs a specific task and returns an integer value.\n", + "\n", + " **Return Value:**\n", + "\n", + " - int: Returns an integer value based on the result of the task performed.\n", + "\n", + "\n", + "**4. Error Handling:**\n", + "\n", + "The following are possible error responses for the functions and classes:\n", + "\n", + "- If incorrect data types are passed as parameters to the functions or attributes, a `TypeError` will be raised.\n", + "- If any other unexpected error occurs during execution, an `Exception` will be raised.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that the AI has generated a detailed and comprehensive API documentation for the hypothetical Python code. The documentation includes an introduction, a section on classes, a section on functions, and a section on error handling. \\n\\nIn the class section, the AI has provided the class name, a description of the class, the attributes and their data types, and the methods within the class. Each method is described in detail, including its parameters and return values.\\n\\nIn the functions section, the AI has provided the function names, descriptions, parameters, return values, and possible error responses. This information is crucial for understanding how to use the functions and what to expect when they are called.\\n\\nIn the error handling section, the AI has outlined the possible error responses that could occur when using the functions and classes. This is helpful for users to understand what could go wrong and how to handle these situations.\\n\\nOverall, the submission is helpful as it provides all the necessary information a user would need to understand and use the API. It is insightful as it goes into detail about each component of the API, and it is appropriate as it follows the structure outlined in the input.\\n\\nTherefore, the submission meets the criterion. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission is supposed to be an API documentation for the provided Python code. However, the reference provided is a different Python code related to \\'sigstore\\' and \\'transparency\\'. The submission does not match the reference code at all. The class and function names, their descriptions, parameters, and return types in the submission do not correspond to those in the reference code.\\n\\nFor instance, the submission mentions a class \\'MyClass\\' with attributes \\'attribute1\\' and \\'attribute2\\', and methods \\'__init__\\', \\'method1\\', and \\'method2\\'. However, the reference code has classes \\'LogInclusionProof\\' and \\'LogEntry\\' with different attributes and methods.\\n\\nSimilarly, the submission mentions functions \\'function1\\' and \\'function2\\', but the reference code does not have these functions.\\n\\nTherefore, the submission is not correct, accurate, or factual as per the provided reference code.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides an introduction that explains the purpose of the documentation. This meets the requirement.\\n\\n2. Class: The submission provides a class name and description, attributes and their data types, and documents each function in the class. This meets the requirement.\\n\\n3. Functions: The submission provides a description for each function, parameters and their data types, and return values. This meets the requirement.\\n\\n4. Error Handling: The submission provides possible error responses. This meets the requirement.\\n\\nThe submission does not hallucinate variable names, function names, class names and the intended API usage. It only generates documentation for the code that is actually present. This meets the requirement.\\n\\nThe submission does not generate generic examples when no code is present in the prompt. This meets the requirement.\\n\\nThe submission avoids speculative information and prioritizes accuracy and completeness. This meets the requirement.\\n\\nTherefore, the submission meets all the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class Name:** LogInclusionProof\n", + "\n", + "**Description:** This class represents an inclusion proof for a log entry in a Merkle Tree. It contains the necessary information to prove that a log entry is part of a larger Merkle Tree structure.\n", + "\n", + "**Attributes:**\n", + "- `checkpoint` (str): The checkpoint of the Merkle Tree.\n", + "- `hashes` (list of str): The hashes of the nodes in the proof path.\n", + "- `log_index` (int): The index of the log entry in the Merkle Tree.\n", + "- `root_hash` (str): The root hash of the Merkle Tree.\n", + "- `tree_size` (int): The size of the Merkle Tree.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `_log_index_positive(v: int) -> int`\n", + " - **Description:** Validates if the log index is positive.\n", + " - **Parameters:** \n", + " - `v` (int): The log index value.\n", + " - **Return Value:** \n", + " - `int`: The validated log index value.\n", + " \n", + "2. `_tree_size_positive(v: int) -> int`\n", + " - **Description:** Validates if the tree size is positive.\n", + " - **Parameters:** \n", + " - `v` (int): The tree size value.\n", + " - **Return Value:** \n", + " - `int`: The validated tree size value.\n", + "\n", + "3. `_log_index_within_tree_size(v: int, info: ValidationInfo, **kwargs: Any) -> int`\n", + " - **Description:** Validates if the log index is within the tree size.\n", + " - **Parameters:** \n", + " - `v` (int): The log index value.\n", + " - `info` (ValidationInfo): Additional information about the validation process.\n", + " - `kwargs` (Any): Additional keyword arguments.\n", + " - **Return Value:** \n", + " - `int`: The validated log index value.\n", + "\n", + "**Error Handling:** \n", + "\n", + "- If the log index is less than 0, a `ValueError` is raised with the message \"Inclusion proof has invalid log index: [log_index] < 0\".\n", + "- If the tree size is less than 0, a `ValueError` is raised with the message \"Inclusion proof has invalid tree size: [tree_size] < 0\".\n", + "- If the log index is greater than or equal to the tree size, a `ValueError` is raised with the message \"Inclusion proof has log index greater than or equal to tree size: [log_index] <= [tree_size]\".\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed and clear documentation for the given Python class code. It includes the class name, description, attributes, functions, and error handling, which are all the required sections according to the task instructions.\\n\\nThe class name and description are accurate and provide a good understanding of what the class is about.\\n\\nThe attributes section lists all the attributes in the class along with their data types and a brief description of what they represent.\\n\\nThe functions section provides a detailed documentation for each function in the class. It includes the function description, parameters, and return values. The descriptions are clear and provide a good understanding of what each function does.\\n\\nThe error handling section lists all the possible errors that can be raised by the functions in the class. It includes the error type and the error message, which can be very helpful for users to understand what went wrong in case of an error.\\n\\nTherefore, the submission is helpful as it provides a comprehensive and clear documentation for the given Python class code. It is insightful as it provides a good understanding of the class and its functions. It is also appropriate as it follows the structure and guidelines provided in the task instructions.\\n\\nSo, the submission meets the criterion.', 'value': 'So, the submission meets the criterion.', 'score': None}\n", + "{'reasoning': 'The criteria for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nLet\\'s evaluate the submission based on this criterion:\\n\\n1. The submission correctly identifies the class name as \"LogInclusionProof\".\\n2. The description provided for the class is accurate and aligns with the reference material.\\n3. The attributes of the class are correctly identified and their data types are accurately mentioned.\\n4. The functions within the class are correctly identified and their descriptions, parameters, and return values are accurately documented.\\n5. The error handling section correctly identifies the possible errors and their corresponding messages.\\n\\nBased on the above evaluation, the submission is correct, accurate, and factual. Therefore, it meets the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\n1. The output begins with the class name and a description, which is required.\\n2. The output then lists all the attributes of the class along with their data types, which is also required.\\n3. The output then documents each function in the class, providing a description, parameters and data types, and return values for each function. This is in line with the requirements.\\n4. The output also includes error handling, detailing the possible error responses, which is required.\\n\\nTherefore, the output is complete and captures all required fields.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Introduction:\n", + "No Code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to be helpful, insightful, and appropriate. \\n\\nLooking at the submission, the AI has correctly identified that no code has been provided in the prompt and has stated so in the introduction. This is appropriate and accurate, as per the special caution in the task instructions. \\n\\nHowever, the criterion also includes being insightful. In this case, the submission is not insightful because it does not provide any additional information or insight beyond stating the obvious fact that no code has been provided. \\n\\nTherefore, the submission does not fully meet the criterion of being helpful, insightful, and appropriate.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion is to assess the correctness of the submission. The submission is supposed to generate API documentation for a given Python code. However, the submission correctly states that \"No Code has been provided in the prompt\". This is accurate as the input does not provide any Python code to document. The reference provided is irrelevant in this case as it is an example of a documented Python code, but no code was provided in the input for the AI to document. Therefore, the submission is correct and meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is whether the output is complete and captures all required fields. \\n\\nThe submission states \"No Code has been provided in the prompt.\" This is in line with the special caution given in the input that if no code is present in the prompt, the AI should state that no code has been provided. \\n\\nTherefore, the submission is complete given the absence of any code to document. It captures the required field of stating that no code has been provided when that is the case. \\n\\nSo, the submission meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to be helpful, insightful, and appropriate. \\n\\nLooking at the input, it\\'s clear that there is Python code provided for which the AI is supposed to generate API documentation. The code includes several classes and methods that need to be documented.\\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect as there is clearly code provided in the input. \\n\\nTherefore, the submission is not helpful or appropriate as it does not provide the required API documentation for the provided code. It is also not insightful as it does not provide any useful information about the code.\\n\\nSo, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria is to assess the correctness of the submission. The submission states \"No code has been provided in the prompt\" which is incorrect. The prompt clearly provides Python code for several classes including Error, NetworkError, TUFError, MetadataError, and RootError. The submission should have provided API documentation for these classes as per the instructions in the prompt. Therefore, the submission does not meet the criteria of correctness. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess if the output is complete and captures all required fields. \\n\\nThe task requires the AI to generate API documentation for the provided Python code. The Python code provided is a set of classes and their methods. The required fields for the documentation include the class name, description, class attributes and data types, function descriptions, parameters and data types, return values, and possible error responses.\\n\\nThe submission, however, states \"No code has been provided in the prompt.\" This is incorrect as there is Python code provided in the input. Therefore, the submission does not capture any of the required fields for the API documentation.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to be helpful, insightful, and appropriate. \\n\\nIn this case, the task was to generate API documentation for a given Python code. However, no code was provided in the prompt. The submission correctly states \"No code has been provided in the prompt\", which is the appropriate response given the instructions. \\n\\nTherefore, the submission is helpful because it correctly identifies the lack of code in the prompt, it is insightful because it follows the special caution instructions, and it is appropriate because it does not generate any speculative or inaccurate information. \\n\\nSo, the submission meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nStep 1: The task requires the AI to generate API documentation for a given Python code. However, the submission states \"No code has been provided in the prompt.\" This is in line with the special caution given in the task that if no code is present, the AI should state so.\\n\\nStep 2: The submission does not generate any generic examples, hallucinate variable names, function names, class names, or the intended API usage. It simply states the fact that no code has been provided.\\n\\nStep 3: The submission is accurate and factual as it correctly identifies that no code has been provided in the prompt.\\n\\nBased on these steps, the submission meets the criterion of correctness.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is to assess if the output is complete and captures all required fields. \\n\\nThe submission states \"No code has been provided in the prompt.\" \\n\\nGiven the input, the AI was supposed to generate API documentation for a Python code. However, no code was provided in the input. \\n\\nThe AI correctly identified this and stated that no code was provided. \\n\\nTherefore, the AI\\'s response is complete and captures all required fields given the lack of code in the input. \\n\\nSo, the submission meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to be helpful, insightful, and appropriate. \\n\\n1. The task prompt provides a Python code that includes several classes and functions. The task is to generate API documentation for the provided code.\\n2. The submission, however, states \"No code has been provided in the prompt.\" This is incorrect as the prompt clearly includes Python code.\\n3. The submission is not helpful or insightful as it does not provide any information about the provided code. It is also not appropriate as it does not follow the task instructions.\\n4. Therefore, the submission does not meet the criterion.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nStep 1: Check if the submission is correct. The submission states \"No code has been provided in the prompt.\" However, the input clearly provides Python code for several classes. Therefore, the submission is not correct.\\n\\nStep 2: Check if the submission is accurate. The submission inaccurately claims that no code has been provided, when in fact there is code present in the input. Therefore, the submission is not accurate.\\n\\nStep 3: Check if the submission is factual. The submission\\'s claim that no code has been provided is not factual, as there is clearly code present in the input. Therefore, the submission is not factual.\\n\\nBased on these steps, the submission does not meet the criterion of correctness.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria is to assess if the output is complete and captures all required fields. \\n\\nThe task was to generate API documentation for the provided Python code. The code provided includes several classes: VerificationResult, VerificationSuccess, VerificationFailure, InvalidMaterials, RekorEntryMissing, and InvalidRekorEntry. Each of these classes has attributes and methods that need to be documented.\\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect as there is clearly code provided in the prompt. Therefore, the submission does not meet the criteria of being complete and capturing all required fields.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# **API Documentation**\n", + "\n", + "## Introduction:\n", + "This API provides a comprehensive documentation for the Python code provided. It includes details about classes, functions, attributes, parameters, return values, and possible error scenarios. \n", + "\n", + "## Class:\n", + "\n", + "- Class Name: `Person`\n", + "- Class Description: This class represents a person and contains attributes and functions related to a person's information.\n", + "\n", + "### Class Attributes:\n", + "- `name` (string): The name of the person.\n", + "- `age` (integer): The age of the person.\n", + "\n", + "### Class Functions:\n", + "\n", + "**1. `__init__`**:\n", + "\n", + "Description: This function is called when a new instance of the `Person` class is initialized. It initializes the `name` and `age` attributes for the person.\n", + "\n", + "Parameters:\n", + "- `self` (object): The instance of the class.\n", + "- `name` (string): The name of the person.\n", + "- `age` (integer): The age of the person.\n", + "\n", + "Return Value: None\n", + "\n", + "---\n", + "\n", + "**2. `get_name`**:\n", + "\n", + "Description: This function returns the name of the person.\n", + "\n", + "Parameters:\n", + "- `self` (object): The instance of the class.\n", + "\n", + "Return Value: The name of the person (string).\n", + "\n", + "---\n", + "\n", + "**3. `get_age`**:\n", + "\n", + "Description: This function returns the age of the person.\n", + "\n", + "Parameters:\n", + "- `self` (object): The instance of the class.\n", + "\n", + "Return Value: The age of the person (integer).\n", + "\n", + "---\n", + "\n", + "**4. `set_name`**:\n", + "\n", + "Description: This function sets the name of the person to the given value.\n", + "\n", + "Parameters:\n", + "- `self` (object): The instance of the class.\n", + "- `name` (string): The new name for the person.\n", + "\n", + "Return Value: None\n", + "\n", + "---\n", + "\n", + "**5. `set_age`**:\n", + "\n", + "Description: This function sets the age of the person to the given value.\n", + "\n", + "Parameters:\n", + "- `self` (object): The instance of the class.\n", + "- `age` (integer): The new age for the person.\n", + "\n", + "Return Value: None\n", + "\n", + "---\n", + "\n", + "## Functions:\n", + "No code has been provided in the prompt. \n", + "\n", + "## Error Handling:\n", + "No code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that the AI has generated a comprehensive API documentation for a hypothetical Python class named `Person`. The documentation includes an introduction, details about the class, its attributes, and functions. It also includes the parameters and return values for each function. \\n\\nThe documentation is helpful as it provides all the necessary details that a developer would need to understand the class and its functions. It is insightful as it explains the purpose of each function and attribute. It is also appropriate as it follows the structure provided in the input and adheres to the special caution about not generating documentation for code that is not present.\\n\\nHowever, there is a discrepancy in the submission. The prompt specifically mentions that if no code is present, the AI should state \"No Code has been provided in the prompt\". In this case, no code was provided, yet the AI generated a detailed documentation for a hypothetical `Person` class. This goes against the instructions provided in the prompt.\\n\\nTherefore, while the submission is helpful, insightful, and appropriate in a general sense, it does not follow the specific instructions provided in the prompt. Hence, it does not meet the criterion of \"helpfulness\" in this context. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission is an API documentation for a hypothetical Python class named `Person`. The documentation includes an introduction, details about the class, its attributes, and its functions. It also mentions that no code for functions or error handling was provided in the prompt.\\n\\nThe documentation is well-structured and follows the structure provided in the input. It includes all the necessary details about the class, its attributes, and its functions. The descriptions are clear and concise, and the data types for the attributes and parameters are correctly identified.\\n\\nHowever, there is a problem with the submission. The input specifically states that the AI should not generate documentation for code that is not present. In this case, no code was provided in the prompt, so the AI should not have generated documentation for a `Person` class. The AI was specifically instructed to state \"No Code has been provided in the prompt\" if no code was present, but it did not follow this instruction.\\n\\nTherefore, the submission is not correct, as it does not follow the instructions provided in the input. It generates documentation for code that is not present, which is against the instructions. \\n\\nSo, the submission does not meet the criterion of correctness.', 'value': 'So, the submission does not meet the criterion of correctness.', 'score': None}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission includes an introduction that describes the purpose of the API documentation. This meets the requirement.\\n\\n2. Class: The submission includes a class named `Person` with a description. It also includes the class attributes `name` and `age` with their data types. This meets the requirement.\\n\\n3. Class Functions: The submission includes five functions (`__init__`, `get_name`, `get_age`, `set_name`, `set_age`) with descriptions, parameters, and return values. This meets the requirement.\\n\\n4. Functions: The submission correctly states that no code has been provided in the prompt for this section. This meets the requirement.\\n\\n5. Error Handling: The submission correctly states that no code has been provided in the prompt for this section. This meets the requirement.\\n\\nThe submission has met all the criteria as it is complete and captures all required fields. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to provide API documentation for the given Python code. \\n\\nThe Python code provided in the input is a series of class definitions. The task requires the assistant to generate API documentation for these classes, including class names, descriptions, attributes, data types, functions, and error handling.\\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect, as there is clearly Python code provided in the input. Therefore, the submission is not helpful or insightful, as it does not provide any of the required information.\\n\\nBased on this analysis, the submission does not meet the criterion of helpfulness.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission states \"No code has been provided in the prompt.\" However, the input clearly contains Python code that needs to be documented. The code includes several classes such as \"_SingleX509ExtPolicy\", \"OIDCIssuer\", \"GitHubWorkflowTrigger\", \"GitHubWorkflowSHA\", \"GitHubWorkflowName\", \"GitHubWorkflowRepository\", \"GitHubWorkflowRef\", \"VerificationPolicy\", \"AnyOf\", \"AllOf\", \"UnsafeNoOp\", and \"Identity\". \\n\\nTherefore, the submission is not correct or accurate as it does not reflect the content of the input. The submission does not meet the criterion.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria asks if the output is complete and captures all required fields. \\n\\nLooking at the input, it is clear that there is a significant amount of Python code provided. The code includes several classes and functions that need to be documented according to the instructions given in the input. \\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect as there is clearly code provided in the input. \\n\\nTherefore, the submission does not meet the criteria as it does not capture all required fields and is not complete. \\n\\nThe answer is No. \\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Tower of Hanoi\n", + "\n", + "## Introduction:\n", + "The Tower of Hanoi is a classic mathematical puzzle that involves moving a tower of disks from one location to another, with the constraint that a larger disk cannot be placed on top of a smaller disk. The puzzle consists of three rods and a number of disks of different sizes which can be moved between the rods. The objective of the puzzle is to move the entire tower to another rod, adhering to the given conditions.\n", + "\n", + "## Functions:\n", + "\n", + "### `tower_of_hanoi`\n", + "\n", + "Description:\n", + "This function solves the Tower of Hanoi puzzle for the given number of disks. It prints a step-by-step solution to move the disks from the source rod to the destination rod.\n", + "\n", + "Parameters: \n", + "- `n` (int): The number of disks in the tower (must be a positive integer).\n", + "- `source` (str): The name of the source rod.\n", + "- `destination` (str): The name of the destination rod.\n", + "- `auxiliary` (str): The name of the auxiliary rod.\n", + "\n", + "Return Values:\n", + "- None\n", + "\n", + "### `main`\n", + "\n", + "Description:\n", + "This function is the main entry point of the program. It prompts the user to input the number of disks and calls the `tower_of_hanoi` function with the specified number of disks and rod names.\n", + "\n", + "Parameters: \n", + "- None\n", + "\n", + "Return Values:\n", + "- None\n", + "\n", + "No error handling has been provided in the code.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\n1. Helpfulness: The submission provides a clear and concise explanation of the Tower of Hanoi problem and the functions used to solve it. It explains the purpose of each function, the parameters they take, and their return values. This would be very helpful for someone trying to understand the code.\\n\\n2. Insightfulness: The submission provides insight into how the Tower of Hanoi problem is solved using recursion. It explains the role of each function in the solution, which would be insightful for someone learning about recursion or the Tower of Hanoi problem.\\n\\n3. Appropriateness: The submission is appropriate for the task. It follows the structure provided in the prompt and provides all the necessary information. It does not include any speculative information or hallucinate any details.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission is a detailed API documentation for a hypothetical Tower of Hanoi Python program. It provides an introduction to the Tower of Hanoi problem, and then documents two functions, `tower_of_hanoi` and `main`. For each function, it provides a description, parameters and their data types, and return values. It also notes that no error handling has been provided in the code.\\n\\nThe submission seems to be correct, accurate, and factual based on the information provided in the input. However, without the actual Python code, it\\'s impossible to verify the accuracy of the documentation. The task instructions specifically state that if no code is present in the prompt, the assistant should state \"No Code has been provided in the prompt\". The assistant has not followed this instruction, instead it has generated a detailed API documentation for a hypothetical code.\\n\\nTherefore, the submission does not meet the criteria. \\n\\nThe reference provided does not seem to be related to the submission or the task instructions, and it\\'s unclear why it was included in the data. It appears to be API documentation for a different Python module, unrelated to the Tower of Hanoi problem. \\n\\nBased on the above reasoning, the answer is: \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides an introduction that explains the concept of the Tower of Hanoi, which is the subject of the API documentation. This meets the requirement for the introduction.\\n\\n2. Class: The prompt does not provide any class, so there is no need for class documentation. The submission does not include any class documentation, which is correct.\\n\\n3. Functions: The submission provides documentation for two functions, `tower_of_hanoi` and `main`. For each function, it provides a description, parameters and their data types, and return values. This meets the requirement for function documentation.\\n\\n4. Error Handling: The submission states that \"No error handling has been provided in the code.\" This meets the requirement for error handling documentation.\\n\\nThe submission does not generate any generic examples, speculative information, or hallucinate any variable names, function names, class names, or intended API usage. It only generates documentation for the code that is actually present, which is in line with the special caution.\\n\\nTherefore, the submission meets all the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class Name:** Signer\n", + "\n", + "**Description:** The Signer class is responsible for signing input data using an identity token and a signing context. It generates ephemeral keys and retrieves a signing certificate for the identity token. It also verifies the SCT (Signed Certificate Timestamp) and signs the input data using the private key. The signed artifact is then used to create a transparency log entry.\n", + "\n", + "**Class Attributes:**\n", + "- `_identity_token` : IdentityToken - The identity token used for signing.\n", + "- `_signing_ctx` : SigningContext - The signing context used for signing.\n", + "- `__cached_private_key` : Optional[ec.EllipticCurvePrivateKey] - Cached private key used for signing.\n", + "- `__cached_signing_certificate` : Optional[FulcioCertificateSigningResponse] - Cached signing certificate.\n", + "\n", + "**Methods:**\n", + "\n", + "1. `_private_key`:\n", + "\n", + " - **Description:** Returns the private key for signing. If a cached private key exists, it returns that. Otherwise, it generates a new private key using the SECP256R1 curve.\n", + " - **Parameters:** None\n", + " - **Return Type:** ec.EllipticCurvePrivateKey\n", + "\n", + "2. `_signing_cert(private_key: ec.EllipticCurvePrivateKey) -> FulcioCertificateSigningResponse`:\n", + "\n", + " - **Description:** Retrieves or generates a signing certificate for the provided private key. It checks if the cached signing certificate exists and if it's expired. If expired, it raises an ExpiredCertificate exception. Otherwise, it retrieves the cached signing certificate. If it doesn't exist, it builds a certificate signing request with the email address from the identity token and sends a request to obtain a signed certificate.\n", + " - **Parameters:**\n", + " - `private_key` : ec.EllipticCurvePrivateKey - The private key used for signing.\n", + " - **Return Type:** FulcioCertificateSigningResponse\n", + "\n", + "3. `sign(input_: IO[bytes]) -> SigningResult`:\n", + "\n", + " - **Description:** Sign the input data using the provided input stream and the private key. Verify if the identity token is expired. If it is, raise an ExpiredIdentity exception. Otherwise, retrieve the signing certificate using the private key. Verify the SCT, sign the artifact, and create a transparency log entry. Return a SigningResult object containing information about the signing operation.\n", + " - **Parameters:**\n", + " - `input_` : IO[bytes] - The input data stream to be signed.\n", + " - **Return Type:** SigningResult\n", + "\n", + "**Error Handling:**\n", + "\n", + "- `ExpiredCertificate` - Raised when the cached signing certificate is expired.\n", + "- `ExpiredIdentity` - Raised when the identity token is expired.\n", + "{'reasoning': \"The criterion for this task is whether the submission is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed and comprehensive documentation for the 'Signer' class. It includes the class name, a description of the class, the class attributes, and the methods within the class. Each method is described in detail, including its purpose, parameters, and return type. The submission also includes potential error handling scenarios.\\n\\nThe submission is helpful because it provides all the necessary information about the 'Signer' class. It is insightful because it explains the purpose and functionality of each method in the class. It is appropriate because it follows the structure provided in the input and adheres to the special caution notes.\\n\\nTherefore, the submission meets the criterion. \\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The submission is being evaluated on the basis of correctness, accuracy, and factualness. \\n\\nThe submission provides a detailed documentation of the 'Signer' class from the provided Python code. It correctly identifies the class name and provides an accurate description of the class's purpose. \\n\\nThe class attributes are correctly identified and their data types are accurately provided. The description of each attribute aligns with the code provided. \\n\\nThe methods of the class are correctly identified and their descriptions are accurate. The parameters and return types of each method are correctly identified and described. \\n\\nThe error handling section correctly identifies the exceptions that can be raised by the methods of the class. \\n\\nThe submission does not include any speculative information and is complete in its documentation of the 'Signer' class. \\n\\nTherefore, the submission meets the criteria of correctness, accuracy, and factualness. \\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nThe output is expected to provide a detailed documentation of the given Python class code. The documentation should include the class name, description, class attributes and their data types, and a detailed documentation of each function in the class. The function documentation should include a description, parameters and their data types, and return values. The output should also include possible error responses.\\n\\nLooking at the provided output, it can be seen that it includes the class name and a detailed description of the class. It also includes the class attributes and their data types. \\n\\nThe output also provides a detailed documentation of each function in the class. For each function, it includes a description, parameters and their data types, and return values. \\n\\nThe output also includes possible error responses, which are the exceptions that can be raised by the functions in the class.\\n\\nTherefore, the output is complete and captures all required fields. \\n\\nThe output meets the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "## 1. Introduction\n", + "\n", + "The provided Python code is an API that performs basic arithmetic operations on two numbers. It includes a class named `Calculator` and several functions to perform addition, subtraction, multiplication, and division.\n", + "\n", + "## 2. Class: Calculator\n", + "\n", + "This class provides methods to perform arithmetic operations on two numbers.\n", + "\n", + "### Class Attributes\n", + "\n", + "- `num1`: A float representing the first number.\n", + "- `num2`: A float representing the second number.\n", + "\n", + "### Methods\n", + "\n", + "#### `add()`\n", + "\n", + "Adds the two numbers `num1` and `num2` and returns the sum.\n", + "\n", + "##### Parameters\n", + "\n", + "None\n", + "\n", + "##### Return Value\n", + "\n", + "- Type: float\n", + "- Description: The sum of `num1` and `num2`.\n", + "\n", + "#### `subtract()`\n", + "\n", + "Subtracts the second number `num2` from the first number `num1` and returns the difference.\n", + "\n", + "##### Parameters\n", + "\n", + "None\n", + "\n", + "##### Return Value\n", + "\n", + "- Type: float\n", + "- Description: The difference between `num1` and `num2`.\n", + "\n", + "#### `multiply()`\n", + "\n", + "Multiplies the two numbers `num1` and `num2` and returns the product.\n", + "\n", + "##### Parameters\n", + "\n", + "None\n", + "\n", + "##### Return Value\n", + "\n", + "- Type: float\n", + "- Description: The product of `num1` and `num2`.\n", + "\n", + "#### `divide()`\n", + "\n", + "Divides the first number `num1` by the second number `num2` and returns the quotient.\n", + "\n", + "##### Parameters\n", + "\n", + "None\n", + "\n", + "##### Return Value\n", + "\n", + "- Type: float\n", + "- Description: The quotient of `num1` divided by `num2`.\n", + "\n", + "\n", + "## Functions\n", + "\n", + "No Code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that the AI has generated a detailed and comprehensive API documentation for a hypothetical Python code. The documentation includes an introduction, a detailed description of a class named \\'Calculator\\', and its methods. The methods are well-documented with their descriptions, parameters, and return values. \\n\\nHowever, there is a discrepancy in the task. The task specifically mentions that the AI should not generate documentation if no code is provided. In this case, no code was provided in the input, but the AI has generated a detailed documentation for a hypothetical \\'Calculator\\' class. This is a violation of the task\\'s special caution.\\n\\nTherefore, the submission is not appropriate as it does not follow the instructions given in the task. It is not helpful as it provides information that is not based on any provided code. It is also not insightful as it does not provide any real insight into any actual Python code.\\n\\nBased on this analysis, the submission does not meet the criterion of \"helpfulness\". \\n\\nSo, the answer is \"N\". \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission is supposed to generate API documentation for a given Python code. However, no Python code was provided in the prompt. The submission should have stated \"No Code has been provided in the prompt\" as per the special caution in the input. \\n\\nInstead, the submission has generated API documentation for a hypothetical Python code involving a class named `Calculator` and several arithmetic functions. This is speculative and not based on any provided code, which goes against the instructions.\\n\\nTherefore, the submission does not meet the criterion of correctness as it is not accurate or factual based on the provided input.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess if the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides an introduction that describes the Python code as an API that performs basic arithmetic operations. This meets the requirement.\\n\\n2. Class: The submission provides a description of the class `Calculator` and its attributes `num1` and `num2`. It also documents the methods `add()`, `subtract()`, `multiply()`, and `divide()`. Each method is described, and their return values are documented. This meets the requirement.\\n\\n3. Functions: The submission correctly states that no code has been provided in the prompt for this section. This meets the requirement.\\n\\n4. Error Handling: The submission does not include any information on possible error responses. This does not meet the requirement.\\n\\nBased on the above analysis, the submission does not meet all the criteria because it does not include information on error handling. \\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code has been provided in the prompt.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to provide API documentation for the given Python code. However, the submission states \"No code has been provided in the prompt\", which is incorrect. The prompt clearly provides Python code for three classes: LogEntryMissing, CertificateVerificationFailure, and Verifier. The submission does not provide any documentation for these classes, which makes it unhelpful and inappropriate. Therefore, the submission does not meet the criterion.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission states \"No code has been provided in the prompt.\" However, the input clearly contains Python code for three classes: LogEntryMissing, CertificateVerificationFailure, and Verifier. The submission is therefore incorrect, as it fails to acknowledge the presence of the code and does not provide the requested API documentation for it.\\n\\nSo, the submission does not meet the criterion of correctness.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe task requires the AI to generate API documentation for the provided Python code. The Python code provided includes three classes: `LogEntryMissing`, `CertificateVerificationFailure`, and `Verifier`. Each class has its own attributes and methods that need to be documented.\\n\\nHowever, the submission states \"No code has been provided in the prompt.\" This is incorrect as there is Python code provided in the input. Therefore, the submission does not meet the criteria of being complete and capturing all required fields, as it does not provide any documentation for the provided Python code.\\n\\nSo, the answer is No, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Function Description:**\n", + "\n", + "The `detect_credential` function is used to detect the credential needed for authorization. It returns the detected credential as a string if it is successfully detected, or `None` if no credential is found.\n", + "\n", + "**Parameters:**\n", + "\n", + "This function does not take any parameters.\n", + "\n", + "**Return Values:**\n", + "\n", + "- Returns a string representing the detected credential if it is successfully detected.\n", + "- Returns `None` if no credential is found.\n", + "\n", + "**Error Handling:**\n", + "\n", + "If the `detect_credential` function encounters an `id.IdentityError` during the credential detection process, it raises an `IdentityError` and handles it using the `raise_from_id` method from the `IdentityError` class. The specific error response and how it is handled is not provided in the code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission:\\n\\n1. The function description is clear and concise, explaining what the function does and what it returns. This is helpful for understanding the function\\'s purpose.\\n\\n2. The submission correctly states that the function does not take any parameters. This is helpful for understanding how to use the function.\\n\\n3. The return values are clearly explained, which is helpful for understanding what to expect when the function is called.\\n\\n4. The error handling section describes what happens when an error is encountered. This is insightful and helpful for understanding how the function handles errors.\\n\\n5. The submission is appropriate as it follows the instructions given in the input and provides accurate information about the function.\\n\\nBased on these points, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nLet's evaluate the submission based on this criterion:\\n\\n1. Function Description: The submission accurately describes the function `detect_credential`. It correctly states that the function is used to detect the credential needed for authorization and returns the detected credential as a string if it is successfully detected, or `None` if no credential is found.\\n\\n2. Parameters: The submission correctly states that the function does not take any parameters.\\n\\n3. Return Values: The submission accurately describes the return values of the function. It correctly states that the function returns a string representing the detected credential if it is successfully detected, or `None` if no credential is found.\\n\\n4. Error Handling: The submission correctly describes the error handling in the function. It accurately states that if the `detect_credential` function encounters an `id.IdentityError` during the credential detection process, it raises an `IdentityError` and handles it using the `raise_from_id` method from the `IdentityError` class.\\n\\nBased on the above analysis, the submission is correct, accurate, and factual. Therefore, it meets the criterion.\\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe required fields for the function documentation are:\\n\\n1. Function Description: The submission provides a clear and accurate description of what the `detect_credential` function does.\\n\\n2. Parameters: The submission correctly states that the function does not take any parameters.\\n\\n3. Return Values: The submission accurately describes the possible return values of the function.\\n\\n4. Error Handling: The submission describes the error handling process in the function, including the type of error that is caught and how it is handled.\\n\\nUpon reviewing the submission, it is clear that it has provided all the required fields in the function documentation. The function description, parameters, return values, and error handling are all accurately and clearly described. Therefore, the submission meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class 1: _OpenIDConfiguration\n", + "Description: This class represents the OpenID configuration, which includes the authorization and token endpoints.\n", + "\n", + "Attributes:\n", + "- authorization_endpoint: A string representing the authorization endpoint.\n", + "- token_endpoint: A string representing the token endpoint.\n", + "\n", + "Class 2: ExpiredIdentity\n", + "Description: This class is an exception class that is raised when an identity token has expired.\n", + "\n", + "Class 3: IdentityToken\n", + "Description: This class represents an identity token and provides methods to retrieve information from it.\n", + "\n", + "Attributes:\n", + "- _raw_token: A string representing the raw identity token.\n", + "- _unverified_claims: A dictionary representing the unverified claims extracted from the identity token.\n", + "- _iss: A string representing the issuer of the identity token.\n", + "- _nbf: An integer or None representing the \"not before\" claim of the identity token.\n", + "- _exp: An integer representing the expiration time of the identity token.\n", + "- _identity: A string representing the identity claim of the identity token.\n", + "- _federated_issuer: A string or None representing the federated issuer of the identity token.\n", + "\n", + "Methods:\n", + "- in_validity_period(): Checks if the identity token is within its validity period. Returns True if it is, False otherwise.\n", + "- identity(): Returns the identity claim of the identity token.\n", + "- issuer(): Returns the issuer of the identity token.\n", + "- expected_certificate_subject(): Returns the expected certificate subject for the identity token.\n", + "- __str__(): Returns the raw identity token as a string.\n", + "\n", + "Class 4: IssuerError\n", + "Description: This class is an exception class that represents an error related to the issuer.\n", + "\n", + "Class 5: Issuer\n", + "Description: This class represents the issuer and provides methods to interact with it.\n", + "\n", + "Attributes:\n", + "- oidc_config: An instance of _OpenIDConfiguration representing the OpenID configuration obtained from the issuer.\n", + "\n", + "Methods:\n", + "- __init__(base_url: str): Initializes the Issuer instance by retrieving the OpenID configuration from the provided base URL.\n", + "- production(): Returns an Issuer instance for the production environment.\n", + "- staging(): Returns an Issuer instance for the staging environment.\n", + "- identity_token(client_id: str, client_secret: str, force_oob: bool): Retrieves an identity token from the issuer using the provided client ID and client secret. Returns an instance of IdentityToken.\n", + "\n", + "Class 6: IdentityError\n", + "Description: This class is an exception class that represents an error related to identity.\n", + "\n", + "Methods:\n", + "- raise_from_id(exc: id.IdentityError): Creates an instance of IdentityError from a given IdentityError exception.\n", + "- diagnostics(): Returns a string with additional diagnostic information about the error.\n", + "\n", + "Function Documentation:\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides detailed documentation for the Python code provided in the input. It includes class names, descriptions, attributes, and methods for each class in the code. The submission also provides the data types for each attribute and method, which is crucial for understanding how to use the classes. \\n\\nThe submission also provides a description for each method, explaining what it does, which is very helpful for understanding the functionality of the class. \\n\\nThe submission is insightful as it provides a deep understanding of the code. It explains not just what the code does, but also why it does it, which is very useful for anyone trying to understand or use the code.\\n\\nThe submission is appropriate as it follows the instructions given in the input. It does not speculate or generate generic examples, but instead provides documentation based on the actual code provided.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate. \\n\\nTherefore, the answer is:\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nThe submission provides a detailed documentation for each class present in the Python code. It includes the class name, description, attributes, and methods. The data types for each attribute and method are also provided. The submission also includes error handling, describing how errors are handled in the code.\\n\\nThe submission is accurate as it correctly describes the functionality of each class and its methods. It also correctly identifies the data types of the attributes and methods.\\n\\nThe submission is factual as it is based on the provided Python code. It does not include any speculative or generic examples.\\n\\nTherefore, the submission meets the criteria of being correct, accurate, and factual. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nThe task requires the documentation of the Python code provided in the prompt. The documentation should include an introduction, class documentation, function documentation, and error handling. \\n\\nLooking at the submission, it can be seen that the introduction is missing. However, the task does not provide any specific information that should be included in the introduction, so this can be overlooked.\\n\\nThe class documentation is present and appears to be complete. It includes the class name, description, attributes, and methods for each class in the code. \\n\\nThe function documentation is also present and appears to be complete. It includes the function description, parameters, and return values.\\n\\nThe error handling is not explicitly documented. However, the submission includes descriptions of the exception classes and their methods, which can be considered as a form of error handling documentation.\\n\\nBased on this analysis, the submission appears to meet the criteria, with the exception of the missing introduction. However, as mentioned earlier, the task does not provide any specific information that should be included in the introduction, so this can be overlooked.\\n\\nTherefore, the submission meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". \\n\\nThe task was to generate API documentation for Python code provided in the prompt. The instructions also stated that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the absence of any Python code. \\n\\nTherefore, the submission is appropriate and helpful in the context of the task and instructions provided. \\n\\nSo, the submission meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nThe input task asks to generate API documentation for Python code provided in the prompt. However, it also states that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", but then proceeds to provide a detailed API documentation for some Python code. This is contradictory and does not follow the instructions given in the input task. The submission should have stopped at \"No code provided\" if there was indeed no code provided.\\n\\nTherefore, the submission does not meet the criteria of correctness as it does not follow the instructions given in the input task.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion is whether the output is complete and captures all required fields. \\n\\nThe task was to generate API documentation for Python code provided in the prompt. The instructions also stated that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given for the scenario where no Python code is provided in the prompt.\\n\\nTherefore, the submission is complete and captures all required fields for the given scenario.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The `LogInclusionProof` class represents an inclusion proof for a log in a Merkle Tree. \n", + "\n", + "Attributes:\n", + "- `checkpoint` (StrictStr): The checkpoint value of the log.\n", + "- `hashes` (List[StrictStr]): The list of hash values in the inclusion proof.\n", + "- `log_index` (StrictInt): The index of the log in the Merkle Tree.\n", + "- `root_hash` (StrictStr): The root hash of the Merkle Tree.\n", + "- `tree_size` (StrictInt): The size of the Merkle Tree.\n", + "\n", + "Methods:\n", + "- `_log_index_positive(cls, v: int) -> int`: Validates that the log index is a positive integer. If the log index is less than 0, a `ValueError` is raised with an error message indicating the invalid index.\n", + "- `_tree_size_positive(cls, v: int) -> int`: Validates that the tree size is a positive integer. If the tree size is less than 0, a `ValueError` is raised with an error message indicating the invalid tree size.\n", + "- `_log_index_within_tree_size(cls, v: int, info: ValidationInfo, **kwargs: Any) -> int`: Validates that the log index is within the bounds of the tree size. If the log index is greater than or equal to the tree size, a `ValueError` is raised with an error message indicating the inconsistency.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed explanation of the `LogInclusionProof` class in the Python code. It includes the class name and a brief description of what the class represents.\\n\\nThe submission also documents each attribute of the class, including their names and data types. This is helpful for understanding what each attribute represents and the type of data it holds.\\n\\nThe submission also documents each method within the class. It provides the method names, a brief description of what each method does, and the conditions under which an error is raised. This is insightful as it gives a clear understanding of the functionality of each method and how errors are handled.\\n\\nThe submission is appropriate as it follows the instructions given in the input. It does not speculate or generate generic examples, but instead provides a detailed and accurate documentation of the provided Python code.\\n\\nTherefore, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nLet's evaluate the submission based on this criterion:\\n\\n1. The submission correctly identifies the class name as `LogInclusionProof`.\\n2. The submission accurately describes the class as representing an inclusion proof for a log in a Merkle Tree. This matches the reference's description of the class as representing an inclusion proof for a transparency log entry.\\n3. The submission correctly identifies and describes the attributes of the class: `checkpoint`, `hashes`, `log_index`, `root_hash`, and `tree_size`.\\n4. The submission correctly identifies and describes the methods of the class: `_log_index_positive`, `_tree_size_positive`, and `_log_index_within_tree_size`.\\n5. The submission accurately describes the error handling in the methods, including the conditions under which `ValueError` is raised.\\n\\nBased on this analysis, the submission appears to be correct, accurate, and factual. It provides a clear and accurate documentation of the provided Python code, meeting the task's requirements.\\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission, it appears to have documented the class `LogInclusionProof` as per the instructions. \\n\\n1. Introduction: The submission starts with a brief introduction about the class `LogInclusionProof`.\\n2. Class Documentation: The submission has documented the class name and provided a description.\\n3. Class Attributes and Data types: The submission has documented all the class attributes along with their data types.\\n4. Documentation for each method within the class: The submission has documented all the methods in the class along with their descriptions, parameters, and return types.\\n5. Error Handling: The submission has described the possible error responses and how they are handled in the code.\\n\\nTherefore, the submission appears to meet all the criteria as it is complete and captures all required fields.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the input, the task was to generate API documentation for Python code provided in the prompt. The instructions also clearly stated that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input. Therefore, the submission is appropriate.\\n\\nHowever, the criterion also includes helpfulness and insightfulness. Since no code was provided, the submission could not provide any insight or be helpful in terms of generating API documentation.\\n\\nBut considering the instructions given in the input, the submission did exactly what was asked when no code is provided. Therefore, it can be considered helpful in the context of the instructions.\\n\\nBased on this reasoning, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nThe input task asks to generate API documentation for Python code provided in the prompt. However, the prompt does not provide any Python code. The instructions clearly state that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the prompt. However, it then goes on to provide a detailed API documentation for some Python code that was not provided in the prompt. This is not in line with the instructions given in the prompt.\\n\\nTherefore, the submission does not meet the criterion of correctness as it does not follow the instructions given in the prompt.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion is to assess if the output is complete and captures all required fields. \\n\\nThe input task was to generate API documentation for Python code provided in the prompt. The task also specified that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the task for the scenario where no Python code is provided.\\n\\nTherefore, the submission is complete and captures all required fields as per the given scenario.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate. \\n\\nStep 1: Check if the submission is helpful.\\nThe task was to generate API documentation for the provided Python code. The submission states \"No code provided\", which is incorrect as there is Python code provided in the input. Therefore, the submission is not helpful.\\n\\nStep 2: Check if the submission is insightful.\\nThe submission does not provide any insights or information about the provided Python code. It does not document the classes, functions, or error handling as requested in the task. Therefore, the submission is not insightful.\\n\\nStep 3: Check if the submission is appropriate.\\nThe submission is not appropriate for the task. The task was to generate API documentation for the provided Python code, but the submission does not do this.\\n\\nBased on these steps, the submission does not meet the criterion of being helpful, insightful, and appropriate.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The task requires the generation of API documentation for the provided Python code. The criteria for assessment is the correctness of the submission.\\n\\nLooking at the submission, it states \"No code provided.\" However, the input clearly provides Python code for several classes including Error, NetworkError, TUFError, MetadataError, and RootError. Therefore, the submission is incorrect as it does not follow the instructions given in the input.\\n\\nThe reference provided also shows a detailed API documentation for the provided Python code, further confirming that the submission is incorrect.\\n\\nTherefore, the submission does not meet the criteria of correctness. \\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe input provided a Python code with several classes and methods that needed to be documented. The instructions clearly stated that if no code is provided, the section should be left blank or state \"No code provided\". However, in this case, code was provided.\\n\\nThe submission, however, states \"No code provided\", which is incorrect as there is Python code provided in the input. Therefore, the submission does not capture all required fields as it does not provide any documentation for the provided Python code.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the input, the task was to generate API documentation for Python code provided in the prompt. The instructions also clearly stated that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input. Therefore, the submission is appropriate.\\n\\nHowever, the criterion also includes helpfulness and insightfulness. Since no code was provided, the submission could not provide any insight or be helpful in terms of generating API documentation.\\n\\nBut considering the instructions given in the input, the submission did exactly what was asked when no code is provided. Therefore, it can be considered helpful in the context of the given instructions.\\n\\nSo, based on the given criterion and the context of the task, the submission can be considered as meeting the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness, which means the submission should be accurate and factual.\\n\\nThe input task asks to generate API documentation for Python code provided in the prompt. It also specifies that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input task. \\n\\nTherefore, the submission is correct and factual as it accurately follows the instructions given in the input task.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is to assess if the output is complete and captures all required fields. \\n\\nThe task was to generate API documentation for Python code provided in the prompt. The instructions also clearly state that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given for the scenario where no Python code is provided.\\n\\nTherefore, the submission has met the criteria of being complete and capturing all required fields for the given scenario.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### VerificationResult\n", + "Class Description: This class represents the result of a verification process. It contains a boolean attribute `success` indicating whether the verification was successful.\n", + "\n", + "Attributes:\n", + "- `success` (bool): Indicates whether the verification was successful.\n", + "\n", + "Methods:\n", + "- `__bool__()` -> bool: This method overrides the built-in `bool()` function and returns the value of the `success` attribute.\n", + "\n", + "### VerificationSuccess\n", + "Class Description: This class represents a successful verification result. It inherits from the `VerificationResult` class and sets the `success` attribute to `True`.\n", + "\n", + "Attributes:\n", + "- `success` (bool): Indicates whether the verification was successful (set to `True`).\n", + "\n", + "### VerificationFailure\n", + "Class Description: This class represents a failed verification result. It inherits from the `VerificationResult` class and sets the `success` attribute to `False`. It also includes a `reason` attribute indicating the reason for the failure.\n", + "\n", + "Attributes:\n", + "- `success` (bool): Indicates whether the verification was successful (set to `False`).\n", + "- `reason` (str): The reason for the verification failure.\n", + "\n", + "### InvalidMaterials\n", + "Class Description: This class represents an error that occurs while parsing verification materials. It inherits from the `Error` class.\n", + "\n", + "Methods:\n", + "- `diagnostics()` -> str: This method returns a string containing diagnostic information about the error.\n", + "\n", + "### RekorEntryMissing\n", + "Class Description: This class represents an exception that occurs when a Rekor entry is missing.\n", + "\n", + "### InvalidRekorEntry\n", + "Class Description: This class represents an error that occurs when a Rekor entry is invalid. It inherits from the `InvalidMaterials` class.\n", + "\n", + "Function Documentation:\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that the user has provided detailed documentation for each class in the provided Python code. The documentation includes class descriptions, attributes, data types, and methods, which are all required elements according to the input. \\n\\nThe descriptions are clear and concise, providing insight into what each class does and how it functions. The user has also correctly identified and documented the inheritance relationships between the classes.\\n\\nThe submission is also appropriate. It sticks to the task of documenting the provided code and does not include any unnecessary or irrelevant information.\\n\\nBased on this analysis, it can be concluded that the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nTo assess this, we need to check if the submitted API documentation accurately describes the provided Python code. \\n\\n1. The submission correctly documents the `VerificationResult` class, including its description, attribute, and method.\\n2. The `VerificationSuccess` class is also correctly documented, with accurate descriptions of its inheritance, attribute, and its value.\\n3. The `VerificationFailure` class documentation is accurate, including its inheritance, attributes, and their values.\\n4. The `InvalidMaterials` class is correctly documented, including its inheritance and method.\\n5. The `RekorEntryMissing` class is correctly documented, with an accurate description.\\n6. The `InvalidRekorEntry` class is correctly documented, including its inheritance.\\n\\nThe submission does not include any function documentation, but this is because no standalone functions are provided in the code. \\n\\nThe submission does not include error handling documentation, but this is because the provided code does not include explicit error handling.\\n\\nTherefore, the submission is correct, accurate, and factual, and it meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is to check if the output is complete and captures all required fields. \\n\\nThe task requires the documentation of Python classes provided in the input. The documentation should include the class name, description, attributes, data types, and methods. \\n\\nLooking at the submission:\\n\\n1. The submission has documented all the classes provided in the input: VerificationResult, VerificationSuccess, VerificationFailure, InvalidMaterials, RekorEntryMissing, and InvalidRekorEntry.\\n\\n2. For each class, the submission has provided the class name and a description.\\n\\n3. The submission has documented the attributes and their data types for the classes VerificationResult, VerificationSuccess, and VerificationFailure.\\n\\n4. The submission has documented the methods and their return types for the classes VerificationResult and InvalidMaterials.\\n\\n5. The submission does not provide any function documentation as there were no standalone functions in the provided code.\\n\\nBased on these observations, the submission appears to be complete and captures all required fields as per the task instructions. The submission does not miss any information from the provided Python code.\\n\\nTherefore, the submission meets the criteria.', 'value': 'Therefore, the submission meets the criteria.', 'score': None}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to generate API documentation for Python code provided in the prompt. However, the prompt clearly states that if no code is provided, the submission should state \"No code provided\". \\n\\nIn this case, the submission correctly states \"No code provided\" as there was indeed no Python code provided in the input. Therefore, the submission is helpful and appropriate as it correctly follows the instructions given in the input. \\n\\nSo, the submission meets the criterion of helpfulness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness, which in this context means the submission should accurately follow the instructions given in the input.\\n\\nThe input asks for API documentation for Python code. However, it also specifies that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input.\\n\\nTherefore, the submission is correct and meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is whether the output is complete and captures all required fields. \\n\\nThe input task asks for API documentation for Python code, but also specifies that if no code is provided, the section should be left blank or state \"No code provided\". \\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input task. \\n\\nTherefore, the submission is complete and captures all required fields as per the given task and criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class Name: `_SingleX509ExtPolicy`\n", + "Description: This class represents a single X509 extension policy. It is an abstract base class (ABC) and provides a blueprint for creating specific extension policies.\n", + "\n", + "Attributes:\n", + "- `oid`: An object identifier representing the OID (Object Identifier) of the extension.\n", + "\n", + "Methods:\n", + "- `__init__(self, value: str) -> None`: Initializes the `_SingleX509ExtPolicy` object with a value.\n", + " - Parameters:\n", + " - `value`: A string representing the value of the extension.\n", + " - Returns: None\n", + " \n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies whether the given certificate contains the specified extension and if its value matches the expected value.\n", + " - Parameters:\n", + " - `cert`: An instance of the `Certificate` class representing the certificate to verify.\n", + " - Returns: An instance of the `VerificationResult` class indicating the result of the verification. Possible results include `VerificationSuccess` or `VerificationFailure`.\n", + "\n", + "Class Name: `OIDCIssuer`\n", + "Description: This class represents an OIDC issuer extension policy and is a subclass of `_SingleX509ExtPolicy`.\n", + "\n", + "Attributes:\n", + "- `oid`: The OID (Object Identifier) of the OIDC issuer extension.\n", + "\n", + "Class Name: `GitHubWorkflowTrigger`\n", + "Description: This class represents a GitHub workflow trigger extension policy and is a subclass of `_SingleX509ExtPolicy`.\n", + "\n", + "Attributes:\n", + "- `oid`: The OID (Object Identifier) of the GitHub workflow trigger extension.\n", + "\n", + "Class Name: `GitHubWorkflowSHA`\n", + "Description: This class represents a GitHub workflow SHA extension policy and is a subclass of `_SingleX509ExtPolicy`.\n", + "\n", + "Attributes:\n", + "- `oid`: The OID (Object Identifier) of the GitHub workflow SHA extension.\n", + "\n", + "Class Name: `GitHubWorkflowName`\n", + "Description: This class represents a GitHub workflow name extension policy and is a subclass of `_SingleX509ExtPolicy`.\n", + "\n", + "Attributes:\n", + "- `oid`: The OID (Object Identifier) of the GitHub workflow name extension.\n", + "\n", + "Class Name: `GitHubWorkflowRepository`\n", + "Description: This class represents a GitHub workflow repository extension policy and is a subclass of `_SingleX509ExtPolicy`.\n", + "\n", + "Attributes:\n", + "- `oid`: The OID (Object Identifier) of the GitHub workflow repository extension.\n", + "\n", + "Class Name: `GitHubWorkflowRef`\n", + "Description: This class represents a GitHub workflow ref extension policy and is a subclass of `_SingleX509ExtPolicy`.\n", + "\n", + "Attributes:\n", + "- `oid`: The OID (Object Identifier) of the GitHub workflow ref extension.\n", + "\n", + "Class Name: `VerificationPolicy`\n", + "Description: This class is a protocol (interface) and specifies the contract for verification policies.\n", + "\n", + "Methods:\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the given certificate based on the implementation-specific logic.\n", + " - Parameters:\n", + " - `cert`: An instance of the `Certificate` class representing the certificate to verify.\n", + " - Returns: An instance of the `VerificationResult` class indicating the result of the verification. Possible results include `VerificationSuccess` or `VerificationFailure`.\n", + "\n", + "Class Name: `AnyOf`\n", + "Description: This class represents a logical OR composition of multiple verification policies.\n", + "\n", + "Methods:\n", + "- `__init__(self, children: list[VerificationPolicy])`: Initializes the `AnyOf` object with a list of child verification policies.\n", + " - Parameters:\n", + " - `children`: A list of `VerificationPolicy` objects representing the child policies.\n", + " - Returns: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the given certificate by applying each child policy until at least one succeeds.\n", + " - Parameters:\n", + " - `cert`: An instance of the `Certificate` class representing the certificate to verify.\n", + " - Returns: An instance of the `VerificationResult` class indicating the result of the verification. Possible results include `VerificationSuccess` or `VerificationFailure`.\n", + "\n", + "Class Name: `AllOf`\n", + "Description: This class represents a logical AND composition of multiple verification policies.\n", + "\n", + "Methods:\n", + "- `__init__(self, children: list[VerificationPolicy])`: Initializes the `AllOf` object with a list of child verification policies.\n", + " - Parameters:\n", + " - `children`: A list of `VerificationPolicy` objects representing the child policies.\n", + " - Returns: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the given certificate by applying each child policy, ensuring that all policies succeed.\n", + " - Parameters:\n", + " - `cert`: An instance of the `Certificate` class representing the certificate to verify.\n", + " - Returns: An instance of the `VerificationResult` class indicating the result of the verification. Possible results include `VerificationSuccess` or `VerificationFailure`.\n", + "\n", + "Class Name: `UnsafeNoOp`\n", + "Description: This class represents an unsafe (no-op) verification policy that performs no actual verification.\n", + "\n", + "Methods:\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the given certificate by logging a warning message and returning a `VerificationSuccess` result.\n", + " - Parameters:\n", + " - `cert`: An instance of the `Certificate` class representing the certificate to verify.\n", + " - Returns: An instance of the `VerificationResult` class indicating the result of the verification, which will always be `VerificationSuccess`.\n", + "\n", + "Class Name: `Identity`\n", + "Description: This class represents a verification policy based on the identity of the certificate.\n", + "\n", + "Methods:\n", + "- `__init__(self, *, identity: str, issuer: str)`: Initializes the `Identity` object with the identity and issuer values.\n", + " - Parameters:\n", + " - `identity`: A string representing the expected identity value.\n", + " - `issuer`: A string representing the OIDC issuer value.\n", + " - Returns: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the given certificate by checking if the issuer is valid, and then verifying if the certificate's subject alternative names (SANs) contain the expected identity.\n", + " - Parameters:\n", + " - `cert`: An instance of the `Certificate` class representing the certificate to verify.\n", + " - Returns: An instance of the `VerificationResult` class indicating the result of the verification. Possible results include `VerificationSuccess` or `VerificationFailure`.\n", + "\n", + "Function Documentation:\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides detailed documentation for each class in the provided Python code. For each class, the submission includes the class name, a description of the class, its attributes, and its methods. For each method, the submission provides a description, the parameters (including their names and data types), and the return values (including their data types). This is very helpful for understanding the functionality and usage of each class.\\n\\nThe submission also provides error handling information, describing the possible error responses and how they are handled in the code. This is insightful as it gives an understanding of how the code behaves in case of errors.\\n\\nThe submission is appropriate as it follows the instructions given in the input. It does not speculate or generate generic examples, but instead provides documentation based on the provided Python code.\\n\\nTherefore, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nTo assess this, we need to compare the submission with the provided Python code and check if the documentation accurately describes the classes, their attributes, and methods.\\n\\n1. The submission correctly identifies and describes the `_SingleX509ExtPolicy` class, its attributes, and methods.\\n2. The submission correctly identifies and describes the `OIDCIssuer`, `GitHubWorkflowTrigger`, `GitHubWorkflowSHA`, `GitHubWorkflowName`, `GitHubWorkflowRepository`, and `GitHubWorkflowRef` classes, which are subclasses of `_SingleX509ExtPolicy`. It correctly notes that their only attribute is `oid`.\\n3. The submission correctly identifies and describes the `VerificationPolicy` class as a protocol (interface) with a `verify` method.\\n4. The submission correctly identifies and describes the `AnyOf` and `AllOf` classes, their attributes, and methods.\\n5. The submission correctly identifies and describes the `UnsafeNoOp` class and its `verify` method.\\n6. The submission correctly identifies and describes the `Identity` class, its attributes, and methods.\\n\\nThe submission does not include any incorrect or misleading information and accurately describes the provided Python code. Therefore, the submission meets the criterion of correctness.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nThe task requires the generation of API documentation for the provided Python code. The documentation should include an introduction, class documentation, function documentation, and error handling. \\n\\nLooking at the submission, it appears that the introduction is missing. However, the class documentation is present and appears to be complete. Each class in the code is documented, including the class name, description, attributes, and methods. The methods are also documented with their descriptions, parameters, and return values. \\n\\nThe function documentation is also missing in the submission. The task prompt does not provide any standalone functions outside of classes, so this might be the reason for the absence of function documentation. \\n\\nThe error handling documentation is also missing in the submission. The code does include error handling, particularly in the `verify` methods of the classes, but these are not documented in the submission.\\n\\nBased on this analysis, the submission does not meet all the criteria as it lacks an introduction, function documentation, and error handling documentation.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to generate API documentation for Python code provided in the prompt. However, the prompt clearly states that if no code is provided, the submission should state \"No code provided\". \\n\\nIn this case, the submission correctly states \"No code provided\" as there was indeed no Python code provided in the input. Therefore, the submission is helpful and appropriate as it correctly follows the instructions given in the input. \\n\\nSo, the submission meets the criterion of helpfulness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nThe input task was to generate API documentation for Python code provided in the prompt. However, the prompt clearly states that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the prompt. Therefore, the submission is correct as it accurately follows the instructions given in the prompt.\\n\\nHowever, the reference provided seems to be an extensive API documentation for a Python code, which is not relevant to the submission as no code was provided in the prompt. The reference does not affect the correctness of the submission in this case.\\n\\nBased on this analysis, the submission meets the criterion of correctness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is to assess if the output is complete and captures all required fields. \\n\\nThe input task requires the generation of API documentation for Python code. However, it also specifies that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input task for a scenario where no Python code is provided.\\n\\nTherefore, the submission is complete and captures all required fields as per the given scenario.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class Signer**\n", + "\n", + "This class represents a signer. It is responsible for generating keys, retrieving signing certificates, and signing artifacts.\n", + "\n", + "**Attributes:**\n", + "- `_identity_token`: An instance of the IdentityToken class that contains the identity information of the signer.\n", + "- `_signing_ctx`: An instance of the SigningContext class that provides access to the Fulcio and Rekor clients.\n", + "- `__cached_private_key`: An optional EllipticCurvePrivateKey object that stores the generated private key.\n", + "- `__cached_signing_certificate`: An optional FulcioCertificateSigningResponse object that stores the signing certificate.\n", + "\n", + "**Methods:**\n", + "\n", + "1. **`__init__(self, identity_token: IdentityToken, signing_ctx: SigningContext, cache: bool = True) -> None:`**\n", + " - Description: Initializes the Signer object.\n", + " - Parameters:\n", + " - `identity_token` (IdentityToken): An instance of the IdentityToken class.\n", + " - `signing_ctx` (SigningContext): An instance of the SigningContext class.\n", + " - `cache` (bool, optional): If `True`, the private key and signing certificate will be cached. Default is `True`.\n", + " - Return Type: None\n", + "\n", + "2. **`_private_key(self) -> ec.EllipticCurvePrivateKey:`**\n", + " - Description: Returns the private key. If the private key is not cached, it generates a new one.\n", + " - Return Type: EllipticCurvePrivateKey\n", + "\n", + "3. **`_signing_cert(self, private_key: ec.EllipticCurvePrivateKey) -> FulcioCertificateSigningResponse:`**\n", + " - Description: Retrieves or generates a signing certificate using the private key.\n", + " - Parameters:\n", + " - `private_key` (EllipticCurvePrivateKey): The private key used to sign the certificate signing request.\n", + " - Return Type: FulcioCertificateSigningResponse\n", + "\n", + "4. **`sign(self, input_: IO[bytes]) -> SigningResult:`**\n", + " - Description: Signs the input artifact using the private key and returns the signing result.\n", + " - Parameters:\n", + " - `input_` (IO[bytes]): An input artifact to be signed.\n", + " - Return Type: SigningResult\n", + "\n", + "\n", + "**Class SigningContext**\n", + "\n", + "This class represents the signing context. It provides access to the Fulcio and Rekor clients.\n", + "\n", + "**Attributes:**\n", + "- `_fulcio`: An instance of the FulcioClient class.\n", + "- `_rekor`: An instance of the RekorClient class.\n", + "\n", + "**Methods:**\n", + "\n", + "1. **`__init__(self, fulcio: FulcioClient, rekor: RekorClient):`**\n", + " - Description: Initializes the SigningContext object.\n", + " - Parameters:\n", + " - `fulcio` (FulcioClient): An instance of the FulcioClient class.\n", + " - `rekor` (RekorClient): An instance of the RekorClient class.\n", + " - Return Type: None\n", + "\n", + "2. **`production(cls) -> SigningContext:`**\n", + " - Description: Creates a signing context for production.\n", + " - Return Type: SigningContext\n", + "\n", + "3. **`staging(cls) -> SigningContext:`**\n", + " - Description: Creates a signing context for staging.\n", + " - Return Type: SigningContext\n", + "\n", + "4. **`signer(self, identity_token: IdentityToken, cache: bool = True) -> Iterator[Signer]:`**\n", + " - Description: Creates a signer within the signing context.\n", + " - Parameters:\n", + " - `identity_token` (IdentityToken): An instance of the IdentityToken class.\n", + " - `cache` (bool, optional): If `True`, the private key and signing certificate will be cached. Default is `True`.\n", + " - Return Type: Iterator[Signer]\n", + "\n", + "\n", + "**Class SigningResult**\n", + "\n", + "This class represents the result of signing an artifact.\n", + "\n", + "**Attributes:**\n", + "- `input_digest`: The hash digest of the input artifact.\n", + "- `cert_pem`: The PEM-encoded signing certificate.\n", + "- `b64_signature`: The base64-encoded artifact signature.\n", + "- `log_entry`: An instance of the LogEntry class representing the transparency log entry.\n", + "\n", + "**Methods:**\n", + "\n", + "1. **`to_bundle(self) -> Bundle:`**\n", + " - Description: Converts the signing result to a bundle object that can be used for verification.\n", + " - Return Type: Bundle\n", + "\n", + "{'reasoning': 'The criterion for this task is \"helpfulness: Is the submission helpful, insightful, and appropriate?\"\\n\\nLet\\'s evaluate the submission based on this criterion:\\n\\n1. The submission provides detailed documentation for the provided Python code. It includes class names, descriptions, attributes, and methods. This is helpful for understanding the code.\\n\\n2. The submission is insightful as it not only lists the methods and attributes but also provides a brief description of what each method does and what each attribute represents. This gives a deeper understanding of the code.\\n\\n3. The submission is appropriate as it follows the instructions given in the input. It does not speculate or generate generic examples. It provides documentation only for the code provided.\\n\\nBased on the above points, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nTherefore, the answer is:\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission is being evaluated on the basis of correctness, accuracy, and factualness. \\n\\nThe submission provides a detailed documentation of the Python classes provided in the input. It includes the class names, descriptions, attributes, and methods. The data types of the attributes and the return types of the methods are also accurately provided. The descriptions of the methods are clear and user-centric, providing an understanding of what each method does. \\n\\nThe submission also correctly follows the instructions in the input. It does not speculate or generate generic examples where code is not provided. \\n\\nBased on this analysis, the submission meets the criteria of correctness, accuracy, and factualness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nThe task requires the documentation of the provided Python code, specifically the classes and their attributes and methods. \\n\\nLooking at the submission:\\n\\n1. The submission has documented all the classes present in the code: `Signer`, `SigningContext`, and `SigningResult`.\\n2. For each class, the submission has provided the class name and a description.\\n3. The submission has documented all the attributes of each class along with their data types.\\n4. The submission has documented all the methods of each class, including their descriptions, parameters (with names and data types), and return values (with data types).\\n\\nTherefore, the submission is complete and captures all required fields. It has followed the instructions in the task and has provided a clear, accurate, and user-centric documentation of the provided Python code.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No code provided.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the input, the task was to generate API documentation for Python code provided in the prompt. The instructions also clearly state that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input. Therefore, the submission is appropriate.\\n\\nAs for helpfulness and insightfulness, since no code was provided, there was no opportunity for the submitter to provide any additional insights or helpful information. However, by stating \"No code provided\", the submitter has given a clear and concise response to the situation, which can be considered helpful in the context of the task.\\n\\nTherefore, based on the given criterion, the submission meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness, which means the submission should be accurate and factual.\\n\\nThe input task asks to generate API documentation for Python code provided in the prompt. However, it also states that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the input task. Therefore, the submission is accurate and factual as it correctly follows the instructions given in the absence of any Python code.\\n\\nSo, the submission meets the criterion of correctness.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion is to assess if the output is complete and captures all required fields. \\n\\nThe input task was to generate API documentation for Python code provided in the prompt. The task also specified that if no code is provided, the section should be left blank or state \"No code provided\".\\n\\nThe submission states \"No code provided\", which is in line with the instructions given in the task for the scenario where no Python code is provided.\\n\\nTherefore, the submission is complete and captures all required fields as per the given scenario.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Class: LogEntryMissing\n", + "\n", + "#### Description:\n", + "This class represents a verification failure due to a missing log entry in the transparency log for the given verification materials.\n", + "\n", + "#### Attributes:\n", + "- `reason` (str): The reason for the verification failure.\n", + "- `signature` (B64Str): The base64-encoded signature of the verification materials.\n", + "- `artifact_hash` (HexStr): The hex-encoded hash of the artifact being verified.\n", + "\n", + "\n", + "### Class: CertificateVerificationFailure\n", + "\n", + "#### Description:\n", + "This class represents a verification failure when trying to verify a signing certificate.\n", + "\n", + "#### Attributes:\n", + "- `model_config`: The model configuration.\n", + "- `reason` (str): The reason for the verification failure.\n", + "- `exception` (Exception): The exception that occurred during the verification.\n", + "\n", + "\n", + "### Class: Verifier\n", + "\n", + "#### Description:\n", + "This class is responsible for verifying the authenticity and integrity of artifacts using a transparency log and a chain of certificates.\n", + "\n", + "#### Attributes:\n", + "- `_rekor` (RekorClient): The client for interacting with the transparency log.\n", + "- `_fulcio_certificate_chain` (List[X509]): The chain of certificates from Fulcio.\n", + "\n", + "#### Methods:\n", + "- `__init__(self, rekor: RekorClient, fulcio_certificate_chain: List[Certificate])`: Initializes a new instance of the Verifier class.\n", + " - Parameters:\n", + " - `rekor` (RekorClient): The client for interacting with the transparency log.\n", + " - `fulcio_certificate_chain` (List[Certificate]): The chain of certificates from Fulcio.\n", + "\n", + "- `production(cls) -> Verifier`: Returns a Verifier instance configured for production use.\n", + " - Returns:\n", + " - Verifier: The Verifier instance.\n", + "\n", + "- `staging(cls) -> Verifier`: Returns a Verifier instance configured for staging use.\n", + " - Returns:\n", + " - Verifier: The Verifier instance.\n", + "\n", + "- `verify(self, materials: VerificationMaterials, policy: VerificationPolicy) -> VerificationResult`: Verifies the authenticity and integrity of the given materials.\n", + " - Parameters:\n", + " - `materials` (VerificationMaterials): The verification materials.\n", + " - `policy` (VerificationPolicy): The verification policy.\n", + " - Returns:\n", + " - VerificationResult: The result of the verification.\n", + "\n", + "\n", + "### Class: VerificationMaterials\n", + "\n", + "#### Description:\n", + "This class represents the materials needed for verifying the authenticity and integrity of an artifact.\n", + "\n", + "#### Methods:\n", + "- `rekor_entry(self, rekor: RekorClient) -> Entry`: Retrieves the Rekor entry for the artifact from the transparency log.\n", + " - Parameters:\n", + " - `rekor` (RekorClient): The client for interacting with the transparency log.\n", + " - Returns:\n", + " - Entry: The Rekor entry for the artifact.\n", + "\n", + "\n", + "### Class: VerificationPolicy\n", + "\n", + "#### Description:\n", + "This class represents the policy for verifying the authenticity and integrity of artifacts.\n", + "\n", + "#### Methods:\n", + "- `verify(self, certificate: X509) -> Optional[VerificationFailure]`: Verifies the certificate against the policy.\n", + " - Parameters:\n", + " - `certificate` (X509): The certificate to verify.\n", + " - Returns:\n", + " - Optional[VerificationFailure]: A VerificationFailure if the certificate does not pass the policy, None otherwise.\n", + "\n", + "\n", + "### Class: VerificationResult\n", + "\n", + "#### Description:\n", + "This class represents the result of a verification process.\n", + "\n", + "#### Methods:\n", + "\n", + "- `__init__(self, success: bool, failure_reason: Optional[str] = None, exception: Optional[Exception] = None)`: Initializes a new instance of the VerificationResult class.\n", + " - Parameters:\n", + " - `success` (bool): True if the verification was successful, False otherwise.\n", + " - `failure_reason` (Optional[str]): The reason for the verification failure, if applicable.\n", + " - `exception` (Optional[Exception]): The exception that occurred during the verification, if applicable.\n", + "\n", + "- `is_success(self) -> bool`: Returns True if the verification was successful, False otherwise.\n", + " - Returns:\n", + " - bool: True if the verification was successful, False otherwise.\n", + "\n", + "- `is_failure(self) -> bool`: Returns True if the verification was a failure, False otherwise.\n", + " - Returns:\n", + " - bool: True if the verification was a failure, False otherwise.\n", + "\n", + "- `get_failure_reason(self) -> Optional[str]`: Returns the reason for the verification failure, if applicable.\n", + " - Returns:\n", + " - Optional[str]: The reason for the verification failure, or None if the verification was successful.\n", + "\n", + "- `get_exception(self) -> Optional[Exception]`: Returns the exception that occurred during the verification, if applicable.\n", + " - Returns:\n", + " - Optional[Exception]: The exception that occurred during the verification, or None if no exception occurred.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides detailed documentation for the provided Python code. It includes descriptions for each class, their attributes, and methods. It also includes the data types for each attribute and method parameters, which is very helpful for understanding the code.\\n\\nThe submission also provides the return types for each method, which is insightful for understanding what each method does and what it returns. This is especially useful for methods that return complex objects or data structures.\\n\\nThe submission is also appropriate. It follows the instructions given in the input and does not include any irrelevant or unnecessary information. It sticks to documenting the provided Python code and does not speculate or generate generic examples.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nTo assess this, we need to compare the provided Python code with the submitted API documentation. The submission should accurately reflect the classes, attributes, methods, and their descriptions in the code.\\n\\n1. The submission correctly documents the `LogEntryMissing` class, including its attributes and their data types.\\n\\n2. The `CertificateVerificationFailure` class is also correctly documented, including its attributes and their data types.\\n\\n3. The `Verifier` class is accurately documented, including its attributes and methods. The methods\\' parameters and return values are also correctly documented.\\n\\n4. The submission includes documentation for `VerificationMaterials`, `VerificationPolicy`, and `VerificationResult` classes. However, these classes are not present in the provided Python code. This is incorrect and does not match the code.\\n\\nBased on this analysis, the submission is not entirely correct, accurate, and factual. It includes documentation for classes that are not present in the provided Python code. Therefore, the submission does not meet the criterion. \\n\\nThe answer is:', 'value': 'The answer is:', 'score': None}\n", + "{'reasoning': 'The criteria for this task is to check if the output is complete and captures all required fields. \\n\\nThe task requires the documentation of the Python code provided in the prompt. The documentation should include the following for each class and function in the code:\\n\\n1. Class Name and Description\\n2. Class Attributes and Data types\\n3. Documentation for each method within the class, including:\\n - Function Description\\n - Parameters, including names and data types.\\n - Return values, including data types.\\n\\nLooking at the submission, it appears that the user has documented all the classes present in the code. For each class, the user has provided the class name, a description, and the attributes with their data types. \\n\\nFor the methods within the classes, the user has provided a description, the parameters with their data types, and the return values with their data types. \\n\\nHowever, the submission includes documentation for classes \"VerificationMaterials\", \"VerificationPolicy\", and \"VerificationResult\" which are not present in the provided code. This is an error and does not meet the criteria of documenting only the provided code.\\n\\nTherefore, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Introduction:\n", + "The detect_credential function is an API endpoint that is used to detect the credentials for the given audience. It is part of the identity module and allows users to identify the credentials associated with a particular audience. The function returns the detected credentials as a string, or None if no credentials are detected.\n", + "\n", + "2. Function:\n", + " - Description:\n", + " - The detect_credential function detects the credentials associated with the given audience.\n", + " \n", + " - Parameters:\n", + " - None\n", + " \n", + " - Return Value:\n", + " - The function returns a string that represents the detected credentials for the audience. If no credentials are detected, the function returns None.\n", + " \n", + "3. Error Handling:\n", + " - The function handles the IdentityError exception that may be raised during the execution of the id.detect_credential function.\n", + " - If an IdentityError exception is raised, the function will re-raise it as an IdentityError.\n", + " - In case of an error, the caller should handle the exception appropriately.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission:\\n\\n1. Introduction: The submission provides a clear and concise introduction to the function, explaining its purpose and intended use. It is helpful and appropriate.\\n\\n2. Function: The submission provides a detailed description of the function, including its parameters and return value. It explains that the function does not take any parameters and returns a string representing the detected credentials or None if no credentials are detected. This is helpful and insightful.\\n\\n3. Error Handling: The submission explains how the function handles errors, specifically the IdentityError exception. It provides appropriate advice on how the caller should handle the exception. This is helpful and appropriate.\\n\\nBased on the above analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness: Is the submission correct, accurate, and factual?\\n\\nLet\\'s evaluate the submission based on this criterion:\\n\\n1. Introduction: The submission correctly describes the purpose of the detect_credential function. It accurately states that the function is used to detect the credentials for a given audience and is part of the identity module.\\n\\n2. Function:\\n - Description: The submission accurately describes what the function does.\\n - Parameters: The submission correctly states that the function does not take any parameters.\\n - Return Value: The submission correctly describes the return value of the function.\\n\\n3. Error Handling: The submission correctly describes the error handling of the function. It accurately states that the function handles the IdentityError exception and re-raises it as an IdentityError.\\n\\nBased on the above evaluation, the submission appears to be correct, accurate, and factual. It accurately describes the function, its parameters, return value, and error handling. The submission also matches the information provided in the reference.\\n\\nTherefore, the submission meets the criterion of correctness. \\n\\nNow, I will print the single character \"Y\" or \"N\" (without quotes or punctuation) on its own line corresponding to the correct answer of whether the submission meets all criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides an introduction that describes the purpose of the API and its intended use. It explains that the detect_credential function is used to detect the credentials for a given audience.\\n\\n2. Functions: The submission documents the detect_credential function, including a description of what the function does, the parameters it takes, and the values it returns. \\n - Description: The submission explains that the function detects the credentials associated with a given audience.\\n - Parameters: The submission states that the function does not take any parameters. However, the function code suggests that it uses a parameter '_DEFAULT_AUDIENCE' which is not mentioned in the documentation. This is a missing piece of information.\\n - Return Values: The submission specifies that the function returns a string representing the detected credentials, or None if no credentials are detected.\\n\\n3. Error Handling: The submission describes the possible error responses and their meanings. It explains that the function handles the IdentityError exception and re-raises it as an IdentityError.\\n\\nBased on this analysis, the submission does not meet the criteria because it does not capture all required fields. The parameter '_DEFAULT_AUDIENCE' is not documented.\\n\\nN\", 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class `Issuer`**\n", + "\n", + "**Introduction:**\n", + "The `Issuer` class represents an OpenID Connect (OIDC) issuer. It is responsible for retrieving the OpenID configuration of the issuer, which includes the authorization and token endpoints.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `__init__(self, base_url: str) -> None`\n", + " - Description: Initializes an instance of the `Issuer` class by retrieving the OpenID configuration of the issuer.\n", + " - Parameters:\n", + " - `base_url` (str): The base URL of the issuer.\n", + " - Return Value: None\n", + "\n", + "2. `production(cls) -> Issuer`\n", + " - Description: Creates an instance of the `Issuer` class for the production environment.\n", + " - Parameters: None\n", + " - Return Value: An instance of the `Issuer` class.\n", + "\n", + "3. `staging(cls) -> Issuer`\n", + " - Description: Creates an instance of the `Issuer` class for the staging environment.\n", + " - Parameters: None\n", + " - Return Value: An instance of the `Issuer` class.\n", + "\n", + "4. `identity_token(self, client_id: str = \"sigstore\", client_secret: str = \"\", force_oob: bool = False) -> IdentityToken`\n", + " - Description: Retrieves an identity token for the client using the authorization code flow.\n", + " - Parameters:\n", + " - `client_id` (str): The client ID.\n", + " - `client_secret` (str): The client secret.\n", + " - `force_oob` (bool): Whether to force the out-of-band (OOB) flow.\n", + " - Return Value: An instance of the `IdentityToken` class representing the identity token.\n", + "\n", + "**Class `IdentityToken`**\n", + "\n", + "**Introduction:**\n", + "The `IdentityToken` class represents an identity token obtained from an issuer. It provides methods to validate and access the claims of the token.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `__init__(self, raw_token: str) -> None`\n", + " - Description: Initializes an instance of the `IdentityToken` class with a raw token.\n", + " - Parameters:\n", + " - `raw_token` (str): The raw identity token.\n", + " - Return Value: None\n", + "\n", + "2. `in_validity_period(self) -> bool`\n", + " - Description: Checks if the identity token is within its validity period.\n", + " - Parameters: None\n", + " - Return Value: A boolean indicating if the identity token is within its validity period.\n", + "\n", + "3. `identity(self) -> str`\n", + " - Description: Returns the identity associated with the identity token.\n", + " - Parameters: None\n", + " - Return Value: The identity as a string.\n", + "\n", + "4. `issuer(self) -> str`\n", + " - Description: Returns the issuer of the identity token.\n", + " - Parameters: None\n", + " - Return Value: The issuer as a string.\n", + "\n", + "5. `expected_certificate_subject(self) -> str`\n", + " - Description: Returns the expected subject of the certificate that should be bound to the identity token.\n", + " - Parameters: None\n", + " - Return Value: The expected certificate subject as a string.\n", + "\n", + "6. `__str__(self) -> str`\n", + " - Description: Returns the raw token as a string.\n", + " - Parameters: None\n", + " - Return Value: The raw token as a string.\n", + "\n", + "**Exceptions:**\n", + "\n", + "1. `IssuerError(Exception)`\n", + " - Description: An exception raised when there is an error with the issuer.\n", + "\n", + "2. `ExpiredIdentity(Exception)`\n", + " - Description: An exception raised when the identity token is expired.\n", + "\n", + "3. `IdentityError(Error)`\n", + " - Description: An exception raised when there is an error with the identity token.\n", + "\n", + "**Error Handling:**\n", + "\n", + "- The `IssuerError` exception can be raised when there is an error with the issuer, such as a failed HTTP request or an invalid OIDC configuration.\n", + "- The `ExpiredIdentity` exception can be raised when the identity token is expired.\n", + "- The `IdentityError` exception can be raised when there is an error with the identity token, such as missing or malformed claims.\n", + "{'reasoning': 'The submission is being evaluated on the criterion of helpfulness. \\n\\nThe submission provides a detailed breakdown of the classes `Issuer` and `IdentityToken`, including their functions and exceptions. It follows the requested output structure, providing an introduction, function documentation, and error handling for each class. \\n\\nThe function documentation includes descriptions, parameters, and return values, which are all clearly explained. The error handling section describes the exceptions that can be raised and under what conditions, which is useful for understanding potential issues.\\n\\nThe submission is insightful as it provides a clear understanding of what each class and function does, and it is appropriate as it follows the requested format and provides accurate information.\\n\\nTherefore, the submission meets the criterion of helpfulness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission provides a detailed and accurate documentation of the provided Python code. It correctly identifies and describes the classes, their methods, parameters, return values, and exceptions. The submission also includes a section on error handling, describing the exceptions that can be raised and their meanings. \\n\\nThe submission correctly follows the output structure provided in the input, creating a clear, concise, accurate, and user-centric API documentation. It avoids speculative information and prioritizes accuracy and completeness, as required by the input.\\n\\nThe submission also matches the reference documentation provided, further confirming its accuracy.\\n\\nTherefore, the submission meets the criteria of correctness, as it is correct, accurate, and factual.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe required fields for the API documentation are:\\n\\n1. Introduction: The submission includes an introduction for each class, describing its purpose and intended use.\\n\\n2. Functions: The submission documents each function in the classes, including a description of what the function does, a list and description of each parameter, and the data type and possible values returned.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings.\\n\\nUpon reviewing the submission, it is clear that it has provided an introduction for each class, documented each function including its description, parameters, and return values, and described the possible error responses and their meanings. \\n\\nTherefore, the submission meets the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "\n", + "This API provides functions for performing basic mathematical operations. It is designed to allow users to perform operations such as addition, subtraction, multiplication, and division on two numbers.\n", + "\n", + "## Functions\n", + "\n", + "### add_numbers\n", + "\n", + "#### Description\n", + "\n", + "This function takes two numbers as input and returns their sum.\n", + "\n", + "#### Parameters\n", + "\n", + "- `num1` (int): The first number.\n", + "- `num2` (int): The second number.\n", + "\n", + "#### Return Values\n", + "\n", + "- (int): The sum of the two input numbers.\n", + "\n", + "### subtract_numbers\n", + "\n", + "#### Description\n", + "\n", + "This function takes two numbers as input and returns their difference.\n", + "\n", + "#### Parameters\n", + "\n", + "- `num1` (int): The first number.\n", + "- `num2` (int): The second number.\n", + "\n", + "#### Return Values\n", + "\n", + "- (int): The difference between the two input numbers.\n", + "\n", + "### multiply_numbers\n", + "\n", + "#### Description\n", + "\n", + "This function takes two numbers as input and returns their product.\n", + "\n", + "#### Parameters\n", + "\n", + "- `num1` (int): The first number.\n", + "- `num2` (int): The second number.\n", + "\n", + "#### Return Values\n", + "\n", + "- (int): The product of the two input numbers.\n", + "\n", + "### divide_numbers\n", + "\n", + "#### Description\n", + "\n", + "This function takes two numbers as input and returns their quotient. It also handles the case of division by zero by returning an error message.\n", + "\n", + "#### Parameters\n", + "\n", + "- `num1` (int): The numerator.\n", + "- `num2` (int): The denominator.\n", + "\n", + "#### Return Values\n", + "\n", + "- (float): The quotient of the two input numbers.\n", + "- (str): An error message if the denominator is zero.\n", + "\n", + "## Error Handling\n", + "\n", + "- If the denominator in the `divide_numbers` function is zero, an error message will be returned indicating that division by zero is not allowed.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". \\n\\nThe submission provides a clear and concise API documentation for a set of mathematical functions. It includes an introduction that describes the purpose of the API and its intended use. \\n\\nFor each function, the submission provides a description of what the function does, lists and describes the parameters, and specifies the return values. This information is helpful for users who need to understand how to use these functions.\\n\\nThe submission also includes a section on error handling, which describes a possible error response and its meaning. This is helpful for users who encounter this error and need to understand what it means.\\n\\nBased on this analysis, the submission meets the criterion of being helpful. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nTo assess this, we need to compare the submission with the input and reference provided.\\n\\nThe input provides a task for the AI to generate API documentation for a given Python code. The documentation should include an introduction, documentation for each function (including description, parameters, and return values), and error handling.\\n\\nThe submission provides API documentation for a set of mathematical functions. It includes an introduction, documentation for each function (including description, parameters, and return values), and error handling. The documentation is clear, concise, accurate, and user-centric.\\n\\nHowever, the reference provided is a completely different API documentation for a transparency log data structure. It includes classes like LogInclusionProof and LogEntry, and a function encode_canonical. This is not related to the mathematical functions in the submission.\\n\\nTherefore, the submission is correct, accurate, and factual in terms of the task given in the input. However, it does not match the reference provided. This suggests that there may have been a mistake in the reference provided for this task.\\n\\nBased on the criteria of correctness, the submission is correct. However, it does not match the reference, which may be a separate issue.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission, it appears to have followed the structure provided in the input. \\n\\n1. Introduction: The introduction is present and describes the purpose of the API and its intended use. \\n\\n2. Functions: The submission documents each function, including a description, parameters, and return values. \\n\\n - Description: Each function has a clear explanation of what it does.\\n - Parameters: Each parameter is listed and described, including data types.\\n - Return Values: The data type and possible values returned are specified for each function.\\n\\n3. Error Handling: The submission describes a possible error response and its meaning, specifically for the divide_numbers function.\\n\\nBased on this analysis, the submission appears to meet all the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Introduction:**\n", + "The `LogInclusionProof` class is a data model that represents an inclusion proof in a Merkle tree-based log. It includes various attributes that provide information about the proof, such as the checkpoint, hashes, log index, root hash, and tree size. This class provides validation and error handling methods to ensure the integrity and consistency of the inclusion proof.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `_log_index_positive(v: int) -> int`:\n", + " - Description: Validates if the log index provided in the inclusion proof is a positive integer.\n", + " - Parameters:\n", + " - `v` (int): The log index to be validated.\n", + " - Return Value: Returns the validated log index if it is a positive integer.\n", + " - Error Handling: Raises a `ValueError` if the log index is less than 0.\n", + "\n", + "2. `_tree_size_positive(v: int) -> int`:\n", + " - Description: Validates if the tree size provided in the inclusion proof is a positive integer.\n", + " - Parameters:\n", + " - `v` (int): The tree size to be validated.\n", + " - Return Value: Returns the validated tree size if it is a positive integer.\n", + " - Error Handling: Raises a `ValueError` if the tree size is less than 0.\n", + "\n", + "3. `_log_index_within_tree_size(v: int, info: ValidationInfo, **kwargs: Any) -> int`:\n", + " - Description: Validates if the log index provided in the inclusion proof is within the range of the tree size.\n", + " - Parameters:\n", + " - `v` (int): The log index to be validated.\n", + " - `info` (ValidationInfo): Additional information about the validation process.\n", + " - `**kwargs` (Any): Additional keyword arguments.\n", + " - Return Value: Returns the validated log index if it is within the range of the tree size.\n", + " - Error Handling: Raises a `ValueError` if the log index is greater than or equal to the tree size.\n", + "\n", + "**Error Handling:**\n", + "The `LogInclusionProof` class handles the following possible error responses:\n", + "\n", + "1. `ValueError(f\"Inclusion proof has invalid log index: {v} < 0\")`: Raised when the log index provided in the inclusion proof is less than 0.\n", + "\n", + "2. `ValueError(f\"Inclusion proof has invalid tree size: {v} < 0\")`: Raised when the tree size provided in the inclusion proof is less than 0.\n", + "\n", + "3. `ValueError(\"Inclusion proof has log index greater than or equal to tree size: {v} <= {info.data['log_index']}\")`: Raised when the log index provided in the inclusion proof is greater than or equal to the tree size.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed explanation of the `LogInclusionProof` class, its methods, and error handling. It follows the structure provided in the input, making it easy to understand and follow.\\n\\nThe introduction gives a brief overview of the class and its purpose. The functions are well-documented, with clear descriptions, parameters, return values, and error handling. The error handling section provides a comprehensive list of possible errors and their meanings.\\n\\nThe submission is insightful as it provides a deep understanding of the class and its methods. It is also appropriate as it follows the structure provided in the input and provides accurate and complete information.\\n\\nTherefore, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria is to assess if the submission is correct, accurate, and factual.\\n\\nLooking at the submission, it provides a detailed explanation of the `LogInclusionProof` class, its methods, and error handling. It correctly describes the purpose of the class and its intended use. The functions are accurately documented with their descriptions, parameters, return values, and error handling. The error handling section also correctly lists the possible errors that can be raised.\\n\\nComparing the submission with the reference, the submission accurately describes the `LogInclusionProof` class and its methods. The reference does not provide as much detail as the submission, but the information that is present matches with the submission.\\n\\nTherefore, the submission is correct, accurate, and factual.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria is to assess if the output is complete and captures all required fields. \\n\\n1. Introduction: The submission provides a clear and concise introduction to the `LogInclusionProof` class, explaining its purpose and intended use. \\n\\n2. Functions: The submission documents all the functions in the class, including their descriptions, parameters, return values, and error handling. \\n\\n3. Error Handling: The submission describes all possible error responses and their meanings. \\n\\nTherefore, the submission meets all the criteria as it is complete and captures all required fields. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "This API provides a set of functions to perform various operations on a given dataset. It includes functions to read, manipulate, and evaluate datasets.\n", + "\n", + "## Functions\n", + "### 1. `read_csv()`\n", + "This function is used to read a CSV file and return the content as a pandas DataFrame.\n", + "\n", + "#### Parameters\n", + "- `file_path` (str): The path to the CSV file.\n", + "\n", + "#### Return Values\n", + "- `data` (pandas.DataFrame): The content of the CSV file as a DataFrame.\n", + "\n", + "### 2. `filter_data()`\n", + "This function is used to filter a DataFrame based on specified criteria.\n", + "\n", + "#### Parameters\n", + "- `data` (pandas.DataFrame): The input DataFrame.\n", + "- `column` (str): The column name to filter on.\n", + "- `value` (any): The value to filter for.\n", + "\n", + "#### Return Values\n", + "- `filtered_data` (pandas.DataFrame): The filtered DataFrame.\n", + "\n", + "### 3. `compute_statistics()`\n", + "This function is used to compute statistics on a DataFrame.\n", + "\n", + "#### Parameters\n", + "- `data` (pandas.DataFrame): The input DataFrame.\n", + "- `column` (str): The column name to compute statistics for.\n", + "\n", + "#### Return Values\n", + "- `statistics` (dict): A dictionary containing the computed statistics. The keys are the statistic names (e.g., \"mean\", \"median\"), and the values are the corresponding statistic values.\n", + "\n", + "## Error Handling\n", + "- If the specified CSV file does not exist or cannot be read, the `read_csv()` function will raise a `FileNotFoundError` exception.\n", + "- If the specified column does not exist in the DataFrame, the `filter_data()` and `compute_statistics()` functions will raise a `KeyError` exception.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a clear and concise API documentation for the given Python code. It follows the required structure and includes all the necessary details.\\n\\n1. Introduction: The introduction gives a brief overview of the API and its intended use. It is clear and concise.\\n\\n2. Functions: The submission documents each function in detail. It includes a description of what each function does, lists and describes each parameter, and specifies the return values. The descriptions are clear and easy to understand.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings. It provides useful information on what exceptions might be raised and under what conditions.\\n\\nBased on these observations, the submission is helpful, insightful, and appropriate. It provides all the necessary information in a clear and concise manner, making it easy for users to understand how to use the API. Therefore, the submission meets the criterion. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria is to assess the correctness, accuracy, and factualness of the submission.\\n\\n1. Correctness: The submission correctly follows the structure provided in the input. It includes an introduction, documentation for each function, and error handling. The functions are correctly documented with their descriptions, parameters, and return values.\\n\\n2. Accuracy: The submission accurately describes the functions and their parameters and return values. It also accurately describes the possible errors and their meanings.\\n\\n3. Factualness: The submission is factual as it provides concrete and specific information about the functions and errors. It does not include speculative or hypothetical information.\\n\\nBased on these assessments, the submission meets all the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission:\\n\\n1. Introduction: The submission includes an introduction that describes the purpose of the API and its intended use. \\n\\n2. Functions: The submission documents each API function, including a description of what the function does, a list and description of each parameter (including data types), and the data type and possible values returned.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings.\\n\\nBased on this analysis, the submission appears to meet all the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Introduction:**\n", + "\n", + "The API provided by this code consists of a set of custom error classes that serve as a foundation for error handling in the sigstore module. These error classes are designed to handle specific types of errors that may occur during the execution of the module. The purpose of this API is to provide a standardized and structured approach to handling errors and providing meaningful error messages to the users.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `Error.diagnostics() -> str`:\n", + " - Description: This function returns a string message containing diagnostics information about the error.\n", + " - Parameters: None.\n", + " - Return Value: A string message containing diagnostics information.\n", + " \n", + "2. `Error.print_and_exit(raise_error: bool = False) -> None`:\n", + " - Description: This function prints the diagnostics information of the error to the standard error stream and optionally raises the error.\n", + " - Parameters:\n", + " - `raise_error` (optional): A boolean flag indicating whether to raise the error or not. Default is `False`.\n", + " - Return Value: None.\n", + " \n", + "3. `NetworkError.diagnostics() -> str`:\n", + " - Description: This function returns a string message specific to network errors.\n", + " - Parameters: None.\n", + " - Return Value: A string message specific to network errors.\n", + " \n", + "4. `TUFError.__init__(message: str)`:\n", + " - Description: This is the constructor method for the TUFError class. It initializes the error message.\n", + " - Parameters:\n", + " - `message`: A string representing the error message.\n", + " - Return Value: None.\n", + " \n", + "5. `TUFError.diagnostics() -> str`:\n", + " - Description: This function returns a string message specific to TUF (The Update Framework) related errors.\n", + " - Parameters: None.\n", + " - Return Value: A string message specific to TUF related errors.\n", + " \n", + "6. `MetadataError.diagnostics() -> str`:\n", + " - Description: This function returns a string message specific to metadata errors.\n", + " - Parameters: None.\n", + " - Return Value: A string message specific to metadata errors.\n", + " \n", + "7. `RootError.diagnostics() -> str`:\n", + " - Description: This function returns a string message specific to root error.\n", + " - Parameters: None.\n", + " - Return Value: A string message specific to root error.\n", + "\n", + "**Error Handling:**\n", + "\n", + "The API provides specific error classes to handle different types of errors that may occur during the execution of the sigstore module. These error classes include:\n", + "\n", + "1. `NetworkError`: An error that occurs due to a network issue.\n", + " - Possible Response: \n", + " - A string message indicating the occurrence of a network issue. The user is advised to check their internet connection and try again.\n", + " - Additional context may be provided if available.\n", + "\n", + "2. `TUFError`: An error that occurs during the execution of TUF (The Update Framework) related operations.\n", + " - Possible Response:\n", + " - A string message indicating the specific error that occurred during TUF operations.\n", + " - Additional details may be provided based on the specific type of TUF error encountered.\n", + "\n", + "3. `MetadataError`: An error that occurs due to issues with metadata.\n", + " - Possible Response:\n", + " - A string message indicating the occurrence of a metadata error.\n", + "\n", + "4. `RootError`: An error that occurs when the root of trust cannot be established.\n", + " - Possible Response:\n", + " - A string message indicating the inability to establish the root of trust. This error may occur when the resources embedded in the distribution of sigstore-python are out of date.\n", + "\n", + "Please note that the specific error messages, details, and responses may vary based on the context in which these errors are raised and handled.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed and structured documentation of the provided Python code. It follows the output structure provided in the input, including an introduction, function documentation, and error handling.\\n\\nThe introduction gives a brief overview of the purpose of the API and its intended use. It explains that the API consists of a set of custom error classes for error handling in the sigstore module.\\n\\nThe function documentation is detailed and clear. It includes a description of what each function does, lists and describes each parameter, and specifies the data type and possible values returned. This information is accurate and complete, meeting the requirements of the task.\\n\\nThe error handling section describes the possible error responses and their meanings. It explains the specific types of errors that the API can handle and provides possible responses for each error. This information is also accurate and complete, meeting the requirements of the task.\\n\\nOverall, the submission is helpful as it provides a clear and detailed documentation of the API. It is insightful as it explains the purpose of the API, the functionality of each function, and the types of errors that can be handled. It is also appropriate as it follows the output structure provided in the input and meets all the requirements of the task.\\n\\nTherefore, the submission meets the criterion of \"helpfulness\". \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The submission is being evaluated on the criterion of correctness, which includes accuracy and factualness.\\n\\n1. The introduction provided in the submission accurately describes the purpose of the API and its intended use. It correctly identifies that the API consists of a set of custom error classes for error handling in the sigstore module.\\n\\n2. The functions are correctly documented in the submission. Each function's description, parameters, and return values are accurately described. The data types of the parameters and return values are also correctly identified.\\n\\n3. The error handling section of the submission accurately describes the different types of errors that the API can handle. It correctly identifies the specific error classes and provides accurate descriptions of the possible responses for each error type.\\n\\n4. The submission does not include any speculative information and prioritizes accuracy and completeness, as per the instructions.\\n\\n5. Comparing the submission with the reference, it is clear that the submission accurately describes the API documentation for the provided Python code. The submission correctly identifies and describes all the classes, functions, and error handling mechanisms in the code.\\n\\nBased on this analysis, the submission meets the criterion of correctness. It is accurate and factual, and it correctly describes the API documentation for the provided Python code. Therefore, the answer is:\", 'value': 'Based on this analysis, the submission meets the criterion of correctness. It is accurate and factual, and it correctly describes the API documentation for the provided Python code. Therefore, the answer is:', 'score': None}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe required fields for this task are:\\n\\n1. Introduction: The submission provides an introduction that describes the purpose of the API and its intended use.\\n\\n2. Functions: The submission documents each API function, including a description of what the function does, a list and description of each parameter, and the data type and possible values returned.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings.\\n\\nLooking at the submission:\\n\\n1. Introduction: The submission provides an introduction that describes the purpose of the API and its intended use. This criterion is met.\\n\\n2. Functions: The submission documents each API function, including a description of what the function does, a list and description of each parameter, and the data type and possible values returned. This criterion is met.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings. This criterion is met.\\n\\nTherefore, the submission meets all the criteria.\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "This API provides a set of functions to manage a user database. It allows users to perform operations such as creating a new user, retrieving user information, updating user details, and deleting a user.\n", + "\n", + "## Functions\n", + "\n", + "### create_user(username: str, password: str) -> bool\n", + "This function creates a new user with the specified username and password.\n", + "\n", + "#### Parameters\n", + "- `username` (str): The username for the new user. It must be a non-empty string.\n", + "- `password` (str): The password for the new user. It must be a non-empty string.\n", + "\n", + "#### Return Value\n", + "- bool: Returns True if the user was successfully created; False otherwise.\n", + "\n", + "### get_user(username: str) -> dict\n", + "This function retrieves information about a user with the specified username.\n", + "\n", + "#### Parameters\n", + "- `username` (str): The username of the user to retrieve. It must be a non-empty string.\n", + "\n", + "#### Return Value\n", + "- dict: Returns a dictionary containing the user information, including the username and any additional details. If the user is not found, an empty dictionary is returned.\n", + "\n", + "### update_user(username: str, details: dict) -> bool\n", + "This function updates the details of a user with the specified username.\n", + "\n", + "#### Parameters\n", + "- `username` (str): The username of the user to update. It must be a non-empty string.\n", + "- `details` (dict): A dictionary containing the updated details for the user. It may include keys such as \"name\", \"email\", \"age\", etc.\n", + "\n", + "#### Return Value\n", + "- bool: Returns True if the user was successfully updated; False otherwise.\n", + "\n", + "### delete_user(username: str) -> bool\n", + "This function deletes a user with the specified username.\n", + "\n", + "#### Parameters\n", + "- `username` (str): The username of the user to delete. It must be a non-empty string.\n", + "\n", + "#### Return Value\n", + "- bool: Returns True if the user was successfully deleted; False otherwise.\n", + "\n", + "## Error Handling\n", + "- If the provided username or password is empty or not a string, the `create_user` function will return False.\n", + "- If a user with the specified username is not found, the `get_user` function will return an empty dictionary.\n", + "- If the `update_user` function fails to update the user details, it will return False.\n", + "- If the `delete_user` function fails to delete the user, it will return False.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\n1. Helpfulness: The submission provides a clear and concise documentation of the API. It explains the purpose of the API and each function in detail, including their parameters and return values. This would be very helpful for a user trying to understand how to use the API.\\n\\n2. Insightfulness: The submission goes beyond just listing the functions and their parameters. It provides insights into what each function does, what the parameters mean, and what the return values signify. This would help users understand not just how to use the API, but also how it works.\\n\\n3. Appropriateness: The submission follows the provided structure and guidelines for creating API documentation. It avoids speculative information and prioritizes accuracy and completeness, which makes it appropriate for the task.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission is being evaluated on the criterion of correctness, which includes accuracy and factualness.\\n\\n1. The submission correctly follows the requested output structure, including an introduction, function documentation, and error handling.\\n2. The introduction accurately describes the purpose of the API and its intended use.\\n3. The function documentation is accurate and factual. Each function is documented with a description, parameters, and return values. The data types and constraints for each parameter are correctly listed.\\n4. The return values are correctly specified with their data types and possible values.\\n5. The error handling section accurately describes possible error responses and their meanings.\\n\\nBased on the above reasoning, the submission meets the criterion of correctness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission includes an introduction that describes the purpose of the API and its intended use. \\n\\n2. Functions: The submission documents each API function, including a description of what the function does, a list and description of each parameter (including data types and any constraints), and the data type and possible values returned.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings.\\n\\nBased on these points, the submission appears to meet all the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Introduction:\n", + "The VerificationResult class is a base model for representing the result of a verification process. It can have two subclasses: VerificationSuccess and VerificationFailure. The VerificationSuccess class represents a successful verification, while the VerificationFailure class represents a failed verification with a reason for the failure. This API is used to handle verification results and perform error handling in a Python application.\n", + "\n", + "2. Functions:\n", + "There are no functions in the VerificationResult class.\n", + "\n", + "3. Error Handling:\n", + "- InvalidMaterials:\n", + " - Description: This error is raised when there is an issue while parsing the verification materials. It indicates that the provided verification materials are malformed and may have been modified maliciously.\n", + " - Parameters: None.\n", + " - Return Values: None.\n", + "\n", + "- RekorEntryMissing:\n", + " - Description: This exception is raised when a Rekor entry is missing during the verification process.\n", + " - Parameters: None.\n", + " - Return Values: None.\n", + "\n", + "- InvalidRekorEntry:\n", + " - Description: This error is raised when a Rekor entry is invalid during the verification process.\n", + " - Parameters: None.\n", + " - Return Values: None.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a clear and concise introduction to the VerificationResult class and its subclasses. It explains the purpose of these classes and how they are used in a Python application, which is helpful for understanding the API.\\n\\nThe submission also provides a detailed description of the error handling in the API. It explains what each error means and when it is raised, which is insightful for users of the API.\\n\\nThe submission does not document any functions in the VerificationResult class because there are none. This is appropriate because the task only requires documenting functions that exist.\\n\\nTherefore, the submission is helpful, insightful, and appropriate, and it meets the criterion.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is correctness, which means the submission should be accurate and factual.\\n\\n1. The introduction provided in the submission accurately describes the purpose of the VerificationResult class and its subclasses, VerificationSuccess and VerificationFailure. It correctly states that these classes are used to handle verification results and perform error handling in a Python application.\\n\\n2. The submission correctly states that there are no functions in the VerificationResult class. However, it misses the __bool__ function in the VerificationResult class and the diagnostics function in the InvalidMaterials class. These are important functions that should be documented.\\n\\n3. The error handling section accurately describes the InvalidMaterials, RekorEntryMissing, and InvalidRekorEntry errors. It correctly states that these errors are raised during the verification process when there are issues with the verification materials or the Rekor entries.\\n\\nBased on the above points, the submission is mostly correct but it misses some important details. Therefore, it does not fully meet the criterion of correctness.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criterion is to assess if the output is complete and captures all required fields. \\n\\nLooking at the submission, the introduction is well written and describes the purpose of the API and its intended use. \\n\\nIn the functions section, the submission states that there are no functions in the VerificationResult class. However, there is a function \"__bool__\" in the VerificationResult class that returns the success attribute. This function is not documented in the submission. \\n\\nIn the error handling section, the submission correctly describes the InvalidMaterials, RekorEntryMissing, and InvalidRekorEntry errors. However, it does not mention the parameters and return values for these errors. \\n\\nTherefore, the submission is not complete as it does not capture all required fields. \\n\\nN', 'value': 'N', 'score': 0}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "This API provides functions to perform various calculations related to geometry. It can be used to calculate the area and perimeter of different shapes such as rectangles and circles.\n", + "\n", + "## Functions\n", + "\n", + "### `calculate_rectangle_area(length: float, width: float) -> float`\n", + "This function calculates the area of a rectangle given its length and width.\n", + "\n", + "- Parameters:\n", + " - `length` (float): The length of the rectangle.\n", + " - `width` (float): The width of the rectangle.\n", + "\n", + "- Return Value:\n", + " - The area of the rectangle as a float.\n", + "\n", + "### `calculate_rectangle_perimeter(length: float, width: float) -> float`\n", + "This function calculates the perimeter of a rectangle given its length and width.\n", + "\n", + "- Parameters:\n", + " - `length` (float): The length of the rectangle.\n", + " - `width` (float): The width of the rectangle.\n", + "\n", + "- Return Value:\n", + " - The perimeter of the rectangle as a float.\n", + "\n", + "### `calculate_circle_area(radius: float) -> float`\n", + "This function calculates the area of a circle given its radius.\n", + "\n", + "- Parameters:\n", + " - `radius` (float): The radius of the circle.\n", + "\n", + "- Return Value:\n", + " - The area of the circle as a float.\n", + "\n", + "### `calculate_circle_circumference(radius: float) -> float`\n", + "This function calculates the circumference of a circle given its radius.\n", + "\n", + "- Parameters:\n", + " - `radius` (float): The radius of the circle.\n", + "\n", + "- Return Value:\n", + " - The circumference of the circle as a float.\n", + "\n", + "## Error Handling\n", + "- If any of the input parameters have invalid values, such as negative lengths or widths, the functions will raise a `ValueError` with an appropriate error message.\n", + "- If the input parameters are of an invalid type, such as a string instead of a number, the functions will raise a `TypeError` with an appropriate error message.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that it is helpful. It provides a detailed explanation of the API, including an introduction that explains the purpose of the API and its intended use. This is helpful for users who are trying to understand what the API does and how to use it.\\n\\nThe submission is also insightful. It provides detailed documentation for each function, including a description of what the function does, a list of parameters with their data types and constraints, and the return values. This is insightful because it provides users with all the information they need to understand how to use each function.\\n\\nFinally, the submission is appropriate. It follows the output structure provided in the input, and it avoids speculative information. It prioritizes accuracy and completeness, as evidenced by the detailed function documentation and the section on error handling.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate. Therefore, the answer is \"Y\". \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The submission is being evaluated on the criterion of correctness, which includes accuracy and factualness.\\n\\n1. The introduction of the API documentation accurately describes the purpose of the API and its intended use. It mentions that the API provides functions to perform various calculations related to geometry, such as calculating the area and perimeter of different shapes. This is correct and factual.\\n\\n2. The functions are documented correctly. Each function's purpose is clearly explained, the parameters are listed with their data types and descriptions, and the return values are specified with their data types. All the information provided is accurate and factual.\\n\\n3. The error handling section correctly describes the possible error responses and their meanings. It mentions that a `ValueError` will be raised if any of the input parameters have invalid values, and a `TypeError` will be raised if the input parameters are of an invalid type. This is correct and factual.\\n\\nBased on the above reasoning, the submission meets the criterion of correctness. It is correct, accurate, and factual. Therefore, the answer is:\\n\\nY\", 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission, it is clear that the output is structured according to the given instructions. \\n\\n1. Introduction: The introduction provides a brief description of the purpose of the API and its intended use. It mentions that the API provides functions to perform various calculations related to geometry.\\n\\n2. Functions: The submission documents each API function, including a description of what the function does, a list and description of each parameter, and the data type and possible values returned. Four functions are documented: `calculate_rectangle_area`, `calculate_rectangle_perimeter`, `calculate_circle_area`, and `calculate_circle_circumference`.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings. It mentions that if any of the input parameters have invalid values or are of an invalid type, the functions will raise a `ValueError` or a `TypeError` respectively.\\n\\nTherefore, the submission appears to meet all the criteria as it is complete and captures all required fields.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Class `_SingleX509ExtPolicy`**\n", + "\n", + "1. Introduction:\n", + "The `_SingleX509ExtPolicy` class is an abstract base class that serves as a template for creating specific X.509 extension policies. It provides common functionality and attributes that can be used by its subclasses.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID (Object Identifier) associated with the X.509 extension policy.\n", + "\n", + "3. Methods:\n", + "- `__init__(self, value: str) -> None`: Initializes an instance of the `_SingleX509ExtPolicy` class.\n", + " - Parameters:\n", + " - `value` (str): The value associated with the X.509 extension policy.\n", + " - Return Type: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies whether the X.509 certificate contains the specified extension and its value matches the expected value.\n", + " - Parameters:\n", + " - `cert` (Certificate): The X.509 certificate to be verified.\n", + " - Return Type: VerificationResult\n", + " - Possible Values: \n", + " - `VerificationSuccess`: If the verification is successful.\n", + " - `VerificationFailure`: If the verification fails.\n", + "\n", + "**Class `OIDCIssuer` (Subclass of `_SingleX509ExtPolicy`)**\n", + "\n", + "1. Introduction:\n", + "The `OIDCIssuer` class is a subclass of the `_SingleX509ExtPolicy` class. It represents an X.509 extension policy for the OpenID Connect (OIDC) Issuer.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID associated with the OIDC Issuer X.509 extension policy.\n", + "\n", + "**Class `GitHubWorkflowTrigger` (Subclass of `_SingleX509ExtPolicy`)**\n", + "\n", + "1. Introduction:\n", + "The `GitHubWorkflowTrigger` class is a subclass of the `_SingleX509ExtPolicy` class. It represents an X.509 extension policy for a GitHub Workflow Trigger.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID associated with the GitHub Workflow Trigger X.509 extension policy.\n", + "\n", + "**Class `GitHubWorkflowSHA` (Subclass of `_SingleX509ExtPolicy`)**\n", + "\n", + "1. Introduction:\n", + "The `GitHubWorkflowSHA` class is a subclass of the `_SingleX509ExtPolicy` class. It represents an X.509 extension policy for a GitHub Workflow SHA.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID associated with the GitHub Workflow SHA X.509 extension policy.\n", + "\n", + "**Class `GitHubWorkflowName` (Subclass of `_SingleX509ExtPolicy`)**\n", + "\n", + "1. Introduction:\n", + "The `GitHubWorkflowName` class is a subclass of the `_SingleX509ExtPolicy` class. It represents an X.509 extension policy for a GitHub Workflow Name.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID associated with the GitHub Workflow Name X.509 extension policy.\n", + "\n", + "**Class `GitHubWorkflowRepository` (Subclass of `_SingleX509ExtPolicy`)**\n", + "\n", + "1. Introduction:\n", + "The `GitHubWorkflowRepository` class is a subclass of the `_SingleX509ExtPolicy` class. It represents an X.509 extension policy for a GitHub Workflow Repository.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID associated with the GitHub Workflow Repository X.509 extension policy.\n", + "\n", + "**Class `GitHubWorkflowRef` (Subclass of `_SingleX509ExtPolicy`)**\n", + "\n", + "1. Introduction:\n", + "The `GitHubWorkflowRef` class is a subclass of the `_SingleX509ExtPolicy` class. It represents an X.509 extension policy for a GitHub Workflow Reference.\n", + "\n", + "2. Class Attributes:\n", + "- `oid` (ObjectIdentifier): The OID associated with the GitHub Workflow Reference X.509 extension policy.\n", + "\n", + "**Class `VerificationPolicy` (Protocol)**\n", + "\n", + "1. Introduction:\n", + "The `VerificationPolicy` class is a protocol that defines the structure for verification policies. It is an abstract base class that other verification policies should inherit from.\n", + "\n", + "2. Methods:\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the X.509 certificate against the implemented policy.\n", + " - Parameters:\n", + " - `cert` (Certificate): The X.509 certificate to be verified.\n", + " - Return Type: VerificationResult\n", + " - Possible Values: \n", + " - `VerificationSuccess`: If the verification is successful.\n", + " - `VerificationFailure`: If the verification fails.\n", + "\n", + "**Class `AnyOf`**\n", + "\n", + "1. Introduction:\n", + "The `AnyOf` class represents a logical OR combination of multiple `VerificationPolicy` instances. It verifies the X.509 certificate against any of the specified policies.\n", + "\n", + "2. Methods:\n", + "- `__init__(self, children: list[VerificationPolicy])`: Initializes an instance of the `AnyOf` class.\n", + " - Parameters:\n", + " - `children` (list[VerificationPolicy]): A list of `VerificationPolicy` instances.\n", + " - Return Type: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the X.509 certificate against any of the specified policies.\n", + " - Parameters:\n", + " - `cert` (Certificate): The X.509 certificate to be verified.\n", + " - Return Type: VerificationResult\n", + " - Possible Values: \n", + " - `VerificationSuccess`: If the verification is successful.\n", + " - `VerificationFailure`: If the verification fails.\n", + "\n", + "**Class `AllOf`**\n", + "\n", + "1. Introduction:\n", + "The `AllOf` class represents a logical AND combination of multiple `VerificationPolicy` instances. It verifies the X.509 certificate against all of the specified policies.\n", + "\n", + "2. Methods:\n", + "- `__init__(self, children: list[VerificationPolicy])`: Initializes an instance of the `AllOf` class.\n", + " - Parameters:\n", + " - `children` (list[VerificationPolicy]): A list of `VerificationPolicy` instances.\n", + " - Return Type: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the X.509 certificate against all of the specified policies.\n", + " - Parameters:\n", + " - `cert` (Certificate): The X.509 certificate to be verified.\n", + " - Return Type: VerificationResult\n", + " - Possible Values: \n", + " - `VerificationSuccess`: If the verification is successful.\n", + " - `VerificationFailure`: If the verification fails.\n", + "\n", + "**Class `UnsafeNoOp`**\n", + "\n", + "1. Introduction:\n", + "The `UnsafeNoOp` class represents a verification policy that performs no actual verification. It serves as a placeholder when no verification is desired or necessary.\n", + "\n", + "2. Methods:\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Performs no verification and returns a success result.\n", + " - Parameters:\n", + " - `cert` (Certificate): The X.509 certificate. This parameter is ignored.\n", + " - Return Type: VerificationResult\n", + " - Possible Values: \n", + " - `VerificationSuccess`: Always returned to indicate no verification performed.\n", + "\n", + "**Class `Identity`**\n", + "\n", + "1. Introduction:\n", + "The `Identity` class represents a verification policy that verifies the X.509 certificate against a specific identity and its associated OIDC issuer.\n", + "\n", + "2. Methods:\n", + "- `__init__(self, identity: str, issuer: str)`: Initializes an instance of the `Identity` class.\n", + " - Parameters:\n", + " - `identity` (str): The identity to be verified against the X.509 certificate's subject alternative names (SANs).\n", + " - `issuer` (str): The OIDC issuer associated with the identity.\n", + " - Return Type: None\n", + "\n", + "- `verify(self, cert: Certificate) -> VerificationResult`: Verifies the X.509 certificate's SANs against the specified identity and the OIDC issuer.\n", + " - Parameters:\n", + " - `cert` (Certificate): The X.509 certificate to be verified.\n", + " - Return Type: VerificationResult\n", + " - Possible Values: \n", + " - `VerificationSuccess`: If the verification is successful.\n", + " - `VerificationFailure`: If the verification fails.\n", + "\n", + "---\n", + "\n", + "Function Documentation:\n", + "{'reasoning': 'The submission is being evaluated on the criterion of helpfulness. \\n\\nThe submission provides a detailed and comprehensive documentation of the provided Python classes. It follows the structure outlined in the task, providing an introduction, description of functions and their parameters, and possible return values for each class. \\n\\nThe submission is insightful as it provides a clear understanding of what each class does, its attributes, and methods. It also explains the purpose of each method and the parameters it takes, along with the possible return values. \\n\\nThe submission is appropriate as it adheres to the task instructions and provides accurate and complete information about the Python classes. It avoids speculative information and prioritizes accuracy and completeness, as instructed in the task.\\n\\nBased on the above reasoning, the submission meets the criterion of helpfulness. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission is being evaluated on the basis of correctness, accuracy, and factualness. \\n\\n1. Correctness: The submission correctly follows the structure provided in the input. It provides an introduction for each class, documents the class attributes and methods, and describes the parameters and return values for each method. The submission also correctly identifies the classes as subclasses of `_SingleX509ExtPolicy` or `VerificationPolicy` where applicable.\\n\\n2. Accuracy: The submission accurately describes the purpose and functionality of each class and method. It correctly identifies the data types of the parameters and return values, and accurately describes the possible return values.\\n\\n3. Factualness: The submission is factual and does not include any speculative or inaccurate information. It accurately describes the functionality of the classes and methods based on the provided Python code.\\n\\nBased on this analysis, the submission meets the criteria of correctness, accuracy, and factualness.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe required fields for the API documentation are:\\n\\n1. Introduction: Briefly describe the purpose of the API and its intended use.\\n2. Functions: Document each API function, including:\\n - Description: Clearly explain what the endpoint or function does.\\n - Parameters: List and describe each parameter, including data types and any constraints.\\n - Return Values: Specify the data type and possible values returned.\\n3. Error Handling: Describe possible error responses and their meanings.\\n\\nLooking at the submission, it appears that the documentation is complete and captures all required fields. \\n\\n1. Introduction: Each class has an introduction that describes its purpose and intended use.\\n2. Functions: Each function within the classes is documented, including a description of what the function does, a list and description of each parameter (including data types), and the data type and possible values returned.\\n3. Error Handling: The documentation describes possible error responses and their meanings in the form of `VerificationFailure` and `VerificationSuccess` return values.\\n\\nTherefore, the submission meets the criteria.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "\n", + "The purpose of this API is to provide a set of functions that perform mathematical calculations. Users can utilize these functions to perform a wide range of mathematical operations, such as addition, subtraction, multiplication, and division. This API is designed to be easy to use and can be integrated into any Python project.\n", + "\n", + "## Functions\n", + "\n", + "### 1. `add(a: int, b: int) -> int`\n", + "\n", + "#### Description\n", + "\n", + "This function takes two integer values, `a` and `b`, and returns their sum.\n", + "\n", + "#### Parameters\n", + "\n", + "- `a` (integer): The first integer value to be added.\n", + "- `b` (integer): The second integer value to be added.\n", + "\n", + "#### Return Values\n", + "\n", + "- Returns an integer value that represents the sum of `a` and `b`.\n", + "\n", + "### 2. `subtract(a: int, b: int) -> int`\n", + "\n", + "#### Description\n", + "\n", + "This function takes two integer values, `a` and `b`, and returns their difference.\n", + "\n", + "#### Parameters\n", + "\n", + "- `a` (integer): The integer value from which the other integer value will be subtracted.\n", + "- `b` (integer): The integer value to be subtracted from `a`.\n", + "\n", + "#### Return Values\n", + "\n", + "- Returns an integer value that represents the difference of `a` and `b`.\n", + "\n", + "### 3. `multiply(a: int, b: int) -> int`\n", + "\n", + "#### Description\n", + "\n", + "This function takes two integer values, `a` and `b`, and returns their product.\n", + "\n", + "#### Parameters\n", + "\n", + "- `a` (integer): The first integer value to be multiplied.\n", + "- `b` (integer): The second integer value to be multiplied.\n", + "\n", + "#### Return Values\n", + "\n", + "- Returns an integer value that represents the product of `a` and `b`.\n", + "\n", + "### 4. `divide(a: int, b: int) -> float`\n", + "\n", + "#### Description\n", + "\n", + "This function takes two integer values, `a` (dividend) and `b` (divisor), and returns the quotient as a floating-point number.\n", + "\n", + "#### Parameters\n", + "\n", + "- `a` (integer): The integer value to be divided.\n", + "- `b` (integer): The integer value by which `a` will be divided.\n", + "\n", + "#### Return Values\n", + "\n", + "- Returns a floating-point number that represents the quotient of `a` divided by `b`.\n", + "\n", + "## Error Handling\n", + "\n", + "In case of any errors or issues, the API functions may raise the following exceptions:\n", + "\n", + "- `ZeroDivisionError`: Raised when the divisor in the `divide` function is zero.\n", + "- `TypeError`: Raised when the input parameters in any function are not of the expected data type (integer).\n", + "\n", + "When any of these exceptions occur, appropriate error messages will be displayed, indicating the cause of the error. It is recommended to handle these exceptions in the calling code to ensure proper error handling and graceful termination of the program if necessary.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that it is helpful. It provides a detailed explanation of the API, its functions, parameters, return values, and error handling. This would be very useful for a user trying to understand how to use the API.\\n\\nThe submission is also insightful. It not only provides the necessary information but also explains it in a way that is easy to understand. The descriptions of the functions and parameters are clear and concise, and the error handling section provides useful information about potential issues and how to handle them.\\n\\nFinally, the submission is appropriate. It follows the requested output structure and provides all the necessary information. It is also written in a professional and user-centric manner, making it suitable for API documentation.\\n\\nBased on these observations, the submission meets the criterion of being helpful, insightful, and appropriate. Therefore, the answer is \"Y\". \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nLooking at the submission, it appears to be a well-structured API documentation for a set of mathematical functions. The submission includes an introduction, detailed descriptions of each function, their parameters, return values, and error handling. \\n\\nThe introduction clearly states the purpose of the API and its intended use. \\n\\nThe function documentation is detailed and accurate. Each function is described with its purpose, parameters, and return values. The data types for the parameters and return values are also correctly specified. \\n\\nThe error handling section describes the possible exceptions that can be raised by the API functions, which is a crucial part of any API documentation.\\n\\nComparing the submission with the reference, it\\'s clear that the submission is not based on the reference. However, the task does not require the submission to be based on the reference. The task is to generate API documentation for provided Python code, but no specific code was provided. Therefore, the reference does not affect the correctness of the submission.\\n\\nBased on the above analysis, the submission is correct, accurate, and factual. It meets the criteria for this task.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\n1. Introduction: The submission includes an introduction that describes the purpose of the API and its intended use. It mentions that the API provides a set of functions for mathematical calculations and can be integrated into any Python project.\\n\\n2. Functions: The submission documents each API function, including a description, parameters, and return values. It covers four functions: `add`, `subtract`, `multiply`, and `divide`. Each function's description clearly explains what it does. The parameters for each function are listed and described, including their data types. The return values for each function are specified, including their data types.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings. It mentions two exceptions that the API functions may raise: `ZeroDivisionError` and `TypeError`, and provides a brief explanation of when these exceptions might occur.\\n\\nBased on this analysis, the submission appears to meet all the criteria. It is complete and captures all required fields.\\n\\nY\", 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Signer**\n", + "\n", + "1. Introduction:\n", + "The Signer class is responsible for signing input data using an identity token and a signing context. It provides a sign() method to generate the digital signature.\n", + "\n", + "2. Methods:\n", + "\n", + " - \\_\\_init\\_\\_(identity_token: IdentityToken, signing_ctx: SigningContext, cache: bool = True) -> None:\n", + " \n", + " - Description: Initializes a new instance of the Signer class.\n", + " \n", + " - Parameters:\n", + " - identity_token: An identity token used for authentication and authorization.\n", + " - Type: IdentityToken.\n", + " - signing_ctx: The signing context used for signing the data.\n", + " - Type: SigningContext.\n", + " - cache: Specifies whether to cache the private key and signing certificate.\n", + " - Type: bool.\n", + " - Default: True.\n", + " \n", + " - _private_key(self) -> ec.EllipticCurvePrivateKey:\n", + " \n", + " - Description: Gets the private key for signing.\n", + " - Parameters: None.\n", + " - Returns: The private key for signing.\n", + " - Type: ec.EllipticCurvePrivateKey.\n", + " \n", + " - _signing_cert(self, private_key: ec.EllipticCurvePrivateKey) -> FulcioCertificateSigningResponse:\n", + " \n", + " - Description: Retrieves the signing certificate for signing.\n", + " \n", + " - Parameters:\n", + " - private_key: The private key used for signing.\n", + " - Type: ec.EllipticCurvePrivateKey.\n", + " \n", + " - Returns: The signing certificate.\n", + " - Type: FulcioCertificateSigningResponse.\n", + " \n", + " - sign(self, input_: IO[bytes]) -> SigningResult:\n", + " \n", + " - Description: Signs the input data and returns the signing result.\n", + " \n", + " - Parameters:\n", + " - input_: The input data to be signed.\n", + " - Type: IO[bytes].\n", + " \n", + " - Returns: The signing result.\n", + " - Type: SigningResult.\n", + "\n", + "**SigningContext**\n", + "\n", + "1. Introduction:\n", + "The SigningContext class represents a signing context that encapsulates the Fulcio client and Rekor client used for signing operations.\n", + "\n", + "2. Methods:\n", + "\n", + " - \\_\\_init\\_\\_(fulcio: FulcioClient, rekor: RekorClient):\n", + " \n", + " - Description: Initializes a new instance of the SigningContext class.\n", + " \n", + " - Parameters:\n", + " - fulcio: The Fulcio client used for signing operations.\n", + " - Type: FulcioClient.\n", + " - rekor: The Rekor client used for signing operations.\n", + " - Type: RekorClient.\n", + " \n", + " - production(cls) -> SigningContext:\n", + " \n", + " - Description: Creates a new SigningContext instance for production use.\n", + " \n", + " - staging(cls) -> SigningContext:\n", + " \n", + " - Description: Creates a new SigningContext instance for staging use.\n", + " \n", + " - \\_\\_enter\\_\\_(self) -> Iterator[Signer]:\n", + " \n", + " - Description: Enters the context manager for signing operations.\n", + " \n", + " - Parameters: None.\n", + " \n", + " - Returns: A Signer instance.\n", + " - Type: Iterator[Signer].\n", + "\n", + "**SigningResult**\n", + "\n", + "1. Introduction:\n", + "The SigningResult class represents the result of a signing operation, including the input digest, signing certificate, digital signature, and log entry.\n", + "\n", + "2. Properties:\n", + "\n", + " - input_digest: The digest of the input data.\n", + " - Type: HexStr.\n", + " \n", + " - cert_pem: The PEM-encoded signing certificate.\n", + " - Type: PEMCert.\n", + " \n", + " - b64_signature: The base64-encoded digital signature.\n", + " - Type: B64Str.\n", + " \n", + " - log_entry: The log entry containing information about the signing operation.\n", + " - Type: LogEntry.\n", + "\n", + " - to_bundle(self) -> Bundle:\n", + "\n", + " - Description: Converts the SigningResult to a Bundle object, which can be used to verify the signature.\n", + " \n", + " - Parameters: None.\n", + " \n", + " - Returns: The Bundle object.\n", + " - Type: Bundle.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is supposed to be helpful, insightful, and appropriate. \\n\\nLooking at the submission, it is clear that it provides a detailed explanation of the provided Python code. The submission has followed the structure provided in the input and has documented each class, its methods, parameters, and return values. \\n\\nThe submission is helpful as it provides a clear understanding of what each class and its methods do. It is insightful as it provides details about the parameters and return values of each method. It is appropriate as it sticks to the task of documenting the provided Python code and does not include any speculative information.\\n\\nTherefore, the submission meets the criterion of being helpful, insightful, and appropriate. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The submission provides a detailed and accurate documentation for the provided Python classes: Signer, SigningContext, and SigningResult. It correctly describes the purpose of each class and their methods, including their parameters and return values. The submission also correctly identifies the data types of the parameters and return values. It does not include any speculative information and prioritizes accuracy and completeness, as required by the task. \\n\\nHowever, the submission does not include any information about error handling, which is one of the required sections in the task. Therefore, the submission does not meet all the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe output is expected to provide API documentation for the provided Python code, including an introduction, documentation for each function (including description, parameters, and return values), and error handling.\\n\\nLooking at the provided submission, it appears to meet most of these requirements:\\n\\n- The submission provides an introduction for each class, explaining its purpose and intended use.\\n- The submission documents each function in the classes, including a description of what the function does, a list of parameters with their data types and constraints, and the data type and possible values returned.\\n- The submission also documents the properties of the SigningResult class.\\n\\nHowever, the submission does not provide any information on error handling. The Python code includes several instances where errors could be raised (for example, the ExpiredCertificate and ExpiredIdentity exceptions in the Signer class), but the submission does not document these potential errors or their meanings.\\n\\nTherefore, the submission does not meet all the criteria. It is complete in terms of documenting the functions and properties of the classes, but it does not capture all required fields because it does not include documentation on error handling. \\n\\nSo, the answer is:', 'value': 'So, the answer is:', 'score': None}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# API Documentation\n", + "\n", + "## Introduction\n", + "This API provides functions to perform operations on a list of numbers.\n", + "\n", + "## Functions\n", + "\n", + "### `add_numbers(numbers: List[int]) -> int`\n", + "This function takes a list of integers as input and returns the sum of all the numbers in the list.\n", + "\n", + "#### Parameters\n", + "- `numbers` (List[int]): A list of integers.\n", + "\n", + "#### Return Value\n", + "- Returns an integer that represents the sum of all the numbers in the list.\n", + "\n", + "### `multiply_numbers(numbers: List[int]) -> int`\n", + "This function takes a list of integers as input and returns the product of all the numbers in the list.\n", + "\n", + "#### Parameters\n", + "- `numbers` (List[int]): A list of integers.\n", + "\n", + "#### Return Value\n", + "- Returns an integer that represents the product of all the numbers in the list.\n", + "\n", + "## Error Handling\n", + "- If the input list is empty, both functions will return 0, as there are no numbers to sum or multiply.\n", + "- If the input list contains non-integer values, both functions will raise a `TypeError` with a message indicating that the input should only contain integers.\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it is clear that it is helpful. It provides a detailed explanation of the API, its functions, parameters, and return values. This would be very useful for a developer trying to understand how to use the API.\\n\\nThe submission is also insightful. It not only explains what each function does, but also what happens in case of errors. This is very important information for a developer, as it helps them understand how to handle potential issues when using the API.\\n\\nFinally, the submission is appropriate. It follows the requested output structure and provides all the necessary information in a clear and concise manner. It avoids speculative information and prioritizes accuracy and completeness, as requested in the task.\\n\\nBased on this analysis, the submission meets the criterion of being helpful, insightful, and appropriate. Therefore, the answer is \"Y\". \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criterion for this task is \"correctness: Is the submission correct, accurate, and factual?\"\\n\\nLooking at the submission, it appears to follow the requested output structure and includes all the required sections: Introduction, Functions, and Error Handling.\\n\\nIn the Introduction, the submission provides a brief description of the API and its intended use, which is to perform operations on a list of numbers.\\n\\nIn the Functions section, the submission documents two functions: `add_numbers` and `multiply_numbers`. For each function, it provides a description, lists and describes the parameters, and specifies the return values. The descriptions are clear and explain what each function does. The parameters are correctly identified as a list of integers. The return values are also correctly identified as integers and the possible values are explained.\\n\\nIn the Error Handling section, the submission describes two possible error responses: if the input list is empty, both functions will return 0, and if the input list contains non-integer values, both functions will raise a `TypeError`. These error responses are accurate and factual.\\n\\nTherefore, the submission appears to be correct, accurate, and factual, meeting the criterion for this task.\\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nLooking at the submission, it includes an introduction that describes the purpose of the API and its intended use. \\n\\nThe submission also documents two functions, `add_numbers` and `multiply_numbers`. For each function, it provides a description explaining what the function does, lists and describes the parameter, and specifies the return value. \\n\\nFinally, the submission includes a section on error handling, describing possible error responses and their meanings. \\n\\nTherefore, the submission appears to meet all the criteria as it is complete and captures all required fields.\\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Introduction:**\n", + "\n", + "The Verifier class is part of a larger API that provides functionality for verifying the authenticity and integrity of artifacts. The Verifier class specifically handles the verification process by performing various checks and validations on the provided signing materials and Rekor entries.\n", + "\n", + "**Functions:**\n", + "\n", + "1. `__init__(\n", + " self, *, rekor: RekorClient, fulcio_certificate_chain: List[Certificate]\n", + " )`\n", + " \n", + " - Description: Initializes an instance of the Verifier class with the provided RekorClient instance and list of Fulcio certificates.\n", + " - Parameters:\n", + " - `rekor` (RekorClient): An instance of the RekorClient class, used for fetching Rekor entries.\n", + " - `fulcio_certificate_chain` (List[Certificate]): A list of Fulcio certificates used for verifying the signing certificate.\n", + " - Return Values: None\n", + "\n", + "2. `production(cls) -> Verifier`\n", + " \n", + " - Description: Returns a production instance of the Verifier class with the necessary configurations.\n", + " - Parameters: None\n", + " - Return Values: `Verifier` - A Verifier instance.\n", + "\n", + "3. `staging(cls) -> Verifier`\n", + " \n", + " - Description: Returns a staging instance of the Verifier class with the necessary configurations.\n", + " - Parameters: None\n", + " - Return Values: `Verifier` - A Verifier instance.\n", + "\n", + "4. `verify(\n", + " self,\n", + " materials: VerificationMaterials,\n", + " policy: VerificationPolicy,\n", + " ) -> VerificationResult`\n", + " \n", + " - Description: Verifies the authenticity and integrity of the provided signing materials and Rekor entries.\n", + " - Parameters:\n", + " - `materials` (VerificationMaterials): The signing materials and Rekor entries for verification.\n", + " - `policy` (VerificationPolicy): The verification policy to apply during the verification process.\n", + " - Return Values: `VerificationResult` - The result of the verification process.\n", + "\n", + "**Error Handling:**\n", + "\n", + "- `CertificateVerificationFailure` - Raised when the signing certificate fails to verify.\n", + "- `LogEntryMissing` - Raised when the Rekor log has no entry for the given verification materials.\n", + "- `VerificationFailure` - Raised when the verification process fails.\n", + "- `InvalidSignature` - Raised when the signature is invalid for the input.\n", + "- `RekorEntryMissingError` - Raised when the Rekor entry for the artifact is missing.\n", + "- `InvalidRekorEntryError` - Raised when the Rekor entry contents do not match the other signing materials.\n", + "- `InvalidInclusionProofError` - Raised when the Rekor inclusion proof is invalid.\n", + "- `CheckpointError` - Raised when the Rekor root hash is invalid.\n", + "- `InvalidSETError` - Raised when the Rekor entry SET is invalid.\n", + "\n", + "{'reasoning': 'The criterion for this task is \"helpfulness\". The submission is to be evaluated based on whether it is helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a detailed explanation of the Verifier class and its methods. It follows the structure provided in the task, including an introduction, function documentation, and error handling. \\n\\nThe introduction gives a brief overview of the Verifier class and its purpose. This is helpful for users who are not familiar with the class and need a quick summary of its functionality.\\n\\nThe function documentation is detailed and includes descriptions, parameters, and return values for each method. This is helpful for users who need to understand how to use the methods and what they can expect in return.\\n\\nThe error handling section lists the possible errors that can be raised during the verification process. This is helpful for users who need to handle these errors in their code.\\n\\nOverall, the submission is helpful as it provides a comprehensive overview of the Verifier class. It is insightful as it explains the purpose and usage of each method. It is appropriate as it follows the structure provided in the task and covers all the necessary information.\\n\\nTherefore, the submission meets the criterion. \\n\\nY', 'value': 'Y', 'score': 1}\n", + "{'reasoning': \"The submission is being evaluated on the criterion of correctness, which includes accuracy and factualness.\\n\\n1. The introduction provided in the submission accurately describes the purpose of the Verifier class and its role in the larger API. It correctly states that the Verifier class is responsible for verifying the authenticity and integrity of artifacts.\\n\\n2. The functions documented in the submission are all present in the provided Python code. The descriptions, parameters, and return values for each function are accurately described.\\n\\n3. The error handling section of the submission correctly lists and describes the possible error responses that can be raised during the execution of the Verifier class's methods. Each error response is present in the provided Python code and is accurately described in the submission.\\n\\nBased on this analysis, the submission is correct, accurate, and factual. It accurately describes the Verifier class, its methods, and possible error responses. Therefore, the submission meets the criterion of correctness. \\n\\nNow, I will provide the final assessment.\", 'value': 'Now, I will provide the final assessment.', 'score': None}\n", + "{'reasoning': 'The criteria for this task is to assess whether the output is complete and captures all required fields. \\n\\nThe required fields for the output are:\\n\\n1. Introduction: The submission provides an introduction that describes the purpose of the API and its intended use.\\n\\n2. Functions: The submission documents each API function, including a description of what the function does, a list and description of each parameter, and the data type and possible values returned.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings.\\n\\nLooking at the submission:\\n\\n1. Introduction: The submission provides an introduction that describes the purpose of the Verifier class and its intended use. This meets the requirement.\\n\\n2. Functions: The submission documents each function in the Verifier class, including a description of what the function does, a list and description of each parameter, and the data type and possible values returned. This meets the requirement.\\n\\n3. Error Handling: The submission describes possible error responses and their meanings. This meets the requirement.\\n\\nTherefore, the submission meets all the criteria. \\n\\nY', 'value': 'Y', 'score': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_224/1423965122.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + " df = df.append(new_row, ignore_index=True)\n" + ] + } + ], + "source": [ + "results_df = pd.DataFrame(columns=['model', 'prompt', 'code_file', 'part', 'response', 'langchain_helpfulness', 'langchain_correctness', 'langchain_logical'])\n", + "\n", + "models = [\"OpenAI/gpt3.5\"]\n", + "instruction_options = [instruction_1, instruction_2, instruction_old]\n", + "code_files = [\"oidc\", \"transparency\", \"errors\", \"verify_models\", \"verify_policy\", \"sign\", \"verify_verifier\"]\n", + "enabled_parts = [\"functions_code\", \"classes_code\"]\n", + "\n", + "for model in models:\n", + " for inst in instruction_options:\n", + " for code_file in code_files:\n", + " for part in enabled_parts:\n", + " prompt, generated_text, actual_doc = get_response(inst, model, code_file, functions=False, classes=False, documentation=False, imports=False, other=False, functions_code=(part==\"functions_code\"), functions_doc=False, classes_code=(part==\"classes_code\"), classes_doc=False)\n", + " results_df = append_row_to_dataframe(results_df, prompt, generated_text)\n", + "\n", + " other_values = {'model': model,\n", + " 'code_file': code_file,\n", + " 'part': part,\n", + " 'instruction': inst\n", + " }\n", + " for column, value in other_values.items():\n", + " results_df.loc[results_df.index[-1], column] = value\n", + "\n", + "results_df['total_langchain_score'] = results_df['langchain_helpfulness'] + results_df['langchain_correctness'] + results_df['langchain_logical']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6e2e8feb-b230-4110-80a3-41a3215521e3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelpromptcode_filepartresponselangchain_helpfulnesslangchain_correctnesslangchain_logicalinstructiontotal_langchain_score
0OpenAI/gpt3.5\\nYou are an AI system specialized at generati...oidcfunctions_code1. Introduction:\\nThis API function is used to...111\\nYou are an AI system specialized at generati...3
1OpenAI/gpt3.5\\nYou are an AI system specialized at generati...oidcclasses_codeNo Code has been provided in the prompt.000\\nYou are an AI system specialized at generati...0
2OpenAI/gpt3.5\\nYou are an AI system specialized at generati...transparencyfunctions_code**1. Introduction:**\\n\\nThe Python script prov...101\\nYou are an AI system specialized at generati...2
3OpenAI/gpt3.5\\nYou are an AI system specialized at generati...transparencyclasses_code**Class Name:** LogInclusionProof\\n\\n**Descrip...None11\\nYou are an AI system specialized at generati...NaN
4OpenAI/gpt3.5\\nYou are an AI system specialized at generati...errorsfunctions_code# Introduction:\\nNo Code has been provided in ...011\\nYou are an AI system specialized at generati...2
5OpenAI/gpt3.5\\nYou are an AI system specialized at generati...errorsclasses_codeNo code has been provided in the prompt.000\\nYou are an AI system specialized at generati...0
6OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_modelsfunctions_codeNo code has been provided in the prompt.111\\nYou are an AI system specialized at generati...3
7OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_modelsclasses_codeNo code has been provided in the prompt.000\\nYou are an AI system specialized at generati...0
8OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_policyfunctions_code# **API Documentation**\\n\\n## Introduction:\\nT...0None1\\nYou are an AI system specialized at generati...NaN
9OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_policyclasses_codeNo code has been provided in the prompt.000\\nYou are an AI system specialized at generati...0
10OpenAI/gpt3.5\\nYou are an AI system specialized at generati...signfunctions_code# Tower of Hanoi\\n\\n## Introduction:\\nThe Towe...101\\nYou are an AI system specialized at generati...2
11OpenAI/gpt3.5\\nYou are an AI system specialized at generati...signclasses_code**Class Name:** Signer\\n\\n**Description:** The...111\\nYou are an AI system specialized at generati...3
12OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_verifierfunctions_code<!-- This task is from test 'Generate API Docu...000\\nYou are an AI system specialized at generati...0
13OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_verifierclasses_codeNo code has been provided in the prompt.000\\nYou are an AI system specialized at generati...0
14OpenAI/gpt3.5\\nGenerate API documentation for Python code p...oidcfunctions_code**Function Description:**\\n\\nThe `detect_crede...111\\nGenerate API documentation for Python code p...3
15OpenAI/gpt3.5\\nGenerate API documentation for Python code p...oidcclasses_codeClass 1: _OpenIDConfiguration\\nDescription: Th...111\\nGenerate API documentation for Python code p...3
16OpenAI/gpt3.5\\nGenerate API documentation for Python code p...transparencyfunctions_codeNo code provided.101\\nGenerate API documentation for Python code p...2
17OpenAI/gpt3.5\\nGenerate API documentation for Python code p...transparencyclasses_codeThe `LogInclusionProof` class represents an in...111\\nGenerate API documentation for Python code p...3
18OpenAI/gpt3.5\\nGenerate API documentation for Python code p...errorsfunctions_codeNo code provided.101\\nGenerate API documentation for Python code p...2
19OpenAI/gpt3.5\\nGenerate API documentation for Python code p...errorsclasses_codeNo code provided.000\\nGenerate API documentation for Python code p...0
20OpenAI/gpt3.5\\nGenerate API documentation for Python code p...verify_modelsfunctions_codeNo code provided.111\\nGenerate API documentation for Python code p...3
21OpenAI/gpt3.5\\nGenerate API documentation for Python code p...verify_modelsclasses_code### VerificationResult\\nClass Description: Thi...11None\\nGenerate API documentation for Python code p...NaN
22OpenAI/gpt3.5\\nGenerate API documentation for Python code p...verify_policyfunctions_codeNo code provided.111\\nGenerate API documentation for Python code p...3
23OpenAI/gpt3.5\\nGenerate API documentation for Python code p...verify_policyclasses_codeClass Name: `_SingleX509ExtPolicy`\\nDescriptio...110\\nGenerate API documentation for Python code p...2
24OpenAI/gpt3.5\\nGenerate API documentation for Python code p...signfunctions_codeNo code provided.111\\nGenerate API documentation for Python code p...3
25OpenAI/gpt3.5\\nGenerate API documentation for Python code p...signclasses_code**Class Signer**\\n\\nThis class represents a si...111\\nGenerate API documentation for Python code p...3
26OpenAI/gpt3.5\\nGenerate API documentation for Python code p...verify_verifierfunctions_codeNo code provided.111\\nGenerate API documentation for Python code p...3
27OpenAI/gpt3.5\\nGenerate API documentation for Python code p...verify_verifierclasses_code### Class: LogEntryMissing\\n\\n#### Description...1None0\\nGenerate API documentation for Python code p...NaN
28OpenAI/gpt3.5\\nYou are an AI system specialized at generati...oidcfunctions_code1. Introduction:\\nThe detect_credential functi...110\\nYou are an AI system specialized at generati...2
29OpenAI/gpt3.5\\nYou are an AI system specialized at generati...oidcclasses_code**Class `Issuer`**\\n\\n**Introduction:**\\nThe `...111\\nYou are an AI system specialized at generati...3
30OpenAI/gpt3.5\\nYou are an AI system specialized at generati...transparencyfunctions_code# API Documentation\\n\\n## Introduction\\n\\nThis...111\\nYou are an AI system specialized at generati...3
31OpenAI/gpt3.5\\nYou are an AI system specialized at generati...transparencyclasses_code**Introduction:**\\nThe `LogInclusionProof` cla...111\\nYou are an AI system specialized at generati...3
32OpenAI/gpt3.5\\nYou are an AI system specialized at generati...errorsfunctions_code# API Documentation\\n\\n## Introduction\\nThis A...111\\nYou are an AI system specialized at generati...3
33OpenAI/gpt3.5\\nYou are an AI system specialized at generati...errorsclasses_code**Introduction:**\\n\\nThe API provided by this ...1None1\\nYou are an AI system specialized at generati...NaN
34OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_modelsfunctions_code# API Documentation\\n\\n## Introduction\\nThis A...111\\nYou are an AI system specialized at generati...3
35OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_modelsclasses_code1. Introduction:\\nThe VerificationResult class...100\\nYou are an AI system specialized at generati...1
36OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_policyfunctions_code# API Documentation\\n\\n## Introduction\\nThis A...111\\nYou are an AI system specialized at generati...3
37OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_policyclasses_code**Class `_SingleX509ExtPolicy`**\\n\\n1. Introdu...111\\nYou are an AI system specialized at generati...3
38OpenAI/gpt3.5\\nYou are an AI system specialized at generati...signfunctions_code# API Documentation\\n\\n## Introduction\\n\\nThe ...111\\nYou are an AI system specialized at generati...3
39OpenAI/gpt3.5\\nYou are an AI system specialized at generati...signclasses_code**Signer**\\n\\n1. Introduction:\\nThe Signer cla...10None\\nYou are an AI system specialized at generati...NaN
40OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_verifierfunctions_code# API Documentation\\n\\n## Introduction\\nThis A...111\\nYou are an AI system specialized at generati...3
41OpenAI/gpt3.5\\nYou are an AI system specialized at generati...verify_verifierclasses_code**Introduction:**\\n\\nThe Verifier class is par...1None1\\nYou are an AI system specialized at generati...NaN
\n", + "
" + ], + "text/plain": [ + " model prompt \\\n", + "0 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "1 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "2 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "3 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "4 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "5 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "6 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "7 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "8 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "9 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "10 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "11 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "12 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "13 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "14 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "15 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "16 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "17 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "18 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "19 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "20 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "21 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "22 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "23 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "24 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "25 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "26 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "27 OpenAI/gpt3.5 \\nGenerate API documentation for Python code p... \n", + "28 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "29 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "30 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "31 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "32 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "33 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "34 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "35 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "36 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "37 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "38 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "39 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "40 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "41 OpenAI/gpt3.5 \\nYou are an AI system specialized at generati... \n", + "\n", + " code_file part \\\n", + "0 oidc functions_code \n", + "1 oidc classes_code \n", + "2 transparency functions_code \n", + "3 transparency classes_code \n", + "4 errors functions_code \n", + "5 errors classes_code \n", + "6 verify_models functions_code \n", + "7 verify_models classes_code \n", + "8 verify_policy functions_code \n", + "9 verify_policy classes_code \n", + "10 sign functions_code \n", + "11 sign classes_code \n", + "12 verify_verifier functions_code \n", + "13 verify_verifier classes_code \n", + "14 oidc functions_code \n", + "15 oidc classes_code \n", + "16 transparency functions_code \n", + "17 transparency classes_code \n", + "18 errors functions_code \n", + "19 errors classes_code \n", + "20 verify_models functions_code \n", + "21 verify_models classes_code \n", + "22 verify_policy functions_code \n", + "23 verify_policy classes_code \n", + "24 sign functions_code \n", + "25 sign classes_code \n", + "26 verify_verifier functions_code \n", + "27 verify_verifier classes_code \n", + "28 oidc functions_code \n", + "29 oidc classes_code \n", + "30 transparency functions_code \n", + "31 transparency classes_code \n", + "32 errors functions_code \n", + "33 errors classes_code \n", + "34 verify_models functions_code \n", + "35 verify_models classes_code \n", + "36 verify_policy functions_code \n", + "37 verify_policy classes_code \n", + "38 sign functions_code \n", + "39 sign classes_code \n", + "40 verify_verifier functions_code \n", + "41 verify_verifier classes_code \n", + "\n", + " response langchain_helpfulness \\\n", + "0 1. Introduction:\\nThis API function is used to... 1 \n", + "1 No Code has been provided in the prompt. 0 \n", + "2 **1. Introduction:**\\n\\nThe Python script prov... 1 \n", + "3 **Class Name:** LogInclusionProof\\n\\n**Descrip... None \n", + "4 # Introduction:\\nNo Code has been provided in ... 0 \n", + "5 No code has been provided in the prompt. 0 \n", + "6 No code has been provided in the prompt. 1 \n", + "7 No code has been provided in the prompt. 0 \n", + "8 # **API Documentation**\\n\\n## Introduction:\\nT... 0 \n", + "9 No code has been provided in the prompt. 0 \n", + "10 # Tower of Hanoi\\n\\n## Introduction:\\nThe Towe... 1 \n", + "11 **Class Name:** Signer\\n\\n**Description:** The... 1 \n", + "12