From 9fd078bfdcb6312fd8799b138253654465725b07 Mon Sep 17 00:00:00 2001 From: gargimaheshwari Date: Thu, 18 Apr 2024 15:10:39 +0530 Subject: [PATCH] fix: staticmethod LyzrLLMFactory and recommendations prompt --- build/lib/lyzr/base/file_utils.py | 2 +- build/lib/lyzr/base/llm.py | 4 ++-- build/lib/lyzr/base/prompt_texts.py | 4 ++-- build/lib/lyzr/data_analyzr/analyzr.py | 11 ++++++----- .../lyzr/data_analyzr/ml_analysis_utils.py | 12 ++++++------ dist/lyzr-0.1.33.tar.gz | Bin 63477 -> 0 bytes ...e-any.whl => lyzr-0.1.34-py3-none-any.whl} | Bin 87826 -> 87853 bytes dist/lyzr-0.1.34.tar.gz | Bin 0 -> 63580 bytes lyzr.egg-info/PKG-INFO | 2 +- lyzr/base/file_utils.py | 2 +- lyzr/base/llm.py | 4 ++-- lyzr/base/prompt_texts.py | 4 ++-- lyzr/data_analyzr/analyzr.py | 11 ++++++----- lyzr/data_analyzr/ml_analysis_utils.py | 12 ++++++------ setup.py | 2 +- 15 files changed, 36 insertions(+), 34 deletions(-) delete mode 100644 dist/lyzr-0.1.33.tar.gz rename dist/{lyzr-0.1.33-py3-none-any.whl => lyzr-0.1.34-py3-none-any.whl} (68%) create mode 100644 dist/lyzr-0.1.34.tar.gz diff --git a/build/lib/lyzr/base/file_utils.py b/build/lib/lyzr/base/file_utils.py index f7479d9..90dcd60 100644 --- a/build/lib/lyzr/base/file_utils.py +++ b/build/lib/lyzr/base/file_utils.py @@ -57,7 +57,7 @@ def describe_dataset( raise ValueError("Please provide a valid pandas DataFrame.") if model is None: - model = LyzrLLMFactory( + model = LyzrLLMFactory.from_defaults( api_key=api_key, api_type=model_type, model=model_name, diff --git a/build/lib/lyzr/base/llm.py b/build/lib/lyzr/base/llm.py index 1e44915..e2beb53 100644 --- a/build/lib/lyzr/base/llm.py +++ b/build/lib/lyzr/base/llm.py @@ -15,8 +15,8 @@ class LyzrLLMFactory: - - def from_defaults(self, model: str = DEFAULT_LLM, **kwargs) -> LLM: + @staticmethod + def from_defaults(model: str = DEFAULT_LLM, **kwargs) -> LLM: # model_type -> api_type # model_name -> model # model_prompts -> Sequence[ChatMessage] diff --git a/build/lib/lyzr/base/prompt_texts.py b/build/lib/lyzr/base/prompt_texts.py index aa2dd86..3010cd7 100644 --- a/build/lib/lyzr/base/prompt_texts.py +++ b/build/lib/lyzr/base/prompt_texts.py @@ -29,7 +29,7 @@ "inputs": "The user asked the following question: {user_input}\nGenerate recommendations that enhance the user's question or are related to it." } }, - "analysis_guide": { + "ml_analysis_guide": { "system": { "context": "You are Business Analyst. You are an expert in your field. You are assisting a data analyst.\nYou are given a dataset and a question. Your job is to analyze these two inputs and determine how to answer the question based on the data.\n\n", "external_context": "{context}", @@ -37,7 +37,7 @@ }, "user": {"inputs": "{df_details}\nQuestion: {question}"}, }, - "analysis_steps": { + "ml_analysis_steps": { "system": { "task": "You are a Senior Data Scientist. You have been asked a question on a dataframe.\nYour job is to analyze the given dataframe `df` to answer the question.\n\nTo assist you, a Business Analyst with domain knowledge has given their insights on the best way to go about your task.\nThe Business Analyst has also shared the names of the columns required in the resultant dataframe.\nFollow their instructions as closely as possible.\n\nMake sure that you clean the data before you analyze it.\n\nYour answer should be in the form of a python JSON object, following the given format:\n{schema}\n\nA. The value of 'analysis_df' should be the name of the dataframe on which this analysis is to be performed.\nB. The value of 'steps' should be a list of dictionaries. Each dictionary should contain the following keys: 'step', 'task', 'type', 'args'.\n The following values are available for these keys. ONLY USE THESE VALUES.\n 1. Step: A number indicating the order of the step. Numbering should start from 1.\n 2. Task: The task to be performed. The task can be one of the following: 'clean_data', 'transform', 'math_operation', 'analysis'\n 3. Type: The type of task to be performed.\n 3a. For task 'clean_data', following types are available: 'convert_to_datetime', 'convert_to_numeric', 'convert_to_categorical'\n 3b. For task 'transform', following types are available: 'one_hot_encode', 'ordinal_encode', 'scale', 'extract_time_period', 'select_indices'\n 3c. For task 'math_operation', following types are available: 'add', 'subtract', 'multiply', 'divide'\n 3d. For task 'analysis', following types are available: 'sortvalues', 'filter', 'mean', 'sum', 'cumsum', 'groupby', 'correlation', 'regression', 'classification', 'clustering', 'forecast'\n 4. Args: The arguments required to perform the task. The arguments should be in the form of a dictionary.\n 4a. For task 'clean_data' - 'columns': list\n 4b. For task 'transform', type 'one_hot_encode', 'ordinal_encode', and 'scale' - 'columns': list\n 4c. For task 'transform', type 'extract_time_period' - 'columns': list, 'period_to_extract': Literal['week', 'month', 'year', 'day', 'hour', 'minute', 'second', 'weekday']\n 4d. For task 'transform', type 'select_indices' - 'columns': list, 'indices': list\n 4e. For task 'math_operation' - 'columns': list, 'result': str (the name of the column to store the result in)\n 4f. For task 'analysis', type 'groupby' - 'columns': list, 'agg': Union[str, list], 'agg_col': Optional[list]\n 4g. For task 'analysis', type 'sortvalues' - columns: list, 'ascending': Optional[bool]\n 4h. For task 'analysis', type 'filter' - 'columns': list, 'values': list[Any] (the values to compare the columns to), 'relations': list[Literal['lessthan', 'greaterthan', 'lessthanorequalto', 'greaterthanorequalto', 'equalto', 'notequalto', 'startswith', 'endswith', 'contains']]\n 4i. For task 'analysis', types 'mean', 'cumsum', and 'sum' - 'columns': list\n 4j. For task 'analysis', type 'correlation' - 'columns': list, 'method': Optional[Literal['pearson', 'kendall', 'spearman']]\n 4k. For task 'analysis', type 'regression' - 'x': list, 'y': list\n 4l. For task 'analysis', type 'classification' - 'x': list, 'y': list\n 4m. For task 'analysis', type 'clustering' - 'x': list, 'y': list\n 4n. For task 'analysis', type 'forecast' - 'time_column': str, 'y_column': str, 'end': Optional[str], 'steps': Optional[int] # you must pass either 'end' - the date until which to forecast or 'steps' - the number of steps to forecast\nC. The value of 'output_columns' should be a list of strings. Each string should be the name of a column in the dataframe. These columns should be the ones that are required to answer the question.\n\nDo not give any explanations. Only give the python JSON as the answer.\nThis JSON will be evaluated using the eval() function in python. Ensure that it is in the correct format, and has no syntax errors.\n\nOnly return this JSON with details of steps. Do not return anything else.\n\nBefore beginning, take a deep breath and relax. You are an expert in your field. You have done this many times before.\nYou may now begin." }, diff --git a/build/lib/lyzr/data_analyzr/analyzr.py b/build/lib/lyzr/data_analyzr/analyzr.py index 7720b13..055e99d 100644 --- a/build/lib/lyzr/data_analyzr/analyzr.py +++ b/build/lib/lyzr/data_analyzr/analyzr.py @@ -88,7 +88,7 @@ def __init__( if analysis_type is None: raise MissingValueError("`analysis_type` is a required parameter.") if model is None: - self.model = LyzrLLMFactory().from_defaults( + self.model = LyzrLLMFactory.from_defaults( model="gpt-4-1106-preview", api_key=api_key, seed=seed ) elif isinstance(model, LiteLLM): @@ -125,7 +125,7 @@ def _legacy_usage( warnings.warn( f"The `{param}` parameter is deprecated and will be removed in a future version. Please use the `analysis_model` parameter to set the analysis model, and the `gen_model` parameter to set the generation model." ) - self.model = model or LyzrLLMFactory().from_defaults( + self.model = model or LyzrLLMFactory.from_defaults( api_key=api_key, api_type=model_type, model=model_name or os.environ.get("MODEL_NAME", "gpt-4-1106-preview"), @@ -246,7 +246,7 @@ def analysis( "No analysis performed. Analysis output is the given dataframe." ) return self.analysis_output - analysis_model = LyzrLLMFactory().from_defaults(model="gpt-3.5-turbo") + analysis_model = LyzrLLMFactory.from_defaults(model="gpt-3.5-turbo") analysis_model.additional_kwargs["logger"] = self.logger if self.analysis_type == "sql" and analysis_steps is None: return self._txt_to_sql_analysis( @@ -393,6 +393,7 @@ def recommendations( if not use_insights: insights = None system_message_sections.append("task_no_insights") + user_message_dict["insights"] = "" else: system_message_sections.append("task_with_insights") user_message_dict["insights"] = ( @@ -412,7 +413,7 @@ def recommendations( ] elif output_type.lower().strip() == "text": system_message_sections.append("text_type") - system_message_dict["n_recommendations"] = n_recommendations + system_message_dict["n_recommendations"] = n_recommendations system_message_sections.append("closing") self.recommendations_output = self.model.run( @@ -461,7 +462,7 @@ def tasks( context=tasks_context.strip() + "\n\n", n_tasks=n_tasks ), LyzrPromptFactory(name="tasks", prompt_type="user").get_message( - user_input=user_input or self.user_input, + user_input=user_input, insights=self.insights_output, recommendations=self.recommendations_output, ), diff --git a/build/lib/lyzr/data_analyzr/ml_analysis_utils.py b/build/lib/lyzr/data_analyzr/ml_analysis_utils.py index a71cad4..2cefd5c 100644 --- a/build/lib/lyzr/data_analyzr/ml_analysis_utils.py +++ b/build/lib/lyzr/data_analyzr/ml_analysis_utils.py @@ -112,12 +112,12 @@ def _get_analysis_guide(self, user_input: str) -> str: output = self.model.run( messages=[ LyzrPromptFactory( - name="analysis_guide", prompt_type="system" + name="ml_analysis_guide", prompt_type="system" ).get_message( context=self.context, ), LyzrPromptFactory( - name="analysis_guide", prompt_type="user" + name="ml_analysis_guide", prompt_type="user" ).get_message( df_details=print_df_details(self.df_dict, self.df_info_dict), question=user_input, @@ -162,10 +162,10 @@ def _get_analysis_steps_messages_kwargs(self, user_input: str) -> tuple: "output columns": ["col1", "col2", "col3"], } messages = [ - LyzrPromptFactory(name="analysis_steps", prompt_type="system").get_message( - schema=schema - ), - LyzrPromptFactory(name="analysis_steps", prompt_type="user").get_message( + LyzrPromptFactory( + name="ml_analysis_steps", prompt_type="system" + ).get_message(schema=schema), + LyzrPromptFactory(name="ml_analysis_steps", prompt_type="user").get_message( df_details=print_df_details(self.df_dict, self.df_info_dict), question=user_input, context=self.analysis_guide, diff --git a/dist/lyzr-0.1.33.tar.gz b/dist/lyzr-0.1.33.tar.gz deleted file mode 100644 index 9d484b1c3f5fed056d09a0d395f8567d37566549..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 63477 zcmZ^~Q*b8E6Ym|{w(VqN+uUShTN~T9ZQHhO+uYdJ^PJ!NulHP@o9}dWbxqAhPt8yOc==^!%S+by*O>|8|OZI>7(ZP0~r9}k!ae!eIZkk+Cqtz 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z;*v$g`6#u@c+=?zpZUP+j1852QE{xsysWq6m_*8k(b%&bQ^~;@mHYC~hlEF4o>xfb zm7g;oy-*mGlX(x+Yj4x0zG{d}E!6N0x)nqIP`X$9|9?{?7taPqotO3y)9?`g57LIr AMF0Q* literal 0 HcmV?d00001 diff --git a/lyzr.egg-info/PKG-INFO b/lyzr.egg-info/PKG-INFO index 719fb1c..f56d583 100644 --- a/lyzr.egg-info/PKG-INFO +++ b/lyzr.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: lyzr -Version: 0.1.33 +Version: 0.1.34 Summary: UNKNOWN Home-page: UNKNOWN Author: lyzr diff --git a/lyzr/base/file_utils.py b/lyzr/base/file_utils.py index f7479d9..90dcd60 100644 --- a/lyzr/base/file_utils.py +++ b/lyzr/base/file_utils.py @@ -57,7 +57,7 @@ def describe_dataset( raise ValueError("Please provide a valid pandas DataFrame.") if model is None: - model = LyzrLLMFactory( + model = LyzrLLMFactory.from_defaults( api_key=api_key, api_type=model_type, model=model_name, diff --git a/lyzr/base/llm.py b/lyzr/base/llm.py index 1e44915..e2beb53 100644 --- a/lyzr/base/llm.py +++ b/lyzr/base/llm.py @@ -15,8 +15,8 @@ class LyzrLLMFactory: - - def from_defaults(self, model: str = DEFAULT_LLM, **kwargs) -> LLM: + @staticmethod + def from_defaults(model: str = DEFAULT_LLM, **kwargs) -> LLM: # model_type -> api_type # model_name -> model # model_prompts -> Sequence[ChatMessage] diff --git a/lyzr/base/prompt_texts.py b/lyzr/base/prompt_texts.py index aa2dd86..3010cd7 100644 --- a/lyzr/base/prompt_texts.py +++ b/lyzr/base/prompt_texts.py @@ -29,7 +29,7 @@ "inputs": "The user asked the following question: {user_input}\nGenerate recommendations that enhance the user's question or are related to it." } }, - "analysis_guide": { + "ml_analysis_guide": { "system": { "context": "You are Business Analyst. You are an expert in your field. You are assisting a data analyst.\nYou are given a dataset and a question. Your job is to analyze these two inputs and determine how to answer the question based on the data.\n\n", "external_context": "{context}", @@ -37,7 +37,7 @@ }, "user": {"inputs": "{df_details}\nQuestion: {question}"}, }, - "analysis_steps": { + "ml_analysis_steps": { "system": { "task": "You are a Senior Data Scientist. You have been asked a question on a dataframe.\nYour job is to analyze the given dataframe `df` to answer the question.\n\nTo assist you, a Business Analyst with domain knowledge has given their insights on the best way to go about your task.\nThe Business Analyst has also shared the names of the columns required in the resultant dataframe.\nFollow their instructions as closely as possible.\n\nMake sure that you clean the data before you analyze it.\n\nYour answer should be in the form of a python JSON object, following the given format:\n{schema}\n\nA. The value of 'analysis_df' should be the name of the dataframe on which this analysis is to be performed.\nB. The value of 'steps' should be a list of dictionaries. Each dictionary should contain the following keys: 'step', 'task', 'type', 'args'.\n The following values are available for these keys. ONLY USE THESE VALUES.\n 1. Step: A number indicating the order of the step. Numbering should start from 1.\n 2. Task: The task to be performed. The task can be one of the following: 'clean_data', 'transform', 'math_operation', 'analysis'\n 3. Type: The type of task to be performed.\n 3a. For task 'clean_data', following types are available: 'convert_to_datetime', 'convert_to_numeric', 'convert_to_categorical'\n 3b. For task 'transform', following types are available: 'one_hot_encode', 'ordinal_encode', 'scale', 'extract_time_period', 'select_indices'\n 3c. For task 'math_operation', following types are available: 'add', 'subtract', 'multiply', 'divide'\n 3d. For task 'analysis', following types are available: 'sortvalues', 'filter', 'mean', 'sum', 'cumsum', 'groupby', 'correlation', 'regression', 'classification', 'clustering', 'forecast'\n 4. Args: The arguments required to perform the task. The arguments should be in the form of a dictionary.\n 4a. For task 'clean_data' - 'columns': list\n 4b. For task 'transform', type 'one_hot_encode', 'ordinal_encode', and 'scale' - 'columns': list\n 4c. For task 'transform', type 'extract_time_period' - 'columns': list, 'period_to_extract': Literal['week', 'month', 'year', 'day', 'hour', 'minute', 'second', 'weekday']\n 4d. For task 'transform', type 'select_indices' - 'columns': list, 'indices': list\n 4e. For task 'math_operation' - 'columns': list, 'result': str (the name of the column to store the result in)\n 4f. For task 'analysis', type 'groupby' - 'columns': list, 'agg': Union[str, list], 'agg_col': Optional[list]\n 4g. For task 'analysis', type 'sortvalues' - columns: list, 'ascending': Optional[bool]\n 4h. For task 'analysis', type 'filter' - 'columns': list, 'values': list[Any] (the values to compare the columns to), 'relations': list[Literal['lessthan', 'greaterthan', 'lessthanorequalto', 'greaterthanorequalto', 'equalto', 'notequalto', 'startswith', 'endswith', 'contains']]\n 4i. For task 'analysis', types 'mean', 'cumsum', and 'sum' - 'columns': list\n 4j. For task 'analysis', type 'correlation' - 'columns': list, 'method': Optional[Literal['pearson', 'kendall', 'spearman']]\n 4k. For task 'analysis', type 'regression' - 'x': list, 'y': list\n 4l. For task 'analysis', type 'classification' - 'x': list, 'y': list\n 4m. For task 'analysis', type 'clustering' - 'x': list, 'y': list\n 4n. For task 'analysis', type 'forecast' - 'time_column': str, 'y_column': str, 'end': Optional[str], 'steps': Optional[int] # you must pass either 'end' - the date until which to forecast or 'steps' - the number of steps to forecast\nC. The value of 'output_columns' should be a list of strings. Each string should be the name of a column in the dataframe. These columns should be the ones that are required to answer the question.\n\nDo not give any explanations. Only give the python JSON as the answer.\nThis JSON will be evaluated using the eval() function in python. Ensure that it is in the correct format, and has no syntax errors.\n\nOnly return this JSON with details of steps. Do not return anything else.\n\nBefore beginning, take a deep breath and relax. You are an expert in your field. You have done this many times before.\nYou may now begin." }, diff --git a/lyzr/data_analyzr/analyzr.py b/lyzr/data_analyzr/analyzr.py index 7720b13..055e99d 100644 --- a/lyzr/data_analyzr/analyzr.py +++ b/lyzr/data_analyzr/analyzr.py @@ -88,7 +88,7 @@ def __init__( if analysis_type is None: raise MissingValueError("`analysis_type` is a required parameter.") if model is None: - self.model = LyzrLLMFactory().from_defaults( + self.model = LyzrLLMFactory.from_defaults( model="gpt-4-1106-preview", api_key=api_key, seed=seed ) elif isinstance(model, LiteLLM): @@ -125,7 +125,7 @@ def _legacy_usage( warnings.warn( f"The `{param}` parameter is deprecated and will be removed in a future version. Please use the `analysis_model` parameter to set the analysis model, and the `gen_model` parameter to set the generation model." ) - self.model = model or LyzrLLMFactory().from_defaults( + self.model = model or LyzrLLMFactory.from_defaults( api_key=api_key, api_type=model_type, model=model_name or os.environ.get("MODEL_NAME", "gpt-4-1106-preview"), @@ -246,7 +246,7 @@ def analysis( "No analysis performed. Analysis output is the given dataframe." ) return self.analysis_output - analysis_model = LyzrLLMFactory().from_defaults(model="gpt-3.5-turbo") + analysis_model = LyzrLLMFactory.from_defaults(model="gpt-3.5-turbo") analysis_model.additional_kwargs["logger"] = self.logger if self.analysis_type == "sql" and analysis_steps is None: return self._txt_to_sql_analysis( @@ -393,6 +393,7 @@ def recommendations( if not use_insights: insights = None system_message_sections.append("task_no_insights") + user_message_dict["insights"] = "" else: system_message_sections.append("task_with_insights") user_message_dict["insights"] = ( @@ -412,7 +413,7 @@ def recommendations( ] elif output_type.lower().strip() == "text": system_message_sections.append("text_type") - system_message_dict["n_recommendations"] = n_recommendations + system_message_dict["n_recommendations"] = n_recommendations system_message_sections.append("closing") self.recommendations_output = self.model.run( @@ -461,7 +462,7 @@ def tasks( context=tasks_context.strip() + "\n\n", n_tasks=n_tasks ), LyzrPromptFactory(name="tasks", prompt_type="user").get_message( - user_input=user_input or self.user_input, + user_input=user_input, insights=self.insights_output, recommendations=self.recommendations_output, ), diff --git a/lyzr/data_analyzr/ml_analysis_utils.py b/lyzr/data_analyzr/ml_analysis_utils.py index a71cad4..2cefd5c 100644 --- a/lyzr/data_analyzr/ml_analysis_utils.py +++ b/lyzr/data_analyzr/ml_analysis_utils.py @@ -112,12 +112,12 @@ def _get_analysis_guide(self, user_input: str) -> str: output = self.model.run( messages=[ LyzrPromptFactory( - name="analysis_guide", prompt_type="system" + name="ml_analysis_guide", prompt_type="system" ).get_message( context=self.context, ), LyzrPromptFactory( - name="analysis_guide", prompt_type="user" + name="ml_analysis_guide", prompt_type="user" ).get_message( df_details=print_df_details(self.df_dict, self.df_info_dict), question=user_input, @@ -162,10 +162,10 @@ def _get_analysis_steps_messages_kwargs(self, user_input: str) -> tuple: "output columns": ["col1", "col2", "col3"], } messages = [ - LyzrPromptFactory(name="analysis_steps", prompt_type="system").get_message( - schema=schema - ), - LyzrPromptFactory(name="analysis_steps", prompt_type="user").get_message( + LyzrPromptFactory( + name="ml_analysis_steps", prompt_type="system" + ).get_message(schema=schema), + LyzrPromptFactory(name="ml_analysis_steps", prompt_type="user").get_message( df_details=print_df_details(self.df_dict, self.df_info_dict), question=user_input, context=self.analysis_guide, diff --git a/setup.py b/setup.py index 4e3a2ea..3124221 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="lyzr", - version="0.1.33", + version="0.1.34", author="lyzr", description="", long_description=open("README.md").read(),