Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

docs(evals): document llm_generate with output parser #1741

Merged
merged 1 commit into from
Nov 14, 2023
Merged

docs(evals): document llm_generate with output parser #1741

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
149 changes: 113 additions & 36 deletions docs/api/evals.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
---
description: >-
Evals are LLM-powered functions that you can use to evaluate the output of
your LLM or generative application
Evals are LLM-powered functions that you can use to evaluate the output of
your LLM or generative application
---

# Evals
Expand All @@ -23,13 +23,13 @@ Class used to store and format prompt templates.

### Parameters

* **text** (str): The raw prompt text used as a template.
* **delimiters** (List\[str]): List of characters used to locate the variables within the prompt template `text`. Defaults to `["{", "}"]`.
- **text** (str): The raw prompt text used as a template.
- **delimiters** (List\[str]): List of characters used to locate the variables within the prompt template `text`. Defaults to `["{", "}"]`.

### Attributes

* **text** (str): The raw prompt text used as a template.
* **variables** (List\[str]): The names of the variables that, once their values are substituted into the template, create the prompt text. These variable names are automatically detected from the template `text` using the `delimiters` passed when initializing the class (see Usage section below).
- **text** (str): The raw prompt text used as a template.
- **variables** (List\[str]): The names of the variables that, once their values are substituted into the template, create the prompt text. These variable names are automatically detected from the template `text` using the `delimiters` passed when initializing the class (see Usage section below).

### Usage

Expand Down Expand Up @@ -72,7 +72,7 @@ print(prompt_template.format(value_dict))

Note that once you initialize the `PromptTemplate` class, you don't need to worry about delimiters anymore, it will be handled for you.

## phoenix.experimental.evals.llm\_classify
## phoenix.experimental.evals.llm_classify

```python
def llm_classify(
Expand All @@ -91,20 +91,20 @@ Classifies each input row of the `dataframe` using an LLM. Returns a `pandas.Dat

### Parameters

* **dataframe (pandas.DataFrame)**: A pandas dataframe in which each row represents a record to be classified. All template variable names must appear as column names in the dataframe (extra columns unrelated to the template are permitted).
* **template (PromptTemplate or str):** The prompt template as either an instance of PromptTemplate or a string. If the latter, the variable names should be surrounded by curly braces so that a call to `.format` can be made to substitute variable values.
* **model (BaseEvalModel):** An LLM model class instance
* **rails (List\[str]):** A list of strings representing the possible output classes of the model's predictions.
* **system\_instruction (Optional\[str]):** An optional system message for modals that support it
* **verbose (bool, optional):** If `True`, prints detailed info to stdout such as model invocation parameters and details about retries and snapping to rails. Default `False`.
* **use\_function\_calling\_if\_available (bool, default=True):** If `True`, use function calling (if available) as a means to constrain the LLM outputs. With function calling, the LLM is instructed to provide its response as a structured JSON object, which is easier to parse.
* **provide\_explanation (bool, default=False):** If `True`, provides an explanation for each classification label. A column named `explanation` is added to the output dataframe. Currently, this is only available for models with function calling.
- **dataframe (pandas.DataFrame)**: A pandas dataframe in which each row represents a record to be classified. All template variable names must appear as column names in the dataframe (extra columns unrelated to the template are permitted).
- **template (PromptTemplate or str):** The prompt template as either an instance of PromptTemplate or a string. If the latter, the variable names should be surrounded by curly braces so that a call to `.format` can be made to substitute variable values.
- **model (BaseEvalModel):** An LLM model class instance
- **rails (List\[str]):** A list of strings representing the possible output classes of the model's predictions.
- **system_instruction (Optional\[str]):** An optional system message for modals that support it
- **verbose (bool, optional):** If `True`, prints detailed info to stdout such as model invocation parameters and details about retries and snapping to rails. Default `False`.
- **use_function_calling_if_available (bool, default=True):** If `True`, use function calling (if available) as a means to constrain the LLM outputs. With function calling, the LLM is instructed to provide its response as a structured JSON object, which is easier to parse.
- **provide_explanation (bool, default=False):** If `True`, provides an explanation for each classification label. A column named `explanation` is added to the output dataframe. Currently, this is only available for models with function calling.

### Returns

* **pandas.DataFrame:** A dataframe where the `label` column (at column position 0) contains the classification labels. If `provide_explanation=True`, then an additional column named `explanation` is added to contain the explanation for each label. The dataframe has the same length and index as the input dataframe. The classification label values are from the entries in the rails argument or "NOT\_PARSABLE" if the model's output could not be parsed.
- **pandas.DataFrame:** A dataframe where the `label` column (at column position 0) contains the classification labels. If `provide_explanation=True`, then an additional column named `explanation` is added to contain the explanation for each label. The dataframe has the same length and index as the input dataframe. The classification label values are from the entries in the rails argument or "NOT_PARSABLE" if the model's output could not be parsed.

## phoenix.experimental.run\_relevance\_eval
## phoenix.experimental.run_relevance_eval

```python
def run_relevance_eval(
Expand All @@ -122,43 +122,120 @@ Given a pandas dataframe containing queries and retrieved documents, classifies

### Parameters

* **dataframe (pd.DataFrame):** A pandas dataframe containing queries and retrieved documents. If both query\_column\_name and reference\_column\_name are present in the input dataframe, those columns are used as inputs and should appear in the following format:
* The entries of the query column must be strings.
* The entries of the documents column must be lists of strings. Each list may contain an arbitrary number of document texts retrieved for the corresponding query.
* If the input dataframe is lacking either query\_column\_name or reference\_column\_name but has query and retrieved document columns in OpenInference trace format named "attributes.input.value" and "attributes.retrieval.documents", respectively, then those columns are used as inputs and should appear in the following format:
* The entries of the query column must be strings.
* The entries of the document column must be lists of OpenInference document objects, each object being a dictionary that stores the document text under the key "document.content".
* **model (BaseEvalModel):** The model used for evaluation.
* **template (Union\[PromptTemplate, str], optional):** The template used for evaluation.
* **rails (List\[str], optional):** A list of strings representing the possible output classes of the model's predictions.
* **query\_column\_name (str, optional):** The name of the query column in the dataframe, which should also be a template variable.
* **reference\_column\_name (str, optional):** The name of the document column in the dataframe, which should also be a template variable.
* **system\_instruction (Optional\[str], optional):** An optional system message.
- **dataframe (pd.DataFrame):** A pandas dataframe containing queries and retrieved documents. If both query_column_name and reference_column_name are present in the input dataframe, those columns are used as inputs and should appear in the following format:
- The entries of the query column must be strings.
- The entries of the documents column must be lists of strings. Each list may contain an arbitrary number of document texts retrieved for the corresponding query.
- If the input dataframe is lacking either query_column_name or reference_column_name but has query and retrieved document columns in OpenInference trace format named "attributes.input.value" and "attributes.retrieval.documents", respectively, then those columns are used as inputs and should appear in the following format:
- The entries of the query column must be strings.
- The entries of the document column must be lists of OpenInference document objects, each object being a dictionary that stores the document text under the key "document.content".
- **model (BaseEvalModel):** The model used for evaluation.
- **template (Union\[PromptTemplate, str], optional):** The template used for evaluation.
- **rails (List\[str], optional):** A list of strings representing the possible output classes of the model's predictions.
- **query_column_name (str, optional):** The name of the query column in the dataframe, which should also be a template variable.
- **reference_column_name (str, optional):** The name of the document column in the dataframe, which should also be a template variable.
- **system_instruction (Optional\[str], optional):** An optional system message.

### Returns

* **evaluations (List\[List\[str]]):** A list of relevant and not relevant classifications. The "shape" of the list should mirror the "shape" of the retrieved documents column, in the sense that it has the same length as the input dataframe and each sub-list has the same length as the corresponding list in the retrieved documents column. The values in the sub-lists are either entries from the rails argument or "NOT\_PARSABLE" in the case where the LLM output could not be parsed.
- **evaluations (List\[List\[str]]):** A list of relevant and not relevant classifications. The "shape" of the list should mirror the "shape" of the retrieved documents column, in the sense that it has the same length as the input dataframe and each sub-list has the same length as the corresponding list in the retrieved documents column. The values in the sub-lists are either entries from the rails argument or "NOT_PARSABLE" in the case where the LLM output could not be parsed.

## phoenix.experimental.evals.llm\_generate
## phoenix.experimental.evals.llm_generate

```python
def llm_generate(
dataframe: pd.DataFrame,
template: Union[PromptTemplate, str],
model: Optional[BaseEvalModel] = None,
system_instruction: Optional[str] = None,
output_parser: Optional[Callable[[str], Dict[str, Any]]] = None,
) -> List[str]
```

Generates a text using a template using an LLM. This function is useful if you want to generate synthetic data, such as irrelevant responses

### Parameters

* **dataframe (pandas.DataFrame)**: A pandas dataframe in which each row represents a record to be used as in input to the template. All template variable names must appear as column names in the dataframe (extra columns unrelated to the template are permitted).
* **template (Union\[PromptTemplate, str])**: The prompt template as either an instance of PromptTemplate or a string. If the latter, the variable names should be surrounded by curly braces so that a call to `format` can be made to substitute variable values.
* **model (BaseEvalModel)**: An LLM model class.
* **system\_instruction (Optional\[str], optional):** An optional system message.
- **dataframe (pandas.DataFrame)**: A pandas dataframe in which each row represents a record to be used as in input to the template. All template variable names must appear as column names in the dataframe (extra columns unrelated to the template are permitted).
- **template (Union\[PromptTemplate, str])**: The prompt template as either an instance of PromptTemplate or a string. If the latter, the variable names should be surrounded by curly braces so that a call to `format` can be made to substitute variable values.
- **model (BaseEvalModel)**: An LLM model class.
- **system_instruction (Optional\[str], optional):** An optional system message.
- **output_parser (Callable[[str], Dict[str, Any]], optional)** An optional function that takes each generated response and parses it to a dictionary. The keys of the dictionary should correspond to the column names of the output dataframe. If None, the output dataframe will have a single column named "output".

### Returns

* **generations (List\[Optional\[str]])**: A list of strings representing the output of the model for each record
- **generations_dataframe (pandas.DataFrame)**: A dataframe where each row represents the generated output

### Usage

Below we show how you can use `llm_generate` to use an llm to generate synthetic data. In this example, we use the `llm_generate` function to generate the capitals of countries but `llm_generate` can be used to generate any type of data such as synthetic questions, irrelevant responses, and so on.

````python

```python
import pandas as pd
from phoenix.experimental.evals import OpenAIModel, llm_generate

countries_df = pd.DataFrame(
{
"country": [
"France",
"Germany",
"Italy",
]
}
)

capitals_df = llm_generate(
dataframe=countries_df,
template="The capital of {country} is ",
model=OpenAIModel(model_name="gpt-4"),
verbose=True,
)

````

`llm_generate` also supports an output parser so you can use this to generate data in a structured format. For example, if you want to generate data in JSON format, you ca prompt for a JSON object and then parse the output using the `json` library.

```python
import json
from typing import Dict

import pandas as pd
from phoenix.experimental.evals import OpenAIModel, PromptTemplate, llm_generate


def output_parser(response: str) -> Dict[str, str]:
try:
return json.loads(response)
except json.JSONDecodeError as e:
return {"__error__": str(e)}

countries_df = pd.DataFrame(
{
"country": [
"France",
"Germany",
"Italy",
]
}
)

template = PromptTemplate("""
Given the country {country}, output the capital city and a description of that city.
The output must be in JSON format with the following keys: "capital" and "description".

response:
""")

capitals_df = llm_generate(
dataframe=countries_df,
template=template,
model=OpenAIModel(
model_name="gpt-4-1106-preview",
model_kwargs={
"response_format": {"type": "json_object"}
}
),
output_parser=output_parser
)
```
3 changes: 2 additions & 1 deletion src/phoenix/experimental/evals/functions/generate.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,8 @@ def llm_generate(
will have a single column named "output". Default None.

Returns:
pandas.DataFrame: A dataframe where each row represents the generated output
generations_dataframe (pandas.DataFrame): A dataframe where each row
represents the generated output

"""
output_parser = output_parser or _no_op_parser
Expand Down
Loading