Evals

Evals are LLM-powered functions that you can use to evaluate the output of your LLM or generative application
Evals are still under experimental and must be installed via pip install arize-phoenix[experimental]
phoenix.experimental.evals.PromptTemplate
class PromptTemplate(
    text: str
    delimiters: List[str]
)
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 ["{", "}"].
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).
Usage
Define a PromptTemplate by passing a text string and the delimiters to use to locate the variables. The default delimiters are { and }.
from phoenix.experimental.evals import PromptTemplate
​
template_text = "My name is {name}. I am {age} years old and I am from {location}."
prompt_template = PromptTemplate(text=template_text)
If the prompt template variables have been correctly located, you can access them as follows:
print(prompt_template.variables)
# Output: ['name', 'age', 'location']
The PromptTemplate class can also understand any combination of delimiters. Following the example above, but getting creative with our delimiters:
template_text = "My name is :/name-!). I am :/age-!) years old and I am from :/location-!)."
prompt_template = PromptTemplate(text=template_text, delimiters=[":/", "-!)"])
print(prompt_template.variables)
# Output: ['name', 'age', 'location']
Once you have a PromptTemplate class instantiated, you can make use of its format method to construct the prompt text resulting from substituting values into the variables. To do so, a dictionary mapping the variable names to the values is passed:
value_dict = {
    "name": "Peter",
    "age": 20,
    "location": "Queens"
}
print(prompt_template.format(value_dict))
# Output: My name is Peter. I am 20 years old and I am from Queens
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
def llm_classify(
    dataframe: pd.DataFrame,
    model: BaseEvalModel,
    template: Union[ClassificationTemplate, PromptTemplate, str],
    rails: List[str],
    system_instruction: Optional[str] = None,
    verbose: bool = False,
    use_function_calling_if_available: bool = True,
    provide_explanation: bool = False,
) -> pd.DataFrame
Classifies each input row of the dataframe using an LLM. Returns a pandas.DataFrame where the first column is named label and contains the classification labels. An optional column named explanation is added when provide_explanation=True.
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 (ClassificationTemplate, 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. Note that this will default to using function calling if available. If the model supplied does not support function calling, llm_classify will need a prompt template that prompts for an explanation. For phoenix's pre-tested eval templates, the template is swapped out for a chain-of-thought based template that prompts for an explanation.
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.
phoenix.experimental.run_relevance_eval
def run_relevance_eval(
    dataframe: pd.DataFrame,
    model: BaseEvalModel,
    template: Union[ClassificationPromptTemplate, PromptTemplate, str] = RAG_RELEVANCY_PROMPT_TEMPLATE,
    rails: List[str] = list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values()),
    system_instruction: Optional[str] = None,
    query_column_name: str = "input",
    document_column_name: str = "reference",
) -> List[List[str]]:
Given a pandas dataframe containing queries and retrieved documents, classifies the relevance of each retrieved document to the corresponding query using an LLM.
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[ClassificationPromptTemplate, PromptTemplate, str], optional): The template 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.
phoenix.experimental.evals.llm_generate
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.
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_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
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.
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
)
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Last modified 7d ago