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[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 (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.

phoenix.experimental.run_relevance_eval

def run_relevance_eval(
    dataframe: pd.DataFrame,
    model: BaseEvalModel,
    template: Union[PromptTemplate, str] = RAG_RELEVANCY_PROMPT_TEMPLATE_STR,
    rails: List[str] = list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values()),
    system_instruction: Optional[str] = None,
    query_column_name: str = "query",
    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[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,
) -> 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.

Returns

• generations (List[Optional[str]]): A list of strings representing the output of the model for each record