Source code for langchain_community.callbacks.uptrain_callback

UpTrain Callback Handler

UpTrain is an open-source platform to evaluate and improve LLM applications. It provides
grades for 20+ preconfigured checks (covering language, code, embedding use cases),
performs root cause analyses on instances of failure cases and provides guidance for
resolving them.

This module contains a callback handler for integrating UpTrain seamlessly into your
pipeline and facilitating diverse evaluations. The callback handler automates various
evaluations to assess the performance and effectiveness of the components within the

The evaluations conducted include:

1. RAG:
   - Context Relevance: Determines the relevance of the context extracted from the query
   to the response.
   - Factual Accuracy: Assesses if the Language Model (LLM) is providing accurate
   information or hallucinating.
   - Response Completeness: Checks if the response contains all the information
   requested by the query.

2. Multi Query Generation:
   MultiQueryRetriever generates multiple variants of a question with similar meanings
   to the original question. This evaluation includes previous assessments and adds:
   - Multi Query Accuracy: Ensures that the multi-queries generated convey the same
   meaning as the original query.

3. Context Compression and Reranking:
   Re-ranking involves reordering nodes based on relevance to the query and selecting
   top n nodes.
   Due to the potential reduction in the number of nodes after re-ranking, the following
   are performed in addition to the RAG evaluations:
   - Context Reranking: Determines if the order of re-ranked nodes is more relevant to
   the query than the original order.
   - Context Conciseness: Examines whether the reduced number of nodes still provides
   all the required information.

These evaluations collectively ensure the robustness and effectiveness of the RAG query
engine, MultiQueryRetriever, and the re-ranking process within the pipeline.

Useful links:


import logging
import sys
from collections import defaultdict
from typing import (
from uuid import UUID

from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.documents import Document
from langchain_core.outputs import LLMResult
from langchain_core.utils import guard_import

logger = logging.getLogger(__name__)
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter("%(message)s")

[docs]def import_uptrain() -> Any: """Import the `uptrain` package.""" return guard_import("uptrain")
[docs]class UpTrainDataSchema: """The UpTrain data schema for tracking evaluation results. Args: project_name (str): The project name to be shown in UpTrain dashboard. Attributes: project_name (str): The project name to be shown in UpTrain dashboard. uptrain_results (DefaultDict[str, Any]): Dictionary to store evaluation results. eval_types (Set[str]): Set to store the types of evaluations. query (str): Query for the RAG evaluation. context (str): Context for the RAG evaluation. response (str): Response for the RAG evaluation. old_context (List[str]): Old context nodes for Context Conciseness evaluation. new_context (List[str]): New context nodes for Context Conciseness evaluation. context_conciseness_run_id (str): Run ID for Context Conciseness evaluation. multi_queries (List[str]): List of multi queries for Multi Query evaluation. multi_query_run_id (str): Run ID for Multi Query evaluation. multi_query_daugher_run_id (str): Run ID for Multi Query daughter evaluation. """
[docs] def __init__(self, project_name: str) -> None: """Initialize the UpTrain data schema.""" # For tracking project name and results self.project_name: str = project_name self.uptrain_results: DefaultDict[str, Any] = defaultdict(list) # For tracking event types self.eval_types: Set[str] = set() ## RAG self.query: str = "" self.context: str = "" self.response: str = "" ## CONTEXT CONCISENESS self.old_context: List[str] = [] self.new_context: List[str] = [] self.context_conciseness_run_id: UUID = UUID(int=0) # MULTI QUERY self.multi_queries: List[str] = [] self.multi_query_run_id: UUID = UUID(int=0) self.multi_query_daugher_run_id: UUID = UUID(int=0)
[docs]class UpTrainCallbackHandler(BaseCallbackHandler): """Callback Handler that logs evaluation results to uptrain and the console. Args: project_name (str): The project name to be shown in UpTrain dashboard. key_type (str): Type of key to use. Must be 'uptrain' or 'openai'. api_key (str): API key for the UpTrain or OpenAI API. (This key is required to perform evaluations using GPT.) Raises: ValueError: If the key type is invalid. ImportError: If the `uptrain` package is not installed. """
[docs] def __init__( self, *, project_name: str = "langchain", key_type: str = "openai", api_key: str = "sk-****************", # The API key to use for evaluation model: str = "gpt-3.5-turbo", # The model to use for evaluation log_results: bool = True, ) -> None: """Initializes the `UpTrainCallbackHandler`.""" super().__init__() uptrain = import_uptrain() self.log_results = log_results # Set uptrain variables self.schema = UpTrainDataSchema(project_name=project_name) self.first_score_printed_flag = False if key_type == "uptrain": settings = uptrain.Settings(uptrain_access_token=api_key, model=model) self.uptrain_client = uptrain.APIClient(settings=settings) elif key_type == "openai": settings = uptrain.Settings( openai_api_key=api_key, evaluate_locally=True, model=model ) self.uptrain_client = uptrain.EvalLLM(settings=settings) else: raise ValueError("Invalid key type: Must be 'uptrain' or 'openai'")
[docs] def uptrain_evaluate( self, evaluation_name: str, data: List[Dict[str, Any]], checks: List[str], ) -> None: """Run an evaluation on the UpTrain server using UpTrain client.""" if self.uptrain_client.__class__.__name__ == "APIClient": uptrain_result = self.uptrain_client.log_and_evaluate( project_name=self.schema.project_name, evaluation_name=evaluation_name, data=data, checks=checks, ) else: uptrain_result = self.uptrain_client.evaluate( project_name=self.schema.project_name, evaluation_name=evaluation_name, data=data, checks=checks, ) self.schema.uptrain_results[self.schema.project_name].append(uptrain_result) score_name_map = { "score_context_relevance": "Context Relevance Score", "score_factual_accuracy": "Factual Accuracy Score", "score_response_completeness": "Response Completeness Score", "score_sub_query_completeness": "Sub Query Completeness Score", "score_context_reranking": "Context Reranking Score", "score_context_conciseness": "Context Conciseness Score", "score_multi_query_accuracy": "Multi Query Accuracy Score", } if self.log_results: # Set logger level to INFO to print the evaluation results logger.setLevel(logging.INFO) for row in uptrain_result: columns = list(row.keys()) for column in columns: if column == "question":"\nQuestion: {row[column]}") self.first_score_printed_flag = False elif column == "response":"Response: {row[column]}") self.first_score_printed_flag = False elif column == "variants":"Multi Queries:") for variant in row[column]:" - {variant}") self.first_score_printed_flag = False elif column.startswith("score"): if not self.first_score_printed_flag:"") self.first_score_printed_flag = True if column in score_name_map:"{score_name_map[column]}: {row[column]}") else:"{column}: {row[column]}") if self.log_results: # Set logger level back to WARNING # (We are doing this to avoid printing the logs from HTTP requests) logger.setLevel(logging.WARNING)
[docs] def on_llm_end( self, response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any, ) -> None: """Log records to uptrain when an LLM ends.""" uptrain = import_uptrain() self.schema.response = response.generations[0][0].text if ( "qa_rag" in self.schema.eval_types and parent_run_id != self.schema.multi_query_daugher_run_id ): data = [ { "question": self.schema.query, "context": self.schema.context, "response": self.schema.response, } ] self.uptrain_evaluate( evaluation_name="rag", data=data, checks=[ uptrain.Evals.CONTEXT_RELEVANCE, uptrain.Evals.FACTUAL_ACCURACY, uptrain.Evals.RESPONSE_COMPLETENESS, ], )
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, name: Optional[str] = None, **kwargs: Any, ) -> None: """Do nothing when chain starts""" if parent_run_id == self.schema.multi_query_run_id: self.schema.multi_query_daugher_run_id = run_id if isinstance(inputs, dict) and set(inputs.keys()) == {"context", "question"}: self.schema.eval_types.add("qa_rag") context = "" if isinstance(inputs["context"], Document): context = inputs["context"].page_content elif isinstance(inputs["context"], list): for doc in inputs["context"]: context += doc.page_content + "\n" elif isinstance(inputs["context"], str): context = inputs["context"] self.schema.context = context self.schema.query = inputs["question"] pass
[docs] def on_retriever_start( self, serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> None: if "contextual_compression" in serialized["id"]: self.schema.eval_types.add("contextual_compression") self.schema.query = query self.schema.context_conciseness_run_id = run_id if "multi_query" in serialized["id"]: self.schema.eval_types.add("multi_query") self.schema.multi_query_run_id = run_id self.schema.query = query elif "multi_query" in self.schema.eval_types: self.schema.multi_queries.append(query)
[docs] def on_retriever_end( self, documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any, ) -> Any: """Run when Retriever ends running.""" uptrain = import_uptrain() if run_id == self.schema.multi_query_run_id: data = [ { "question": self.schema.query, "variants": self.schema.multi_queries, } ] self.uptrain_evaluate( evaluation_name="multi_query", data=data, checks=[uptrain.Evals.MULTI_QUERY_ACCURACY], ) if "contextual_compression" in self.schema.eval_types: if parent_run_id == self.schema.context_conciseness_run_id: for doc in documents: self.schema.old_context.append(doc.page_content) elif run_id == self.schema.context_conciseness_run_id: for doc in documents: self.schema.new_context.append(doc.page_content) context = "\n".join( [ f"{index}. {string}" for index, string in enumerate(self.schema.old_context, start=1) ] ) reranked_context = "\n".join( [ f"{index}. {string}" for index, string in enumerate(self.schema.new_context, start=1) ] ) data = [ { "question": self.schema.query, "context": context, "concise_context": reranked_context, "reranked_context": reranked_context, } ] self.uptrain_evaluate( evaluation_name="context_reranking", data=data, checks=[ uptrain.Evals.CONTEXT_CONCISENESS, uptrain.Evals.CONTEXT_RERANKING, ], )