Source code for langchain.chains.qa_with_sources.vector_db

"""Question-answering with sources over a vector database."""

import warnings
from typing import Any, Dict, List

from langchain_core.callbacks import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.vectorstores import VectorStore

from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain


[docs]class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain): """Question-answering with sources over a vector database.""" vectorstore: VectorStore = Field(exclude=True) """Vector Database to connect to.""" k: int = 4 """Number of results to return from store""" reduce_k_below_max_tokens: bool = False """Reduce the number of results to return from store based on tokens limit""" max_tokens_limit: int = 3375 """Restrict the docs to return from store based on tokens, enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true""" search_kwargs: Dict[str, Any] = Field(default_factory=dict) """Extra search args.""" def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]: num_docs = len(docs) if self.reduce_k_below_max_tokens and isinstance( self.combine_documents_chain, StuffDocumentsChain ): tokens = [ self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) for doc in docs ] token_count = sum(tokens[:num_docs]) while token_count > self.max_tokens_limit: num_docs -= 1 token_count -= tokens[num_docs] return docs[:num_docs] def _get_docs( self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun ) -> List[Document]: question = inputs[self.question_key] docs = self.vectorstore.similarity_search( question, k=self.k, **self.search_kwargs ) return self._reduce_tokens_below_limit(docs) async def _aget_docs( self, inputs: Dict[str, Any], *, run_manager: AsyncCallbackManagerForChainRun ) -> List[Document]: raise NotImplementedError("VectorDBQAWithSourcesChain does not support async") @root_validator() def raise_deprecation(cls, values: Dict) -> Dict: warnings.warn( "`VectorDBQAWithSourcesChain` is deprecated - " "please use `from langchain.chains import RetrievalQAWithSourcesChain`" ) return values @property def _chain_type(self) -> str: return "vector_db_qa_with_sources_chain"