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437 lines
16 KiB
Python
437 lines
16 KiB
Python
"""Interface for vector stores."""
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from __future__ import annotations
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import asyncio
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import warnings
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from abc import ABC, abstractmethod
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from functools import partial
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from typing import (
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Any,
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ClassVar,
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Collection,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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Type,
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TypeVar,
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)
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from pydantic import BaseModel, Field, root_validator
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.schema import BaseRetriever
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VST = TypeVar("VST", bound="VectorStore")
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class VectorStore(ABC):
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"""Interface for vector stores."""
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@abstractmethod
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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kwargs: vectorstore specific parameters
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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async def aadd_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore."""
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raise NotImplementedError
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def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
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"""Run more documents through the embeddings and add to the vectorstore.
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Args:
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documents (List[Document]: Documents to add to the vectorstore.
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Returns:
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List[str]: List of IDs of the added texts.
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"""
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# TODO: Handle the case where the user doesn't provide ids on the Collection
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texts = [doc.page_content for doc in documents]
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metadatas = [doc.metadata for doc in documents]
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return self.add_texts(texts, metadatas, **kwargs)
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async def aadd_documents(
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self, documents: List[Document], **kwargs: Any
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) -> List[str]:
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"""Run more documents through the embeddings and add to the vectorstore.
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Args:
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documents (List[Document]: Documents to add to the vectorstore.
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Returns:
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List[str]: List of IDs of the added texts.
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"""
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texts = [doc.page_content for doc in documents]
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metadatas = [doc.metadata for doc in documents]
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return await self.aadd_texts(texts, metadatas, **kwargs)
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def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:
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"""Return docs most similar to query using specified search type."""
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if search_type == "similarity":
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return self.similarity_search(query, **kwargs)
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elif search_type == "mmr":
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return self.max_marginal_relevance_search(query, **kwargs)
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else:
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raise ValueError(
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f"search_type of {search_type} not allowed. Expected "
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"search_type to be 'similarity' or 'mmr'."
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)
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async def asearch(
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self, query: str, search_type: str, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query using specified search type."""
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if search_type == "similarity":
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return await self.asimilarity_search(query, **kwargs)
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elif search_type == "mmr":
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return await self.amax_marginal_relevance_search(query, **kwargs)
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else:
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raise ValueError(
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f"search_type of {search_type} not allowed. Expected "
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"search_type to be 'similarity' or 'mmr'."
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)
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@abstractmethod
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query."""
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def similarity_search_with_relevance_scores(
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self,
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query: str,
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k: int = 4,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return docs and relevance scores in the range [0, 1].
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0 is dissimilar, 1 is most similar.
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Args:
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query: input text
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k: Number of Documents to return. Defaults to 4.
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**kwargs: kwargs to be passed to similarity search. Should include:
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score_threshold: Optional, a floating point value between 0 to 1 to
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filter the resulting set of retrieved docs
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Returns:
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List of Tuples of (doc, similarity_score)
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"""
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docs_and_similarities = self._similarity_search_with_relevance_scores(
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query, k=k, **kwargs
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)
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if any(
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similarity < 0.0 or similarity > 1.0
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for _, similarity in docs_and_similarities
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):
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warnings.warn(
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"Relevance scores must be between"
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f" 0 and 1, got {docs_and_similarities}"
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)
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score_threshold = kwargs.get("score_threshold")
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if score_threshold is not None:
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docs_and_similarities = [
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(doc, similarity)
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for doc, similarity in docs_and_similarities
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if similarity >= score_threshold
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]
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if len(docs_and_similarities) == 0:
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warnings.warn(
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"No relevant docs were retrieved using the relevance score"
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f" threshold {score_threshold}"
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)
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return docs_and_similarities
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def _similarity_search_with_relevance_scores(
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self,
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query: str,
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k: int = 4,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return docs and relevance scores, normalized on a scale from 0 to 1.
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0 is dissimilar, 1 is most similar.
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"""
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raise NotImplementedError
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async def asimilarity_search_with_relevance_scores(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query."""
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# This is a temporary workaround to make the similarity search
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# asynchronous. The proper solution is to make the similarity search
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# asynchronous in the vector store implementations.
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func = partial(self.similarity_search_with_relevance_scores, query, k, **kwargs)
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return await asyncio.get_event_loop().run_in_executor(None, func)
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async def asimilarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query."""
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# This is a temporary workaround to make the similarity search
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# asynchronous. The proper solution is to make the similarity search
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# asynchronous in the vector store implementations.
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func = partial(self.similarity_search, query, k, **kwargs)
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return await asyncio.get_event_loop().run_in_executor(None, func)
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def similarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query vector.
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"""
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raise NotImplementedError
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async def asimilarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to embedding vector."""
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# This is a temporary workaround to make the similarity search
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# asynchronous. The proper solution is to make the similarity search
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# asynchronous in the vector store implementations.
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func = partial(self.similarity_search_by_vector, embedding, k, **kwargs)
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return await asyncio.get_event_loop().run_in_executor(None, func)
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def max_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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raise NotImplementedError
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async def amax_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance."""
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# This is a temporary workaround to make the similarity search
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# asynchronous. The proper solution is to make the similarity search
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# asynchronous in the vector store implementations.
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func = partial(
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self.max_marginal_relevance_search, query, k, fetch_k, lambda_mult, **kwargs
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)
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return await asyncio.get_event_loop().run_in_executor(None, func)
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def max_marginal_relevance_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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raise NotImplementedError
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async def amax_marginal_relevance_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance."""
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raise NotImplementedError
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@classmethod
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def from_documents(
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cls: Type[VST],
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documents: List[Document],
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embedding: Embeddings,
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**kwargs: Any,
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) -> VST:
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"""Return VectorStore initialized from documents and embeddings."""
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texts = [d.page_content for d in documents]
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metadatas = [d.metadata for d in documents]
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return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
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@classmethod
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async def afrom_documents(
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cls: Type[VST],
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documents: List[Document],
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embedding: Embeddings,
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**kwargs: Any,
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) -> VST:
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"""Return VectorStore initialized from documents and embeddings."""
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texts = [d.page_content for d in documents]
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metadatas = [d.metadata for d in documents]
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return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
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@classmethod
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@abstractmethod
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def from_texts(
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cls: Type[VST],
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> VST:
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"""Return VectorStore initialized from texts and embeddings."""
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@classmethod
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async def afrom_texts(
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cls: Type[VST],
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> VST:
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"""Return VectorStore initialized from texts and embeddings."""
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raise NotImplementedError
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def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
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return VectorStoreRetriever(vectorstore=self, **kwargs)
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class VectorStoreRetriever(BaseRetriever, BaseModel):
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vectorstore: VectorStore
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search_type: str = "similarity"
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search_kwargs: dict = Field(default_factory=dict)
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allowed_search_types: ClassVar[Collection[str]] = (
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"similarity",
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"similarity_score_threshold",
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"mmr",
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)
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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@root_validator()
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def validate_search_type(cls, values: Dict) -> Dict:
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"""Validate search type."""
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search_type = values["search_type"]
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if search_type not in cls.allowed_search_types:
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raise ValueError(
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f"search_type of {search_type} not allowed. Valid values are: "
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f"{cls.allowed_search_types}"
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)
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if search_type == "similarity_score_threshold":
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score_threshold = values["search_kwargs"].get("score_threshold")
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if (score_threshold is None) or (not isinstance(score_threshold, float)):
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raise ValueError(
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"`score_threshold` is not specified with a float value(0~1) "
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"in `search_kwargs`."
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)
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return values
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def get_relevant_documents(self, query: str) -> List[Document]:
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if self.search_type == "similarity":
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docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
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elif self.search_type == "similarity_score_threshold":
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docs_and_similarities = (
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self.vectorstore.similarity_search_with_relevance_scores(
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query, **self.search_kwargs
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)
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)
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docs = [doc for doc, _ in docs_and_similarities]
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elif self.search_type == "mmr":
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docs = self.vectorstore.max_marginal_relevance_search(
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query, **self.search_kwargs
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)
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else:
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raise ValueError(f"search_type of {self.search_type} not allowed.")
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return docs
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async def aget_relevant_documents(self, query: str) -> List[Document]:
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if self.search_type == "similarity":
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docs = await self.vectorstore.asimilarity_search(
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query, **self.search_kwargs
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)
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elif self.search_type == "similarity_score_threshold":
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docs_and_similarities = (
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await self.vectorstore.asimilarity_search_with_relevance_scores(
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query, **self.search_kwargs
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)
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)
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docs = [doc for doc, _ in docs_and_similarities]
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elif self.search_type == "mmr":
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docs = await self.vectorstore.amax_marginal_relevance_search(
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query, **self.search_kwargs
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)
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else:
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raise ValueError(f"search_type of {self.search_type} not allowed.")
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return docs
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def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
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"""Add documents to vectorstore."""
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return self.vectorstore.add_documents(documents, **kwargs)
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async def aadd_documents(
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self, documents: List[Document], **kwargs: Any
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) -> List[str]:
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"""Add documents to vectorstore."""
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return await self.vectorstore.aadd_documents(documents, **kwargs)
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