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858 lines
30 KiB
Python
858 lines
30 KiB
Python
"""**Vector store** stores embedded data and performs vector search.
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One of the most common ways to store and search over unstructured data is to
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embed it and store the resulting embedding vectors, and then query the store
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and retrieve the data that are 'most similar' to the embedded query.
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**Class hierarchy:**
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.. code-block::
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VectorStore --> <name> # Examples: Annoy, FAISS, Milvus
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BaseRetriever --> VectorStoreRetriever --> <name>Retriever # Example: VespaRetriever
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**Main helpers:**
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.. code-block::
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Embeddings, Document
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""" # noqa: E501
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from __future__ import annotations
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import logging
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import math
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import warnings
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from abc import ABC, abstractmethod
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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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 langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.retrievers import BaseRetriever
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from langchain_core.runnables.config import run_in_executor
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if TYPE_CHECKING:
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from langchain_core.callbacks.manager import (
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AsyncCallbackManagerForRetrieverRun,
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CallbackManagerForRetrieverRun,
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)
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from langchain_core.documents import Document
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logger = logging.getLogger(__name__)
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VST = TypeVar("VST", bound="VectorStore")
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class VectorStore(ABC):
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"""Interface for vector store."""
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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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@property
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def embeddings(self) -> Optional[Embeddings]:
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"""Access the query embedding object if available."""
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logger.debug(
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f"{Embeddings.__name__} is not implemented for {self.__class__.__name__}"
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)
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return None
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
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"""Delete by vector ID or other criteria.
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Args:
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ids: List of ids to delete.
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**kwargs: Other keyword arguments that subclasses might use.
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Returns:
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Optional[bool]: True if deletion is successful,
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False otherwise, None if not implemented.
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"""
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raise NotImplementedError("delete method must be implemented by subclass.")
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async def adelete(
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self, ids: Optional[List[str]] = None, **kwargs: Any
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) -> Optional[bool]:
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"""Delete by vector ID or other criteria.
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Args:
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ids: List of ids to delete.
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**kwargs: Other keyword arguments that subclasses might use.
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Returns:
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Optional[bool]: True if deletion is successful,
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False otherwise, None if not implemented.
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"""
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return await run_in_executor(None, self.delete, ids, **kwargs)
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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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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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return await run_in_executor(None, self.add_texts, texts, metadatas, **kwargs)
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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: Documents to add to the vectorstore.
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Returns:
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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: Documents to add to the vectorstore.
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Returns:
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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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Args:
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query: Input text
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search_type: Type of search to perform. Can be "similarity",
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"mmr", or "similarity_score_threshold".
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**kwargs: Arguments to pass to the search method.
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"""
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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 == "similarity_score_threshold":
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docs_and_similarities = self.similarity_search_with_relevance_scores(
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query, **kwargs
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)
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return [doc for doc, _ in docs_and_similarities]
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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', 'similarity_score_threshold' 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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Args:
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query: Input text.
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search_type: Type of search to perform. Can be "similarity",
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"mmr", or "similarity_score_threshold".
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**kwargs: Arguments to pass to the search method.
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"""
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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 == "similarity_score_threshold":
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docs_and_similarities = await self.asimilarity_search_with_relevance_scores(
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query, **kwargs
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)
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return [doc for doc, _ in docs_and_similarities]
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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', 'similarity_score_threshold' 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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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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Returns:
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List of Documents most similar to the query.
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"""
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@staticmethod
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def _euclidean_relevance_score_fn(distance: float) -> float:
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"""Return a similarity score on a scale [0, 1]."""
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# The 'correct' relevance function
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# may differ depending on a few things, including:
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# - the distance / similarity metric used by the VectorStore
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# - the scale of your embeddings (OpenAI's are unit normed. Many
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# others are not!)
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# - embedding dimensionality
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# - etc.
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# This function converts the euclidean norm of normalized embeddings
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# (0 is most similar, sqrt(2) most dissimilar)
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# to a similarity function (0 to 1)
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return 1.0 - distance / math.sqrt(2)
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@staticmethod
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def _cosine_relevance_score_fn(distance: float) -> float:
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"""Normalize the distance to a score on a scale [0, 1]."""
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return 1.0 - distance
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@staticmethod
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def _max_inner_product_relevance_score_fn(distance: float) -> float:
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"""Normalize the distance to a score on a scale [0, 1]."""
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if distance > 0:
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return 1.0 - distance
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return -1.0 * distance
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def _select_relevance_score_fn(self) -> Callable[[float], float]:
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"""
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The 'correct' relevance function
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may differ depending on a few things, including:
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- the distance / similarity metric used by the VectorStore
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- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
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- embedding dimensionality
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- etc.
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Vectorstores should define their own selection based method of relevance.
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"""
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raise NotImplementedError
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def similarity_search_with_score(
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self, *args: Any, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search with distance.
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Returns:
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List of Tuples of (doc, similarity_score)
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"""
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raise NotImplementedError
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async def asimilarity_search_with_score(
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self, *args: Any, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Run similarity search with distance.
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Returns:
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List of Tuples of (doc, similarity_score)
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"""
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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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return await run_in_executor(
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None, self.similarity_search_with_score, *args, **kwargs
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)
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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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"""
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Default similarity search with relevance scores. Modify if necessary
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in subclass.
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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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relevance_score_fn = self._select_relevance_score_fn()
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docs_and_scores = self.similarity_search_with_score(query, k, **kwargs)
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return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
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async def _asimilarity_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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"""
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Default similarity search with relevance scores. Modify if necessary
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in subclass.
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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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relevance_score_fn = self._select_relevance_score_fn()
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docs_and_scores = await self.asimilarity_search_with_score(query, k, **kwargs)
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return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
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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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score_threshold = kwargs.pop("score_threshold", None)
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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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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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async def asimilarity_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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score_threshold = kwargs.pop("score_threshold", None)
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docs_and_similarities = await self._asimilarity_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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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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|
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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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|
|
Args:
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query: Input text.
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|
k: Number of Documents to return. Defaults to 4.
|
|
|
|
Returns:
|
|
List of Documents most similar to the query.
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"""
|
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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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return await run_in_executor(None, self.similarity_search, query, k=k, **kwargs)
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|
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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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|
|
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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|
|
# 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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|
return await run_in_executor(
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None, self.similarity_search_by_vector, embedding, k=k, **kwargs
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)
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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]:
|
|
"""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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|
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# This is a temporary workaround to make the similarity search
|
|
# asynchronous. The proper solution is to make the similarity search
|
|
# asynchronous in the vector store implementations.
|
|
return await run_in_executor(
|
|
None,
|
|
self.max_marginal_relevance_search,
|
|
query,
|
|
k=k,
|
|
fetch_k=fetch_k,
|
|
lambda_mult=lambda_mult,
|
|
**kwargs,
|
|
)
|
|
|
|
def max_marginal_relevance_search_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
k: int = 4,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs selected using the maximal marginal relevance.
|
|
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
|
|
among selected documents.
|
|
|
|
Args:
|
|
embedding: Embedding to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
|
lambda_mult: Number between 0 and 1 that determines the degree
|
|
of diversity among the results with 0 corresponding
|
|
to maximum diversity and 1 to minimum diversity.
|
|
Defaults to 0.5.
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
async def amax_marginal_relevance_search_by_vector(
|
|
self,
|
|
embedding: List[float],
|
|
k: int = 4,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
**kwargs: Any,
|
|
) -> List[Document]:
|
|
"""Return docs selected using the maximal marginal relevance.
|
|
|
|
Maximal marginal relevance optimizes for similarity to query AND diversity
|
|
among selected documents.
|
|
|
|
Args:
|
|
embedding: Embedding to look up documents similar to.
|
|
k: Number of Documents to return. Defaults to 4.
|
|
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
|
|
lambda_mult: Number between 0 and 1 that determines the degree
|
|
of diversity among the results with 0 corresponding
|
|
to maximum diversity and 1 to minimum diversity.
|
|
Defaults to 0.5.
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
return await run_in_executor(
|
|
None,
|
|
self.max_marginal_relevance_search_by_vector,
|
|
embedding,
|
|
k=k,
|
|
fetch_k=fetch_k,
|
|
lambda_mult=lambda_mult,
|
|
**kwargs,
|
|
)
|
|
|
|
@classmethod
|
|
def from_documents(
|
|
cls: Type[VST],
|
|
documents: List[Document],
|
|
embedding: Embeddings,
|
|
**kwargs: Any,
|
|
) -> VST:
|
|
"""Return VectorStore initialized from documents and embeddings.
|
|
|
|
Args:
|
|
documents: List of Documents to add to the vectorstore.
|
|
embedding: Embedding function to use.
|
|
"""
|
|
texts = [d.page_content for d in documents]
|
|
metadatas = [d.metadata for d in documents]
|
|
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
|
|
|
|
@classmethod
|
|
async def afrom_documents(
|
|
cls: Type[VST],
|
|
documents: List[Document],
|
|
embedding: Embeddings,
|
|
**kwargs: Any,
|
|
) -> VST:
|
|
"""Return VectorStore initialized from documents and embeddings.
|
|
|
|
Args:
|
|
documents: List of Documents to add to the vectorstore.
|
|
embedding: Embedding function to use.
|
|
"""
|
|
texts = [d.page_content for d in documents]
|
|
metadatas = [d.metadata for d in documents]
|
|
return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
|
|
|
|
@classmethod
|
|
@abstractmethod
|
|
def from_texts(
|
|
cls: Type[VST],
|
|
texts: List[str],
|
|
embedding: Embeddings,
|
|
metadatas: Optional[List[dict]] = None,
|
|
**kwargs: Any,
|
|
) -> VST:
|
|
"""Return VectorStore initialized from texts and embeddings.
|
|
|
|
Args:
|
|
texts: Texts to add to the vectorstore.
|
|
metadatas: Optional list of metadatas associated with the texts.
|
|
embedding: Embedding function to use.
|
|
"""
|
|
|
|
@classmethod
|
|
async def afrom_texts(
|
|
cls: Type[VST],
|
|
texts: List[str],
|
|
embedding: Embeddings,
|
|
metadatas: Optional[List[dict]] = None,
|
|
**kwargs: Any,
|
|
) -> VST:
|
|
"""Return VectorStore initialized from texts and embeddings.
|
|
|
|
Args:
|
|
texts: Texts to add to the vectorstore.
|
|
metadatas: Optional list of metadatas associated with the texts.
|
|
embedding: Embedding function to use.
|
|
"""
|
|
return await run_in_executor(
|
|
None, cls.from_texts, texts, embedding, metadatas, **kwargs
|
|
)
|
|
|
|
def _get_retriever_tags(self) -> List[str]:
|
|
"""Get tags for retriever."""
|
|
tags = [self.__class__.__name__]
|
|
if self.embeddings:
|
|
tags.append(self.embeddings.__class__.__name__)
|
|
return tags
|
|
|
|
def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
|
|
"""Return VectorStoreRetriever initialized from this VectorStore.
|
|
|
|
Args:
|
|
search_type (Optional[str]): Defines the type of search that
|
|
the Retriever should perform.
|
|
Can be "similarity" (default), "mmr", or
|
|
"similarity_score_threshold".
|
|
search_kwargs (Optional[Dict]): Keyword arguments to pass to the
|
|
search function. Can include things like:
|
|
k: Amount of documents to return (Default: 4)
|
|
score_threshold: Minimum relevance threshold
|
|
for similarity_score_threshold
|
|
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
|
|
lambda_mult: Diversity of results returned by MMR;
|
|
1 for minimum diversity and 0 for maximum. (Default: 0.5)
|
|
filter: Filter by document metadata
|
|
|
|
Returns:
|
|
VectorStoreRetriever: Retriever class for VectorStore.
|
|
|
|
Examples:
|
|
|
|
.. code-block:: python
|
|
|
|
# Retrieve more documents with higher diversity
|
|
# Useful if your dataset has many similar documents
|
|
docsearch.as_retriever(
|
|
search_type="mmr",
|
|
search_kwargs={'k': 6, 'lambda_mult': 0.25}
|
|
)
|
|
|
|
# Fetch more documents for the MMR algorithm to consider
|
|
# But only return the top 5
|
|
docsearch.as_retriever(
|
|
search_type="mmr",
|
|
search_kwargs={'k': 5, 'fetch_k': 50}
|
|
)
|
|
|
|
# Only retrieve documents that have a relevance score
|
|
# Above a certain threshold
|
|
docsearch.as_retriever(
|
|
search_type="similarity_score_threshold",
|
|
search_kwargs={'score_threshold': 0.8}
|
|
)
|
|
|
|
# Only get the single most similar document from the dataset
|
|
docsearch.as_retriever(search_kwargs={'k': 1})
|
|
|
|
# Use a filter to only retrieve documents from a specific paper
|
|
docsearch.as_retriever(
|
|
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
|
|
)
|
|
"""
|
|
tags = kwargs.pop("tags", None) or [] + self._get_retriever_tags()
|
|
return VectorStoreRetriever(vectorstore=self, tags=tags, **kwargs)
|
|
|
|
|
|
class VectorStoreRetriever(BaseRetriever):
|
|
"""Base Retriever class for VectorStore."""
|
|
|
|
vectorstore: VectorStore
|
|
"""VectorStore to use for retrieval."""
|
|
search_type: str = "similarity"
|
|
"""Type of search to perform. Defaults to "similarity"."""
|
|
search_kwargs: dict = Field(default_factory=dict)
|
|
"""Keyword arguments to pass to the search function."""
|
|
allowed_search_types: ClassVar[Collection[str]] = (
|
|
"similarity",
|
|
"similarity_score_threshold",
|
|
"mmr",
|
|
)
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
arbitrary_types_allowed = True
|
|
|
|
@root_validator()
|
|
def validate_search_type(cls, values: Dict) -> Dict:
|
|
"""Validate search type."""
|
|
search_type = values["search_type"]
|
|
if search_type not in cls.allowed_search_types:
|
|
raise ValueError(
|
|
f"search_type of {search_type} not allowed. Valid values are: "
|
|
f"{cls.allowed_search_types}"
|
|
)
|
|
if search_type == "similarity_score_threshold":
|
|
score_threshold = values["search_kwargs"].get("score_threshold")
|
|
if (score_threshold is None) or (not isinstance(score_threshold, float)):
|
|
raise ValueError(
|
|
"`score_threshold` is not specified with a float value(0~1) "
|
|
"in `search_kwargs`."
|
|
)
|
|
return values
|
|
|
|
def _get_relevant_documents(
|
|
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
|
) -> List[Document]:
|
|
if self.search_type == "similarity":
|
|
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
|
|
elif self.search_type == "similarity_score_threshold":
|
|
docs_and_similarities = (
|
|
self.vectorstore.similarity_search_with_relevance_scores(
|
|
query, **self.search_kwargs
|
|
)
|
|
)
|
|
docs = [doc for doc, _ in docs_and_similarities]
|
|
elif self.search_type == "mmr":
|
|
docs = self.vectorstore.max_marginal_relevance_search(
|
|
query, **self.search_kwargs
|
|
)
|
|
else:
|
|
raise ValueError(f"search_type of {self.search_type} not allowed.")
|
|
return docs
|
|
|
|
async def _aget_relevant_documents(
|
|
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
|
|
) -> List[Document]:
|
|
if self.search_type == "similarity":
|
|
docs = await self.vectorstore.asimilarity_search(
|
|
query, **self.search_kwargs
|
|
)
|
|
elif self.search_type == "similarity_score_threshold":
|
|
docs_and_similarities = (
|
|
await self.vectorstore.asimilarity_search_with_relevance_scores(
|
|
query, **self.search_kwargs
|
|
)
|
|
)
|
|
docs = [doc for doc, _ in docs_and_similarities]
|
|
elif self.search_type == "mmr":
|
|
docs = await self.vectorstore.amax_marginal_relevance_search(
|
|
query, **self.search_kwargs
|
|
)
|
|
else:
|
|
raise ValueError(f"search_type of {self.search_type} not allowed.")
|
|
return docs
|
|
|
|
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
|
|
"""Add documents to the vectorstore.
|
|
|
|
Args:
|
|
documents: Documents to add to the vectorstore.
|
|
|
|
Returns:
|
|
List of IDs of the added texts.
|
|
"""
|
|
return self.vectorstore.add_documents(documents, **kwargs)
|
|
|
|
async def aadd_documents(
|
|
self, documents: List[Document], **kwargs: Any
|
|
) -> List[str]:
|
|
"""Add documents to the vectorstore.
|
|
|
|
Args:
|
|
documents: Documents to add to the vectorstore.
|
|
|
|
Returns:
|
|
List of IDs of the added texts.
|
|
"""
|
|
return await self.vectorstore.aadd_documents(documents, **kwargs)
|