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125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
"""Interface for vector stores."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import Any, Iterable, List, Optional
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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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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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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@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_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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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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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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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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def max_marginal_relevance_search_by_vector(
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self, embedding: List[float], k: int = 4, fetch_k: int = 20
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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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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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@classmethod
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def from_documents(
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cls,
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documents: List[Document],
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embedding: Embeddings,
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**kwargs: Any,
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) -> VectorStore:
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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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@abstractmethod
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def from_texts(
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cls,
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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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) -> VectorStore:
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"""Return VectorStore initialized from texts and embeddings."""
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