mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-22 09:15:31 -04:00
seanaedmiston
f0a258555b
Alternate implementation to PR #960 Again - only FAISS is implemented. If accepted can add this to other vectorstores or leave as NotImplemented? Suggestions welcome...
106 lines
3.4 KiB
Python
106 lines
3.4 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, texts: Iterable[str], metadatas: Optional[List[dict]] = None
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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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Returns:
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List of ids from adding the texts into the vectorstore.
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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_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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