mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-19 21:33:31 -04:00
Harrison Chase
2163d064f3
not actually sure the desired return in add_example to example selector is actually general/good - whats the use case?
42 lines
1.2 KiB
Python
42 lines
1.2 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(self, query: str, k: int = 4) -> List[Document]:
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"""Return docs most similar to query."""
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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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