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# src/vectorstores/base_vectorstore.py
from abc import ABC, abstractmethod
from typing import List, Callable, Any, Dict, Optional
class BaseVectorStore(ABC):
@abstractmethod
def add_documents(
self,
documents: List[str],
embeddings: Optional[List[List[float]]] = None
) -> None:
"""
Add documents to the vector store
Args:
documents (List[str]): List of document texts
embeddings (Optional[List[List[float]]]): Corresponding embeddings.
If not provided, they will be generated using the embedding function.
"""
pass
@abstractmethod
def similarity_search(
self,
query_embedding: List[float],
top_k: int = 3,
**kwargs
) -> List[str]:
"""
Perform similarity search
Args:
query_embedding (List[float]): Embedding of the query
top_k (int): Number of top similar documents to retrieve
**kwargs: Additional search parameters
Returns:
List[str]: List of most similar documents
"""
pass
@abstractmethod
def get_all_documents(
self,
include_embeddings: bool = False
) -> List[Dict[str, Any]]:
"""
Retrieve all documents from the vector store
Args:
include_embeddings (bool): Whether to include embeddings in the response
Returns:
List[Dict[str, Any]]: List of documents with their IDs and optionally embeddings
"""
pass |