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from typing import Callable, List
def create_vector_store(
docs: List,
metric: str = 'cos',
top_k: int = 4
) -> Callable:
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
# Embed your documents and combine with the raw text in a pseudo db.
# Note: This will make an API call to OpenAI
docsearch = FAISS.from_documents(docs, embeddings)
# Retriver object
retriever = docsearch.as_retriever()
# Retriver configs
retriever.search_kwargs['distance_metric'] = metric
retriever.search_kwargs['k'] = top_k
return retriever |