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from langchain_community.vectorstores import FAISS
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
embeddings = FastEmbedEmbeddings()
def retriever(docs, k=5, search_type='mmr', lambda_mult=None):
"""
Creates a document retriever using FAISS (Facebook AI Similarity Search).
Parameters:
-----------
docs : List[Document]
A list of documents to be indexed for similarity search.
k : int, optional, default=5
The number of top-k results to return for a query.
search_type : str, optional, default='mmr'
The type of search to perform. Options include 'mmr' and 'similarity'.
lambda_mult : float, optional, default=None
Lambda multiplier for Maximal Marginal Relevance (MMR) search.
Returns:
--------
retriever : Retriever
A retriever object for querying relevant documents.
"""
# Create FAISS index from documents
vector_store = FAISS.from_documents(docs, embedding=embeddings)
# Prepare search kwargs with optional lambda_mult
search_kwargs = {'k': k}
if lambda_mult is not None:
search_kwargs['lambda_mult'] = lambda_mult
# Return the retriever
return vector_store.as_retriever(search_type=search_type, search_kwargs=search_kwargs)
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