import pinecone from langchain import hub from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.load import dumps, loads from langchain.schema.output_parser import StrOutputParser from langchain.vectorstores import Pinecone def reciprocal_rank_fusion(results: list[list], k=60): fused_scores = {} for docs in results: # Assumes the docs are returned in sorted order of relevance for rank, doc in enumerate(docs): doc_str = dumps(doc) if doc_str not in fused_scores: fused_scores[doc_str] = 0 fused_scores[doc_str] += 1 / (rank + k) reranked_results = [ (loads(doc), score) for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True) ] return reranked_results pinecone.init(api_key="...", environment="...") prompt = hub.pull("langchain-ai/rag-fusion-query-generation") generate_queries = ( prompt | ChatOpenAI(temperature=0) | StrOutputParser() | (lambda x: x.split("\n")) ) vectorstore = Pinecone.from_existing_index("rag-fusion", OpenAIEmbeddings()) retriever = vectorstore.as_retriever() chain = ( {"original_query": lambda x: x} | generate_queries | retriever.map() | reciprocal_rank_fusion )