mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
from langchain import hub
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.load import dumps, loads
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema.output_parser import StrOutputParser
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from langchain.vectorstores import Pinecone
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def reciprocal_rank_fusion(results: list[list], k=60):
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fused_scores = {}
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for docs in results:
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# Assumes the docs are returned in sorted order of relevance
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for rank, doc in enumerate(docs):
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doc_str = dumps(doc)
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if doc_str not in fused_scores:
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fused_scores[doc_str] = 0
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fused_scores[doc_str] += 1 / (rank + k)
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reranked_results = [
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(loads(doc), score)
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for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)
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]
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return reranked_results
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prompt = hub.pull("langchain-ai/rag-fusion-query-generation")
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generate_queries = (
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prompt | ChatOpenAI(temperature=0) | StrOutputParser() | (lambda x: x.split("\n"))
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)
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vectorstore = Pinecone.from_existing_index("rag-fusion", OpenAIEmbeddings())
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retriever = vectorstore.as_retriever()
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chain = (
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{"original_query": lambda x: x}
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| generate_queries
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| retriever.map()
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| reciprocal_rank_fusion
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)
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# Add typed inputs to chain for playground
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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