Update models/langOpen.py
Browse files- models/langOpen.py +11 -1
models/langOpen.py
CHANGED
@@ -9,6 +9,10 @@ from langchain.embeddings import OpenAIEmbeddings
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from langchain.prompts import PromptTemplate
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from langchain_pinecone import PineconeVectorStore
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prompt_template = """Answer the question using the given context to the best of your ability.
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If you don't know, answer I don't know.
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Context: {context}
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@@ -33,7 +37,13 @@ class LangOpen:
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def get_response(self, query_str):
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print("query_str: ", query_str)
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print("model_name: ", self.llm.model_name)
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-
docs = self.index.similarity_search(query_str, k=4)
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inputs = [{"context": doc.page_content, "topic": query_str} for doc in docs]
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result = self.chain.apply(inputs)[0]["text"]
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return result
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from langchain.prompts import PromptTemplate
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from langchain_pinecone import PineconeVectorStore
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from langchain.retrievers import ContextualCompressionRetriever
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from langchain.retrievers.document_compressors import CohereRerank
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from langchain_community.llms import Cohere
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prompt_template = """Answer the question using the given context to the best of your ability.
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If you don't know, answer I don't know.
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Context: {context}
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def get_response(self, query_str):
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print("query_str: ", query_str)
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print("model_name: ", self.llm.model_name)
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#docs = self.index.similarity_search(query_str, k=4)
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vectorstore_retriever = self.index.as_retriever(search_type="similarity", search_kwargs={"k": 10})
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compressor = CohereRerank()
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compression_retriever = ContextualCompressionRetriever(
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base_compressor=compressor, base_retriever=vectorstore_retriever
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)
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docs = compression_retriever.get_relevant_documents(query_str)
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inputs = [{"context": doc.page_content, "topic": query_str} for doc in docs]
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result = self.chain.apply(inputs)[0]["text"]
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return result
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