def reciprocal_rank_fusion(results: list[list], k=60):""" Reciprocal_rank_fusion that takes multiple lists of ranked documents and an optional parameter k used in the RRF formula """
# Initialize a dictionary to hold fused scores for each unique documentfused_scores = {}
# Iterate through each list of ranked documentsfor docs in results:# Iterate through each document in the list, with its rank (position in the list)for rank, doc in enumerate(docs):# Convert the document to a string format to use as a key (assumes documents can be serialized to JSON)doc_str = dumps(doc)# If the document is not yet in the fused_scores dictionary, add it with an initial score of 0if doc_str not in fused_scores:fused_scores[doc_str] = 0# Retrieve the current score of the document, if anyprevious_score = fused_scores[doc_str]# Update the score of the document using the RRF formula: 1 / (rank + k)fused_scores[doc_str] += 1 / (rank + k)
# Sort the documents based on their fused scores in descending order to get the final reranked resultsreranked_results = [(loads(doc), score)for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)]
# Return the reranked results as a list of tuples, each containing the document and its fused scorereturn reranked_results

from langchain_community.llms import Coherefrom langchain.retrievers importContextualCompressionRetrieverfrom langchain.retrievers.document_compressors import CohereRerankfrom langchain.retrievers.document_compressors import CohereRerank
retriever = vectorstore.as_retriever(search_kwargs={"k": 10})
# Re-rankcompressor = CohereRerank()compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.get_relevant_documents(question)

from langchain.retrievers import ContextualCompressionRetrieverfrom langchain.retrievers.document_compressors import LLMChainExtractorfrom langchain_openai import OpenAI
llm = OpenAI(temperature=0)compressor = LLMChainExtractor.from_llm(llm)compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.invoke("What did the president say about Ketanji Jackson Brown")

from langchain.retrievers.document_compressors import LLMChainFilter
_filter = LLMChainFilter.from_llm(llm)compression_retriever = ContextualCompressionRetriever(base_compressor=_filter, base_retriever=retriever)
compressed_docs = compression_retriever.invoke("What did the president say about Ketanji Jackson Brown")pretty_print_docs(compressed_docs)

from langchain_core.prompts import ChatPromptTemplatefrom langchain_core.pydantic_v1 import BaseModel, Fieldfrom langchain_openai import ChatOpenAI
# Data modelclass GradeDocuments(BaseModel):"""Binary score for relevance check on retrieved documents."""
binary_score: str = Field(description="Documents are relevant to the question, 'yes' or 'no'")
# LLM with function callllm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)structured_llm_grader = llm.with_structured_output(GradeDocuments)
# Promptsystem = """You are a grader assessing relevance of a retrieved document to a user question. \n If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant. \nGive a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
grade_prompt = ChatPromptTemplate.from_messages([("system", system),("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),])
retrieval_grader = grade_prompt | structured_llm_graderquestion = "agent memory"docs = retriever.get_relevant_documents(question)doc_txt = docs[1].page_contentprint(retrieval_grader.invoke({"question": question, "document": doc_txt}))