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docsearch.as_retriever(search_type="mmr")

# Retrieve more documents with higher diversity- useful if your dataset has many similar documents
docsearch.as_retriever(search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25})

# Fetch more documents for the MMR algorithm to consider, but only return the top 5
docsearch.as_retriever(search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50})

# Only retrieve documents that have a relevance score above a certain threshold
docsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8})

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}})
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Thu Nov 02 2023 00:27:25 GMT+0000 (Coordinated Universal Time) https://python.langchain.com/docs/use_cases/question_answering/vector_db_qa

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