54 retriever = vs.as_retriever( 55 search_type="mmr", search_kwargs={"k": 10, "lambda_mult": 0.25} 56 ) 57 memory = ConversationBufferWindowMemory( 58 memory_key="chat_history", k=5, return_messages=True 59 ) 60 61 # initilize the conversation chain 62 conversation_chain = Conve...
(chunk_size=1500, chunk_overlap=150)texts= text_splitter.split_documents(documents)embeddings= OpenAIEmbeddings()vectordb= Chroma.from_documents(texts,embeddings)chain= RetrievalQA.from_chain_type(llm=OpenAI(temperature=0.0),chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"),...
search_type(Dropdown, options=["Similarity", "MMR"], advanced, value="Similarity") number_of_results(Integer, advanced, info="Number of results to return.", value=10) limit(Integer, advanced, info="Limit the number of records to compare when Allow Duplicates is False.")[1][2]. ...
LlamaIndex is a data framework for your LLM applications - llama_index/CHANGELOG.md at feature/lindormsearch-vector-db · Rainy-GG/llama_index
Maximal Marginal Relevance (MMR) search. ImplementRAGby using Atlas Vector Search to answer questions on your data. Background LangChain is an open-source framework that simplifies the creation ofLLMapplications through the use of "chains." Chains are LangChain-specific components that can be combi...
{ "maximal_marginal_relevance": True, "distance_metric": "cos", "fetch_k": 100, "k": 10, } retriever = vectordb.as_retriever(search_type="mmr", search_kwargs=search_kwargs) chain = ConversationalRetrievalChain.from_llm( llm, retriever=retriever, chain_type="stuff", verbose=True, ...
Maximal Marginal Relevance (MMR) search. ImplementRAGby using Atlas Vector Search to answer questions on your data. Background LangChain is an open-source framework that simplifies the creation ofLLMapplications through the use of "chains." Chains are LangChain-specific components that can be combi...