faiss_retriever = db.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 3}, max_tokens_limit=1000) # Define a QA chain qa_chain = RetrievalQA.from_chain_type( llm, retriever=faiss_retriever, chain_type_kwargs={"prompt": QA_PROMPT} ) query = 'What versions of TLS supported...
faiss_retriever = db.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 3}, max_tokens_limit=1000) # Define a QA chain qa_chain = RetrievalQA.from_chain_type( llm, retriever=faiss_retriever, chain_type_kwargs={"prompt": QA_PROMPT} ) query = 'What versions of TLS supported...
n_ctx=2048)# vectorize and create a retrieverdb = LoadFVectorize.load_db() faiss_retriever = db.as_retriever(search_type="mmr", search_kwargs={'fetch_k':3}, max_tokens_limit=1000)# Define a QA chainqa_chain = RetrievalQA.from_chain_type( llm, retriever=faiss_retriever, chain_type_...
db=LoadFVectorize.load_db()faiss_retriever=db.as_retriever(search_type="mmr",search_kwargs={'fetch_k':3},max_tokens_limit=1000)# Define aQAchain qa_chain=RetrievalQA.from_chain_type(llm,retriever=faiss_retriever,chain_type_kwargs={"prompt":QA_PROMPT})query='What versions of TLS supporte...
Run Code Online (Sandbox Code Playgroud) 小智7 您可以按照您所说的那样使用以下内容作为 VectorStoreRetriever,但带有 search_type 参数。 retriever = dbFAISS.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": .5,"k": top_k})...
# vectorize and create a retrieverdb = LoadFVectorize.load_db()faiss_retriever = db.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 3}, max_tokens_limit=1000)# Define a QA chainqa_chain = RetrievalQA.from_chain_type(llm...
db=LoadFVectorize.load_db()faiss_retriever=db.as_retriever(search_type="mmr",search_kwargs={'fetch_k':3},max_tokens_limit=1000)# Define aQAchain qa_chain=RetrievalQA.from_chain_type(llm,retriever=faiss_retriever,chain_type_kwargs={"prompt":QA_PROMPT})query='What versions of TLS supporte...
defbuild_index(data,index_type="IndexFlatL2",quantizer_type="IndexFlatL2",nlist=1,m=1,nbits=8,dist_metric=0,*args,**kwargs):d=len(data[0])index=Noneifindex_type=="IndexFlatL2":index=faiss.IndexFlatL2(d)elif index_type=="IndexFlatIP":index=faiss.IndexFlatIP(d)elif index_type==...
Agents / Agent Executors Tools / Toolkits Chains Callbacks/Tracing Async Reproduction CreateFAISSvectorstore Callas_retriever(search_type='similarity_score_threshold', k=4, search_kwargs={'score_threshold': 0.6}) Expected behavior To not raise error ...
FAISS(FacebookAISimilaritySearch)是Facebook开源的一个用于高效相似度搜索的库,特别适 用于大规模的向量集合。它提供了多种索引结构和搜索算法,能够在海量数据中快速找到与查询 向量最相似的向量。FAISS支持多种向量距离度量,如欧氏距离、余弦相似度等,适用于图像检 ...