vector_store = FAISS.load_local(vs_path, self.embeddings) FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector vector_store.chunk_size = self.chunk_size # 利用faiss求相似 related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k...
similarity_search_with_relevance_scores:这个方法与similarity_search_with_score类似,但是它会将分数转换为一个介于0和1之间的相关度评分,这个评分表示查询和doc对象之间的语义相关程度,评分越高越相似。代码与similarity_search_with_score类似,不再额外示例 max_marginal_relevance_search:这个方法是基于最大边际相关性...
使用了LangChain中的similarity_search_with_score就可以获取所需的top_k个文案片段,并且返回其score。结果显示score差别不是很大。 def searchSimDocs(query,vectorstore,top_k=3,scoreThershold=5): packs=vectorstore.similarity_search_with_score(query,k=top_k) contentList=[] for pack in packs: doc,score...
LLM可以输出更加精准的内容。所以通过FAISS提供的similarity_search_with_score接口函数。我们可以得到跟query 相关的内容。从而构建更有效的Prompt。 相关代码 和LLM交互得到输出结果 相关代码 延展内容 不同向量数据库的相似度检索方案对比 不同向量数据库的常用接口 本文为稀土掘金博主「wang_...
similarity_search_with_relevance_scores:这个方法与similarity_search_with_score类似,但是它会将分数转换为一个介于0和1之间的相关度评分,这个评分表示查询和doc对象之间的语义相关程度,评分越高越相似。代码与similarity_search_with_score类似,不再额外示例 ...
From what I understand, you encountered a TypeError when using the similarity_search_with_score_by_vector() function with the 'score_threshold' keyword argument. SDcodehub suggested an alternative approach to resolve the issue. Before we close this issue, could you please confirm if it is still...
From what I understand, the issue is about the inconsistency in scoring between FAISS and Pinecone when using the similarity_search_with_score function. It seems that the problem has been identified and fixed, and the expected behavior of having all Vector Stores use the same scoring has been ...
similarity_score_thresholdでは以下のfaiss._similarity_search_with_relevance_scoresが利用されるためここを修正します。 langchain/vectorstores/faiss.py def_similarity_search_with_relevance_scores(self,query:str,k:int=4,filter:Optional[Dict[str,Any]]=None,fetch_k:int=20,**kwargs:Any,)->List[Tu...
vectordb.similarity_search() and vectordb.similarity_search_with_score() return exactly the same top n chucks in the same order. similarity_search_with_score() also has score data. I think this data is important for filtering out irrelevant chucks. On the other hand, I have r...
As you can see I am also using similarity_search_with_score(), see below. I would like to confirm with you the following: Do you also use distance_metric="cos" for CHROMA? The documentation doesn't explicitly say this, but I believe it's possible, since it has this parame...