FAISS.load_local(self.vector_store_path, self.embeddings) 从langchain-ChatGLM拆解如何对FAISS进行query向量相似性查询: query = "本项目使用的embedding模型是什么,消耗多少显存" related_docs_with_score = vector_store.similarity_search_with_score(query, k=self.top_k) 3 LLM模型给出最终Answer:ChatGLM ...
I used the FAISS as the vector store. it seems that thesimilarity_search_with_score(supposedly ranked by distance: low to high) andsimilarity_search_with_relevance_scores((supposedly ranked by relevance: high to low) produce conflicting results when specifyingMAX_INNER_PRODUCTas the distance strate...
在本案例中我们使用的是FAISS向量数据库。更多了解可参考LangChain - Vectorstores 相关代码 2.2 通过向量数据库进行相似性查找并构造Prompt 我们知道通过向LLM输入更多的上下文信息。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...
Seems this is for relevance score, is it same as the score defined insimilarity_search_with_score? I notice it only invoked herehttps://github.com/hwchase17/langchain/blob/f0cfed636f37ea7c5171541e0df3f814858f1550/langchain/vectorstores/faiss.py#L475-L488.similarity_search_with_score_by_...
When utilizing langchain's Faiss vector library and the GTE embedding model, I've encountered an issue: even though my query sentence is present in the vector library file, the similarity score obtained through thesimilarity_search_with_score() is only 0.9. Here is the code snippet I...
# faiss_db.save_local('test1') # 保存向量数据库# faiss_db= FAISS.load_local('test1',embeddings=doc2embdding) # 从保存的向量数据库中构建数据库,可将 构建Lanchain的Document类“和 ”构建faiss数据库“步骤去掉 # 在向量数据库查询 k = faiss_db.similarity_search_with_score(query="北京是中国最...
这里我们使用 Chroma 作为检索引擎,在 LangChain 中,Chroma 默认使用 cosine distance 作为向量相似度的评估方法, 同时可以通过调整 db.as_retriever(search_type= "similarity_score_threshold"),或是 db.as_retriever(search_type= "mmr")来更改默认搜索策略,前者为带阈值的相似度搜索,后者为 max_marginal_relevanc...
embeddings = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese", model_kwargs={'device': ""}) from langchain.vectorstores.faiss import FAISS vector_store = FAISS.from_documents(docs, embeddings) related_docs_with_score = vector_store.similarity_search_with_score(query, ...
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...
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 ...