使用FAISS的方法如下: -初始化方法:传入嵌入函数、索引、文档存储和索引到文档存储id的字典等信息进行初始化。 - from_texts方法:根据文本数据和嵌入式向量模型计算文本向量,初始化FAISS数据库,并返回FAISS封装对象。 - save_local方法:将FAISS索引、文档存储和索引到文档存储id保存到指定的文件夹路径中。 - load_loc...
三、存在faiss.index vector_store = load_vector_store(vs_path, self.embeddings) 这里做了lru_cache缓存机制, MyFAISS调用静态方法load_local @lru_cache(CACHED_VS_NUM) def load_vector_store(vs_path, embeddings): return MyFAISS.load_local(vs_path, embeddings) ...
Save the FAISS vector store. Load the FAISS vector store with the distance_strategy = "MAX_INNER_PRODUCT" Compare the saved.index and loaded.index objects. One is IndexFlatIP and the other is an IndexFlat class. Expected behavior When loading the index from load_local, it should still be...
from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = FAISS.from_documents(texts,...
if os.path.exists("{你的地址}/my_faiss_store.faiss") == False: vector_store = FAISS.from_documents(docs,embeddings) vector_store.save_local("{你的地址}/my_faiss_store.faiss") # 如果faiss仓库已存在,则直接读取 else: vector_store = FAISS.load_local( ...
docsearch=FAISS.load_local(folder_path=embedding_path, embeddings=embedding_model) First, create aConversationalRetrievalChainchain using ChatNVIDIA. In this chain, I demonstrate the use of one LLM. llm=ChatNVIDIA(model="ai-llama2-70b", temperature=0.1, max_tokens=1000, top_p=1.0) ...
model_kwargs={'device': 'cpu'}) vectordb = FAISS.load_local('vectorstore/db_faiss', embeddings) qa_prompt = set_qa_prompt() dbqa = build_retrieval_qa(llm, qa_prompt, vectordb) return dbqa6、代码整合 最后一步就是是将前面的组件组合到main.py脚本中。使用argparse模块是因为...
self.db = FAISS.load_local(faiss_index, embeddings) retriever = self.db.as_retriever( search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 10} ) prompt = PromptTemplate.from_template( "Summarize this content: {context}" )
#Loadembeddings model embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device':'cpu'}) # Buildandpersist FAISS vector store vectorstore = FAISS.from_documents(texts, embeddings) vectorstore.save_local('vectorstore/db_faiss') ...
如今各类AI模型层出不穷,百花齐放,大佬们开发的速度永远遥遥领先于学习者的学习速度。。为了解放生产力,不让应用层开发人员受限于各语言模型的生产部署中..LangChain横空出世界。