# 向量化 embedding model: m3e-base model_name="./m3e-base" model_kwargs={'device':'cpu'} encode_kwargs={'normalize_embeddings':True} embedding=HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为文本生成向量表示用于...
model_kwargs = {'device': device} 在这里,我们设置了将用于嵌入的预训练模型的路径。我们还配置了设备设置,如果可用,则使用 GPU 以加快计算速度,否则默认为 CPU。 初始化拥HuggingFace 嵌入和 FAISS 矢量存储 embeddings = HuggingFaceEmbeddings( model_name=modelPath, model_kwargs=model_kwargs, ) # Made ...
# 向量化 embedding model: m3e-base model_name = "./m3e-base" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': True} embedding = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为文本...
model_kwargs = {'device': 'cuda'})# 如果没有本地faiss仓库,先读取doc向量库,再将向量库保存到本地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fa...
model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs )fromlangchain_community.vectorstoresimportFAISS vector = FAISS.from_documents(all_splits, bgeEmbeddings) 5、向量库检索 接下来尝试下使用向量库进行检索。 retriever = vector.as_retriever(search_type="similarity", search...
model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': False} hf = HuggingFaceEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) 1. 2. 3. 4. 5. 6. 7. 8. 9.
overal_temperature = 0.1 flan_t5xxl = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":overal_temperature, "max_new_tokens":200} ) llm = flan_t5xxl tools = load_tools(["llm-math"], llm=llm) 复制 创建总结链 chain = load_summarize_chain(llm, chain_type="...
model_kwargs={"temperature": 0, "max_length":200}, huggingfacehub_api_token=HUGGING_FACE_API_KEY) chain = LLMChain(prompt=prompt, llm=model) temperature表示输出的随机性程度。max_length则为我们令牌的最大长度 现在就可以载入模型: hf_embeddings = HuggingFaceEmbeddings(model_name='sentence-transform...
model= HuggingFaceHub(repo_id="facebook/mbart-large-50",model_kwargs={"temperature":0,"max_length":200},huggingfacehub_api_token=HUGGING_FACE_API_KEY)chain= LLMChain(prompt=prompt, llm=model) temperature表示输出的随机性程度。max_length则为我们令牌的最大长度 ...
model_kwargs={"sample_model_args": False} ) return llm # 连接到 VectorDB def connect_vectorstore(self,clear_db): print("Start connecting to VectorDB.") VDB_URL = self.config['vdb_config']['VDB_URL'] VDB_USERNAME = self.config['vdb_config']['VDB_USERNAME'] VDB_KE...