"""Arbitrary metadata about the page content (e.g., source, relationships to other documents, etc.). """ type: Literal["Document"] = "Document" @classmethod def is_lc_serializable(cls) -> bool: """Return whether this class is serializable.""" return True @classmethod def get_lc_name...
.0f} characters (smaller pieces)") Now you have 62 documents that have an average of 2,846 characters (smaller pieces) # 设置 embedding 引擎 embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) # Embed 文档,然后使用伪数据库
from_documents(docs, embeddings) 代码语言:javascript 复制 # Get our retriever ready qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()) 代码语言:javascript 复制 query = "What function do I use if I want to find the most similar item in a...
创建向量数据库,灌入数据from_documents: 查询similarity_search: 运行结果: 它也接受传入一个向量来进行向量检索similarity_search_by_vector。以下是代码示例。在以下代码中可能体现的价值不是很大,但是在实际项目中,如果出现只知道向量值,不知道具体文字的情况,这个接口就...
cache_folder="models") vectorstore = FAISS.from_documents(docs, embeddings) result = vectorstore....
# 导入并初始化Pinecone客户端import osimport pineconefrom langchain.vectorstores import Pineconepinecone.init( api_key=os.getenv('PINECONE_API_KEY'), environment=os.getenv('PINECONE_ENV') ) # 上传向量到Pineconeindex_name = "langchain-quickstart"search = Pinecone.from_documents(texts, em...
"https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()# Splittext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)splits = text_splitter.split_documents(data)# VectorDBembedding = OpenAIEmbeddings()vectordb = Chroma.from_documents(documents=split...
from langchain.vectorstores import Chromafrom zhipuai_embedding import ZhipuAIEmbeddingspersist_directory = '../../data_base/vector_db/chroma' # 数据库持久化路径vectordb = Chroma.from_documents( documents=split_docs[:100], embedding=embedding, persist_directory=persist_directory # =将persist_direct...
# 创建Promptprompt = ChatPromptTemplate.from_template('基于上下文:{context}\n回答:{input}') # 创建输出解析器output_parser = StrOutputParser() # 模拟文档docs = [Document(page_content="TuGraph是蚂蚁开源的图数据库产品")] # 文档嵌入splits = RecursiveCharacterTextSplitter().split_documents(docs)ve...
RefineDocumentsChain: 是通过迭代更新的方式获取答案。先处理第一个文档,作为 context 传递给 llm,获取中间结果 intermediate answer。然后将第一个文档的中间结果以及第二个文档发给 llm 进行处理,后续的文档类似处理。 MapReduceDocumentsChain: 先通过 LLM 对每个 document 进行处理,然后将所有文档的答案在通过 LLM ...