os.environ["ZHIPUAI_API_KEY"]="zhipuai_api_key"model=ChatZhipuAI(model="glm-4",temperature=0,streaming=True,)chunks=[]asyncforchunkinmodel.astream("你好关于降本增效你都知道什么?"):#采用异步比同步输出更快 chunks.append(chunk)p
if run_manager: run_manager.on_text("Log something about this run") return {self.output_key: response.generations[0][0].text} async def _acall( self, inputs: Dict[str, Any], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: # Your custom chain...
按向量搜索（Search by Vector）：通过输入查询向量，查找与之最相似的向量数据。异步操作（Async Operatio...
Deprecated since version 0.2.13: This function is deprecated and will be removed in langchain 1.0. See API reference for replacement: https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.openapi.get_openapi_chain.html chains.openai_functions.qa_with_structure.create...
Async Reproduction Steps to reproduce: Create a Chroma store which is locally persisted store = Chroma.from_texts( texts=docs, embedding=embeddings, metadatas=metadatas, persist_directory=environ["DB_DIR"] ) Get the errorYou are using a deprecated configuration of Chroma. Please pip install chrom...
fromTemplate(`{test}`), ]), new ChatGoogleGenerativeAI({ model: 'gemini-2.0-flash' }), new StringOutputParser(), ]); async function run() { try { const result = await someChain.invoke({ test: 'bla bla bla' }); console.log(result); } catch (e) { console.log(e); } } run...
In computer science, asynchronous (async) functions are those that operate independently of other processes, thereby enabling several API requests to be run concurrently without waiting for each other. In LangChain, these async functions let you make many API requests all at once, not one after ...
We send a question and print out the response: // code/rag/src/app/page.js 'use client' import {useState} from "react" export default function Home() { const [answer, setAnswer] = useState() const askQuestion = async (e) => { e.preventDefault() const question = e.target.question....
These models natively support streaming, and as is the case with all LangChain LLMs they expose a batch method to handle concurrent requests, as well as async methods for invoke, stream, and batch. Below are a few examples. print(llm.batch(["What's 2*3?","What's 2*6?"])) ...
异步操作(Async Operations):支持异步执行索引操作,提高系统的并发处理能力和响应速度。 按分数搜索(Search with Score):在进行向量搜索的同时,返回每个匹配结果的相似度分数,以便评估结果的相关性。 优化思路:添加元数据(Add Metadata) 在向量数据库中,元数据(Metadata) 是指与每个向量相关联的附加信息。这些信息通常...