}, ], stream=True, ) for chunk in response: print(chunk.choices[0].delta) Python 调用示例 代码语言:javascript 代码运行次数:0 运行 AI代码解释 data: {"id":"8313807536837492492","created":1706092316,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"土"}...
astream:异步流式返回响应的块 ainvoke:输入中异步调用链 abatch:多个批量输入中异步调用链 不同组件的输入类型各不相同: Prompt输入类型 Prompt 字典(Dictionary) Retriever 单个字符串(Single string) Model 单个字符串(Single string)、对话消息列表(list of chat messages)或 PromptValue 输出类型也因组件而异: ...
{"type":"function","function": {"name":"query_train_info","description":"根据用户提供的信息,查询对应的车次","parameters": {"type":"object","properties": {"departure": {"type":"string","description":"出发城市或车站", },"destination": {"type":"string","description":"目的地城市或车...
prompt = hub.pull("rlm/rag-prompt") print(prompt.invoke({"context": "填充内容", "question": "填充问题"}).to_string()) Human: You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just ...
Langchain 的套路是先 load 数据;再做成 retrieval;再搞成 chain;最后通过 stream/astream 串起来用,所以先看最终效果,得益于 embedding 层,中英文混着用也没有问题: 图0:最终效果 载入colbert 权重: 图一:初始化 colbert 第一次 retriveal 的时间: 图二:第一次载入时间比较久 以后的 retrieval 就很爽了!
InputStream inputStream = source.inputStream(); // 此处需要将编码格式设置为UTF_8,解决 InputStream 流读取时的中文乱码问题 BufferedReader reader = new BufferedReader(new InputStreamReader(inputStream, StandardCharsets.UTF_8)); int byteRead; int preRead = 0; String str = ""; while ((byte...
# 定义 BaseLLM 抽象基类,它从 BaseLanguageModel[str] 和 ABC(Abstract Base Class)继承 class BaseLLM(BaseLanguageModel[str], ABC): """Base LLM abstract interface. It should take in a prompt and return a string.""" # 定义可选的缓存属性,其初始值为 None cache: Optional[bool] = None # ...
基于提供的我了解的信息内容，我们可以了解到如何构建一个简单的React前端项目来支持流式输出。此项目将与后端接口进行交互，该接口地址为http://.../ai/chatStream?input=...，返回类型为flux<String>。下面是详细的步骤说明：构建项目并填写代码 首先，你需要设置一个新的React应用环境，并安装必要的依赖包。npx ...
)# runnable = RunnableParallel(joke=joke_chain, poem=poem_chain)runnable = RunnableParallel({"joke":joke_chain,"poem":poem_chain}).assign(answer=itemgetter("poem"))# Display streamoutput = {key:""forkey, _inrunnable.output_schema()}forchunkinrunnable.stream({"topic":"bear"}):# for key...
LLMs 实现Runnable 接口,这是LangChain 表达式语言 (LCEL)的基本构建块。这意味着它们支持invoke、 ainvoke、stream、astream、batch、abatch、astream_log调用。 LLM 接受字符串作为输入,或可以强制为字符串提示的对象,包括List[BaseMessage]和PromptValue。