> ## Documentation Index > Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # ChatSambanova 集成 > 使用 LangChain Python 集成 ChatSambanova 聊天模型。 本指南将帮助您开始使用 SambaNova [聊天模型](/oss/python/langchain/models/). 有关所有 `ChatSambaNova` 功能和配置的详细文档，请前往 [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview). **[SambaNova](https://sambanova.ai/)'s** [SambaCloud](http://cloud.sambanova.ai?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup) is a cloud platform for performing inference with open-source models ## 概述 ### 集成详情 | 类 | 包 | 可序列化 | JS 支持 | 下载量 | 版本 | | :--------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :--: | :---: | :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | | [`ChatSambaNova`](https://docs.sambanova.ai/cloud/docs/get-started/overview) | [`langchain-sambanova`](/oss/python/integrations/providers/sambanova/) | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square\&label=%20) | ### 模型功能 | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | 音频输入 | 视频输入 | [Token-level streaming](/oss/python/langchain/streaming/) | 原生异步 | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) | | :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :--: | :--: | :-------------------------------------------------------: | :--: | :-----------------------------------------------------: | :--------------------------------------------------------: | | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ## 设置 要访问 SambaNova models you will need to create a [SambaCloud](http://cloud.sambanova.ai?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup) account, get an API key, install the `langchain_sambanova` 集成包。 ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-sambanova ``` ### 凭证 Get an API Key from [cloud.sambanova.ai](http://cloud.sambanova.ai/apis?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signupapis) 完成后设置 SAMBANOVA\_API\_KEY 环境变量： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import getpass import os if not os.getenv("SAMBANOVA_API_KEY"): os.environ["SAMBANOVA_API_KEY"] = getpass.getpass( "Enter your SambaNova API key: " ) ``` 要启用模型调用的自动追踪，请设置您的 [LangSmith](/langsmith/home) API key: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} os.environ["LANGSMITH_TRACING"] = "true" os.environ["LANGSMITH_API_KEY"] = getpass.getpass("请输入您的 LangSmith API 密钥: ") ``` ### 安装 LangChain 的 **SambaNova** 集成位于 `langchain_sambanova` 包中： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-sambanova ``` ## 实例化 现在我们可以实例化模型对象并生成聊天补全： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_sambanova import ChatSambaNova llm = ChatSambaNova( model="Meta-Llama-3.3-70B-Instruct", max_tokens=1024, temperature=0.7, top_p=0.01, # 其他参数... ) ``` ## 调用 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} messages = [ ( "system", "You are a helpful assistant that translates English to French. " "Translate the user sentence.", ), ("human", "I love programming."), ] ai_msg = llm.invoke(messages) ai_msg ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} AIMessage(content="J'adore la programmation.", additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 7, 'completion_tokens': 8, 'completion_tokens_after_first_per_sec': 195.0204119588971, 'completion_tokens_after_first_per_sec_first_ten': 618.3422770734173, 'completion_tokens_per_sec': 53.25837044790076, 'end_time': 1731535338.1864908, 'is_last_response': True, 'prompt_tokens': 55, 'start_time': 1731535338.0133238, 'time_to_first_token': 0.13727331161499023, 'total_latency': 0.15021112986973353, 'total_tokens': 63, 'total_tokens_per_sec': 419.4096672772185}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535338}, id='f04b7c2c-bc46-47e0-9c6b-19a002e8f390') ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(ai_msg.content) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} J'adore la programmation. ``` ## 流式输出 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} system = "You are a helpful assistant with pirate accent." human = "I want to learn more about this animal: {animal}" prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)]) chain = prompt | llm for chunk in chain.stream({"animal": "owl"}): print(chunk.content, end="", flush=True) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Yer lookin' fer some knowledge about owls, eh? Alright then, matey, settle yerself down with a pint o' grog and listen close. Owls be a fascinatin' lot, with their big round eyes and silent wings. They be birds o' prey, which means they hunt other creatures fer food. There be over 220 species o' owls, rangin' in size from the tiny Elf Owl (which be smaller than a parrot) to the Great Grey Owl (which be as big as a small eagle). One o' the most interestin' things about owls be their eyes. They be huge, with some species havin' eyes that be as big as their brains! This lets 'em see in the dark, which be perfect fer nocturnal huntin'. They also have special feathers on their faces that help 'em hear better, and their ears be specially designed to pinpoint sounds. Owls be known fer their silent flight, which be due to the special shape o' their wings. They be able to fly without makin' a sound, which be perfect fer sneakin' up on prey. They also be very agile, with some species able to fly through tight spaces and make sharp turns. Some o' the most common species o' owls include: * Barn Owl: A medium-sized owl with a heart-shaped face and a screechin' call. * Tawny Owl: A large owl with a distinctive hootin' call and a reddish-brown plumage. * Great Horned Owl: A big owl with ear tufts and a deep hootin' call. * Snowy Owl: A white owl with a round face and a soft, hootin' call. Owls be found all over the world, in a variety o' habitats, from forests to deserts. They be an important part o' many ecosystems, helpin' to keep populations o' small mammals and birds under control. So there ye have it, matey! Owls be amazin' creatures, with their big eyes, silent wings, and sharp talons. Now go forth and spread the word about these fascinatin' birds! ``` ## 异步 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} prompt = ChatPromptTemplate.from_messages( [ ( "human", "what is the capital of {country}?", ) ] ) chain = prompt | llm await chain.ainvoke({"country": "France"}) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} AIMessage(content='The capital of France is Paris.', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 1, 'completion_tokens': 7, 'completion_tokens_after_first_per_sec': 442.126212227688, 'completion_tokens_after_first_per_sec_first_ten': 0, 'completion_tokens_per_sec': 46.28540439646366, 'end_time': 1731535343.0321083, 'is_last_response': True, 'prompt_tokens': 42, 'start_time': 1731535342.8808727, 'time_to_first_token': 0.137664794921875, 'total_latency': 0.15123558044433594, 'total_tokens': 49, 'total_tokens_per_sec': 323.99783077524563}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535342}, id='c4b8c714-df38-4206-9aa8-fc8231f7275a') ``` ## Async streaming ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} prompt = ChatPromptTemplate.from_messages( [ ( "human", "in less than {num_words} words explain me {topic} ", ) ] ) chain = prompt | llm async for chunk in chain.astream({"num_words": 30, "topic": "quantum computers"}): print(chunk.content, end="", flush=True) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Quantum computers use quantum bits (qubits) to process info, leveraging superposition and entanglement to perform calculations exponentially faster than classical computers for certain complex problems. ``` ## 工具调用 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from datetime import datetime from langchain.messages import HumanMessage, ToolMessage from langchain.tools import tool @tool def get_time(kind: str = "both") -> str: """Returns current date, current time or both. Args: kind(str): date, time or both Returns: str: current date, current time or both """ if kind == "date": date = datetime.now().strftime("%m/%d/%Y") return f"Current date: {date}" elif kind == "time": time = datetime.now().strftime("%H:%M:%S") return f"Current time: {time}" else: date = datetime.now().strftime("%m/%d/%Y") time = datetime.now().strftime("%H:%M:%S") return f"Current date: {date}, Current time: {time}" tools = [get_time] def invoke_tools(tool_calls, messages): available_functions = {tool.name: tool for tool in tools} for tool_call in tool_calls: selected_tool = available_functions[tool_call["name"]] tool_output = selected_tool.invoke(tool_call["args"]) print(f"Tool output: {tool_output}") messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"])) return messages ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} llm_with_tools = llm.bind_tools(tools=tools) messages = [ HumanMessage( content="I need to schedule a meeting for two weeks from today. " "Can you tell me the exact date of the meeting?" ) ] ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} response = llm_with_tools.invoke(messages) while len(response.tool_calls) > 0: print(f"Intermediate model response: {response.tool_calls}") messages.append(response) messages = invoke_tools(response.tool_calls, messages) response = llm_with_tools.invoke(messages) print(f"final response: {response.content}") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Intermediate model response: [{'name': 'get_time', 'args': {'kind': 'date'}, 'id': 'call_7352ce7a18e24a7c9d', 'type': 'tool_call'}] Tool output: Current date: 11/13/2024 final response: The meeting should be scheduled for two weeks from November 13th, 2024. ``` ## 结构化输出 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from pydantic import BaseModel, Field class Joke(BaseModel): """Joke to tell user.""" setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") structured_llm = llm.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} Joke(setup='Why did the cat join a band?', punchline='Because it wanted to be the purr-cussionist!') ``` ## Input image ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} multimodal_llm = ChatSambaNova( model="Llama-4-Maverick-17B-128E-Instruct", max_tokens=1024, temperature=0.7, top_p=0.01, ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import base64 import httpx image_url = ( "https://images.pexels.com/photos/147411/italy-mountains-dawn-daybreak-147411.jpeg" ) image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") message = HumanMessage( content=[ {"type": "text", "text": "describe the weather in this image in 1 sentence"}, { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}, }, ], ) response = multimodal_llm.invoke([message]) print(response.content) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} The weather in this image is a serene and peaceful atmosphere, with a blue sky and white clouds, suggesting a pleasant day with mild temperatures and gentle breezes. ``` *** ## API 参考 有关所有 `SambaNova` 功能和配置的详细文档，请前往 API reference: [docs.sambanova.ai/cloud/docs/get-started/overview](https://docs.sambanova.ai/cloud/docs/get-started/overview) ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/chat/sambanova.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).