LangChain 集成

使用 LangChain Python 集成 ChatSambanova 对话模型。

这将帮助您开始使用 SambaNova 对话模型。有关所有 ChatSambaNova 功能和配置的详细文档，请访问 API 参考。

SambaNova的 SambaCloud 是一个用于使用开源模型进行推理的云平台

概述

集成详情

类	包	可序列化	JS 支持	下载	版本
ChatSambaNova	langchain-sambanova	beta	❌

模型功能

工具调用	结构化输出	图像输入	音频输入	视频输入	Token 级流式输出	原生异步	Token 使用	Logprobs
✅	✅	✅	✅	❌	✅	✅	✅	❌

设置

要访问 SambaNova 模型，您需要创建一个 SambaCloud 账户，获取一个 API 密钥，安装 langchain_sambanova 集成包。

pip install langchain-sambanova

凭证

从 cloud.sambanova.ai 获取 API 密钥。完成此操作后，设置 SAMBANOVA_API_KEY 环境变量：

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跟踪，设置您的 LangSmith API 密钥：

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

安装

LangChain SambaNova 集成位于 langchain_sambanova 包中：

pip install -qU langchain-sambanova

实例化

现在我们可以实例化我们的模型对象并生成对话补全：

from langchain_sambanova import ChatSambaNova

llm = ChatSambaNova(
    model="Meta-Llama-3.3-70B-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.01,
    # other params...
)

调用

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

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')

print(ai_msg.content)

J'adore la programmation.

流式输出

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)

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

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "what is the capital of {country}?",
        )
    ]
)

chain = prompt | llm
await chain.ainvoke({"country": "France"})

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')

异步流式输出

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)

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

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

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?"
    )
]

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}")

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

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")

Joke(setup='Why did the cat join a band?', punchline='Because it wanted to be the purr-cussionist!')

输入图像

multimodal_llm = ChatSambaNova(
    model="Llama-4-Maverick-17B-128E-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.01,
)

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)

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.

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API 参考

有关所有 SambaNova 功能和配置的详细文档，请访问 API 参考：docs.sambanova.ai/cloud/docs/get-started/overview

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官方原文：src/oss/python/integrations/chat/sambanova.mdx · 非官方翻译，译文由 AI 生成，以英文原文为准。