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ChatSambaNova云

这将帮助您开始使用SambaNovaCloud 聊天模型。有关所有ChatSambaNovaCloud功能和配置的详细文档，请访问API参考。

SambaNova's SambaNova Cloud 是一个用于执行开源模型推理的平台

概述​

集成详情​

类包本地可序列化JS支持包下载包最新
ChatSambaNovaCloudlangchain-community❌❌❌PyPI - 下载量PyPI - 版本

模型特性​

工具调用结构化输出JSON模式图像输入音频输入视频输入令牌级流式传输原生异步令牌使用Logprobs
✅✅✅✅❌❌✅✅✅❌

设置​

要访问ChatSambaNovaCloud模型，您需要创建一个SambaNovaCloud账户，获取一个API密钥，安装langchain_community集成包，并安装SSEClient包。

pip install langchain-community
pip install sseclient-py

凭证​

从cloud.sambanova.ai获取API密钥，并将其添加到您的环境变量中：

export SAMBANOVA_API_KEY="your-api-key-here"
import getpass
import os

if not os.getenv("SAMBANOVA_API_KEY"):
    os.environ["SAMBANOVA_API_KEY"] = getpass.getpass(
        "Enter your SambaNova Cloud API key: "
    )

如果你想获取模型调用的自动追踪，你也可以通过取消注释以下内容来设置你的LangSmith API密钥：

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

安装​

LangChain 的 SambaNovaCloud 集成位于 langchain_community 包中：

%pip install -qU langchain-community
%pip install -qu sseclient-py

实例化​

现在我们可以实例化我们的模型对象并生成聊天完成：

from langchain_community.chat_models.sambanova import ChatSambaNovaCloud

llm = ChatSambaNovaCloud(
    model="Meta-Llama-3.1-70B-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_k=1,
    top_p=0.01,
)
API Reference:ChatSambaNovaCloud

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.

链式调用​

我们可以链式我们的模型与一个提示模板，如下所示：

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 2.3333333333333335, 'completion_tokens': 6, 'completion_tokens_after_first_per_sec': 106.06729752831038, 'completion_tokens_after_first_per_sec_first_ten': 204.92722183833433, 'completion_tokens_per_sec': 26.32497272023831, 'end_time': 1731535339.9997504, 'is_last_response': True, 'prompt_tokens': 50, 'start_time': 1731535339.7539687, 'time_to_first_token': 0.19864177703857422, 'total_latency': 0.22792046410696848, 'total_tokens': 56, 'total_tokens_per_sec': 245.6997453888909}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535339}, id='dfe0bee6-b297-472e-ac9d-29906d162dcb')

流处理​

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​

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_core.messages import HumanMessage, ToolMessage
from langchain_core.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
API Reference:HumanMessage | ToolMessage | tool
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 = ChatSambaNovaCloud(
    model="Llama-3.2-11B-Vision-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_k=1,
    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.

API参考​

有关所有ChatSambaNovaCloud功能和配置的详细文档，请前往API参考：https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html

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