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caution

您当前所在的页面记录了将Fireworks模型用作文本完成模型的使用方法。许多流行的Fireworks模型是聊天完成模型。

你可能在寻找这个页面。

Fireworks 通过创建一个创新的AI实验和生产平台，加速生成式AI的产品开发。

本示例介绍了如何使用LangChain与Fireworks模型进行交互。

概述​

集成详情​

类包本地可序列化JS 支持包下载量包最新版本
Fireworkslangchain_fireworks❌❌✅PyPI - VersionPyPI - Version

设置​

凭证​

登录Fireworks AI获取API密钥以访问我们的模型，并确保将其设置为FIREWORKS_API_KEY环境变量。 3. 使用模型ID设置您的模型。如果未设置模型，默认模型为fireworks-llama-v2-7b-chat。查看完整的最新模型列表，请访问fireworks.ai。

import getpass
import os

if "FIREWORKS_API_KEY" not in os.environ:
    os.environ["FIREWORKS_API_KEY"] = getpass.getpass("Fireworks API Key:")

安装​

您需要安装langchain_fireworks python包才能使笔记本的其余部分正常工作。

%pip install -qU langchain-fireworks
Note: you may need to restart the kernel to use updated packages.

实例化​

from langchain_fireworks import Fireworks

# Initialize a Fireworks model
llm = Fireworks(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    base_url="https://api.fireworks.ai/inference/v1/completions",
)
API Reference:Fireworks

调用​

你可以直接用字符串提示调用模型来获取补全。

output = llm.invoke("Who's the best quarterback in the NFL?")
print(output)
 If Manningville Station, Lions rookie EJ Manuel's

使用多个提示调用​

# Calling multiple prompts
output = llm.generate(
    [
        "Who's the best cricket player in 2016?",
        "Who's the best basketball player in the league?",
    ]
)
print(output.generations)
[[Generation(text=" We're not just asking, we've done some research. We'")], [Generation(text=' The conversation is dominated by Kobe Bryant, Dwyane Wade,')]]

使用附加参数调用​

# Setting additional parameters: temperature, max_tokens, top_p
llm = Fireworks(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    temperature=0.7,
    max_tokens=15,
    top_p=1.0,
)
print(llm.invoke("What's the weather like in Kansas City in December?"))

December is a cold month in Kansas City, with temperatures of

链式调用​

你可以使用LangChain表达式语言来创建一个简单的非聊天模型链。

from langchain_core.prompts import PromptTemplate
from langchain_fireworks import Fireworks

llm = Fireworks(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    temperature=0.7,
    max_tokens=15,
    top_p=1.0,
)
prompt = PromptTemplate.from_template("Tell me a joke about {topic}?")
chain = prompt | llm

print(chain.invoke({"topic": "bears"}))
API Reference:PromptTemplate | Fireworks
 What do you call a bear with no teeth? A gummy bear!

流处理​

如果你愿意，你可以流式传输输出。

for token in chain.stream({"topic": "bears"}):
    print(token, end="", flush=True)
 Why do bears hate shoes so much? They like to run around in their

API 参考​

有关所有Fireworks LLM功能和配置的详细文档，请参阅API参考：https://python.langchain.com/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks

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