Javelin AI 网关教程
本 Jupyter Notebook 将探讨如何使用 Python SDK 与 Javelin AI Gateway 进行交互。Javelin AI Gateway 通过提供安全且统一的端点，简化了 OpenAI、Cohere、Anthropic 等大型语言模型 (LLM) 的使用。网关本身提供了一种集中机制，用于系统地推出模型，提供访问安全、企业策略和成本控制等功能。
有关 Javelin 的所有功能和优势的完整列表，请访问 www.getjavelin.io
步骤 1：简介
Javelin AI Gateway 是一款针对 AI 应用的企业级 API 网关。它集成了强大的访问安全功能，确保与大型语言模型的安全交互。在 官方文档 中了解更多信息。
步骤 2：安装
在开始之前，我们必须安装 javelin_sdk 并将 Javelin API 密钥设置为环境变量。
pip install 'javelin_sdk'
Requirement already satisfied: javelin_sdk in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (0.1.8)
Requirement already satisfied: httpx<0.25.0,>=0.24.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from javelin_sdk) (0.24.1)
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Requirement already satisfied: certifi in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpx<0.25.0,>=0.24.0->javelin_sdk) (2023.5.7)
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Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from pydantic<2.0.0,>=1.10.7->javelin_sdk) (4.7.1)
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Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.0->javelin_sdk) (3.7.1)
Note: you may need to restart the kernel to use updated packages.
步骤 3：完成示例
本节将演示如何与 Javelin AI Gateway 交互以从大型语言模型获取完成结果。以下 Python 脚本演示了这一点：（注意）假设您已经在网关中设置了名为“eng_dept03”的路由。
from langchain.chains import LLMChain
from langchain_community.llms import JavelinAIGateway
from langchain_core.prompts import PromptTemplate
route_completions = "eng_dept03"
gateway = JavelinAIGateway(
gateway_uri="http://localhost:8000", # replace with service URL or host/port of Javelin
route=route_completions,
model_name="gpt-3.5-turbo-instruct",
)
prompt = PromptTemplate("Translate the following English text to French: {text}")
llmchain = LLMChain(llm=gateway, prompt=prompt)
result = llmchain.run("podcast player")
print(result)
---------------------------------------------------------------------------
``````output
ImportError Traceback (most recent call last)
``````output
Cell In[6], line 2
1 from langchain.chains import LLMChain
----> 2 from langchain.llms import JavelinAIGateway
3 from langchain.prompts import PromptTemplate
5 route_completions = "eng_dept03"
``````output
ImportError: cannot import name 'JavelinAIGateway' from 'langchain.llms' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/llms/__init__.py)
步骤 4：嵌入示例
本节演示如何使用 Javelin AI Gateway 获取文本查询和文档的嵌入。以下 Python 脚本说明了这一点：（注意）假设您已经在网关中设置了名为“embeddings”的路由。
from langchain_community.embeddings import JavelinAIGatewayEmbeddings
embeddings = JavelinAIGatewayEmbeddings(
gateway_uri="http://localhost:8000", # replace with service URL or host/port of Javelin
route="embeddings",
)
print(embeddings.embed_query("hello"))
print(embeddings.embed_documents(["hello"]))
API 参考：JavelinAIGatewayEmbeddings
---------------------------------------------------------------------------
``````output
ImportError Traceback (most recent call last)
``````output
Cell In[9], line 1
----> 1 from langchain.embeddings import JavelinAIGatewayEmbeddings
2 from langchain.embeddings.openai import OpenAIEmbeddings
4 embeddings = JavelinAIGatewayEmbeddings(
5 gateway_uri="http://localhost:8000", # replace with service URL or host/port of Javelin
6 route="embeddings",
7 )
``````output
ImportError: cannot import name 'JavelinAIGatewayEmbeddings' from 'langchain.embeddings' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/embeddings/__init__.py)
步骤 5：聊天示例
本节说明如何与 Javelin AI Gateway 交互以促进与大型语言模型的聊天。以下 Python 脚本演示了这一点：（注意）假设您已经在网关中设置了名为“mychatbot_route”的路由。
from langchain_community.chat_models import ChatJavelinAIGateway
from langchain_core.messages import HumanMessage, SystemMessage
messages = [
SystemMessage(
content="You are a helpful assistant that translates English to French."
),
HumanMessage(
content="Artificial Intelligence has the power to transform humanity and make the world a better place"
),
]
chat = ChatJavelinAIGateway(
gateway_uri="http://localhost:8000", # replace with service URL or host/port of Javelin
route="mychatbot_route",
model_name="gpt-3.5-turbo",
params={"temperature": 0.1},
)
print(chat(messages))
---------------------------------------------------------------------------
``````output
ImportError Traceback (most recent call last)
``````output
Cell In[8], line 1
----> 1 from langchain.chat_models import ChatJavelinAIGateway
2 from langchain.schema import HumanMessage, SystemMessage
4 messages = [
5 SystemMessage(
6 content="You are a helpful assistant that translates English to French."
(...)
10 ),
11 ]
``````output
ImportError: cannot import name 'ChatJavelinAIGateway' from 'langchain.chat_models' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/chat_models/__init__.py)
步骤 6：结论 本教程介绍了 Javelin AI Gateway 并演示了如何使用 Python SDK 与其进行交互。请务必查看 Javelin Python SDK 以获取更多示例，并探索官方文档以获取更多详细信息。
祝您编码愉快！