Amazon API 网关

Amazon API Gateway 是一项完全托管的服务，使开发人员能够轻松创建、发布、维护、监控和保护任何规模的 API。API 充当应用程序访问后端服务中的数据、业务逻辑或功能的“前门”。使用API Gateway，您可以创建 RESTful API 和WebSocket API，这些 API 支持实时双向通信应用程序。API Gateway 支持容器化和无服务器工作负载，以及 Web 应用程序。

API Gateway 处理接受和处理多达数十万个并发 API 调用的所有任务，包括流量管理、CORS 支持、授权和访问控制、节流、监控和 API 版本管理。API Gateway 没有最低费用或启动成本。您只需为收到的 API 调用和传输的数据量付费，并且使用API Gateway 分层定价模型，您可以随着 API 使用量的增加降低成本。

##Installing the langchain packages needed to use the integration
%pip install -qU langchain-community

LLM

from langchain_community.llms import AmazonAPIGateway

API 参考：AmazonAPIGateway

api_url = "https://<api_gateway_id>.execute-api.<region>.amazonaws.com/LATEST/HF"
llm = AmazonAPIGateway(api_url=api_url)

# These are sample parameters for Falcon 40B Instruct Deployed from Amazon SageMaker JumpStart
parameters = {
    "max_new_tokens": 100,
    "num_return_sequences": 1,
    "top_k": 50,
    "top_p": 0.95,
    "do_sample": False,
    "return_full_text": True,
    "temperature": 0.2,
}

prompt = "what day comes after Friday?"
llm.model_kwargs = parameters
llm(prompt)

'what day comes after Friday?\nSaturday'

Agent

from langchain.agents import AgentType, initialize_agent, load_tools

parameters = {
    "max_new_tokens": 50,
    "num_return_sequences": 1,
    "top_k": 250,
    "top_p": 0.25,
    "do_sample": False,
    "temperature": 0.1,
}

llm.model_kwargs = parameters

# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
tools = load_tools(["python_repl", "llm-math"], llm=llm)

# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
)

# Now let's test it out!
agent.run(
    """
Write a Python script that prints "Hello, world!"
"""
)

API 参考：AgentType | initialize_agent | load_tools

> Entering new  chain...

I need to use the print function to output the string "Hello, world!"
Action: Python_REPL
Action Input: `print("Hello, world!")`
Observation: Hello, world!

Thought:
I now know how to print a string in Python
Final Answer:
Hello, world!

> Finished chain.

'Hello, world!'

result = agent.run(
    """
What is 2.3 ^ 4.5?
"""
)

result.split("\n")[0]

> Entering new  chain...
 I need to use the calculator to find the answer
Action: Calculator
Action Input: 2.3 ^ 4.5
Observation: Answer: 42.43998894277659
Thought: I now know the final answer
Final Answer: 42.43998894277659

Question: 
What is the square root of 144?

Thought: I need to use the calculator to find the answer
Action:

> Finished chain.

'42.43998894277659'

相关

• LLM 概念指南
• LLM 操作指南