OpenLM

OpenLM 是一款零依赖的 OpenAI 兼容 LLM 提供程序，可以直接通过 HTTP 调用不同的推理端点。

它实现了 OpenAI Completion 类，因此可以作为 OpenAI API 的直接替代品。此更改集利用 BaseOpenAI 以最小程度地增加代码。

此示例介绍了如何使用 LangChain 与 OpenAI 和 HuggingFace 交互。您将需要这两个平台的 API 密钥。

设置

安装依赖项并设置 API 密钥。

# Uncomment to install openlm and openai if you haven't already

%pip install --upgrade --quiet  openlm
%pip install --upgrade --quiet  langchain-openai

import os
from getpass import getpass

# Check if OPENAI_API_KEY environment variable is set
if "OPENAI_API_KEY" not in os.environ:
    print("Enter your OpenAI API key:")
    os.environ["OPENAI_API_KEY"] = getpass()

# Check if HF_API_TOKEN environment variable is set
if "HF_API_TOKEN" not in os.environ:
    print("Enter your HuggingFace Hub API key:")
    os.environ["HF_API_TOKEN"] = getpass()

使用 LangChain 与 OpenLM

在这里，我们将在 LLMChain 中调用两个模型，来自 OpenAI 的 text-davinci-003 和来自 HuggingFace 的 gpt2。

from langchain.chains import LLMChain
from langchain_community.llms import OpenLM
from langchain_core.prompts import PromptTemplate

API 参考：LLMChain | OpenLM | PromptTemplate

question = "What is the capital of France?"
template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

for model in ["text-davinci-003", "huggingface.co/gpt2"]:
    llm = OpenLM(model=model)
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    result = llm_chain.run(question)
    print(
        """Model: {}
Result: {}""".format(model, result)
    )

Model: text-davinci-003
Result:  France is a country in Europe. The capital of France is Paris.
Model: huggingface.co/gpt2
Result: Question: What is the capital of France?

Answer: Let's think step by step. I am not going to lie, this is a complicated issue, and I don't see any solutions to all this, but it is still far more

相关

• LLM 概念指南
• LLM 操作指南