【LangChain系列 15】语言模型——LLMs(一)

原文地址：【LangChain系列 15】语言模型——LLMs(一)

本文速读：

• 异步API

• 自定义LLM

• Fake LLM

• HumanInput LLM

本文将介绍LLMs在LangChain中的一些用法，帮助我们更好地了解LLM模块。

01 异步API

---

LangChain通过异步库实现了对异步的支持，异步对于多LLM的并发调用是非常有用的。目前，OpenAI、PromptLayerOpenAI、ChatOpenAI、Anthropic、Cohere都是支持异步的，其它LLM的异步支持已经在规划中。

在LangChain中，可以通过agenerate方法去异步地调用OpenAI LLM。

import time
import asyncio

from langchain.llms import OpenAI


def generate_serially():
    llm = OpenAI(temperature=0.9)
    for _ in range(10):
        resp = llm.generate(["Hello, how are you?"])
        print(resp.generations[0][0].text)


async def async_generate(llm):
    resp = await llm.agenerate(["Hello, how are you?"])
    print(resp.generations[0][0].text)


async def generate_concurrently():
    llm = OpenAI(temperature=0.9)
    tasks = [async_generate(llm) for _ in range(10)]
    await asyncio.gather(*tasks)


s = time.perf_counter()
# If running this outside of Jupyter, use asyncio.run(generate_concurrently())
await generate_concurrently()
elapsed = time.perf_counter() - s
print(f"Concurrent executed in {elapsed:0.2f} seconds.")

s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print(f"Serial executed in {elapsed:0.2f} seconds.")

执行代码，输出结果：

I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, how about you?
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about yourself?
  
  
  I'm doing well, thank you! How about you?
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you! How about you?
  
  
  I'm doing well, thank you. How about you?
  Concurrent executed in 1.39 seconds.
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about you?
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about yourself?
  
  
  I'm doing well, thanks for asking. How about you?
  
  
  I'm doing well, thanks! How about you?
  
  
  I'm doing well, thank you. How about you?
  
  
  I'm doing well, thank you. How about yourself?
  
  
  I'm doing well, thanks for asking. How about you?
  Serial executed in 5.77 seconds.

02 自定义LLM

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通过自定义LLM，你可以定义一个自己的LLM，也可以基于LangChain已有的LLM封装成一个新的LLM。

封装一个LLM，你唯一要实现的方法是_call，这个方法接收一个字符串和一些可选的停止词，然后返回一个字符串；另外，还有一个可选的_identifying_params属性，你可以根据需要决定是否实现，它主要作用是辅助这个类信息的打印输出。

下面我们动手实现一个自定义的LLM。

from typing import Any, List, Mapping, Optional

from langchain.callbacks.man