AI模型（三） OpenAi + LangChain

鲫小鱼
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hello world

from openai import OpenAI
import os

OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')

client = OpenAI(
    api_key=OPENAI_API_KEY
)

completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "什么是js事件循环"}
    ]
)

print(completion.choices[0].message)

tiktoken

  • 计算有多少token的
import openai
import tiktoken

encoding = tiktoken.encoding_for_model("gpt-4")

chinese = """在未来还没有到来的时候，总要有人把它创造出来，那个人应该是我们啊。"""

tokens = encoding.encode(chinese)

print(tokens)

num_of_tokens_in_chinese = len(encoding.encode(chinese))

print(f"chinese:{chinese} ; {num_of_tokens_in_chinese} tokens\n") //  37 tokens

LangChain 连接大模型和应用的框架

1. 提示词模板 PromptTemplate

from langchain import PromptTemplate, OpenAI, LLMChain

from langchain.chat_models import AzureChatOpenAI
# from langchain.chat_models import ChatOpenAI #直接访问OpenAI的GPT服务

prompt_template = "What is a good name for a company that makes {product}? And only return the best one."

#llm = ChatOpenAI(model_name="gpt-4", temperature=0) #直接访问OpenAI的GPT服务
llm = AzureChatOpenAI(deployment_name = deployment, model_name=model, temperature=0, max_tokens=200) # 通过Azure的OpenAI服务
llm_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(prompt_template)
)
llm_chain.run("colorful socks")
  • 也可以添加多个
products = [{"product":"'cloudnative devops platform'"},
            {"product":"'Noise cancellation headphone'"}, 
            {"product":"colorful socks"}]
llm_chain.apply(products)

2. API调用链 LLMRequestsChain

  • 通过API获取信息，然后通过LLM给出最终答案
  • 例如：http request chain 使用请求库从url获取html结果，然后使用llm分析结果
from langchain import PromptTemplate, OpenAI, LLMChain
from langchain.chains import LLMRequestsChain
from langchain.chat_models import AzureChatOpenAI
# from langchain.chat_models import ChatOpenAI #直接访问OpenAI的GPT服务

#llm = ChatOpenAI(model_name="gpt-4", temperature=0) #直接访问OpenAI的GPT服务
llm = AzureChatOpenAI(deployment_name = deployment, model_name=model, temperature=0, max_tokens=200) #通过Azure的OpenAI服务

def query_baidu(question):
      template = """Between >>> and <<< are the raw search result text from web.
      Extract the answer to the question '{query}' or say "not found" if the information is not contained.
      Use the format
      Extracted:<answer or "not found">
      >>> {requests_result} <<<
      Extracted:"""

      PROMPT = PromptTemplate(
          input_variables=["query", "requests_result"],
          template=template,
      )

      inputs = {
          "query": question,
          "url": "http://www.baidu.com/s?wd=" + question.replace(" ", "+")
      }
      requests_chain = LLMRequestsChain(llm_chain = LLMChain(llm=llm, prompt=PROMPT), output_key="query_info", verbose=True)
      res = requests_chain.run(inputs)
      return res

query_baidu("今天北京天气？")
  • 查询百度天气，inputs 接收 url，然后请求完后的结果返回给组装好的llm大模型，根据模板分析出要的结果
  • 也有封装好的谷歌搜索的例子
# import os
# os.environ["SERPER_API_KEY"] = ""
# https://serper.dev
from langchain.utilities import GoogleSerperAPIWrapper
def query_web(question):
    search = GoogleSerperAPIWrapper()
    return search.run(question)

---
query_web("今天北京天气？")

3. 调用链SequentialChain

  • 链式调用
from langchain import PromptTemplate, OpenAI, LLMChain
from langchain.chat_models import AzureChatOpenAI
from langchain.chains import SequentialChain
from langchain.chat_models import AzureChatOpenAI
# from langchain.chat_models import ChatOpenAI #直接访问OpenAI的GPT服务

#llm = ChatOpenAI(model_name="gpt-4", temperature=0) #直接访问OpenAI的GPT服务
llm = AzureChatOpenAI(deployment_name = deployment, model_name=model, temperature=0, max_tokens=200)

summarizing_prompt_template = """
Summarize the following content into a sentence less than 20 words:
---
{content}

"""
summarizing_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(summarizing_prompt_template),
    output_key = "summary"
)

translating_prompt_template = """
translate "{summary}" into Chinese:

"""

translating_chain = LLMChain(
    llm = llm,
    prompt=PromptTemplate.from_template(translating_prompt_template),
    output_key = "translated"
)

overall_chain = SequentialChain(
    chains=[summarizing_chain, translating_chain],
    input_variables=["content"],
    output_variables=[ "summary","translated"],
    verbose=True
)

res = overall_chain("""
LangChain is a framework for developing applications powered by language models. It enables applications that are:

Data-aware: connect a language model to other sources of data
Agentic: allow a language model to interact with its environment
The main value props of LangChain are:

Components: abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
Off-the-shelf chains: a structured assembly of components for accomplishing specific higher-level tasks
Off-the-shelf chains make it easy to get started. For more complex applications and nuanced use-cases, components make it easy to customize existing chains or build new ones.
""")

print("summary:"+res["summary"])

print("中文:"+res["translated"])

让 ChatGPT有记忆功能 - ConversationBufferWindowMemory

  • 利用 Cradio 快速构建原型

1. 自己记住对话历史

import openai
def get_response(msg):
    # print(msg)
    response = openai.ChatCompletion.create(
        engine=deployment, # engine = "deployment_name".
        messages=msg,
        temperature = 0.9, 
        max_tokens = 600
    )
    return response.choices[0].message.content
    


def history_to_prompt(chat_history): # 将对话内容保存在一个List里
    msg = [{"role": "system", "content": "You are an AI assistant."}]
    i = 0
    for round_trip in chat_history: # 将List里的内容，组成 ChatCompletion的 messages部分，{role，content} dict
        msg.append({"role": "user", "content": round_trip[0]})
        msg.append({"role": "assistant", "content": round_trip[1]})
    return msg

def respond(message, chat_history):
    his_msg = history_to_prompt(chat_history) #并装历史会话，ChatCompletion的 messages部分格式
    his_msg.append({"role": "user", "content": message}) # 放入当前用户问题
    bot_message = get_response(his_msg)
    chat_history.append((message, bot_message)) # 将用户问题和返回保存到 历史记录 List
    return "", chat_history



import gradio as gr
with gr.Blocks() as demo:
    chatbot = gr.Chatbot(height=480) #just to fit the notebook
    msg = gr.Textbox(label="Prompt")
    btn = gr.Button("Submit")
    clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")

    btn.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot]) #Press enter to submit
gr.close_all()
demo.launch(share=True)

2. ConversationBufferWindowMemory

from langchain.chat_models import AzureChatOpenAI
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationChain
# from langchain.chat_models import ChatOpenAI #直接访问OpenAI的GPT服务

#llm = ChatOpenAI(model_name="gpt-4", temperature=0) #直接访问OpenAI的GPT服务
llm = AzureChatOpenAI(deployment_name = deployment, model_name=model, temperature=0, max_tokens=200) #通过Azure的OpenAI服务


memory = ConversationBufferWindowMemory(k=10)   # 记住10轮


def get_response(input):
    print("------------")
    print(memory.load_memory_variables({}))
    print("------------")
    conversation_with_memory = ConversationChain(
        llm=llm, 
        memory=memory,
        verbose=False
    )
    return conversation_with_memory.predict(input=input)

import gradio as gr
def respond(message, chat_history):
    bot_message = get_response(message)
    chat_history.append((message, bot_message))
    return "", chat_history

with gr.Blocks() as demo:
    chatbot = gr.Chatbot(height=300) #just to fit the notebook
    msg = gr.Textbox(label="Prompt")
    btn = gr.Button("Submit")
    clear = gr.ClearButton(components=[msg, chatbot], value="Clear console")

    btn.click(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot]) #Press enter to submit
gr.close_all()
demo.launch(share=True)

启发新的交互方式

  • 多用gpt的联想，推理能力，多和外界的内容链接起来
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