三、记忆模块开发，让聊天机器人有记忆

1. LangChain缓冲记忆组件的使用与解析

缓冲窗口记忆示例

from operator import itemgetter

import dotenv
from langchain.memory import ConversationTokenBufferMemory
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough, RunnableLambda
from langchain_openai import ChatOpenAI

dotenv.load_dotenv()

# 1.创建提示模板&记忆
prompt = ChatPromptTemplate.from_messages([
    ("system", "你是OpenAI开发的聊天机器人，请根据对应的上下文回复用户问题"),
    MessagesPlaceholder("history"),  # 需要的history其实是一个列表
    ("human", "{query}"),
])
memory = ConversationTokenBufferMemory(
    return_messages=True,
    input_key="query",
    llm=ChatOpenAI()
)

# 2.创建大语言模型
llm = ChatOpenAI(model="gpt-3.5-turbo-16k")

# 3.构建链应用
chain = RunnablePassthrough.assign(
    history=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
) | prompt | llm | StrOutputParser()

# 4.死循环构建对话命令行
while True:
    query = input("Human: ")

    if query == "q":
        exit(0)

    chain_input = {"query": query, "language": "中文"}

    response = chain.stream(chain_input)
    print("AI: ", flush=True, end="")
    output = ""
    for chunk in response:
        output += chunk
        print(chunk, flush=True, end="")
    memory.save_context(chain_input, {"output": output})
    print("")
    print("history: ", memory.load_memory_variables({}))

摘要缓冲混合记忆示例

from operator import itemgetter

import dotenv
from langchain.memory import ConversationSummaryBufferMemory
from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough, RunnableLambda

dotenv.load_dotenv()

# 1.创建提示模板&记忆
prompt = ChatPromptTemplate.from_messages([
    ("system", "你是OpenAI开发的聊天机器人，请根据对应的上下文回复用户问题"),
    MessagesPlaceholder("history"),  # 需要的history其实是一个列表
    ("human", "{query}"),
])
memory = ConversationSummaryBufferMemory(
    max_token_limit=300,
    return_messages=True,
    input_key="query",
    llm=QianfanChatEndpoint(),
    # llm=ChatOpenAI(model="gpt-3.5-turbo-16k"),
)

# 2.创建大语言模型
# llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
llm = QianfanChatEndpoint()

# 3.构建链应用
chain = RunnablePassthrough.assign(
    history=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
) | prompt | llm | StrOutputParser()

# 4.死循环构建对话命令行
while True:
    query = input("Human: ")

    if query == "q":
        exit(0)

    chain_input = {"query": query, "language": "中文"}

    response = chain.stream(chain_input)
    print("AI: ", flush=True, end="")
    output = ""
    for chunk in response:
        output += chunk
        print(chunk, flush=True, end="")
    memory.save_context(chain_input, {"output": output})
    print("")
    print("history: ", memory.load_memory_variables({}))

实体记忆组件示例

from operator import itemgetter

import dotenv
from langchain.memory import ConversationSummaryBufferMemory
from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough, RunnableLambda

dotenv.load_dotenv()

# 1.创建提示模板&记忆
prompt = ChatPromptTemplate.from_messages([
    ("system", "你是OpenAI开发的聊天机器人，请根据对应的上下文回复用户问题"),
    MessagesPlaceholder("history"),  # 需要的history其实是一个列表
    ("human", "{query}"),
])
memory = ConversationSummaryBufferMemory(
    max_token_limit=300,
    return_messages=True,
    input_key="query",
    llm=QianfanChatEndpoint(),
    # llm=ChatOpenAI(model="gpt-3.5-turbo-16k"),
)

# 2.创建大语言模型
# llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
llm = QianfanChatEndpoint()

# 3.构建链应用
chain = RunnablePassthrough.assign(
    history=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
) | prompt | llm | StrOutputParser()

# 4.死循环构建对话命令行
while True:
    query = input("Human: ")

    if query == "q":
        exit(0)

    chain_input = {"query": query, "language": "中文"}

    response = chain.stream(chain_input)
    print("AI: ", flush=True, end="")
    output = ""
    for chunk in response:
        output += chunk
        print(chunk, flush=True, end="")
    memory.save_context(chain_input, {"output": output})
    print("")
    print("history: ", memory.load_memory_variables({}))

2. 记忆组件的持久化与第三方集成

internal---handler---app_handler.py

import uuid
from dataclasses import dataclass
from operator import itemgetter
from uuid import UUID

from injector import inject
from langchain.memory import ConversationBufferWindowMemory
from langchain_community.chat_message_histories import FileChatMessageHistory
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough, RunnableLambda
from langchain_openai import ChatOpenAI

from internal.exception import FailException
from internal.schema.app_schema import CompletionReq
from internal.service import AppService
from pkg.response import success_json, validate_error_json, success_message


@inject
@dataclass
class AppHandler:
    """应用控制器"""
    app_service: AppService

    def create_app(self):
        """调用服务创建新的APP记录"""
        app = self.app_service.create_app()
        return success_message(f"应用已经成功创建，id为{app.id}")

    def get_app(self, id: uuid.UUID):
        app = self.app_service.get_app(id)
        return success_message(f"应用已经成功获取，名字是{app.name}")

    def update_app(self, id: uuid.UUID):
        app = self.app_service.update_app(id)
        return success_message(f"应用已经成功修改，修改的名字是:{app.name}")

    def delete_app(self, id: uuid.UUID):
        app = self.app_service.delete_app(id)
        return success_message(f"应用已经成功删除，id为:{app.id}")

    def debug(self, app_id: UUID):
        """聊天接口"""
        # 1.提取从接口中获取的输入，POST
        req = CompletionReq()
        if not req.validate():
            return validate_error_json(req.errors)

        # 2.创建prompt与记忆
        prompt = ChatPromptTemplate.from_messages([
            ("system", "你是一个强大的聊天机器人，能根据用户的提问回复对应的问题"),
            MessagesPlaceholder("history"),
            ("human", "{query}"),
        ])
        memory = ConversationBufferWindowMemory(
            k=3,
            input_key="query",
            output_key="output",
            return_messages=True,
            chat_memory=FileChatMessageHistory("./storage/memory/chat_history.txt"),
        )

        # 3.创建llm
        llm = ChatOpenAI(model="gpt-3.5-turbo-16k")

        # 4.创建链应用
        chain = RunnablePassthrough.assign(
            history=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
        ) | prompt | llm | StrOutputParser()

        # 5.调用链生成内容
        chain_input = {"query": req.query.data}
        content = chain.invoke(chain_input)
        memory.save_context(chain_input, {"output": content})

        return success_json({"content": content})

    def ping(self):
        raise FailException("数据未找到")

3. 运行流程拆解与基础数据表设计

postgreSql下执行命令行 CREATE EXTENSION "uuid-ossp"

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