网页设计网站大全网络营销的作用
分类目录：《自然语言处理从入门到应用》总目录
Cassandra聊天消息记录
Cassandra是一种分布式数据库，非常适合存储大量数据，是存储聊天消息历史的良好选择，因为它易于扩展，能够处理大量写入操作。
# List of contact points to try connecting to Cassandra cluster.
contact_points = ["cassandra"]from langchain.memory import CassandraChatMessageHistorymessage_history = CassandraChatMessageHistory(contact_points=contact_points, session_id="test-session"
)message_history.add_user_message("hi!")message_history.add_ai_message("whats up?")
message_history.messages
[HumanMessage(content='hi!', additional_kwargs={}, example=False),
AIMessage(content='whats up?', additional_kwargs={}, example=False)]
DynamoDB聊天消息记录
首先确保我们已经正确配置了AWS CLI，并再确保我们已经安装了boto3。接下来，创建我们将存储消息 DynamoDB表：
import boto3# Get the service resource.
dynamodb = boto3.resource('dynamodb')# Create the DynamoDB table.
table = dynamodb.create_table(TableName='SessionTable',KeySchema=[{'AttributeName': 'SessionId','KeyType': 'HASH'}],AttributeDefinitions=[{'AttributeName': 'SessionId','AttributeType': 'S'}],BillingMode='PAY_PER_REQUEST',
)# Wait until the table exists.
table.meta.client.get_waiter('table_exists').wait(TableName='SessionTable')# Print out some data about the table.
print(table.item_count)
输出：
0
DynamoDBChatMessageHistory
from langchain.memory.chat_message_histories import DynamoDBChatMessageHistoryhistory = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="0")
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
输出：
[HumanMessage(content='hi!', additional_kwargs={}, example=False),
AIMessage(content='whats up?', additional_kwargs={}, example=False)]
使用自定义端点URL的DynamoDBChatMessageHistory
有时候在连接到AWS端点时指定URL非常有用，比如在本地使用Localstack进行开发。对于这种情况，我们可以通过构造函数中的endpoint_url参数来指定URL。
from langchain.memory.chat_message_histories import DynamoDBChatMessageHistoryhistory = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="0", endpoint_url="http://localhost.localstack.cloud:4566")
Agent with DynamoDB Memory
from langchain.agents import Tool
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.utilities import PythonREPL
from getpass import getpassmessage_history = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="1")
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=message_history, return_messages=True)
python_repl = PythonREPL()# You can create the tool to pass to an agent
tools = [Tool(name="python_repl",description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",func=python_repl.run
)]
llm=ChatOpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain.run(input="Hello!")
日志输出：
> Entering new AgentExecutor chain...
{"action": "Final Answer","action_input": "Hello! How can I assist you today?"
}> Finished chain.
输出：
'Hello! How can I assist you today?'
输入：
agent_chain.run(input="Who owns Twitter?")
日志输出：
> Entering new AgentExecutor chain...
{"action": "python_repl","action_input": "import requests\nfrom bs4 import BeautifulSoup\n\nurl = 'https://en.wikipedia.org/wiki/Twitter'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.content, 'html.parser')\nowner = soup.find('th', text='Owner').find_next_sibling('td').text.strip()\nprint(owner)"
}
Observation: X Corp. (2023–present)Twitter, Inc. (2006–2023)Thought:{"action": "Final Answer","action_input": "X Corp. (2023–present)Twitter, Inc. (2006–2023)"
}> Finished chain.
输出：
'X Corp. (2023–present)Twitter, Inc. (2006–2023)'
输入：
agent_chain.run(input="My name is Bob.")
日志输出：
> Entering new AgentExecutor chain...
{"action": "Final Answer","action_input": "Hello Bob! How can I assist you today?"
}> Finished chain.
输出：
'Hello Bob! How can I assist you today?'
输入：
agent_chain.run(input="Who am I?")
日志输出：
> Entering new AgentExecutor chain...
{"action": "Final Answer","action_input": "Your name is Bob."
}> Finished chain.
输出：
'Your name is Bob.'
Momento聊天消息记录
本节介绍如何使用Momento Cache来存储聊天消息记录，我们会使用MomentoChatMessageHistory类。需要注意的是，默认情况下，如果不存在具有给定名称的缓存，我们将创建一个新的缓存。我们需要获得一个Momento授权令牌才能使用这个类。这可以直接通过将其传递给momento.CacheClient实例化，作为MomentoChatMessageHistory.from_client_params的命名参数auth_token，或者可以将其设置为环境变量MOMENTO_AUTH_TOKEN。
from datetime import timedelta
from langchain.memory import MomentoChatMessageHistorysession_id = "foo"
cache_name = "langchain"
ttl = timedelta(days=1)
history = MomentoChatMessageHistory.from_client_params(session_id, cache_name,ttl,
)history.add_user_message("hi!")history.add_ai_message("whats up?")
history.messages
输出：
[HumanMessage(content='hi!', additional_kwargs={}, example=False),
AIMessage(content='whats up?', additional_kwargs={}, example=False)]
MongoDB聊天消息记录
本节介绍如何使用MongoDB存储聊天消息记录。MongoDB是一个开放源代码的跨平台文档导向数据库程序。它被归类为NoSQL数据库程序，使用类似JSON的文档，并且支持可选的模式。MongoDB由MongoDB Inc.开发，并在服务器端公共许可证（SSPL）下许可。
# Provide the connection string to connect to the MongoDB database
connection_string = "mongodb://mongo_user:password123@mongo:27017"
from langchain.memory import MongoDBChatMessageHistorymessage_history = MongoDBChatMessageHistory(connection_string=connection_string, session_id="test-session")message_history.add_user_message("hi!")message_history.add_ai_message("whats up?")
message_history.messages
输出：
[HumanMessage(content='hi!', additional_kwargs={}, example=False),
AIMessage(content='whats up?', additional_kwargs={}, example=False)]
Postgres聊天消息历史记录
本节介绍了如何使用 Postgres 来存储聊天消息历史记录。
from langchain.memory import PostgresChatMessageHistoryhistory = PostgresChatMessageHistory(connection_string="postgresql://postgres:mypassword@localhost/chat_history", session_id="foo")history.add_user_message("hi!")history.add_ai_message("whats up?")
history.messages
Redis聊天消息历史记录
本节介绍了如何使用Redis来存储聊天消息历史记录。
from langchain.memory import RedisChatMessageHistoryhistory = RedisChatMessageHistory("foo")history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
输出：
[AIMessage(content='whats up?', additional_kwargs={}),
HumanMessage(content='hi!', additional_kwargs={})]
参考文献：
[1] LangChain官方网站：https://www.langchain.com/
[2] LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发：https://www.langchain.com.cn/
[3] LangChain中文网 - LangChain 是一个用于开发由语言模型驱动的应用程序的框架：http://www.cnlangchain.com/