SQL（SQLAlchemy）

Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is particularly useful in handling structured data, i.e., data incorporating relations among entities and variables.

SQLAlchemy is an open-source SQL toolkit and object-relational mapper (ORM) for the Python programming language released under the MIT License.

本笔记本介绍了 SQLChatMessageHistory 类，该类允许将聊天历史存储在任何由 SQLAlchemy 支持的数据库中。

请注意，要与其他数据库（而非 SQLite）一起使用它，您需要安装相应的数据库驱动程序。

设置

集成位于 langchain-community 包中，因此我们需要安装它。我们还需要安装 SQLAlchemy 包。

pip install -U langchain-community SQLAlchemy langchain-openai

设置LangSmith 以获得一流的可观测性也很有帮助（但不是必需的）。

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

使用

要使用存储，您只需要提供两样东西：

1. 会话 ID - 会话的唯一标识符，例如用户名、电子邮件、聊天 ID 等。
2. 连接字符串 - 一个指定数据库连接的字符串。它将传递给 SQLAlchemy 的 create_engine 函数。

from langchain_community.chat_message_histories import SQLChatMessageHistory

chat_message_history = SQLChatMessageHistory(
    session_id="test_session", connection_string="sqlite:///sqlite.db"
)

chat_message_history.add_user_message("Hello")
chat_message_history.add_ai_message("Hi")

API 参考：SQLChatMessageHistory

chat_message_history.messages

[HumanMessage(content='Hello'), AIMessage(content='Hi')]

链式调用

我们可以轻松地将此消息历史类与LCEL Runnables结合

要做到这一点，我们需要使用 OpenAI，因此需要安装它。

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI

API 参考：ChatPromptTemplate | MessagesPlaceholder | RunnableWithMessageHistory | ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{question}"),
    ]
)

chain = prompt | ChatOpenAI()

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: SQLChatMessageHistory(
        session_id=session_id, connection_string="sqlite:///sqlite.db"
    ),
    input_messages_key="question",
    history_messages_key="history",
)

# This is where we configure the session id
config = {"configurable": {"session_id": "<SESSION_ID>"}}

chain_with_history.invoke({"question": "Hi! I'm bob"}, config=config)

AIMessage(content='Hello Bob! How can I assist you today?')

chain_with_history.invoke({"question": "Whats my name"}, config=config)

AIMessage(content='Your name is Bob! Is there anything specific you would like assistance with, Bob?')