How to Connect Langchain Database Agent with SqlServer – Langchain

The Problem:

It is necessary to connect Langchain’s Database Agent with a Microsoft SQL Server database. However, the documentation solely focuses on establishing a connection with SQLite, and no guidance is provided for SQL Server integration. Attempts to replace the SQLite connection code with the appropriate SQL Server connection string resulted in an error: ‘sqlalchemy.exc.InterfaceError: (pyodbc.InterfaceError) (‘IM002’, ‘[IM002] [Microsoft][ODBC Driver Manager] Data source name not found and no default driver specified (0) (SQLDriverConnect)’) ‘. Provide a comprehensive solution that addresses this error and demonstrates how to successfully connect Langchain’s Database Agent with a SQL Server database.

The Solutions:

Solution 1: Create connection with SQL Server Database

To connect your Langchain Database Agent with a SQL Server, you can use the following steps:

1. Obtain Database Credentials:
   
   Gather the necessary credentials, including the database server name, database name, username, and password, to establish a connection to your SQL Server database.
2. Construct Connection URI:
   
   Construct the connection URI using the mssql+pyodbc format. The general syntax is as follows:
   
   
uri = "mssql+pyodbc://DBUsername:DBPassword@ServerName:Port/DBName?driver=ODBC+Driver+17+for+SQL+Server"

   
   Replace DBUsername, DBPassword, ServerName, Port, and DBName with your actual values. Ensure that the ODBC driver is properly installed on your system.
3. Instantiate SQLDatabase Object:
   
   Instantiate the SQLDatabase object using the SQLDatabase.from_uri() method. Pass the constructed connection URI as an argument to establish the connection.
   
   
db = SQLDatabase.from_uri(uri)

4. Utilize SQLDatabaseToolkit:
   
   Instantiate the SQLDatabaseToolkit with the connected SQLDatabase object and the desired Language Model (LLM). This toolkit provides the necessary functionalities to interact with the SQL Server database.
   
   
toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))

5. Integrate with Agent:
   
   Create an instance of the AgentExecutor and incorporate the SQLDatabaseToolkit. This enables seamless data retrieval and manipulation from the SQL Server database within your Langchain Agent.
   
   
agent_executor = AgentExecutor(agent_type=AgentType.CHAT, toolkit=toolkit)


By following these steps, you can successfully connect your Langchain Database Agent to a SQL Server database, enabling data access and manipulation from within your agent.

Video Explanation:

The following video, titled "LangChain SQL Database agent with OpenAI to query PostgreSQL ...", provides additional insights and in-depth exploration related to the topics discussed in this post.

In this video we discover how to set up the LangChain SQL Database Agent with PostgreSQL and OpenAI using LangChain.