How to Generate SQL Scripts with Proper Column Specifications Using SQL LangChain Agent?

Below is a list of potential solutions you can try:

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1. Ensure the Agent Is Schema-Aware

LangChain’s SQL agent is designed to work by first examining the database schema via tools (e.g., the list and info tools) before generating SQL queries. In your case, a plain “Describe” prompt might cause the agent to generate an incomplete query like “SELECT FROM reports_details …” because it hasn’t been provided enough information about the table’s columns. In the tutorials, the agent is instructed to first list the tables and then retrieve the schema before generating an answer.

How to address:
• Invoke the schema lookup tools (for example, using the sql_db_schema tool through the agent’s normal step-by-step process) so that the agent is aware of which columns exist.
• Alternatively, modify your prompt to be more explicit, e.g., “List the columns for the reports_details table” rather than “Describe …” so that the agent knows you expect a column list.

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2. Check Your Agent’s Configuration & System Prompt

The provided SQL agent system prompt in LangChain includes detailed instructions regarding querying only relevant columns and enforcing a sequence that includes schema retrieval prior to querying. Ensure you have configured your agent with the appropriate system message (or instructive prefix) that guides it to first examine the schema. This can prevent queries with missing column specifications.

For example, the LangChain documentation uses a system prompt like:

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SQL_PREFIX = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table; only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

To start you should ALWAYS look at the tables in the database to see what you can query.
Do NOT skip this step.
Then you should query the schema of the most relevant tables."""

Make sure your agent is set up similarly so that it follows a structured process.

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3. Validate Placeholder Usage

In your code, the agent is invoked as follows:

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response = agent_executor.invoke(f"Describe the {TABLE_NAME} table")

Verify that the variable TABLE_NAME is defined and correctly matches the actual table name in your SQLite database (for instance, "reports_details"). An undefined or mismatched variable could lead to unexpected query outputs.

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4. Consider Changing the Input Prompt

If your application needs a human-friendly prompt (like “Describe …”), ensure the agent’s chain of thought includes schema introspection. For example, you could:

• First have the agent list available tables using a command like List available tables
• Then query the schema using something such as What are the columns in reports_details?
• Finally, combine the retrieved schema information to answer the “describe” question fully.

This chaining mirrors the examples in the LangChain SQL agent tutorials.

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Summary

The incomplete query you're seeing is commonly due to the agent not having the necessary schema context when generating the SQL query. Adjust your prompt to trigger the lookup of schema details, ensure your system prompt is configured as shown in the documentation, and verify that any placeholder values such as TABLE_NAME are defined correctly.

Follow these steps based on the LangChain SQL agent documentation and tutorials, and you should see the issue resolved.