SQL-Agent

Overview

Chat with your database with natural language. Inspired by the pattern in ai world chat with your 'X' where X could be anything you desire eg. your database, your application, your spreadsheet, your pdfs etc...

Following is the process that SQL agent follows.

1. Question is passed to LLM to generate query.
2. LLM generate the SQL query.
3. Query is executed using db.
4. Query result is passed to LLM again for making proper response.

This project is build to top of langchain and langserve modules.

Here is the general architecture (stolen from langchain website)

⚠️ Security Note ⚠️

This agent is not production ready and may do unexpected results. How ever you can experiment it with your local database. You can control agent behaviour using prompt engineering by limiting access to certain DML statements eg. INSERT, UPDATE, DELETE, DROP, etc... Or you can use SQL user-connection with limited privileges to certain tables or views only.

There still some work is required on this project that allow SQL agent to use tools to add advance question and reasioning capabilities.

Installation

Install the LangChain CLI if you haven't already

# Global installation of langchain cli
# Used for scafolding boilerplate for making agents
pip install -U langchain-cli

# Create virtual env
python -m venv venv

# Activate virtual env windows
venv/Scripts/activate

# Active virtual env unix
source venv/bin/activate

# For Unix users please ensure you have mysql-client and pkg-config installed already 
# If not follow the steps mentioned here https://github.com/PyMySQL/mysqlclient

# Install dependencies
pip install -r requirements.txt

# Copy environment file
cp sample.env .env

# > Make sure you have added your environment config in .env file

# Run agent locally
langchain serve

# Now explore swagger and try your queries using swagger
start http://localhost:8000/docs

# Or you can experiment using langchain playground
start http://localhost:8000/agent/playground

Adding additional packages (optional)

# adding packages from 
# https://github.com/langchain-ai/langchain/tree/master/templates
langchain app add $PROJECT_NAME

# adding custom GitHub repo packages
langchain app add --repo $OWNER/$REPO
# or with whole git string (supports other git providers):
# langchain app add git+https://github.com/hwchase17/chain-of-verification

# with a custom api mount point (defaults to `/{package_name}`)
langchain app add $PROJECT_NAME --api_path=/my/custom/path/rag

Note: you remove packages by their api path

langchain app remove my/custom/path/rag

Setup LangSmith (Optional)

LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

Launch LangServe

langchain serve

Running in Docker

This project folder includes a Dockerfile that allows you to easily build and host your LangServe app.

Building the Image

To build the image, you simply:

docker build . -t sql-agent

If you tag your image with something other than sql-agent, note it for use in the next step.

Running the Image Locally

To run the image, you'll need to include any environment variables necessary for your application.

In the below example, we inject the OPENAI_API_KEY and DB_URL environment variable with the value set in my local environment ($OPENAI_API_KEY) and ($DB_URL)

We also expose port 8080 with the -p 8080:8080 option.

docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -e DB_URL=$DB_URL -p 8080:8080 sql-agent

License

MIT - Free to use, modify or publish without any warranty

References

• Langchain SQL Agent Tutorial.
• Building Rest apis using langchain.
• Prompt Engineering.
• Lang Graph.
• Guide to Langchain Graps.
• Langchain Hub for finding engineered prompts.
• Instoduction to Langserve.