Chat with Structured Data using LangChain and PandasAI
AI Summary
Summary: SQL Database and Pandas DataFrame Agents with Langchain and Pandas AI
SQL Database Agent with Langchain
- Introduction
- SQL database agent interacts with SQL databases using natural language.
- Requires installation of necessary packages.
- Setup
- Import modules, load environment variables, and set up OpenAI API key.
- Initialize an LLM with GPT-3.5 turbo model at temperature 0.7.
- Use Chinook database for examples.
- Usage
- Connect to the database and initialize toolkit with OpenAI model.
- Create SQL database agent with verbosity and agent type settings.
- Run natural language queries to interact with database tables.
- Examples include listing total sales per country and counting tracks in playlists.
Pandas DataFrame Agent with Langchain
- Introduction
- Pandas DataFrame agent performs data analysis tasks using natural language.
- Setup
- Import necessary libraries and load Titanic dataset into a DataFrame.
- Usage
- Create Pandas DataFrame agent with similar parameters to SQL agent.
- Run queries such as counting rows and finding people with more than three siblings.
- Utilizes Python REPL AST tool to generate and execute code.
Pandas AI Library
- Introduction
- Enhances pandas with generative AI capabilities for conversational data interaction.
- Usage
- Install Pandas AI library and import necessary components.
- Create Smart DataFrame and use LLM to ask data-related questions.
- Perform operations like plotting charts and summing GDP of countries.
- Smart Data Lake allows working with multiple DataFrames.
- Configuration
- Config parameters include log saving, verbosity, privacy enforcement, chart saving, caching, error correction, and custom prompts.
- Callbacks can be used to interact with the code generation process.
Conclusion
- Explored Langchain’s SQL database and Pandas DataFrame agents.
- Demonstrated Pandas AI library features.
- Encouraged viewers to like, share, and subscribe for more tech insights.