
Advanced-QA-and-RAG-Series
This repository contains advanced LLM-based chatbots for Q&A using LLM agents, and Retrieval Augmented Generation (RAG) and with different databases. (VectorDB, GraphDB, SQLite, CSV, XLSX, etc.)
Stars: 143

This repository contains advanced LLM-based chatbots for Retrieval Augmented Generation (RAG) and Q&A with different databases. It provides guides on using AzureOpenAI and OpenAI API for each project. The projects include Q&A and RAG with SQL and Tabular Data, and KnowledgeGraph Q&A and RAG with Tabular Data. Key notes emphasize the importance of good column names, read-only database access, and familiarity with query languages. The chatbots allow users to interact with SQL databases, CSV, XLSX files, and graph databases using natural language.
README:
This repository contains advanced LLM-based chatbots for Retrieval Augmented Generation (RAG) and Q&A with different databases. (VectorDB, GraphDB, SQLite, CSV, XLSX, etc.). The repository provides guide on using both AzureOpenAI and OpenAI API for each project.
- [x] AgentGraph-Intelligent-Q&A-and-RAG-System
- [x] Q&A-and-RAG-with-SQL-and-TabularData
- [x] KnowledgeGraph-Q&A-and-RAG-with-TabularData
Project-folder
├── README.md <- The top-level README for developers using this project.
├── HELPER.md <- Contains extra information that might be useful to know for executing the project.
├── .env <- dotenv file for local configuration.
├── .here <- Marker for project root.
├── configs <- Holds yml files for project configs
├── explore <- Contains my exploration notebooks and the teaching material for YouTube videos.
├── data <- Contains the sample data for the project.
├── src <- Contains the source code(s) for executing the project.
| └── utils <- Contains all the necessary project modules.
└── images <- Contains all the images used in the user interface and the README file.
NOTE: This is the general structure of the projects, however there might be small changes duo to the specific needs of each project.
Key Note 1: All the project uses Azure OpenAI or OpenAI models. So, to use OpenAI API directly, just change the credentials and switch the models completions.
Key Note 2 : When we interact with databases using LLM agents, good informative column names can help the agents to navigate easier through the database.
Key Note 3: When we interact with databases using LLM agents, remember to NOT use the database with WRITE privileges. Use only READ and limit the scope. Otherwise your user can manupulate the data (e.g ask your chain to delete data).
Key Note 4: Familiarity with database query languages such as Pandas for Python, SQL, and Cypher can enhance the user's ability to ask more better questions and have a richer interaction with the graph agent.
This project demonstrates how to build an agentic system using Large Language Models (LLMs) that can interact with multiple databases and utilize various tools. It highlights the use of SQL agents to efficiently query large databases. The key frameworks used in this project include OpenAI, LangChain, LangGraph, LangSmith, and Gradio. The end product is an end-to-end chatbot, designed to perform these tasks, with LangSmith used to monitor the performance of the agents.
Features:
- Handles unstructured data with RAG and structured data with SQL agents.
- Built-in web search when needed.
- Automatically chooses the best tool for each task.
- Scalable for large databases.
- Easily connects to multiple databases.
Databases:
- To be added
YouTube video:: - To be added
Q&A-and-RAG-with-SQL-and-TabularData
is a chatbot project that utilizes GPT 3.5, Langchain, SQLite, and ChromaDB and allows users to interact (perform Q&A and RAG) with SQL databases, CSV, and XLSX files using natural language.
Features:
- Chat with SQL data.
- Chat with preprocessed CSV and XLSX data.
- Chat with uploaded CSV and XSLX files during the interaction with the user interface.
- RAG with Tabular datasets.
Databases:
YouTube video: Link
KnowledgeGraph-Q&A-and-RAG-with-TabularData
is a chatbot project that utilizes knowledge graph, GPT 3.5, Langchain graph agent, and Neo4j graph database and allows users to interact (perform Q&A and RAG) with Tabular databases (CSV, XLSX, etc.) using natural language. This project also demonstrates an approach for constructing the knowledge graph from unstructured data by leveraging LLMs.
Features:
- Chat with a graphDB created from tabular data.
- RAG with a graphDB created from tabular data.
Databases:
YouTube video:: Link
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