metadata

title: Personal ChatBot
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.0.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Krishna's Persona Chat Bot using Multi RAG network

🧠 Krishna's Personal AI Chatbot

A memory-grounded, retrieval-augmented AI assistant built with LangChain, FAISS, BM25, and Llama3 — personalized to Krishna Vamsi Dhulipalla’s career, projects, and technical profile.

⚡️ Ask me anything about Krishna — skills, experience, goals, or even what tools he used at Virginia Tech.

📌 Features

✅ Hybrid Retrieval: Combines dense vector search (FAISS) + keyword search (BM25) for precise, high-recall chunk selection
🤖 LLM-Powered Pipelines: Uses OpenAI GPT-4o and NVIDIA NIMs (e.g. LLaMA-3, Mixtral) for rewriting, validation, and final answer generation
🧠 Memory Module: Stores user preferences, recent topics, and inferred tone using a structured KnowledgeBase schema
🛠️ Custom Architecture:
- Query → Rewriting → Hybrid Retriever → Scope Validator → LLM Answer
- Fallback humor model (Mixtral) for out-of-scope queries
🧩 Document Grounding: Powered by Krishna’s actual markdown files like profile.md, goals.md, and chatbot_architecture.md
📊 Enriched Vector Store: Chunks include LLM-generated summaries and synthetic queries for better search performance
🎛️ Gradio Frontend: Responsive, markdown-formatted interface for natural, real-time interaction

🏗️ Architecture

User Query
   ↓
[LLM1] → Rephrase into 3 diverse subqueries
   ↓
Hybrid Retrieval (BM25 + FAISS)
   ↓
[LLM2] → Classify: In-scope or Out-of-scope
   ↓
   ├─ In-scope → Top-k Chunks → GPT-4o
   └─ Out-of-scope → Mixtral (funny fallback)
   ↓
Final Answer + Async Memory Update

📂 Project Structure

.
├── app.py                      # Main Gradio app and pipeline logic
├── Vector_storing.py          # Chunking, LLM-based enrichment, and FAISS store creation
├── requirements.txt           # Python package dependencies
├── faiss_store/               # Saved FAISS vector index
├── all_chunks.json            # JSON of enriched document chunks
├── personal_data/             # Source markdown files (right now excluded)
├── README.md

🧠 Knowledge Sources

All answers are grounded in curated markdown files:

File Name	Description
`profile.md`	Krishna’s full technical profile and education
`goals.md`	Short- and long-term personal goals
`chatbot_architecture.md`	System-level breakdown of this AI assistant
`personal_interests.md`	Hobbies, cultural identity, food preferences
`conversations.md`	Sample queries and expected response tone

🧪 How It Works

User input is rewritten into subqueries (LLM1)
Retriever fetches relevant chunks using BM25 and FAISS
Classifier LLM decides if results are relevant to Krishna
GPT-4o generates final answer using top-k chunks
Memory is updated asynchronously with every turn

💬 Example Queries

What programming languages does Krishna know?
Tell me about Krishna’s chatbot architecture
Can this chatbot explain Krishna's work at Virginia Tech?
What tools has Krishna used for data engineering?

🚀 Setup & Usage

# 1. Clone the repo
git clone https://github.com/krishna-creator/krishna-personal-chatbot.git
cd krishna-personal-chatbot

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set your API keys (OpenAI, NVIDIA)
export OPENAI_API_KEY=...
export NVIDIA_API_KEY=...

# 4. Launch the chatbot
python app.py

🔮 Model Stack

Purpose	Model Name	Provider
Query Rewriting	`phi-3-mini-4k-instruct`	NVIDIA
Scope Classifier	`llama-3-70b-instruct`	NVIDIA
Answer Generator	`gpt-4o`	OpenAI
Fallback Humor LLM	`mixtral-8x22b-instruct`	NVIDIA

📌 Acknowledgments

Built as part of Krishna's exploration into LLM orchestration and agentic RAG
Inspired by LangChain, SentenceTransformers, and NVIDIA RAG Agents Course

Spaces:

krishnadhulipalla
/

Personal_ChatBot

Sleeping