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--- |
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title: Personal ChatBot |
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emoji: 💬 |
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colorFrom: yellow |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 5.0.1 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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short_description: Krishna's Persona Chat Bot using Multi RAG network |
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--- |
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# 🧠 Krishna's Personal AI Chatbot |
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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. |
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> ⚡️ Ask me anything about Krishna — skills, experience, goals, or even what tools he used at Virginia Tech. |
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--- |
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## 📌 Features |
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- ✅ **Hybrid Retrieval**: Combines dense vector search (FAISS) + keyword search (BM25) for precise, high-recall chunk selection |
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- 🤖 **LLM-Powered Pipelines**: Uses OpenAI GPT-4o and NVIDIA NIMs (e.g. LLaMA-3, Mixtral) for rewriting, validation, and final answer generation |
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- 🧠 **Memory Module**: Stores user preferences, recent topics, and inferred tone using a structured `KnowledgeBase` schema |
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- 🛠️ **Custom Architecture**: |
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- Query → Rewriting → Hybrid Retriever → Scope Validator → LLM Answer |
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- Fallback humor model (Mixtral) for out-of-scope queries |
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- 🧩 **Document Grounding**: Powered by Krishna’s actual markdown files like `profile.md`, `goals.md`, and `chatbot_architecture.md` |
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- 📊 **Enriched Vector Store**: Chunks include LLM-generated summaries and synthetic queries for better search performance |
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- 🎛️ **Gradio Frontend**: Responsive, markdown-formatted interface for natural, real-time interaction |
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--- |
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## 🏗️ Architecture |
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```text |
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User Query |
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↓ |
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[LLM1] → Rephrase into 3 diverse subqueries |
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↓ |
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Hybrid Retrieval (BM25 + FAISS) |
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↓ |
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[LLM2] → Classify: In-scope or Out-of-scope |
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↓ |
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├─ In-scope → Top-k Chunks → GPT-4o |
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└─ Out-of-scope → Mixtral (funny fallback) |
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↓ |
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Final Answer + Async Memory Update |
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``` |
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--- |
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## 📂 Project Structure |
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``` |
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. |
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├── app.py # Main Gradio app and pipeline logic |
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├── Vector_storing.py # Chunking, LLM-based enrichment, and FAISS store creation |
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├── requirements.txt # Python package dependencies |
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├── faiss_store/ # Saved FAISS vector index |
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├── all_chunks.json # JSON of enriched document chunks |
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├── personal_data/ # Source markdown files (right now excluded) |
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├── README.md |
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``` |
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--- |
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## 🧠 Knowledge Sources |
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All answers are grounded in curated markdown files: |
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| File Name | Description | |
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| ------------------------- | ---------------------------------------------- | |
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| `profile.md` | Krishna’s full technical profile and education | |
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| `goals.md` | Short- and long-term personal goals | |
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| `chatbot_architecture.md` | System-level breakdown of this AI assistant | |
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| `personal_interests.md` | Hobbies, cultural identity, food preferences | |
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| `conversations.md` | Sample queries and expected response tone | |
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--- |
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## 🧪 How It Works |
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1. **User input** is rewritten into subqueries (LLM1) |
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2. **Retriever** fetches relevant chunks using BM25 and FAISS |
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3. **Classifier LLM** decides if results are relevant to Krishna |
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4. **GPT-4o** generates final answer using top-k chunks |
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5. **Memory is updated** asynchronously with every turn |
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--- |
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## 💬 Example Queries |
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- What programming languages does Krishna know? |
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- Tell me about Krishna’s chatbot architecture |
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- Can this chatbot explain Krishna's work at Virginia Tech? |
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- What tools has Krishna used for data engineering? |
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--- |
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## 🚀 Setup & Usage |
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```bash |
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# 1. Clone the repo |
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git clone https://github.com/krishna-creator/krishna-personal-chatbot.git |
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cd krishna-personal-chatbot |
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# 2. Install dependencies |
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pip install -r requirements.txt |
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# 3. Set your API keys (OpenAI, NVIDIA) |
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export OPENAI_API_KEY=... |
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export NVIDIA_API_KEY=... |
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# 4. Launch the chatbot |
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python app.py |
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``` |
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--- |
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## 🔮 Model Stack |
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| Purpose | Model Name | Provider | |
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| ------------------ | ------------------------ | -------- | |
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| Query Rewriting | `phi-3-mini-4k-instruct` | NVIDIA | |
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| Scope Classifier | `llama-3-70b-instruct` | NVIDIA | |
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| Answer Generator | `gpt-4o` | OpenAI | |
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| Fallback Humor LLM | `mixtral-8x22b-instruct` | NVIDIA | |
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--- |
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## 📌 Acknowledgments |
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- Built as part of Krishna's exploration into **LLM orchestration and agentic RAG** |
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- Inspired by LangChain, SentenceTransformers, and NVIDIA RAG Agents Course |
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--- |
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## 📜 License |
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MIT License © Krishna Vamsi Dhulipalla |
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