Personal AI Research Assistant (Local)

A 100% local, privacy-focused AI research assistant that can answer questions about your own documents using a Retrieval-Augmented Generation (RAG) pipeline.

All processing — including the database, vector search, and AI inference — happens entirely on your machine. No internet calls, no API keys, no data leaks.

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✨ Features

• 📚 Document Ingestion — Supports PDF (.pdf), Markdown (.md), and Text (.txt) files.
• 🧠 Local Question Answering — Fully local AI inference, no cloud required.
• 🌐 Simple Web Interface — Easy-to-use browser UI.
• 🗣 Text-to-Speech — Convert answers to speech instantly.
• 🔒 Completely Private — Your data never leaves your computer.
• ⚙ Extensible — Modular architecture for adding features easily.

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🛠 Tech Stack

Backend: Python, FastAPI, Uvicorn AI / ML: LangChain, Sentence Transformers (Hugging Face), PyTorch Vector Database: ChromaDB Text-to-Speech: pyttsx3 Frontend: HTML, CSS, JavaScript

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📂 Project Structure

personal-ai-research-assistant/
├── data/                  # Your .pdf, .md, and .txt files
├── models/                # Local AI model file(s)
├── services/
│   ├── ingestion/         # Document loading and database creation
│   ├── langchain_api/     # FastAPI backend
│   ├── tts/               # Text-to-speech logic
│   └── web/               # Frontend (HTML/CSS/JS)
├── .env                   # Configuration file (create this)
├── requirements.txt       # Python dependencies
└── run_all.py             # Main startup script

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🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/YourUsername/personal-ai-research-assistant.git
cd personal-ai-research-assistant

2️⃣ Set Up a Virtual Environment

python -m venv .venv
# Activate (Windows Git Bash)
source .venv/Scripts/activate
# Activate (Linux/Mac)
source .venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Configure Environment Variables

Create a .env file in the project root:

# --- ChromaDB Settings ---
CHROMA_DIR=./chroma_db
CHROMA_COLLECTION_NAME=personal_ai_research_assistant

# --- Embedding Model ---
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# --- Local AI Settings ---
LLM_MODEL_PATH=./models/

5️⃣ Ingest Your Documents

Place .pdf, .txt, or .md files into data/. Then run:

python services/ingestion/ingest.py

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▶ Running the Application

python run_all.py

This starts Uvicorn at http://0.0.0.0:8000.

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💡 Usage

Web Interface (Recommended)

1. Open services/web/index.html in your browser.
2. Type a question and click Ask.
3. Click Speak Answer to hear it aloud.

(Tip: In VS Code, right-click index.html → Open with Live Server for instant reload.)

API

Example using curl:

curl -X POST "http://127.0.0.1:8000/ask" \
-H "Content-Type: application/json" \
-d '{"query": "Summarize the main points of the research documents."}'