From documents to decisions — powered by AI, secured locally.
ChatBot AI is a early stage startup comprising a powerful and private document question-answering app powered by LLaMA 2 and LangChain. Upload your PDF, DOCX, or TXT files, and ask intelligent questions. All data is processed locally to maintain maximum privacy.
- 📄 Supports PDF, DOCX, and TXT files
- 🧠 Local LLaMA 2 model inference (no cloud dependencies)
- 🔍 Intelligent search & similarity-based retrieval
- 📦 Built with Streamlit for an interactive UI
- 🖼️ Custom dark-themed background support
- 🧵 Efficient multi-threaded document parsing
- Python 3.9+
- Download the LLaMA 2 model from HERE!
GEN_AI_FINAL_PROJECT/
├── Gen_AI.py # Main application script
├── HuggingFaceEmbeddings.ipynb # Jupyter notebook for embedding exploration
├── requirements.txt # Project dependencies
├── evaluation_log.json # Evaluation log file
│
├── Model/ # LLaMA model folder
│ └── llama-2-7b-chat.Q4_K_M.gguf # Quantized LLaMA model file
│
├── models/
│ └── sentence_transformers/
│ └── all-MiniLM-L6-v2/
│ ├── 1_Pooling/
│ ├── onnx/
│ ├── openvino/
│ ├── .cache/
│ ├── config_sentence_transformers.json
│ ├── config.json
│ ├── data_config.json
│ ├── model.safetensors
│ ├── modules.json
│ ├── pytorch_model.bin
│ ├── rust_model.ot
│ ├── sentence_bert_config.json
│ ├── special_tokens_map.json
│ ├── tf_model.h5
│ ├── tokenizer_config.json
│ ├── tokenizer.json
│ ├── vocab.txt
│ ├── README.md
│ └── train_script.py # Optional training script
│
├── db/
│ └── faiss/
│ ├── index.faiss # FAISS index file
│ └── index.pkl # Serialized metadata or index data
│
├── faiss_index/ # FAISS vector store (auto-generated)
│ ├── index.faiss
│ └── index.pkl
│
├── evaluation_logs/ # Auto-generated logs
│ └── evaluation_results.json
│
├── Images/
│ ├── 767.jpg
│ ├── ChatGPT_Image.png
│ └── freepik__adjust__9850.jpeg
│
├── Documents/
│ ├── Advanced_Facts_Octopus.pdf
│ ├── Advanced_Velociraptor_Text.txt
│ ├── Dire_Wolf.docx
│ └── Saravanan Data Science Resume ATS match without emoji.pdf
│
└── env/ # Python virtual environment
├── Include/
├── Lib/
├── Scripts/
└── pyvenv.cfg
# Clone the repo
git clone https://github.com/yourusername/AskDocs_GEN-AI.git
cd AskDocs_GEN-AI
# (Optional) Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run
streamlit run app.py- Upload one or more PDF, DOCX, or TXT documents.
- Click Start The Fun!
- Ask your question in natural language.
- Get answers with cited document sources.
- streamlit
- PyMuPDF
- python-docx
- docx2txt
- requests
- numpy
- langchain
- sentence-transformers
- nltk
- rouge-score
- ctransformers
- faiss-cpu
- huggingface-hub
- You need to download and manually place the LLaMA model.
- Ensure GPU support is configured if needed (set gpu_layers accordingly).
- Image background is customizable — make sure the image path is valid.