🦅 PrivateFalcon

PrivateFalcon is a Python script that allows you to locally query documents using the Falcon-7b Language Model (L.L.M) from HuggingFace. This script is designed to work with documents that have been ingested into a VectorStore using the ingest.py file. With PrivateFalcon, you can perform efficient and accurate document retrieval and similarity searches.

🔧 Prerequisites

Before you begin, make sure you have the following prerequisites in place:

• Python 3.x
• A pre-trained Falcon-7b model (Will be installed by the script)
• Documents Placed in the data/ directory
• .env file containing:

DB_DIRECTORY=vectors
EMBEDDINGS_MODEL=all-MiniLM-L6-v2
SOURCE_CHUNKS=<Number of chunks used to create an answer, if lost - put 4>
MAX_NEW_TOKENS=200
CHUNK_SIZE=<The size of each chunk, if lost - put 1000>
CHUNK_OVERLAP=<The overlapping of different chunks, if lost - put 100>

You can put any other embeddings model into that variable.

📥 Installation

1. Clone the repository

git repo clone https://github.com/AdiKsOnDev/PrivateFalcon.git

2. Install the dependencies

pip install -r requirements

📊 Usage

PrivateFalcon is easy to use:

1. Place your documents into the data/ directory.
2. Run:

python ingest.py

3. After creating a VectorStore, run:

python main.py

📂 Directory Structure

PrivateFalcon/
├── main.py             # Ask questions
├── ingest.py           # Script that ingests your documents
├── vectors/            # Directory with ingested documents
├── data/               # Directory with the source documents
├── requirements.txt    # .txt file with all the dependencies

📂 Allowed file extensions

• .csv
• .doc
• .docx
• .enex
• .epub
• .html
• .md
• .odt
• .pdf
• .ppt
• .pptx
• .txt

🤝 Contributing

If you want to contribute to PrivateFalcon, feel free to submit a pull request or make an Issue

📧 Contact

For any questions or issues, please contact me at [email protected]

Happy querying with PrivateFalcon! 🦅🔍