PrivateGPT with Llama 2 uncensored

example.video.mp4

Note: this example is a slightly modified version of PrivateGPT using models such as Llama 2 Uncensored. All credit for PrivateGPT goes to Iván Martínez who is the creator of it, and you can find his GitHub repo here.

Setup

Set up a virtual environment (optional):

python3 -m venv .venv
source .venv/bin/activate

Install the Python dependencies:

pip install -r requirements.txt

Pull the model you'd like to use:

ollama pull zephyr:7b-alpha-q4_K_M

Ask questions

python privateGPT.py --hide-source

Enter a query: How many locations does WeWork have?

> Answer (took 17.7 s.):
As of June 2023, WeWork has 777 locations worldwide, including 610 Consolidated Locations (as defined in the section entitled Key Performance Indicators).

Try a different model:

ollama pull llama2:13b
MODEL=llama2:13b python privateGPT.py

Adding more files

Put any and all your files into the source_documents directory

The supported extensions are:

• .csv: CSV,
• .docx: Word Document,
• .doc: Word Document,
• .enex: EverNote,
• .eml: Email,
• .epub: EPub,
• .html: HTML File,
• .md: Markdown,
• .msg: Outlook Message,
• .odt: Open Document Text,
• .pdf: Portable Document Format (PDF),
• .pptx : PowerPoint Document,
• .ppt : PowerPoint Document,
• .txt: Text file (UTF-8),

Ingesting files

python ingest.py

Output should look like this:

Creating new vectorstore
Loading documents from source_documents
Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00,  1.73s/it]
Loaded 1 new documents from source_documents
Split into 90 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Using embedded DuckDB with persistence: data will be stored in: db
Ingestion complete! You can now run privateGPT.py to query your documents