ollama/examples/privategpt

privateGPT with Llama 2 Uncensored

Note: this example is a simplified version of PrivateGPT that works with Llama 2 Uncensored.

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 llama2-uncensored

Getting WeWork's latest quarterly report

mkdir source_documents
curl https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf  -o source_documents/wework.pdf

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

Ask questions

python privateGPT.py

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),