ollama/examples/privategpt

privateGPT with Llama 2 Uncensored

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

Setup

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

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

Ingesting data

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).

Adding your own data

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),