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 llama2-uncensored

Getting WeWork's latest quarterly earnings report (10-Q)

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),

albinvar / langchain-python-rag-privategpt-ollama