1.9 KiB
1.9 KiB
privateGPT with Llama 2 Uncensored
Note: this example is a simplified version of PrivateGPT that works with Llama 2 Uncensored.
Setup
Optionally set up a virtual environment:
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
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),