Inspired from (https://github.com/imartinez/privateGPT) and (https://github.com/jmorganca/ollama)

Step 1: Step a Virtual Environment

Step 2: Install the Requirements

pip install -r requirements.txt

Step 3: Pull the models (if you already have models loaded in Ollama, then not required)

Make sure to have Ollama running on your system from https://ollama.ai

ollama pull mistral

Step 4: put your files in the source_documents folder after making a directory

mkdir source_documents

Step 5: Ingest the files (use python3 if on mac)

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,  1.99s/it]
Loaded 235 new documents from source_documents
Split into 1268 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Ingestion complete! You can now run privateGPT.py to query your documents

Step 6: Run this command (use python3 if on mac)

python privateGPT.py

Play with your docs

Enter a query: How many locations does WeWork have?

Try with a different model:

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

Add 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),