Practical overview
When it fits
Use it when you want to build a code-based Q&A workflow over a defined set of web pages, documents or YouTube material, rather than use a ready-made chat website.
Who it is for
- Python developers building question-answer workflows over selected web or local sources.
- Teams that want to retrieve relevant passages from PDFs, DOCX or PPTX files, or YouTube transcripts and audio.
Documented uses
- Extract source text from a website, document or video.
- Compare a question embedding with stored knowledge embeddings to find relevant context.
- Pass retrieved context to a language model and return an answer through code.
Requirements & limits
- The README documents Python installation through PyPI or the repository and requires the spaCy English parsing model.
- The illustrated web-scraping workflow configures an OpenAI API key for answer generation; the embedding extractor can be set to Hugging Face.
- The README lists FAISS support and Dockerization; it does not describe a web interface.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Knowledge Gpt from PyPI
Install the library and the documented spaCy parsing model.
Before you start
- A terminal with Python and pip.
- Internet access to install the package and language model.
Steps
Run the command below.
pip install knowledgegptRun the command below.
python3 -m spacy download en_core_web_sm
Expected outcome
The package and the README's English parsing model are installed.
Guide limitations
- This covers setup only; using an extractor and generating answers requires further configuration.
- The README's OpenAI example uses an API key for answer generation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- geeks-of-data/knowledge-gpt · Official homepage title · retained in its source language

