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Bert Faiss Qa System

Bert Faiss Qa System is a Python application for asking questions about a document collection. It uses DistilBERT to create dense embeddings for documents and questions, Faiss to retrieve similar document vectors, and extractive answering to present relevant passages through a Streamlit web interface.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bert Faiss Qa System — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to explore a self-hosted Q&A workflow that combines DistilBERT document embeddings, Faiss similarity search and extractive answers.

Who it is for

  • Developers setting up a self-hosted question-answering interface for a document collection.
  • Teams or researchers exploring dense-vector retrieval and extractive Q&A with DistilBERT and Faiss.

Documented uses

  • Search document collections with natural-language questions.
  • Retrieve passages whose embeddings are similar to a question.
  • Present extracted answer passages in a web interface.
Before you choose

Requirements & limits

The README describes running the application locally with Streamlit. It lists Python 3.x, PyTorch, Transformers, Faiss and Streamlit as requirements.

  • The documented answer flow retrieves and extracts relevant passages rather than describing a fully generative answer system.
  • The setup requires preparing both a DistilBERT model and a Faiss index before opening the interface.
  • The README does not explain how documents are supplied to or updated in the index.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and open the local document Q&A interface

Prepare the DistilBERT model and Faiss index, then start the Streamlit interface.

Before you start

  • Python 3.x
  • PyTorch
  • Transformers
  • Faiss
  • Streamlit for the web interface

Steps

  1. Clone the repository and install its requirements with pip install -r requirements.txt.

  2. Run python3 trainer.py to train and download the DistilBERT model.

  3. Run python3 faiss_index.py to build the Faiss vector database.

  4. Start the interface with streamlit run app.py and open http://localhost:8501/ in a browser.

Expected outcome

A locally served Streamlit Q&A interface using the prepared model and Faiss index.

Guide limitations

  • The README does not specify how document files are added or configured.
  • The README does not describe the implementation of its closed generative Q&A demo.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
VuBacktracking/bert-faiss-qa-system · Official homepage title · retained in its source language
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