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RAG & Knowledge Systems

Pdf Qa Rag System

Pdf Qa Rag System is a Streamlit application that lets users upload a PDF and ask questions about its contents. It extracts and chunks text, indexes it in a vector store, retrieves relevant passages, and uses an Ollama language model to generate context-based answers.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pdf Qa Rag 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 have a PDF to investigate and can run a Python application with Ollama and its required models locally.

Who it is for

  • Readers researching PDFs such as notes, papers and reports.
  • Developers who want a Python-based local PDF question-answering application.

Documented uses

  • Ask a question about a PDF and receive an answer based on retrieved document context.
  • Find relevant material in notes, research papers or reports without manually scanning the full document.
  • Explore a local RAG workflow combining PDF extraction, chunking, vector search and language-model answers.
Before you choose

Requirements & limits

The documented setup runs locally and requires Python 3.9 or later, Ollama, and the llama3.1 and nomic-embed-text models. The public repository identifies an MIT license.

  • The app's workflow is based on extracted and chunked PDF text, vector retrieval, and an Ollama-generated response.
  • Chroma or FAISS is identified as the vector-search backend.
  • Local setup requires installing dependencies and managing Ollama models; it is not presented as a hosted service.
  • The repository describes answers as context-based, but does not establish an accuracy guarantee.

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 the local PDF question-answering app

Prepare the documented local environment and launch the Streamlit interface.

Before you start

  • Python 3.9 or later
  • Ollama installed and running locally
  • The Ollama models llama3.1 and nomic-embed-text
  • The repository and its requirements.txt

Steps

  1. Clone the repository and, optionally, create and activate a Python virtual environment.

  2. Install the project dependencies with pip install -r requirements.txt.

  3. Pull llama3.1 and nomic-embed-text through Ollama.

  4. Start the interface with streamlit run app.py and open the local URL Streamlit provides.

Expected outcome

A locally served Streamlit interface for uploading PDFs and asking questions about their contents.

Guide limitations

  • The README's example virtual-environment activation command is for Windows.
  • The app requires Ollama and the specified models to be available locally.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Pdf Qa Rag SystemAsk a question about a PDF and receive an answer based on retrieved document context.WebThe documented setup runs locally and requires Python 3.9 or later, Ollama, and the llama3.1 and nomic-embed-text models. The public repository identifies an MIT license.
ServietteAnswer questions from a maintained folder of documents and cite the sources used.Linux, Self-hosted, ServerServiette is self-hosted and MIT-licensed. Setup requires a free Pathway license key. Provider API keys are needed for the corresponding API-based embedding or generated-answer options.
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
muqadasejaz/PDF-QA-RAG-System · Official homepage title · retained in its source language
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