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Talk To Your Pdf

Talk To Your Pdf is a Python application that lets users upload PDF documents and ask questions about their contents. It extracts text, creates embeddings, retrieves relevant passages through a PostgreSQL and pgvector similarity search, and uses a language model to generate answers. The project provides a Streamlit interface and documents OpenAI, Supabase/PostgreSQL, pgvector, and Google Drive API components.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Talk To Your Pdf — 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 query information in a PDF through a question-and-answer interface, or when you want to examine the project’s documented RAG architecture and setup process.

Who it is for

  • Readers who need to find information in PDF documents through questions rather than manual navigation.
  • Developers exploring a Python-based RAG application for PDF question answering.

Documented uses

  • Upload a PDF and ask questions about its contents.
  • Retrieve semantically relevant text snippets from an indexed PDF.
  • Explore a custom RAG workflow using OpenAI embeddings and PostgreSQL with pgvector.
  • Prepare the documented project environment and configure its OpenAI, Supabase, and Google Drive credentials.
Before you choose

Requirements & limits

The repository links to a hosted Streamlit app and identifies the project as MIT-licensed open-source software. The documented self-setup requires OpenAI, Google Cloud Platform, and Supabase accounts.

  • The answer workflow relies on extracting text from PDFs and matching questions to embedded document snippets.
  • OpenAI is used for embeddings, intent detection, and response generation.
  • Supabase provides PostgreSQL database management; pgvector supports vector storage and similarity search.
  • Google Drive API is listed for PDF storage, so self-setup involves Google Cloud credentials as well as database and OpenAI configuration.
  • The repository says the app is built without a framework such as LangChain.

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

Prepare the project environment

Set up the documented Python project environment and configure the required service credentials.

Before you start

  • Python installed; the README specifies Python 3.11+.
  • OpenAI API account and API key.
  • Google Cloud Platform account for Google Drive API setup.
  • Supabase account for database setup.

Steps

  1. Clone the repository and enter its project directory.

  2. Create and activate a Python virtual environment.

  3. Install dependencies from requirements.txt.

  4. Add Google credentials, the Supabase PostgreSQL URL, and the OpenAI API key to the environment configuration.

  5. In Google Cloud Console, create a project, enable the Google Drive API, and create credentials.

Expected outcome

The documented environment setup and service configuration are in place for the project’s next stage.

Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
semasuka/Talk-to-your-PDF · Official homepage title · retained in its source language
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