[PR #359] [CLOSED] Proposal : Add RAG-Based LLM PromptBot (OpenRouter + Files) #9964

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GiteaMirror · 2026-06-12T10:37:44-05:00

GiteaMirror commented

2026-06-12 10:37:44 -05:00

📋 Pull Request Information

Original PR: https://github.com/Shubhamsaboo/awesome-llm-apps/pull/359
Author: @VathareVinayak
Created: 10/12/2025
Status: ❌ Closed

Base: main ← Head: add-streamlit-rag-app

📝 Commits (2)

ca4db5d Add Streamlit RAG Chatbot (OpenRouter + PDF) project
a8c7ed8 Updated Streamlit RAG Chatbot (OpenRouter + PDF) project

📊 Changes

8 files changed (+197 additions, -0 deletions)

View changed files

➕ rag_tutorials/openrouter_rag_based_chatbot/.env.example (+1 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/.gitignore (+3 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/README.md (+40 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/app.py (+92 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/requirements.txt (+40 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/snaps/OpenrouterResting.png (+0 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/snaps/testSnaps.png (+0 -0)
➕ rag_tutorials/openrouter_rag_based_chatbot/utils/pdf_reader.py (+21 -0)

📄 Description

Streamlit RAG PromptBot

@Shubhamsaboo , This PR adds a full working example of a Retrieval-Augmented Chatbot using Streamlit and OpenRouter API. The app reads PDF files as a knowledge base and answers user queries intelligently without storing any data in a database — all chat is kept in session memory.

Features

RAG-based responses using PDF context.
Upload your own PDF knowledge files.
Streamlit interactive UI with chat interface.
Session-only memory; no database required.
OpenRouter API integration for LLM responses.
Error handling for API issues.

Tech Stack

Streamlit (Frontend UI)
OpenRouter API (gpt-3.5-turbo) for LLM
Python 3.10+
PyPDF2 for PDF text extraction
python-dotenv for environment variables

Project Folder

rag_tutorials/openrouter_rag_based_chatbot/
├── app.py
├── utils/pdf_reader.py
├── requirements.txt
├── .gitignore
├── .env.example
├── README.md
└── snaps/
├── OpenrouterResting.png
└── testSnaps.png

Installation & Running

Clone the repo or fork.
Create a Python virtual environment and activate it.
Install dependencies: pip install -r requirements.txt.
Add your OpenRouter API key in .env.
Run: streamlit run app.py.

Performance & Notes

Response time: ~1-2.038 seconds (also depending on PDF contains tables , imagnes or not and also API)
Best performance with PDFs < 20 MB.
Lightweight, no database used.

Screenshots

Upload PDF Interface:

Chat Interface:

This PR adds a ready-to-run, self-contained example project for users to try RAG with Streamlit + OpenRouter API.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/Shubhamsaboo/awesome-llm-apps/pull/359 **Author:** [@VathareVinayak](https://github.com/VathareVinayak) **Created:** 10/12/2025 **Status:** ❌ Closed **Base:** `main` ← **Head:** `add-streamlit-rag-app` --- ### 📝 Commits (2) - [`ca4db5d`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/ca4db5df5cdeaa3211e751f81d48521ea6eda5bf) Add Streamlit RAG Chatbot (OpenRouter + PDF) project - [`a8c7ed8`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/a8c7ed8e63275a45cb463f0910938d016d1d8752) Updated Streamlit RAG Chatbot (OpenRouter + PDF) project ### 📊 Changes **8 files changed** (+197 additions, -0 deletions) <details> <summary>View changed files</summary> ➕ `rag_tutorials/openrouter_rag_based_chatbot/.env.example` (+1 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/.gitignore` (+3 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/README.md` (+40 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/app.py` (+92 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/requirements.txt` (+40 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/snaps/OpenrouterResting.png` (+0 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/snaps/testSnaps.png` (+0 -0) ➕ `rag_tutorials/openrouter_rag_based_chatbot/utils/pdf_reader.py` (+21 -0) </details> ### 📄 Description ## Streamlit RAG PromptBot @Shubhamsaboo , This PR adds a full working example of a **Retrieval-Augmented Chatbot** using Streamlit and OpenRouter API. The app reads PDF files as a knowledge base and answers user queries intelligently without storing any data in a database — all chat is kept in session memory. ### Features - RAG-based responses using PDF context. - Upload your own PDF knowledge files. - Streamlit interactive UI with chat interface. - Session-only memory; no database required. - OpenRouter API integration for LLM responses. - Error handling for API issues. ### Tech Stack - Streamlit (Frontend UI) - OpenRouter API (`gpt-3.5-turbo`) for LLM - Python 3.10+ - PyPDF2 for PDF text extraction - python-dotenv for environment variables ### Project Folder rag_tutorials/openrouter_rag_based_chatbot/ ├── app.py ├── utils/pdf_reader.py ├── requirements.txt ├── .gitignore ├── .env.example ├── README.md └── snaps/ ├── OpenrouterResting.png └── testSnaps.png ### Installation & Running 1. Clone the repo or fork. 2. Create a Python virtual environment and activate it. 3. Install dependencies: `pip install -r requirements.txt`. 4. Add your OpenRouter API key in `.env`. 5. Run: `streamlit run app.py`. ### Performance & Notes - Response time: ~1-2.038 seconds (also depending on PDF contains tables , imagnes or not and also API) - Best performance with PDFs < 20 MB. - Lightweight, no database used. ### Screenshots ### Screenshots **Upload PDF Interface:** ![Upload PDF](rag_tutorials/openrouter_rag_based_chatbot/snaps/OpenrouterResting.png) **Chat Interface:** ![Chat Example](rag_tutorials/openrouter_rag_based_chatbot/snaps/testSnaps.png) This PR adds a ready-to-run, self-contained example project for users to try RAG with Streamlit + OpenRouter API. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-06-12 10:37:44 -05:00

GiteaMirror closed this issue

2026-06-12 10:37:46 -05:00

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Reference: github-starred/awesome-llm-apps#9964