[PR #907] [CLOSED] feat: add bedrock_pdf_rag — Amazon Bedrock RAG chatbot with FAISS and page-level citations #14065

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2026-07-11T19:31:10-05:00

GiteaMirror commented

2026-07-11 19:31:10 -05:00

📋 Pull Request Information

Original PR: https://github.com/Shubhamsaboo/awesome-llm-apps/pull/907
Author: @Rajeshdevandla
Created: 6/28/2026
Status: ❌ Closed

Base: main ← Head: main

📝 Commits (4)

cd84f2b Add README for Amazon Bedrock PDF RAG Agent
a28a38f feat: add rag_agent.py - FastAPI + Streamlit PDF RAG with Bedrock and FAISS
a0f8717 Add requirements.txt for project dependencies
33a8753 Create .env.example for AWS and app configuration

📊 Changes

4 files changed (+313 additions, -0 deletions)

View changed files

➕ rag_tutorials/bedrock_pdf_rag/.env.example (+21 -0)
➕ rag_tutorials/bedrock_pdf_rag/README.md (+84 -0)
➕ rag_tutorials/bedrock_pdf_rag/rag_agent.py (+197 -0)
➕ rag_tutorials/bedrock_pdf_rag/requirements.txt (+11 -0)

📄 Description

Summary

Adds a new RAG tutorial: rag_tutorials/bedrock_pdf_rag/

A RAG-powered PDF chatbot using Amazon Bedrock (Claude 3 Haiku) and FAISS for in-memory vector search with page-level citations.

What it does

Upload any PDF and chat with it in natural language
- Every answer cites the exact page it came from (e.g. [Page 14])
- Local embeddings via sentence-transformers — no embedding API cost on uploads
- Session isolation — multiple users supported simultaneously
- Sub-2s response time

Files

README.md — setup instructions, architecture, API reference
- rag_agent.py — FastAPI backend + Streamlit UI in one file
- requirements.txt — pinned dependencies

Stack

Amazon Bedrock (Claude 3 Haiku) · FAISS · sentence-transformers · FastAPI · Streamlit · pypdf · Docker

Why it's different

Most RAG examples use OpenAI or open-source models. This is the only Bedrock-native RAG tutorial in this collection, making it uniquely useful for AWS teams.

_{🔄 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/907 **Author:** [@Rajeshdevandla](https://github.com/Rajeshdevandla) **Created:** 6/28/2026 **Status:** ❌ Closed **Base:** `main` ← **Head:** `main` --- ### 📝 Commits (4) - [`cd84f2b`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/cd84f2b4dc4dc8c3e767476222c67c3ad265cf41) Add README for Amazon Bedrock PDF RAG Agent - [`a28a38f`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/a28a38f3c96bc134ffdd49b1240e2210a03f0569) feat: add rag_agent.py - FastAPI + Streamlit PDF RAG with Bedrock and FAISS - [`a0f8717`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/a0f87179de5678962595fc58375db577814d02eb) Add requirements.txt for project dependencies - [`33a8753`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/33a87530209b13621d2cfaaa80281b162cac76d2) Create .env.example for AWS and app configuration ### 📊 Changes **4 files changed** (+313 additions, -0 deletions) <details> <summary>View changed files</summary> ➕ `rag_tutorials/bedrock_pdf_rag/.env.example` (+21 -0) ➕ `rag_tutorials/bedrock_pdf_rag/README.md` (+84 -0) ➕ `rag_tutorials/bedrock_pdf_rag/rag_agent.py` (+197 -0) ➕ `rag_tutorials/bedrock_pdf_rag/requirements.txt` (+11 -0) </details> ### 📄 Description ## Summary Adds a new RAG tutorial: `rag_tutorials/bedrock_pdf_rag/` A RAG-powered PDF chatbot using **Amazon Bedrock (Claude 3 Haiku)** and **FAISS** for in-memory vector search with page-level citations. ## What it does - Upload any PDF and chat with it in natural language - - Every answer cites the exact page it came from (e.g. `[Page 14]`) - - Local embeddings via sentence-transformers — no embedding API cost on uploads - - Session isolation — multiple users supported simultaneously - - Sub-2s response time ## Files - `README.md` — setup instructions, architecture, API reference - - `rag_agent.py` — FastAPI backend + Streamlit UI in one file - - `requirements.txt` — pinned dependencies ## Stack Amazon Bedrock (Claude 3 Haiku) · FAISS · sentence-transformers · FastAPI · Streamlit · pypdf · Docker ## Why it's different Most RAG examples use OpenAI or open-source models. This is the only Bedrock-native RAG tutorial in this collection, making it uniquely useful for AWS teams. --- <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 2026-07-11 19:31:10 -05:00

GiteaMirror closed this issue

2026-07-11 19:31:11 -05:00

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Reference: github-starred/awesome-llm-apps#14065