fetchai · davi713albano-coder · Jun 27, 2026
diff --git a/README.md b/README.md
@@ -158,6 +158,7 @@ innovation-lab-examples/
 |---------|-------------|------------|------------|
 | [contributors/community_agent](contributors/community_agent/) | AI community growth agent for events and hackathons | Python, uAgents, ASI:One, Tavily | 🟡 Intermediate |
 | [contributors/news-summarizer-agent](contributors/news-summarizer-agent/) | Fetches top headlines for a topic via NewsAPI and summarizes them with ASI:One, via Chat Protocol | Python, uAgents, NewsAPI, ASI:One | 🟡 Intermediate |
+| [contributors/rag-document-qa-agent](contributors/rag-document-qa-agent/) | RAG-powered Document Q&A with LangChain + ChromaDB + Gemini 2.0 Flash | Python, uAgents, LangChain, ChromaDB, Gemini | 🟡 Intermediate |
 
 ### 🌐 Web3 & Blockchain
 

diff --git a/contributors/CHANGELOG.md b/contributors/CHANGELOG.md
@@ -8,6 +8,7 @@ Format follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 
 ### Added
 
+- `rag-document-qa-agent/`: RAG-powered Document Q&A Agent using uAgents + LangChain + Gemini 2.0 Flash + ChromaDB (@davi713albano-coder)
 - `gemini-research-agent/`: Added Gemini-powered research assistant demonstrating the standard Agent Chat Protocol (@Kavurubuvanesh)
 - `contributors/` folder and contribution guide for community agent examples
 - `contributors/community_agent/` — moved from repository root; AI community growth agent for events and hackathons

diff --git a/contributors/rag-document-qa-agent/.env.example b/contributors/rag-document-qa-agent/.env.example
@@ -0,0 +1,8 @@
+# Required: Google Gemini API key for LLM responses
+GEMINI_API_KEY=your_gemini_api_key_here
+
+# Optional: path to a document (PDF or .txt) to auto-ingest on startup
+DOCUMENT_PATH=
+
+# Optional: Agentverse mailbox key (if deploying to Agentverse)
+AGENTVERSE_API_KEY=your_agentverse_key_here
diff --git a/contributors/rag-document-qa-agent/.gitignore b/contributors/rag-document-qa-agent/.gitignore
@@ -0,0 +1,6 @@
+chroma_db/
+downloads/
+.env
+__pycache__/
+*.pyc
+.venv/
diff --git a/contributors/rag-document-qa-agent/README.md b/contributors/rag-document-qa-agent/README.md
@@ -0,0 +1,207 @@
+# RAG-powered Document Q&A Agent
+
+![uAgents](https://img.shields.io/badge/uAgents-chat--protocol-blue)
+![RAG](https://img.shields.io/badge/RAG-LangChain%20%2B%20ChromaDB-green)
+![LLM](https://img.shields.io/badge/LLM-Gemini%202.0%20Flash-orange)
+![Python](https://img.shields.io/badge/python-3.10+-blue)
+
+A Retrieval-Augmented Generation (RAG) agent that ingests PDF or plain-text documents, chunks and embeds them using HuggingFace sentence-transformers, stores vectors in ChromaDB, and answers natural-language questions about the documents via the uAgents Chat Protocol — powered by Google Gemini 2.0 Flash.
+
+- **Category:** `RAG`, `LLM`, `Integration`
+- **Difficulty:** Intermediate
+
+---
+
+## What it does
+
+1. You provide a document (PDF, .txt, .md, or .csv) via the `ingest` command or a `DOCUMENT_PATH` env var.
+2. The document is split into chunks, embedded with `all-MiniLM-L6-v2`, and stored in a local ChromaDB vector store.
+3. You ask questions via the Chat Protocol, and the agent retrieves relevant chunks and generates grounded answers using Gemini 2.0 Flash.
+4. Answers are strictly based on document content — no hallucinated information.
+
+---
+
+## Tech stack
+
+| Layer | Technology |
+|-------|------------|
+| Agent runtime | [uAgents](https://docs.fetch.ai/agents/uaagents/) + Chat Protocol |
+| RAG framework | [LangChain](https://python.langchain.com/) |
+| LLM | [Google Gemini 2.0 Flash](https://ai.google.dev/) (free tier) |
+| Embeddings | [HuggingFace sentence-transformers](https://www.sbert.net/) (`all-MiniLM-L6-v2`) |
+| Vector store | [ChromaDB](https://www.trychroma.com/) (local) |
+| PDF parsing | [pypdf](https://pypdf.readthedocs.io/) |
+| Language | Python 3.10+ |
+
+**Flow:** User question → ChromaDB retrieval (top-4 chunks) → context injected into prompt → Gemini 2.0 Flash generates answer → plain-text reply.
+
+---
+
+## Prerequisites
+
+- **Python 3.10+**
+- **Google Gemini API key** — [Get one here](https://aistudio.google.com/apikey) (free tier available)
+
+---
+
+## Environment variables
+
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `GEMINI_API_KEY` | Yes | Google Gemini API key for LLM responses |
+| `DOCUMENT_PATH` | No | Path to a document to auto-ingest on agent startup |
+| `AGENTVERSE_API_KEY` | No | Agentverse key for mailbox deployment |
+
+---
+
+## Installation
+
+```bash
+# Navigate to this folder
+cd contributors/rag-document-qa-agent
+
+# Create a virtual environment
+python -m venv .venv
+source .venv/bin/activate    # Windows: .venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+---
+
+## Setup
+
+### 1. Set up environment variables
+
+```bash
+cp .env.example .env
+```
+
+Edit `.env` and fill in your API key:
+
+```env
+GEMINI_API_KEY=your_gemini_api_key_here
+DOCUMENT_PATH=
+```
+
+### 2. (Optional) Pre-ingest a document
+
+You can ingest a document before starting the agent:
+
+```bash
+python ingest.py path/to/your/document.pdf
+```
+
+Or set `DOCUMENT_PATH` in your `.env` to auto-ingest on startup.
+
+---
+
+## Run the Agent
+
+```bash
+python agent.py
+```
+
+The agent registers on Agentverse (if `AGENTVERSE_API_KEY` is set) or runs locally. Send it a Chat Protocol message to interact.
+
+---
+
+## Usage
+
+Once the agent is running, interact via the Chat Protocol:
+
+| Command | Description |
+|---------|-------------|
+| `ingest <path>` | Load a PDF or text document into the vector store |
+| `status` | Check if a document is currently loaded |
+| `<your question>` | Ask a question about the loaded document |
+
+### Example interaction
+
+```text
+You:   ingest research-paper.pdf
+Agent: Document ingested successfully!
+       Chunks stored: 47
+       You can now ask questions about the document.
+
+You:   What is the main contribution of this paper?
+Agent: Based on the document, the main contribution is a novel
+       approach to retrieval-augmented generation that reduces
+       hallucination rates by 40% compared to baseline methods...
+
+You:   What methodology was used?
+Agent: The paper uses a combination of quantitative evaluation
+       on standard benchmarks and qualitative analysis of
+       generated outputs...
+```
+
+---
+
+## Project structure
+
+```
+contributors/rag-document-qa-agent/
+├── agent.py              # uAgent with Chat Protocol
+├── rag.py                # RAG pipeline (loading, chunking, embedding, QA)
+├── ingest.py             # Standalone document ingestion script
+├── requirements.txt      # Python dependencies
+├── .env.example          # Environment variable template
+├── assets/
+│   └── demo.png          # Demo screenshot
+└── README.md             # This file
+```
+
+---
+
+## Architecture
+
+```text
+Document ──► pypdf / text loader
+                │
+                ▼
+      RecursiveCharacterTextSplitter
+                │
+                ▼
+      HuggingFaceEmbeddings (all-MiniLM-L6-v2)
+                │
+                ▼
+           ChromaDB (local)
+                │
+                ▼
+        Retriever (top-4 chunks)
+                │
+                ▼
+     ChatPromptTemplate + Gemini 2.0 Flash
+                │
+                ▼
+            Answer
+```
+
+---
+
+## Troubleshooting
+
+| Issue | Fix |
+|-------|-----|
+| `GEMINI_API_KEY is not set` | Add your Gemini API key to `.env` |
+| `No document is loaded` | Run `ingest <path>` or set `DOCUMENT_PATH` in `.env` |
+| `File not found` | Use an absolute path or path relative to the working directory |
+| `ImportError: sentence_transformers` | Run `pip install sentence-transformers` |
+| ChromaDB persistence errors | Delete the `chroma_db/` folder and re-ingest |
+
+---
+
+## Demo
+
+![RAG Document Q&A Demo](./assets/demo.png)
+
+## Agent Profile
+
+[View Agent Profile](https://agentverse.ai/)
+
+---
+
+## License
+
+Apache 2.0 (see repository root [LICENSE](../../LICENSE)).