80 lines
2.8 KiB
Markdown
80 lines
2.8 KiB
Markdown
# Retrieval Augmented Generation (RAG)
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This project demonstrates a simplified RAG system that retrieves relevant documents based on user queries and generates answers using an LLM. This implementation is based directly on the tutorial: [Retrieval Augmented Generation (RAG) from Scratch — Tutorial For Dummies](https://zacharyhuang.substack.com/p/retrieval-augmented-generation-rag).
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## Features
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- Document chunking for processing long texts
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- FAISS-powered vector-based document retrieval
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- LLM-powered answer generation
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## How to Run
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1. Set your API key:
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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```
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Or update it directly in `utils.py`
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Let's do a quick check to make sure your API key is working properly:
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```bash
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python utils.py
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```
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2. Install and run with the default query:
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```bash
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pip install -r requirements.txt
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python main.py
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```
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3. Run the application with a sample query:
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```bash
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python main.py --"How does the Q-Mesh protocol achieve high transaction speeds?"
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```
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## How It Works
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The magic happens through a two-phase pipeline implemented with PocketFlow:
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```mermaid
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graph TD
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subgraph OfflineFlow[Offline Document Indexing]
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ChunkDocs[ChunkDocumentsNode] --> EmbedDocs[EmbedDocumentsNode] --> CreateIndex[CreateIndexNode]
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end
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subgraph OnlineFlow[Online Processing]
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EmbedQuery[EmbedQueryNode] --> RetrieveDoc[RetrieveDocumentNode] --> GenerateAnswer[GenerateAnswerNode]
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end
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```
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Here's what each part does:
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1. **ChunkDocumentsNode**: Breaks documents into smaller chunks for better retrieval
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2. **EmbedDocumentsNode**: Converts document chunks into vector representations
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3. **CreateIndexNode**: Creates a searchable FAISS index from embeddings
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4. **EmbedQueryNode**: Converts user query into the same vector space
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5. **RetrieveDocumentNode**: Finds the most similar document using vector search
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6. **GenerateAnswerNode**: Uses an LLM to generate an answer based on the retrieved content
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## Example Output
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```
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✅ Created 5 chunks from 5 documents
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✅ Created 5 document embeddings
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🔍 Creating search index...
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✅ Index created with 5 vectors
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🔍 Embedding query: How to install PocketFlow?
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🔎 Searching for relevant documents...
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📄 Retrieved document (index: 0, distance: 0.3427)
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📄 Most relevant text: "Pocket Flow is a 100-line minimalist LLM framework
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Lightweight: Just 100 lines. Zero bloat, zero dependencies, zero vendor lock-in.
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Expressive: Everything you love—(Multi-)Agents, Workflow, RAG, and more.
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Agentic Coding: Let AI Agents (e.g., Cursor AI) build Agents—10x productivity boost!
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To install, pip install pocketflow or just copy the source code (only 100 lines)."
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🤖 Generated Answer:
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To install PocketFlow, use the command `pip install pocketflow` or simply copy its 100 lines of source code.
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```
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