pocketflow/cookbook/pocketflow-rag/README.md

Retrieval Augmented Generation (RAG)

This project demonstrates a simplified RAG system that retrieves relevant documents based on user queries.

Features

• Simple vector-based document retrieval
• Two-stage pipeline (offline indexing, online querying)
• FAISS-powered similarity search

Getting Started

1. Install the required dependencies:

pip install -r requirements.txt

2. Run the application with a sample query:

python main.py --"Large Language Model"

3. Or run without arguments to use the default query:

python main.py

API Key

By default, demo uses dummy embedding based on character frequencies. To use real OpenAI embedding:

1. Edit nodes.py to replace the dummy get_embedding with get_openai_embedding:

# Change this line:
query_embedding = get_embedding(query)
# To this:
query_embedding = get_openai_embedding(query)

# And also change this line:
return get_embedding(text)
# To this:
return get_openai_embedding(text)

2. Make sure your OpenAI API key is set:

export OPENAI_API_KEY="your-api-key-here"

How It Works

The magic happens through a two-stage pipeline implemented with PocketFlow:

graph TD
    subgraph OfflineFlow[Offline Document Indexing]
        EmbedDocs[EmbedDocumentsNode] --> CreateIndex[CreateIndexNode]
    end
    
    subgraph OnlineFlow[Online Query Processing]
        EmbedQuery[EmbedQueryNode] --> RetrieveDoc[RetrieveDocumentNode]
    end

Here's what each part does:

1. EmbedDocumentsNode: Converts documents into vector representations
2. CreateIndexNode: Creates a searchable FAISS index from embeddings
3. EmbedQueryNode: Converts user query into the same vector space
4. RetrieveDocumentNode: Finds the most similar document using vector search

Example Output

✅ Created 5 document embeddings
🔍 Creating search index...
✅ Index created with 5 vectors
🔍 Embedding query: Large Language Model
🔎 Searching for relevant documents...
📄 Retrieved document (index: 3, distance: 0.3296)
📄 Most relevant text: "PocketFlow is a 100-line Large Language Model Framework."

Files

• main.py: Main entry point for running the RAG demonstration
• flow.py: Configures the flows that connect the nodes
• nodes.py: Defines the nodes for document processing and retrieval
• utils.py: Utility functions including the embedding function