Skool RAG Pipeline

Goal

Query Skool community content using a RAG (Retrieval-Augmented Generation) pipeline with vector search and reranking.

Scripts

• ./scripts/skool_rag_prepare.py - Prepare content for indexing
• ./scripts/skool_rag_index.py - Index content in Pinecone
• ./scripts/skool_rag_query.py - Query the knowledge base

Pipeline

1. Prepare Content

python3 ./scripts/skool_rag_prepare.py --community makerschool

Scrapes and chunks community content.

2. Index in Pinecone

python3 ./scripts/skool_rag_index.py --input .tmp/skool_chunks.json

Creates OpenAI embeddings and stores in Pinecone.

3. Query

python3 ./scripts/skool_rag_query.py --query "How do I get my first client?"

Pipeline:

1. OpenAI embeddings for query
2. Pinecone vector search
3. Cohere reranking
4. Claude response generation

Environment

PINECONE_API_KEY=your_key
OPENAI_API_KEY=your_key
COHERE_API_KEY=your_key
ANTHROPIC_API_KEY=your_key

---

Schema

Inputs

Name	Type	Required	Description
query	string	Yes	Natural language question to search for
community	string	No	Community slug to index (default: makerschool)

Outputs

Name	Type	Description
answer	string	AI-generated answer with source references

Credentials

Name	Source
PINECONE_API_KEY	.env
OPENAI_API_KEY	.env
COHERE_API_KEY	.env
ANTHROPIC_API_KEY	.env

Composable With

Skills that chain well with this one: skool-monitor

Cost

Pinecone + OpenAI embeddings + Cohere reranking + Claude