RAG Pipeline Starter#

Production-grade RAG pipeline setup with chunking strategies, embedding benchmarks, and retrieval tuning for 50K-500K row datasets.

Overview#

This skill provides a complete toolkit for building and optimizing RAG (Retrieval-Augmented Generation) pipelines. It analyzes your data, recommends optimal chunking strategies, benchmarks embedding models, and helps tune retrieval parameters for maximum accuracy.

When to Use#

• Building a new RAG system from scratch
• Optimizing an existing RAG pipeline's retrieval quality
• Choosing the right embedding model for your domain
• Processing large document collections (50K-500K rows)
• Need to balance speed vs. accuracy for your use case

Scripts#

chunking_analyzer.py#

Analyzes documents and recommends optimal chunking strategies based on content structure.

Usage:

bash

# Assess data and get strategy recommendation
python chunking_analyzer.py --assess ./data

# Apply chunking strategy to documents
python chunking_analyzer.py --strategy recursive --input ./data/doc.txt --output ./chunks/ --chunk-size 500 --overlap 50

Options:

• --assess <dir> - Analyze documents and recommend strategy
• --strategy <name> - Chunking strategy: fixed, semantic, recursive, hierarchical
• --input <path> - Input file or directory
• --output <dir> - Output directory for chunks
• --chunk-size <int> - Chunk size (default: 500)
• --overlap <int> - Overlap between chunks (default: 50)

embedding_benchmark.py#

Tests multiple embedding models on your data to find the best fit for your domain.

Usage:

bash

python embedding_benchmark.py --data ./chunks/ --domain finance --output results.json

Options:

• --embeddings <models> - Embedding models to test (space-separated)
• --data <dir> - Directory with chunked text files (required)
• --domain <name> - Domain name for context-specific recommendations
• --output <file> - Output file for results (JSON)

Supported Embeddings:

• sentence-transformers/all-MiniLM-L6-v2 (384 dims, fast, free)
• sentence-transformers/all-mpnet-base-v2 (768 dims, medium, free)
• openai/text-embedding-ada-002 (1536 dims, fast, paid)
• cohere/embed-english-v3.0 (1024 dims, fast, paid)
• bm25 (sparse, fast, free)

retrieval_tuner.py#

Optimizes retrieval parameters (top-k, similarity threshold) for your specific use case.

Usage:

bash

python retrieval_tuner.py --index ./vector_store/ --queries ./test_queries.json --output tuning_results.json

Options:

• --index <dir> - Vector store index directory
• --queries <file> - JSON file with test queries and expected results
• --output <file> - Output file for tuning results
• --top-k-range <min> <max> - Range of top-k values to test (default: 1 20)
• --threshold-range <min> <max> <step> - Similarity threshold range

vector_store_manager.py#

Manages vector store operations: create, update, search, and maintain indexes.

Usage:

bash

# Create index from chunks
python vector_store_manager.py --create --chunks ./chunks/ --index ./vector_store/ --embedding sentence-transformers/all-MiniLM-L6-v2

# Search index
python vector_store_manager.py --search --index ./vector_store/ --query "your search query" --top-k 5

Options:

• --create - Create new index from chunks
• --chunks <dir> - Directory with chunked text files
• --index <dir> - Vector store directory
• --embedding <model> - Embedding model to use
• --search - Search existing index
• --query <text> - Search query
• --top-k <int> - Number of results to return (default: 5)
• --update - Update index with new documents
• --stats - Show index statistics

Pricing Strategy#

Free tier (this skill): Core chunking + embedding benchmark tools

Paid guide ($49): Complete production RAG setup with:

• Multi-modal document processing
• Hybrid search (dense + sparse)
• Re-ranking pipeline
• Evaluation framework
• Deployment scripts

Workflow#

1. Assess your data bash

   python chunking_analyzer.py --assess ./your_data/
   
   

2. Apply chunking strategy bash

   python chunking_analyzer.py --strategy recursive --input ./data/ --output ./chunks/
   
   

3. Benchmark embeddings bash

   python embedding_benchmark.py --data ./chunks/ --domain your_domain
   
   

4. Create vector store bash

   python vector_store_manager.py --create --chunks ./chunks/ --index ./vector_store/ --embedding <recommended_model>
   
   

5. Tune retrieval (optional) bash

   python retrieval_tuner.py --index ./vector_store/ --queries ./test_queries.json
   
   

Requirements#

• Python 3.8+
• Dependencies: numpy, sentence-transformers (optional for real embeddings)

Files#

• chunking_analyzer.py - Document analysis and chunking
• embedding_benchmark.py - Embedding model benchmarking
• retrieval_tuner.py - Retrieval parameter optimization
• vector_store_manager.py - Vector store operations
• skill.json - Skill metadata