rag-architect

RAG Architect - POWERFUL

Overview

The RAG (Retrieval-Augmented Generation) Architect skill provides comprehensive tools and knowledge for designing, implementing, and optimizing production-grade RAG pipelines. This skill covers the entire RAG ecosystem from document chunking strategies to evaluation frameworks, enabling you to build scalable, efficient, and accurate retrieval systems.

Core Competencies

1. Document Processing & Chunking Strategies

Fixed-Size Chunking

• Character-based chunking: Simple splitting by character count (e.g., 512, 1024, 2048 chars)
• Token-based chunking: Splitting by token count to respect model limits
• Overlap strategies: 10-20% overlap to maintain context continuity
• Pros: Predictable chunk sizes, simple implementation, consistent processing time
• Cons: May break semantic units, context boundaries ignored
• Best for: Uniform documents, when consistent chunk sizes are critical

Sentence-Based Chunking

• Sentence boundary detection: Using NLTK, spaCy, or regex patterns
• Sentence grouping: Combining sentences until size threshold is reached
• Paragraph preservation: Avoiding mid-paragraph splits when possible
• Pros: Preserves natural language boundaries, better readability
• Cons: Variable chunk sizes, potential for very short/long chunks
• Best for: Narrative text, articles, books

Paragraph-Based Chunking

• Paragraph detection: Double newlines, HTML tags, markdown formatting
• Hierarchical splitting: Respecting document structure (sections, subsections)
• Size balancing: Merging small paragraphs, splitting large ones
• Pros: Preserves logical document structure, maintains topic coherence
• Cons: Highly variable sizes, may create very large chunks
• Best for: Structured documents, technical documentation

Semantic Chunking

• Topic modeling: Using TF-IDF, embeddings similarity for topic detection
• Heading-aware splitting: Respecting document hierarchy (H1, H2, H3)
• Content-based boundaries: Detecting topic shifts using semantic similarity
• Pros: Maintains semantic coherence, respects document structure
• Cons: Complex implementation, computationally expensive
• Best for: Long-form content, technical manuals, research papers

Recursive Chunking

• Hierarchical approach: Try larger chunks first, recursively split if needed
• Multi-level splitting: Different strategies at different levels
• Size optimization: Minimize number of chunks while respecting size limits
• Pros: Optimal chunk utilization, preserves context when possible
• Cons: Complex logic, potential performance overhead
• Best for: Mixed content types, when chunk count optimization is important

Document-Aware Chunking

• File type detection: PDF pages, Word sections, HTML elements
• Metadata preservation: Headers, footers, page numbers, sections
• Table and image handling: Special processing for non-text elements
• Pros: Preserves document structure and metadata
• Cons: Format-specific implementation required
• Best for: Multi-format document collections, when metadata is important

2. Embedding Model Selection

Dimension Considerations

• 128-256 dimensions: Fast retrieval, lower memory usage, suitable for simple domains
• 512-768 dimensions: Balanced performance, good for most applications
• 1024-1536 dimensions: High quality, better for complex domains, higher cost
• 2048+ dimensions: Maximum quality, specialized use cases, significant resources

Speed vs Quality Tradeoffs

• Fast models: sentence-transformers/all-MiniLM-L6-v2 (384 dim, ~14k tokens/sec)
• Balanced models: sentence-transformers/all-mpnet-base-v2 (768 dim, ~2.8k tokens/sec)
• Quality models: text-embedding-ada-002 (1536 dim, OpenAI API)
• Specialized models: Domain-specific fine-tuned models

Model Categories

• General purpose: all-MiniLM, all-mpnet, Universal Sentence Encoder
• Code embeddings: CodeBERT, GraphCodeBERT, CodeT5
• Scientific text: SciBERT, BioBERT, ClinicalBERT
• Multilingual: LaBSE, multilingual-e5, paraphrase-multilingual

3. Vector Database Selection

Pinecone

• Managed service: Fully hosted, auto-scaling
• Features: Metadata filtering, hybrid search, real-time updates
• Pricing: $70/month for 1M vectors (1536 dim), pay-per-use scaling
• Best for: Production applications, when managed service is preferred
• Cons: Vendor lock-in, costs can scale quickly

Weaviate

• Open source: Self-hosted or cloud options available
• Features: GraphQL API, multi-modal search, automatic vectorization
• Scaling: Horizontal scaling, HNSW indexing
• Best for: Complex data types, when GraphQL API is preferred
• Cons: Learning curve, requires infrastructure management

Qdrant

• Rust-based: High performance, low memory footprint
• Features: Payload filtering, clustering, distributed deployment
• API: REST and gRPC interfaces
• Best for: High-performance requirements, resource-constrained environments
• Cons: Smaller community, fewer integrations

Chroma

• Embedded database: SQLite-based, easy local development
• Features: Collections, metadata filtering, persistence
• Scaling: Limited, suitable for prototyping and small deployments
• Best for: Development, testing, small-scale applications
• Cons: Not suitable for production scale

pgvector (PostgreSQL)

• SQL integration: Leverage existing PostgreSQL infrastructure
• Features: ACID compliance, joins with relational data, mature ecosystem
• Performance: ivfflat and HNSW indexing, parallel query processing
• Best for: When you already use PostgreSQL, need ACID compliance
• Cons: Requires PostgreSQL expertise, less specialized than purpose-built DBs

4. Retrieval Strategies

Dense Retrieval

• Semantic similarity: Using embedding cosine similarity
• Advantages: Captures semantic meaning, handles paraphrasing well
• Limitations: May miss exact keyword matches, requires good embeddings
• Implementation: Vector similarity search with k-NN or ANN algorithms

Sparse Retrieval

• Keyword-based: TF-IDF, BM25, Elasticsearch
• Advantages: Exact keyword matching, interpretable results
• Limitations: Misses semantic similarity, vulnerable to vocabulary mismatch
• Implementation: Inverted indexes, term frequency analysis

Hybrid Retrieval

• Combination approach: Dense + sparse retrieval with score fusion
• Fusion strategies: Reciprocal Rank Fusion (RRF), weighted combination
• Benefits: Combines semantic understanding with exact matching
• Complexity: Requires tuning fusion weights, more complex infrastructure

Reranking

• Two-stage approach: Initial retrieval followed by reranking
• Reranking models: Cross-encoders, specialized reranking transformers
• Benefits: Higher precision, can use more sophisticated models for final ranking
• Tradeoff: Additional latency, computational cost

5. Query Transformation Techniques

HyDE (Hypothetical Document Embeddings)

• Approach: Generate hypothetical answer, embed answer instead of query
• Benefits: Improves retrieval by matching document style rather than query style
• Implementation: Use LLM to generate hypothetical document, embed that
• Use cases: When queries and documents have different styles

Multi-Query Generation

• Approach: Generate multiple query variations, retrieve for each, merge results
• Benefits: Increases recall, handles query ambiguity
• Implementation: LLM generates 3-5 query variations, deduplicate results
• Considerations: Higher cost and latency due to multiple retrievals

Step-Back Prompting

• Approach: Generate broader, more general version of specific query
• Benefits: Ret