#atom

Enhancing language models with external knowledge retrieval

Core Idea: Retrieval-Augmented Generation combines information retrieval with text generation, retrieving relevant documents from external sources to provide context for language model responses, especially for knowledge-intensive tasks.

Key Elements

System Architecture

• Document Processing Pipeline:
  • Ingestion of knowledge sources (documents, databases, websites)
  • Chunking content into manageable segments
  • Converting text into vector embeddings
  • Storing in vector databases for efficient retrieval
• Retrieval Mechanism:
  • Query processing and vectorization
  • Similarity search using vector embeddings
  • Hybrid search combining semantic and keyword approaches
  • Reranking results for relevance refinement
• Generation Component:
  • Context assembly from retrieved documents
  • Prompt engineering for effective integration
  • Response generation with contextual guidance
  • Source attribution and citation (optional)

Implementation Approaches

• Basic RAG: Direct retrieval and context insertion
• Advanced Techniques:
  • Recursive RAG: Multiple retrieval steps for complex queries
  • Self-RAG: Model evaluates its own retrieval needs
  • ReAct: Interleaving reasoning and retrieval steps
  • IRCoT: Retrieval integrated with chain-of-thought reasoning
  • Agentic RAG: Giving agents multiple retrieval tools and strategic reasoning

Performance Factors

• Retrieval Quality:
  • Embedding model selection and quality
  • Chunking strategy and granularity
  • Search algorithm effectiveness
  • Reranking precision
• Local vs. Cloud Implementation:
  • Performance gap between local and cloud LLMs
  • Integration challenges with local models (Ollama)
  • Hallucination rates and context handling differences
• Optimization Techniques:
  • Query reformulation for better retrieval
  • Hybrid retrieval methods
  • Context length management
  • Result diversity balancing

Known Limitations

• Context Fragmentation: Partial retrieval of important information
• Limited Data Analysis: Struggles with structured data like tables and spreadsheets
• Cross-Document Challenges: Difficulty connecting information across documents
• See: RAG Limitations for detailed exploration of constraints

Connections

• Related Concepts: Vector Search (retrieval mechanism), Document Processing Pipeline (ingestion system), Document Ingestion (knowledge preparation)
• Broader Context: Information Retrieval (theoretical foundation), Knowledge-Enhanced AI (paradigm)
• Applications: n8n (automation platform), Ollama (local LLM implementation)
• Components: Local LLMs vs Cloud LLMs (implementation comparison), Hybrid Search (retrieval approach)
• Extensions: Structured Data in RAG Systems (handling tabular data), Document Exploration Strategies for LLMs (beyond vector search)
• Architecture: Knowledge Base Architecture for AI Agents (system design)
• Comparison: RAG vs Agentic RAG (different approaches)

References

1. Lazaridou et al. "Internet augmented language models through few-shot prompting for open-domain question answering"
2. Liu et al. "Generate rather than retrieve: Large language models are strong context generators"
3. Reddit discussion on n8n + Ollama RAG implementation challenges (2025)
4. Lewis et al. "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", 2020

#rag #retrieval #external-knowledge #information-retrieval #context-augmentation #vector-search

Sources:

• From: Lil'log Prompt Engineering