[DSC Europe 24] Sofia Konchakova - Effective Context Tuning Strategies for Retrieval-Augmented Generation (RAG) Systems
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Retrieval-Augmented Generation (RAG) systems represent a cutting-edge approach to natural language processing, blending the strengths of retrieval-based models with generative architectures. Central to the effectiveness of RAG systems is the nuanced management of context. This abstract investigates various strategies for context tuning in RAG systems, ranging from the selection and weighting of relevant passages to the dynamic adjustment of context size. Through case studies and empirical analysis, we uncover insights into how different tuning strategies impact the quality and coherence of generated text. Join us as we navigate the landscape of context tuning in RAG systems, unlocking pathways to more effective and adaptable language generation.
[DSC Europe 24] Sofia Konchakova - Effective Context Tuning Strategies for Retrieval-Augmented Generation (RAG) Systems
- 1. SOFIA KONCHAKOVA DSC EUROPE NOVEMBER 2024 CONTENT MANAGEMENT FOR RAG SYSTEMS
- 2. ABOUT ME ABOUT ML for IoT RAG Systems MLOps AI Consulting Topics of Interest:
- 3. AGENDA 1 2 3 RAG 101 SHORT ABOUT DATA RETRIEVER MANAGEMENT
- 4. REMIND THE BASICS RAGOPS WHAT IS RAG? 1 1 2
- 5. USER QUERY RETRIEVER RETRIEVAL AUGMENTED GENERATION (RAG) 1 RELEVANT CONTENT EXTRACTION PROMPT AUGMENTATI ON RESPONSE R E T R I E V E D C O N T E N T FULL PROMPT
- 6. WHY DO WE NEED RAG? We no longer have training costs by enabling smaller models to retrieve information on-demand, eliminating the need to store all knowledge within the model. Cost Reduction It increases accuracy by retrieving relevant, up-to- date information during generation, reducing inaccuracies and "hallucinations" often seen in generative AI. RAG frameworks are versatile, enabling retrieval from various data sources and adapting across domains, making them ideal for custom applications in different industries. Higher Accuracy Flexibility
- 7. TRADITIONAL MLOPS Business Problem Definition Data Management Machine Learning Deployment & Monitoring
- 8. RAGOPS Data Management Model Management Prompt Management System Management & Deployment
- 9. DATA TYPES DATA REQUIREMENTS SHORT ABOUT DATA 1 2 2
- 10. PDF WHAT DATA CAN WE USE?
- 11. THOUGH RAG SUPPORTS VARIOUS DATA FORMATS, IT MAY NOT ALWAYS BE THE BEST OPTION. DASHBOARDS CONNECTED TO A DATA WAREHOUSE MIGHT BE MORE EFFICIENT FOR SOME CASES.
- 12. REQUIREMENTS FOR THE DATA MANAGEMENT PIPELINE Scalability and Performance Efficiency: Efficient handling of increasing volumes of data and complex processing tasks without performance degradation and manual intervention. Monitoring and Observability: Continuous tracking of the performance and health of the data pipeline using metrics, logs, and alerts. Data Transformation and Enrichment: The ability to clean, normalize, and enrich data as it moves through the pipeline. Automation and Orchestration: Automating repetitive tasks, including timely data updates, and coordinating complex workflows between different pipeline stages.
- 13. GARBAGE IN - GARBAGE OUT! WE WANT OUR SYSTEM TO HAVE ACCESS TO RELEVANT DATA.
- 14. RETRIEVER & GENERATOR CHUNKING STRATEGIES RETRIEVER MANAGEMENT 1 2 2 RETRIEVAL STRATEGIES 3 RERANKING STRATEGIES 4
- 15. Retriever Generator Responsible for fetching relevant information from an external knowledge base based on the input query. It acts as the system's non-parametric "memory," allowing it to access and utilize up-to- date or domain-specific information. Language model that produces the final response by conditioning on both the input query and the retrieved documents. It generates coherent and contextually appropriate text as an answer to the query. RETRIEVAL AUGMENTED GENERATION (RAG) 1
- 16. RETRIEVAL AUGMENTED GENERATION (RAG) 1 Reranker Embedding Model Optimal Chunk Size Sparse vs. Dense Retriever Named Entity Recognition Similarity Metrics Retriever
- 17. TO CHUNK OR NOT TO CHUNK?
- 18. OPTIMAL CHUNK SIZE Content Coherence vs. Granularity: Balance between semantic preservation and retrieval precision Large chunks: risk relevance dilution Small chunks: may break context flow Model Context Window Limitations: Consider LLM token capacity constraints Reserve space for context and response Optimize for efficient token utilization Query-Content Alignment: Match chunk size to content type Technical docs: smaller, focused chunks Narrative content: larger, context-preserving chunks And remember there's no one-size-fits-all approach!
- 19. CHUNKING ISN’T ALWAYS NECESSARY! FOR SHORTER DOCUMENTS, IT’S OFTEN BEST TO KEEP THEM INTACT.
- 20. FIXED-SIZED CHUNKING RECURSIVE CHUNKING CHUNKING ALGORITHMS Chunks are created by setting a fixed number of tokens per chunk, with optional overlap to retain semantic context. Simple and efficient Consistent chunk sizes Potential loss of context Inflexible with content length Splits text by trying larger separators (e.g., paragraphs) first, then progressively uses smaller separators until reaching the desired chunk size. Preserves natural document structure Reduces context fragmentation Higher computational overhead More complex implementation
- 21. SEMANTIC CHUNKING AGENTIC CHUNKING CHUNKING ALGORITHMS Splits text based on meaningful units and topical boundaries rather than length limits, using embedding similarity to identify and maintain coherent content blocks while preserving context. Better preservation of context Reduced information loss Requires sophisticated NLP models Produces chunks of varying sizes Uses LLMs as active agents to split text intelligently based on content, context, and user needs, rather than fixed rules or similarity metrics. More intelligent splits Purpose-aware chunking High computational cost & latency Limited standardization and maturity as a new technology
- 23. SPARSE RETRIEVER A sparse retriever uses keyword-based matching, creating vectors where only present words get values while all other positions are zero. Text: “I am speaking at DSC Europe. I speak about RAG.”
- 24. SPARSE RETRIEVER A sparse retriever uses keyword-based matching, creating vectors where only present words get values while all other positions are zero. Fast and efficient Good for exact matches Easy to interpret results Low computational cost Misses semantic relationships Sensitive to wording variations Poor with synonyms No understanding of context BM25, TF-IDF
- 25. DENSE RETRIEVER A dense retriever uses embeddings to convert text into meaningful vectors that capture semantic meaning. Text: “I am speaking at DSC Europe. I speak about RAG.”
- 26. DENSE RETRIEVER A dense retriever uses embeddings to convert text into meaningful vectors that capture semantic meaning. Better semantic understanding Handles synonyms well Good with paraphrasing Context-aware Computationally expensive Requires more storage Harder to interpret May miss exact matches MTEB Leaderboard on HuggingFace ChromaDB, FAISS
- 27. HYBRID RETRIEVER SPARSE DENSE H Y B R I D A hybrid retriever merges keyword-based search with embedding-based search to leverage the strengths of both approaches. Better overall accuracy Combines benefits of both methods Most complex to implement More maintenance overhead Weaviate Pinecone LangChain's hybrid retrievers
- 28. BALANCED KEYWORD-FOCUSED WITH METADATA SEMANTIC-FOCUSED 50% 50% 70% 30% 30% 70% DENSE SPARSE 40% 20% 40% META HYBRID RETRIEVER
- 29. NAMED ENTITY RECOGNITION (NER) Technique that identifies and classifies named entities (like names, organizations, locations, dates) in text into predefined categories. These extracted entities can be stored as metadata in databases, enabling enhanced search capabilities through metadata filtering. GLiNER, Flair, StanfordNER
- 31. TIME-BASED WEIGHTING Method that adjusts document relevance scores based on their age, applying a decay factor to give more weight to recent documents while gradually reducing the importance of older ones. Easy to implement Prioritises fresh content Configurable decay rates Can over-penalise “classics” Domain-specific tuning required Not suitable for evergreen content
- 32. SOURCE AUTHORITY WEIGHTING Method that modifies document relevance scores based on the credibility and reliability of their sources, considering factors like domain expertise, publication authority and etc. Promotes reliable sources Supports quality control Reduces misinformation risk May miss emerging experts Subjective authority tuning Need regular updates of authority scores
- 33. CROSS-ENCODER RERANKING A cross-encoder is a neural architecture that processes a query and document together as a single input to determine their relevance. Think of it as having two people watching and discussing a movie together, rather than separately writing reviews and comparing them later. USER QUERY RETRIEVER TOP-5 CHUNKS BI-ENCODER
- 34. CROSS-ENCODER RERANKING A cross-encoder is a neural architecture that processes a query and document together as a single input to determine their relevance. Think of it as having two people watching and discussing a movie together, rather than separately writing reviews and comparing them later. USER QUERY RETRIEVER TOP-50 CHUNKS CROSS-ENCODER RERANKER TOP-5 CHUNKS
- 35. CROSS-ENCODER RERANKING Higher accuracy Better understanding of query- passage relationships More nuanced ranking decisions Can capture complex relevance patterns Computationally expensive Slower inference time Higher memory requirements Not suitable for initial retriever Sentence-Transformers, HuggingFace Transformers, PyTerrier
- 37. RETRIEVER CHUNKER RERANKER RECALL@k PRECISION@K COST LATENCY SEMANTIC COHERENCE EVALUATION METRICS
- 38. Extraction of a representative slice of the real data Evaluation Metric Definition Creating QA data frame Generating questions to the real data with the LLM Cross-check of outputs by another LLM and human evaluator Clarity Specificity Realism EVALUATION DATASET
- 39. Baseline Characteristics Chunking Choice Chunk Size Chunk Approach Create Different chunks with the use of different Chunk sizes and chunking methods Store in local store Conduct experiments Characteristics of the best previous iterations Update Embeddings Chunk Size Chunk Approach Embedding the best performers of the iteration A with the new model Store in local store Conduct experiments Embedding Methods Create Sparse Vectors Store in local store Conduct experiments Characteristics of the best previous iterations Chunk Size Chunk Approach Implementing Sparse Vectors and Hybrid Search for the best performers of the iteration B Embedding Methods Create Sparse Vectors A B C A B C TESTING & EVALUATION
- 40. THANK YOU! SOFIA KONCHAKOVA DSC EUROPE NOVEMBER 2024