RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
/ quick answer
To augment the raw retrieved documents in a RAG system with additional, structured, or synthesized information, creating a more comprehensive and actionable context for the LLM. A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
- Vector Database (e.g., Milvus, Chroma): Stores embeddings of core documents and potentially enriched metadata.
- Knowledge Graph (e.g., Neo4j, Grakn): Connects entities and relationships, providing structured factual context.
- Entity Extraction/Named Entity Recognition (NER) (e.g., spaCy, custom LLM call): Identifies key entities from retrieved text.
- Query Expansion Module (e.g., keyword extraction, semantic similarity with LLM): Rewrites or expands user queries for better retrieval.
- Context Summarization/Abstractive Generation (e.g., T5, Llama 2): Condenses verbose retrieved information or creates concise overviews.
- Schema-based Output Generator (e.g., Pydantic, Instructor): Structures the enriched context into a consistent format for the LLM.
How does context enrichment help with complex queries?
For complex queries, enrichment can break down the query into sub-questions, retrieve diverse types of information, and then synthesize them. For example, it might identify entities in the query, search for those entities in a knowledge graph, and add relevant attributes to the context, providing a richer understanding to the LLM.
Is this stack suitable for real-time applications?
Yes, but careful optimization of each component is required to minimize latency. Techniques like caching, efficient API calls, and optimized data processing pipelines are crucial for maintaining real-time performance while performing complex enrichment steps.