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.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →Knowledge Graph
A network of entities and the relationships between them, queryable like a map.
- →Entity Extraction
Pulling structured entities (people, places, orgs, dates) from text.
- →Semantic Search
Finding information by meaning rather than exact keyword match.
Related workflows
Turn this into a repeatable process.
- →Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
- →Dynamic Context Insertion Workflow
This workflow details how to dynamically inject context-specific information into LLM prompts based on user queries or application state, improving response accuracy and relevance.
- →Multi-Turn Context Management Workflow
This workflow manages conversation history and other dynamic context in multi-turn interactions with LLMs, ensuring coherence and relevance over extended dialogues.
Related tool stacks
The tools that run it in production.
- →LLM Context Management Stack
A technology stack for effectively managing and optimizing the context provided to large language models, ensuring efficient, relevant, and cost-effective operations.
- →Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
- →Indie SaaS Launch Stack
Everything a solo founder needs to ship and monetize a SaaS in weeks.
- →AI Marketing Ops Stack
The control center for an AI-augmented marketing team of one to five.
Comparisons & alternatives
Pick between the options.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.