Design RAG System

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An enterprise-grade, production-ready knowledge base system leveraging Agentic GraphRAG combines semantic vector search, structured graph relationships, and autonomous multi-agent reasoning. This architecture eliminates context drift, handles multi-hop reasoning, and scales efficiently across millions of documents.

1. System Architecture Blueprint


graph TB
A[Raw Documents<br/>PDF, Code, Wikis]
B[Ingestion & Parsing Pipeline<br/>Layout-aware chunking<br/>Unstructured/LlamaParse]
C[Entity Extraction]
D[Hierarchical Chunking]
E[Graph DB<br/>Neo4j]
F[Vector DB<br/>Milvus/Qdrant]
G[Agentic Orchestration Layer<br/>State Graph / Router / Planner]
H[Graph Traversal]
I[Vector Similarity Search]
J[Cross-Encoder Reranker & Guardrails]
K[Final Grounded Output + Citations]

A --> B
B --> C
B --> D
C --> E
D --> F
E --> G
F --> G
G --> H
G --> I
H --> J
I --> J
J --> K

2. Core Components & Detailed Key Points

Component A: Ingestion & Knowledge Graph Construction

  • Layout-Aware Parsing: Extracting text, nested tables, and markdown structures while maintaining parent-child document hierarchies.
  • Entity & Relation Extraction: Using structured extraction schemas (via instructor or Pydantic with LLMs) to map out nodes (entities, concepts, components) and edges (dependencies, ownership, temporal relations).
  • Community Detection: Running algorithms like Leiden or Louvain on the graph to cluster subgraphs and generating hierarchical summaries for broad corpus queries.

💡 Interview Questions

  1. Chunking Strategies: How do you handle document chunking for complex files containing nested tables and code blocks without breaking semantic context?
  2. Entity Resolution: What strategies do you use for entity deduplication and disambiguation when multiple documents refer to the same entity using different aliases?
  3. Graph Summarization: Explain how you implement hierarchical community summaries (similar to Microsoft GraphRAG) to handle global, corpus-level queries instead of narrow fact retrieval.
  4. Incremental Updates: How do you handle real-time document updates, modifications, and deletions in both the graph database and vector index without requiring a costly full re-index?

Component B: Hybrid Storage & Indexing Layer

  • Multi-Model Persistence: Combining a Graph Database (e.g., Neo4j, NebulaGraph) for structural traversal, a Vector Database (e.g., Milvus, Qdrant) for dense semantic search, and a relational metadata store (Postgres) for ACL/RBAC permissions.
  • ID Synchronization: Maintaining atomicity and consistency between vector chunks and graph node IDs during concurrent write operations.

💡 Interview Questions

  1. Consistency Challenges: What synchronization and consistency models do you use between your graph database and your vector index when updates occur?
  2. Metadata Filtering & RBAC: How do you integrate user-level access control lists (ACLs) into the hybrid retrieval layer so users only retrieve authorized graph nodes and chunks?
  3. Scaling Graph Queries: How do you optimize Cypher or Gremlin traversal queries to prevent memory exhaustion and latency spikes on deep multi-hop lookups?

Component C: Agentic Orchestration & Retrieval Strategy

  • Stateful Agentic Workflows: Utilizing frameworks like LangGraph or custom state machines where an LLM router dynamically decides the retrieval path.
  • Query Decomposition & Rewriting: Breaking complex user prompts into sequential sub-tasks (e.g., separating temporal constraints from semantic entities).
  • Multi-Tool Execution: The agent dynamically chooses between Vector Search (semantic similarity), Graph Traversal (structural relationships), and Full-Text Search (exact keyword IDs).

💡 Interview Questions

  1. Agentic Loops & Latency: Walk through an Agentic RAG workflow. How do you prevent infinite routing loops, excessive API calls, and runaway latency?
  2. Dynamic Routing: How do you design a router classifier to decide when to trigger a graph traversal versus a dense vector search?
  3. Tool-Use Failures: How do you handle errors or hallucinated parameters when an agent attempts to query structured APIs or SQL databases alongside the knowledge graph?
  4. Self-Correction (Self-RAG): Explain how you implement critique loops to evaluate retrieved context sufficiency before passing it to the generator.

Component D: Generation, Reranking & Guardrails

  • Cross-Encoder Reranking: Filtering noisy chunks using models like BGE-Reranker or Cohere Rerank to optimize context window efficiency.
  • Context Window Optimization: Mitigating the “lost in the middle” phenomenon by placing the most relevant retrieved nodes/chunks at the extreme edges of the context window.
  • Deterministic Guardrails: Employing frameworks like NeMo Guardrails to block prompt injection, data exfiltration, and toxic generations.

💡 Interview Questions

  1. Context Window Management: How do you mitigate the “lost in the middle” phenomenon when passing dozens of graph neighborhoods and vector chunks into the context window?
  2. Hallucination Mitigation: What mechanisms do you enforce to ensure the LLM strictly cites explicit graph node references or chunk IDs for every factual claim?
  3. Prompt Injection & Security: How do you design an enterprise-grade security layer to prevent indirect prompt injection embedded inside unstructured ingestion documents?

Component E: Observability & Automated Evaluation

  • The RAG Triad: Continuously monitoring Context Relevance, Groundedness, and Answer Relevance using frameworks like Ragas, TruLens, or Phoenix.
  • Tracing & Debugging: Tracking agent decision paths, token costs, and latency breakdowns per execution step.

💡 Interview Questions

  1. Evaluation Metrics: What automated metrics do you prioritize in a CI/CD pipeline to evaluate retrieval quality independently from generation quality?
  2. Cost & Latency Optimization: How do you balance caching strategies (e.g., semantic caching of query embeddings and agent routing paths) with real-time freshness requirements?