CRAG MultiHop Reasoning Engine: Self-Grading RAG with Query Decomposition
- Automatically decomposes complex questions into logical sub-queries (up to 3 hops)
- Self-grading retrieval system evaluates context quality before generation
- Hybrid dense/sparse search with local Jina reranker for precision
- Real-time WebSocket streaming shows pipeline progress visually
- Graceful degradation maintains functionality during partial failures
The Challenge: Why CRAG MultiHop Reasoning Engine Was Built
Traditional Retrieval-Augmented Generation (RAG) systems face two critical limitations:
- Multi-Hop Questions: Complex queries requiring intermediate reasoning steps often fail because standard RAG performs single-step retrieval.
- Noisy Contexts: Weak or irrelevant retrieved documents lead to hallucinated answers when fed to LLMs.
The CRAG MultiHop Reasoning Engine addresses these through a novel pipeline combining:
1. Sequential Question Decomposition
2. Self-Grading Retrieval (Corrective RAG)
3. Hybrid Dense+Sparse Search with Local Reranking
4. Real-Time Pipeline VisualizationCore Architecture & Technical Stack
Containerized Microservices
- Frontend: React + Vite with WebSocket event streaming
- Backend: Django ASGI (Daphne) with Celery task queues
- Vector DB: ChromaDB for dense retrieval
- Search: BM25 sparse retrieval + Jina Reranker v3
- LLM: OpenRouter with Qwen 30B for generation
Model Pipeline
| Component | Model | Execution |
|---|---|---|
| Embeddings | multilingual-e5-small | Local CPU |
| Reranker | jina-reranker-v3 | Local CPU |
| Generator | Qwen 30B | Cloud (OpenRouter) |
Key Features Breakdown
1. Multi-Hop Query Decomposition
Breaks complex questions like “What were the economic impacts of the 2021 Suez Canal obstruction on European manufacturing?” into sequenced sub-queries:
- Identify key events during 2021 Suez Canal obstruction
- Find European manufacturing sectors dependent on Suez routes
- Cross-reference economic reports from impacted industries
2. Self-Grading Corrective RAG
Uses multilingual-e5-small to classify retrieved chunks as:
- Correct: Directly relevant (proceeds to generation)
- Ambiguous: Triggers query refinement
- Incorrect: Falls back to external web search
Real-World Use Cases
- Investigative Research: Connect facts across legal documents or medical studies
- Technical Support: Diagnose issues requiring multi-step manual lookups
- Academic Literature Reviews: Synthesize findings from disparate papers
How It Works: Step-by-Step Workflow
- User submits query via WebSocket connection
- System decomposes into sub-queries (if multi-hop enabled)
- Executes hybrid dense/sparse retrieval against ChromaDB
- Grades results using CRAG evaluator
- Reranks merged results with Jina Cross-Encoder
- Generates answer with Qwen 30B
- Streams verification scores back to UI
Comparison: CRAG vs Traditional RAG
| Feature | Traditional RAG | CRAG MultiHop |
|---|---|---|
| Query Complexity | Single-step | Multi-hop (3+ steps) |
| Retrieval QA | Passes all results to LLM | Self-grades context quality |
| Fallback | None | External web search |
Frequently Asked Questions
How does multi-hop differ from chain-of-thought prompting?
Multi-hop performs sequential retrievals with each step’s results modifying subsequent queries, while CoT maintains a single context window.
What hardware requirements does the system have?
Designed for 4GB+ RAM VPS environments with CPU-only support for local models (jina-reranker-v3, multilingual-e5).
Can I customize the retrieval pipeline?
Yes – the UI allows toggling hybrid search, multi-hop depth, CRAG grading, and reranking per query.
Conclusion & Next Steps
The CRAG MultiHop Reasoning Engine represents a significant evolution in RAG architectures by combining self-assessment with sequential reasoning. For developers building complex QA systems, it provides:
- A reference implementation for agentic RAG workflows
- Production-ready Django/React codebase patterns
- Configurable pipeline components
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