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Processing Analytics
AI Configuration
Processing Pipeline
Architecture Comparison
SimpleRAG
Best For:
- Quick factual queries
- Single-context questions
- When response time is critical
Limitations:
- May miss nuanced context
- Less accurate for complex queries
Example Usage:
Q: "What is the revenue for 2023?" Best: SimpleRAG (direct fact lookup)
HybridRAG
Best For:
- Multi-part questions
- When keyword matching is important
- Balance of speed and accuracy
Limitations:
- Slightly slower than SimpleRAG
- May return redundant information
Example Usage:
Q: "Compare the sales in Europe vs Asia" Best: HybridRAG (combines semantic & keyword search)
ReRankerRAG
Best For:
- Complex analytical questions
- When accuracy is crucial
- Multi-context synthesis
Limitations:
- Slower processing time
- Higher computational cost
Example Usage:
Q: "What are the implications of the policy changes?" Best: ReRankerRAG (precise context ranking)
About RAG
RAG Technology
Retrieval-Augmented Generation (RAG) enhances LLM responses with context from your documents:
- Real-time document processing
- Semantic search capabilities
- Context-aware responses
- Source verification
Architecture Comparison
SimpleRAG
Fast vector similarity search using embeddings. Best for straightforward queries.
HybridRAG
Combines semantic and keyword search. Ideal for complex queries needing precise matching.
ReRankerRAG
Advanced result reranking for highest accuracy. Best when precision is critical.
Technical Stack
Performance Metrics
Pro Tips
- •Use specific questions for better context retrieval
- •Compare architectures for different query types
- •Check source contexts to verify accuracy
- •Use HybridRAG for complex queries