Beyond Basic RAG
Standard RAG has limitations in certain scenarios:Complex Reasoning
Large Document Sets
Diverse Information Types
Domain-Specific Nuances
Multi-Turn Conversations
Dynamic Information
Advanced RAG Architectures
Prisme.ai supports several advanced RAG architectures that you can implement based on your specific needs:- Multi-Stage Retrieval
- Recursive Retrieval
- Hypothetical Document Embeddings
- Knowledge Graph RAG
- Self-Reflective RAG
- First stage performs efficient but less precise retrieval (e.g., BM25 keyword search)
- Second stage applies more intensive semantic filtering on first-stage results
- Final stage re-ranks candidates using cross-encoders or other precise methods
- Only the highest quality content is passed to the LLM
Advanced Context Processing
Beyond retrieval architectures, sophisticated methods for processing retrieved context can significantly improve response quality:Context Compression
Context Compression
- LLM-Based Summarization: Using a model to create concise summaries of retrieved documents
- Semantic Compression: Removing redundant information while preserving meaning
- Information Distillation: Extracting only the most relevant facts and details
- Token Optimization: Maximizing information density within token constraints
- Makes more efficient use of context window
- Reduces noise and distractions
- Allows inclusion of more diverse sources
- Improves response coherence
Contextual Fusion
Contextual Fusion
- Hierarchical Aggregation: Organizing information at different levels of detail
- Cross-Document Coreference: Identifying when different documents refer to the same entities
- Information Reconciliation: Resolving contradictions between sources
- Narrative Threading: Creating a coherent flow across document fragments
- Creates unified context from fragmented sources
- Reduces contradictions and inconsistencies
- Preserves important relationships between facts
- Presents information in logical progression
Contextual Routing
Contextual Routing
- Query Classification: Categorizing questions by type and intent
- Domain Detection: Identifying the knowledge domain of the question
- Complexity Assessment: Determining question difficulty and required approach
- Pipeline Selection: Choosing the optimal processing strategy
- Applies specialized approaches for different question types
- Optimizes resource allocation
- Improves handling of diverse queries
- Enables domain-specific customizations
Semantic Enrichment
Semantic Enrichment
- Entity Recognition: Identifying and tagging named entities
- Concept Linking: Connecting text to knowledge base concepts
- Semantic Annotation: Adding metadata about meaning and relationships
- Ontology Mapping: Relating content to domain-specific knowledge structures
- Enhances retrieval precision
- Enables concept-based rather than just keyword-based retrieval
- Supports reasoning about relationships
- Facilitates domain-specific understanding
Multi-Agent RAG Systems
For particularly complex knowledge applications, multiple specialized agents can work together:Query Analysis
- Intent identification
- Domain classification
- Complexity assessment
- Subtask identification
Knowledge Retrieval
- Document specialist for textual knowledge
- Structured data agent for databases and tables
- Knowledge graph navigator for entity relationships
- Media analyzer for images and diagrams
Information Synthesis
- Resolving contradictions
- Organizing information logically
- Identifying information gaps
- Creating unified context
Response Generation
- Appropriate format and style
- Clear explanation logic
- Accurate source attribution
- Addressing all aspects of the query
Self-Reflection
- Factual accuracy
- Comprehensiveness
- Clarity and coherence
- Appropriate detail level
Advanced RAG Implementation with Prisme.ai
Implementing advanced RAG architectures in Prisme.ai follows a structured approach:- Configuration Approach
- AI Builder Approach
- Custom Development
- Multi-stage retrieval configuration
- Query preprocessing settings
- Context handling parameters
- Response generation strategies
Webhook Integration for Advanced RAG
- Implement custom processing logic
- Integrate with specialized AI systems
- Override various stages of the RAG pipeline
- Create sophisticated multi-step workflows
Webhook Subscription Events
You can subscribe to different events in the AI Knowledge lifecycle:Document Management Events
Document Management Events
documents_created
: Triggered when new documents are addeddocuments_updated
: Triggered when existing documents are modifieddocuments_deleted
: Triggered when documents are removed
- Custom document processing pipelines
- Content moderation and validation
- Metadata enrichment
- Document transformation
Query Events
Query Events
queries
: Triggered when users ask questions
- Custom context retrieval
- Specialized prompt engineering
- Complete answer generation
- Parameter customization
Test Events
Test Events
tests_results
: Triggered for each test case execution
- Custom evaluation criteria
- Specialized test analytics
- Integration with quality systems
- Performance benchmarking
Webhook Response Options
Depending on the event type, your webhook can return different responses to influence the RAG process:- Context Retrieval
- Prompt Generation
- Complete Answer
- Parameter Override
- Test Evaluation
- Custom retrieval strategies
- External knowledge sources
- Specialized context processing
- Dynamic information integration
Setting Up Webhook Integration
To implement webhook integration for advanced RAG:Create External Service
- HTTPS endpoint
- Ability to process webhook requests
- Business logic implementation
- Response generation
Configure AI Builder
- Create a new automation in AI Builder
- Configure event subscriptions on AI Knowledge
- Connect to your webhook endpoint
- Set up authentication
Subscribe to Events
- Document management events
- Query processing events
- Test evaluation events
Test Integration
- Monitor webhook requests
- Validate response formats
- Check integration behavior
- Troubleshoot any issues
Use Case Examples
Medical Knowledge Advisor
Challenge: Providing accurate medical information from diverse sources including research papers, clinical guidelines, and drug databases.
Advanced RAG Solution: Multi-stage retrieval with knowledge graph integration
Key Features:
- Entity recognition for medical terms
- Relationship tracking between conditions, treatments, and medications
- Source prioritization based on evidence quality
- Self-reflective validation for factual accuracy
Legal Research Assistant
Challenge: Navigating complex legal documents, precedents, and statutes with precise citation and reasoning.
Advanced RAG Solution: Recursive retrieval with contextual routing
Key Features:
- Hierarchical decomposition of legal questions
- Jurisdiction-aware retrieval pathways
- Citation tracking and verification
- Temporal reasoning about law changes
Technical Support Advisor
Challenge: Troubleshooting complex technical issues spanning multiple products, versions, and systems.
Advanced RAG Solution: Multi-agent RAG with self-reflection
Key Features:
- Problem classification and decomposition
- Product-specific knowledge agents
- Step-by-step solution synthesis
- Verification against known issues database
Financial Analyst
Challenge: Analyzing financial data from reports, market trends, and news to provide investment insights.
Advanced RAG Solution: Hypothetical document embeddings with structured data integration
Key Features:
- Financial query expansion and reformulation
- Integration of numerical data analysis
- Time-sensitive information prioritization
- Data visualization for complex insights
Advanced RAG Best Practices
Architecture Selection
Architecture Selection
- Match architecture complexity to actual needs
- Consider maintenance requirements and technical expertise
- Start with simpler approaches and add complexity as needed
- Validate architecture choices with realistic test scenarios
- Document architecture decisions and rationales
Implementation Strategy
Implementation Strategy
- Use configuration options for moderate customization needs
- Leverage AI Builder for complex but codeless implementations
- Reserve custom development for highly specialized requirements
- Implement iteratively with continuous testing
- Create reusable components for common patterns
Performance Optimization
Performance Optimization
- Monitor and optimize retrieval precision and recall
- Balance response quality with latency requirements
- Consider resource usage for production-scale deployments
- Implement caching strategies where appropriate
- Profile and optimize bottlenecks in the pipeline
Webhook Integration
Webhook Integration
- Ensure webhook endpoints are reliable and performant
- Implement proper error handling and fallback mechanisms
- Use appropriate authentication and security measures
- Monitor webhook performance and reliability
- Document webhook interfaces and expected behaviors