Knowledge Base Architecture
The knowledge base architecture provides the foundation for our agent intelligence, enabling comprehensive understanding, contextual responses, and personalized interactions. This architecture combines multiple data storage and retrieval approaches to create a sophisticated, multi-layered knowledge system.Core Knowledge Components
Our knowledge base consists of several key components that work together to provide agents with the information they need:Vector Database
Semantic search capabilities for understanding meaning and context in natural language
Document Store
Structured content with metadata for organized information retrieval
Graph Database
Relationship mapping between entities to understand connections and hierarchies
Metadata Index
Rapid filtering and categorization based on attributes and properties
Domain-Specific Knowledge
Each agent accesses specialized knowledge relevant to its domain:Brand Knowledge
- Company history and values
- Product information and specifications
- Brand voice and messaging guidelines
- Marketing materials and campaigns
- Customer success stories
Domain Expertise
- Fragrance science and composition
- Wellness and self-care principles
- Learning and development methodologies
- Community building best practices
- Technical development standards
User Context
- Individual user preferences and history
- Segment-specific insights and trends
- Interaction patterns and feedback
- Purchase history and product usage
- Learning progress and interests
Knowledge Organization and Access
1
Knowledge Ingestion
New information is processed through our content ingestion pipeline, which extracts key concepts, generates embeddings, and identifies relationships.
2
Storage Allocation
Content is distributed to appropriate storage systems based on type, usage patterns, and relationship complexity.
3
Metadata Enrichment
Information is tagged with relevant metadata to enhance discoverability and contextual retrieval.
4
Access Control
Permissions are applied based on agent roles and information sensitivity.
5
Retrieval Optimization
Access patterns are monitored to optimize retrieval speed for frequently used information.
Vector Search Capabilities
Our vector search system enables semantic understanding and retrieval:Key Features
- Semantic matching that understands meaning beyond keyword matching
- Contextual relevance scoring that considers user history and preferences
- Multi-modal vectors that represent text, images, and other content types
- Hybrid retrieval combining vector search with traditional filtering
- Continuous learning that improves over time based on interaction results
Knowledge Update Mechanisms
Our knowledge base remains current through several update mechanisms:Scheduled Updates
Scheduled Updates
Regular imports from content management systems, product databases, and learning materials ensure information remains current.
Event-Driven Updates
Event-Driven Updates
Changes in connected systems trigger immediate updates to relevant knowledge areas to maintain consistency.
User Feedback Integration
User Feedback Integration
Feedback on agent responses helps identify knowledge gaps or inaccuracies that need correction.
Automated Discovery
Automated Discovery
Monitoring systems identify potential new knowledge sources and suggest additions to the knowledge base.
Integration with Agent System
The knowledge base integrates with the agent system through several mechanisms:- Real-time query API for retrieving information during conversations
- Background knowledge retrieval that preemptively gathers relevant context
- Knowledge gap identification to flag areas where agent training may be needed
- Usage analytics to prioritize knowledge enhancements
- Personalization filtering to adapt knowledge retrieval to individual preferences
Implementation Technologies
Our knowledge base implementation leverages several technologies:- Pinecone for vector database and semantic search capabilities
- Supabase for structured data storage and relationships
- Neo4j for graph relationships and complex connection modeling
- Langchain for orchestrating retrievals and combining different knowledge sources
- Custom embedding pipeline for generating specialized, domain-specific embeddings
Future Enhancements
Planned enhancements to the knowledge base architecture include:- Enhanced multi-modal understanding combining text, images, and audio
- More sophisticated relationship inference between knowledge entities
- Improved personalization through deeper understanding of individual context
- Automated knowledge quality assessment and enhancement
- Real-time knowledge updates from live interactions
This documentation covers the shared knowledge base infrastructure. Each agent also maintains specialized knowledge retrieval methods optimized for their specific domain and responsibilities.