Key Components
A comprehensive architecture that handles everything from data ingestion to secure deployment
Ingestion Layer
Multiple memory types supported including Google Docs, PDFs, Confluence, Jira, and audio transcripts.
Embedding & Vectorization
Content chunking and vector embeddings using OpenAI or Microsoft Azure AI Services.
Storage & Indexing
Advanced vector storage with metadata optimization and relevance filtering.
Retrieval Engine
High-performance similarity search with batch processing for datasets with 90K+ rows.
LLM Generation
Context-aware response generation with OpenAI GPT, Mistral AI, and Perplexity.ai enrichment.
Security & Orchestration
Production deployment on GKE with Nest.js, access controls, monitoring, and security-conscious data boundaries.
Workflow Example
See how Hana's RAG pipeline processes data from ingestion to accurate response generation
Ingestion
User adds memory via dashboard or chat command
Example: @Hana /memory add "Q3 goals: Increase efficiency by 20%"
Processing
Content is chunked, embedded, and stored in MongoDB Atlas
Example: Vector embeddings generated with metadata
Query
User asks a question in natural language
Example: @Hana What are our Q3 goals?
Retrieval
Similarity search retrieves relevant chunks
Example: Score threshold >0.5 for high relevance
Generation
LLM generates contextualized response
Example: Based on memory: Q3 goals include 20% efficiency increase
Output
Accurate, cited response delivered to user
Example: Response with source attribution
Unique Optimizations
Advanced techniques that make Hana's RAG pipeline reliable, efficient, and enterprise-ready
Multi-Stage RAG
Initial semantic search followed by re-ranking for better precision.
Auto-Resync
Daily automatic updates keep data fresh without manual intervention.
Deep Integrations
Real-time data from Google Workspace, Stability.ai, and more.
Efficiency Optimized
Batch processing, metadata grouping, score thresholds reduce costs.
Enterprise Ready
CASA Tier-2, HIPAA compliant, no data used for external training.
Build yourRAG pipeline
Let's discuss how to implement a grounded knowledge workflow with the ingestion, retrieval, permissions, evaluation, and production controls the use case requires.