Problem Statement
The organisation building generative AI solutions face two major integration challenges:
1. Fragmented Data Access:
Multiple REST APIs and Fabric-specific connectors force developers to manually aggregate responses.
Overfetching/underfetching leads to high network costs and slower AI model input preparation.
2. Inefficient Query Processing for AI:
LLMs need semantically relevant, structured data — but current query flows are not optimized for RAG.
Schema changes in underlying data sources cause frequent API breakages, slowing AI feature delivery.
Proposed Solution
Stage 1: GraphQL Integration in Microsoft Fabric
Implement schema introspection and auto-generation to unify Fabric datasets under a single GraphQL endpoint.
Configure resolvers for direct Fabric queries, avoiding unnecessary ETL steps.
Apply query batching and caching to reduce network calls.
Stage 2: AI Optimization Layer
Integrate vector search APIs for semantic filtering before LLM ingestion.
Apply schema annotations for entity-type mapping, enabling RAG pipelines to retrieve only relevant data.
Build a schema evolution module that automatically updates GraphQL types when Fabric schemas change.
Key Tools & Techniques
Microsoft Fabric: Unified storage & compute for enterprise data.
GraphQL Server: Apollo Federation for multi-source stitching.
Caching & Query Batching: DataLoader pattern to cut down repeated calls.
AI Integration: RAG via LangChain + Azure OpenAI with GraphQL-based retrieval.
Monitoring: Apollo Studio metrics for resolver performance.
Implementation Details
GraphQL Setup:
Ran automated schema discovery across 12 Fabric datasets.
Created federated services for warehouse, lakehouse, and real-time datasets.
Optimization:
Reduced overfetching by enforcing field-level query constraints.
Implemented persisted queries to cut query compile time by ~40%.
AI Enablement:
Added vector index mappings for AI-powered retrieval.
Created GraphQL directives for specifying embedding models per entity type.
Results & KPIs
Latency Reduction: Avg. query latency down by 38%.
Network Efficiency: API payload sizes reduced by 45%.
AI Pipeline Speed: RAG ingestion time improved by 32%.
Developer Productivity: Reduced new AI feature delivery time from ~3 weeks to ~9 days.
Future Enhancements
AI-assisted query rewriting to further optimize resolver performance.
Auto-scaling GraphQL servers based on AI pipeline load.
Expanding schema federation beyond Fabric to external ERP/CRM APIs.
