RAG
Strict grounded-answer retrieval engine for tenant-isolated knowledge bases.
RAG knowledge system combining hybrid sparse-dense vector retrieval, re-ranking, and strict provenance citation. Designed to ingest PDF manuals, documentation, source code, and knowledge bases for strictly grounded answers.
Hallucination-free document intelligence with verifiable source attribution.
Engineered with production-grade isolation, deterministic reliability, and seamless integration across the XIK infrastructure ecosystem.
Core Capabilities
4 Functional ModulesMulti-format Document Ingestion
Extracts clean semantic text from PDFs, Markdown, Notion, GitHub repos, and raw database dumps.
Context-Aware Chunking
Hierarchical chunking preserving headers, tables, and parent-child document relationships.
Strict Provenance & Citations
Every statement in the generated answer contains clickable footnotes pointing to exact source pages.
Tenant-isolated Knowledge Partitioning
Strict security boundaries guaranteeing that organizational knowledge never leaks across workspaces.
Technical Specifications
- Identifier
- rag
- System Stack
- Python LlamaIndex, Qdrant / pgvector, BGE / OpenAI Embeddings, Next.js UI
- Runtime Architecture
- Kubernetes Cluster
- Deployment Model
- Private VPC
- Supported Interfaces
- REST Query APIVector gRPCWebhook Sync