RAG Orchestration
Build Custom Retrieval Pipelines
Design end-to-end RAG pipelines by connecting VectorDB, AI Function Tools, and Custom LLMs into stateful workflows — grounded answers, live data, and full control.
Build Blocks for Custom RAG
Connect your data, tools, and models — then orchestrate retrieval end-to-end.
VectorDB Integration
Use MonoChat embedded VectorDBs or connect your own with credentials — keep embeddings where you want.
- Embedded or external VectorDB
- Credential-based connections
- Metadata filters & access control
- Secure data isolation
Multi-Step RAG Pipelines
Chain retrieval steps: query rewrite, search, rerank, cite, respond — all inside one orchestration flow.
- Query rewrite & expansion
- Hybrid retrieval patterns
- Reranking and scoring
- Citations & traceability
AI Function Tools
Let LLMs take actions: fetch live data, call internal APIs, run business logic, and return structured results.
- Tool schemas & validation
- Secure API calls
- Structured outputs
- Reusable tool library
Custom LLM Routing
Route tasks to the best model — fast vs. accurate — with fallback and cost controls.
- Multi-provider support
- Per-step model selection
- Fallback & retries
- Cost/performance guardrails
Stateful Workflows
Keep state across steps and sessions. Build journeys that remember context and progress reliably.
- State + memory variables
- Session-aware flows
- Conditional branching
- Event-driven triggers
Production Controls
Deploy safely with monitoring, permissions, and auditability — iterate without breaking ops.
- Observability & metrics
- Role-based controls
- Audit logs
- Versioned workflows
How It Works
A simple path to production-grade RAG — without vendor lock-in.
1) Connect VectorDB
Use embedded vector stores or connect your own by pasting credentials.
2) Add Tools
Build AI Function Tools to fetch live data, call APIs, or run business actions.
3) Route Models
Choose the best LLM per step. Use fallbacks to balance cost and quality.
4) Orchestrate Pipeline
Chain steps: rewrite → retrieve → rerank → cite → respond — with stateful logic.
Typical Pipelines
Common RAG blueprints you can ship quickly — then customize forever.
Support RAG
Ground answers in policies, manuals, and tickets — escalate to agents with full context.
Sales RAG
Answer product questions with catalog + pricing + CRM tools, and convert in-chat.
Operations RAG
Turn SOP documents into guided actions with approvals, dashboards, and automation.
Key Benefits
More reliable answers, faster ops, and full control — built for production.
Higher Answer Quality
Reduce hallucinations by grounding every response in retrieval + tools.
Faster Resolution
Multi-step automation + routing means fewer back-and-forth messages.
Lower Cost via Routing
Use smaller models for most steps and reserve strong models for final reasoning.
Full Data Control
Keep embeddings and sources where you decide — embedded or bring your own VectorDB.
Build Your First RAG Pipeline
Connect your VectorDB, add AI tools, route models, and ship reliable retrieval workflows in MonoChat.