Generate RAG Pipeline Diagrams with AI

Visualize your entire Retrieval-Augmented Generation system — from document ingestion and embedding through vector retrieval and LLM response generation. Describe your stack in plain English and get a professional architecture diagram ready for design reviews, stakeholder presentations, or technical documentation.

The challenge

RAG systems are deceptively complex. A production RAG pipeline involves an ingestion path (document loading, chunking, embedding, vector upsert) and a separate query path (query embedding, ANN search, metadata filtering, reranking, context injection, LLM generation, citation mapping). Both paths have different latency requirements, different failure modes, and different scaling characteristics. Communicating this architecture to product managers, security reviewers, or new team members is genuinely difficult without a clear diagram.

The solution

Describe your RAG pipeline the way you'd explain it to a colleague:

"Documents are loaded from Confluence and S3, split into 512-token chunks, and embedded using text-embedding-3-small. Chunks land in Pinecone with tenant_id and doc_type metadata. At query time, the user's question is embedded, Pinecone retrieves top-10 chunks filtered by tenant_id, a Cohere reranker picks the best 5, and those are injected into a Claude claude-sonnet-4-6 prompt with conversation history from Redis. Responses stream to the React frontend and include citations."

From that description, you get a full RAG architecture diagram showing the ingestion path, query path, every component, and the data flows between them. Use chat-based editing to add batch ingestion scheduling, adjust metadata filters, or annotate cost boundaries.

RAG diagrams we support

Document ingestion pipelines
End-to-end ingestion flows from source documents (PDFs, Confluence, Notion, GitHub) through chunking, embedding, and vector database upsert, including async queuing and dead-letter queues.
Hybrid search architectures
Diagrams showing dense vector search and sparse BM25 keyword search running in parallel, with Reciprocal Rank Fusion and reranking stages.
Agentic RAG with query routing
Multi-path retrieval with a router LLM deciding between vector search, SQL lookup, and direct generation — including decision logic and feedback loops.
Multi-tenant RAG systems
Enterprise RAG architectures showing namespace isolation, tenant-scoped metadata filters, access control, and audit logging per query.

Perfect for

AI engineering team design documentation
Stakeholder presentations on RAG system architecture
Security and compliance reviews of data access patterns
Onboarding new engineers to your RAG stack
Architecture reviews before scaling to production
Debugging retrieval quality by visualizing the full pipeline

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