ArchGen

RAG Pipeline Architecture Template

A retrieval-augmented generation architecture diagram template for ingestion, vector search, and LLM synthesis. Also works as a block diagram or system diagram for RAG systems.

Generate system diagrams, system block diagrams, and software architecture diagrams from text.

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Template: RAG PipelineStyle: Corp
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Detailed RAG Pipeline Architecture (Ingestion -> Vector Retrieval -> Context Fusion -> LLM Synthesis) has 4 layers: Data Sources Layer, Ingestion Pipeline (Index Build & Refresh), Retrieval & Generation Pipeline (Online Serving), Supporting Services (Governance, Observability, Security).

Style gallery

Pick a style and jump straight into generation.

Clean
Best for product docs and software architecture diagrams.
DocsSpecs
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Classic
Enterprise reviews and system architecture diagram templates.
EnterpriseReview
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Dark
Low-light presentations and technical briefings.
DecksBriefing
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Hand
Workshop whiteboarding and early-stage discovery.
WorkshopIdeation
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Blueprint
Blueprint-style architecture reviews.
BlueprintReview
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Brutal
Bold internal narratives and strategic alignment.
StrategyBold
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Soft
Storytelling decks and stakeholder updates.
StoryStakeholders
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Glass
Pitch-ready visuals for demos and sales.
PitchDemo
Use Glass
Terminal
Infra, ops, and observability handoffs.
OpsInfra
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Corp
Formal stakeholder updates and compliance decks.
FormalCompliance
Use Corp

What you get

  • Layered architecture blocks with clear responsibilities.
  • Ready-to-edit module names and duty lists.
  • Key flows to explain system behavior quickly.
  • A block diagram of a web application architecture for quick overviews.
  • All supported styles in a single template page.
  • Shareable links for team reviews and feedback.
  • Instant export to high-resolution PNG.

How to use this template

  1. Pick a style from the gallery above.
  2. Generate your diagram and edit titles or modules.
  3. Export a PNG or share the diagram link.

Default structure

This architecture diagram template uses default layers: Data Sources Layer, Ingestion Pipeline (Index Build & Refresh), Retrieval & Generation Pipeline (Online Serving), Supporting Services (Governance, Observability, Security).

Who it's for

  • AI product teams
  • Knowledge base search
  • Support copilots

Who it's not for

  • Apps without searchable knowledge
  • Pure offline batch ML workflows

Best for

  • Enterprise knowledge assistants
  • Customer support copilots
  • Document summarization systems

Key layers

Show 4 layersClick to expand
  • Data Sources Layer: Modules include Unstructured Document Sources, Document Connectors.
  • Ingestion Pipeline (Index Build & Refresh): Modules include Document Parser & Cleaner, Text Chunker, Embedding Service, Vector Index Writer.
  • Retrieval & Generation Pipeline (Online Serving): Modules include Query API / Chat Gateway, RAG Orchestration Layer, Query Embedding Model, Vector Retrieval (Top-K).
  • Supporting Services (Governance, Observability, Security): Modules include Metadata Store & ACL, Caching & Rate Control, Monitoring & Evaluation.

Module responsibilities

Show 13 itemsClick to expand
  • Data Sources Layer / Unstructured Document Sources: Provide raw knowledge content; Serve as the system-of-record for documents
  • Data Sources Layer / Document Connectors: Fetch documents and metadata; Detect updates and deletions; Normalize into ingestion payloads
  • Ingestion Pipeline (Index Build & Refresh) / Document Parser & Cleaner: Convert heterogeneous formats to clean text; Preserve structure (headings, sections, tables tags); Attach metadata (source, author, timestamp)
  • Ingestion Pipeline (Index Build & Refresh) / Text Chunker: Split documents into retrieval-friendly chunks; Balance recall vs precision using overlap; Emit stable chunk identifiers for updates
  • Ingestion Pipeline (Index Build & Refresh) / Embedding Service: Generate dense vectors for chunks; Ensure consistent embedding model/version usage; Handle rate limits and failures
  • Ingestion Pipeline (Index Build & Refresh) / Vector Index Writer: Persist embeddings into vector store; Maintain metadata for filtering and citations; Support incremental updates and deletions
  • Retrieval & Generation Pipeline (Online Serving) / Query API / Chat Gateway: Receive user queries and context; Enforce access control and quotas; Route requests to RAG orchestrator
  • Retrieval & Generation Pipeline (Online Serving) / RAG Orchestration Layer: Manage end-to-end RAG workflow; Apply routing, filtering, and policies; Assemble prompts and control context budget
  • Retrieval & Generation Pipeline (Online Serving) / Query Embedding Model: Produce query embedding compatible with chunk vectors; Ensure stable retrieval behavior across versions; Optimize latency via batching/caching
  • Retrieval & Generation Pipeline (Online Serving) / Vector Retrieval (Top-K): Retrieve the most relevant chunks for the query; Enforce security filters before returning chunks; Provide candidates for reranking and fusion
  • Supporting Services (Governance, Observability, Security) / Metadata Store & ACL: Enforce authorization during retrieval; Provide traceable provenance for citations; Support document lifecycle (delete/expire)
  • Supporting Services (Governance, Observability, Security) / Caching & Rate Control: Reduce latency and cost; Protect backend services under load; Stabilize performance during bursts
  • Supporting Services (Governance, Observability, Security) / Monitoring & Evaluation: Measure RAG effectiveness and drift; Detect failures (empty retrieval, hallucinations); Support iterative tuning of chunking and prompts

Key flows

Show 3 flowsClick to expand
  • Ingestion pipeline: connectors pull PDFs/Wikis, a parser cleans and normalizes content, the Text Chunker splits into token-aware overlapping chunks, the Embedding Model generates vectors, and the Vector Index Writer upserts vectors + metadata into Pinecone for incremental refresh and deletion handling.
  • Retrieval pipeline: a user query enters the Query API, the orchestrator (LangChain) normalizes it and generates a query embedding, then performs top-k similarity search in Pinecone with metadata/ACL filters; optional reranking and MMR improve precision and diversity.
  • Generation pipeline: the orchestrator fuses the selected chunks into a context window within the token budget, injects it into a prompt, and calls the LLM (GPT-4) to synthesize a grounded final answer while preserving citations back to chunk_id/doc sources.

Template prompt

Generate a detailed RAG (Retrieval-Augmented Generation) pipeline architecture. The flow should start with an Ingestion Pipeline where unstructured documents (PDFs/Wikis) are processed via a Text Chunker and passed to an Embedding Model to generate vectors, stored in a Vector Database (e.g., Pinecone). The Retrieval Pipeline should show a User Query being vectorized, matching against the Vector DB for top-k relevant chunks, and then being fused into a Context Window sent to an LLM (e.g., GPT-4) for final answer synthesis. Include an Orchestration layer (e.g., LangChain) managing this workflow.

Recommended tweaks

  • Swap the vector database and embedding model
  • Add re-rankers for better relevance
  • Introduce evaluation and feedback loops
  • Add guardrails, PII filters, and policy checks
  • Support streaming ingestion for fresh data
  • Annotate it as a software/system architecture diagram for stakeholders.

FAQ

  • Do I need a vector database?
    For scale and semantic retrieval, yes. Use a hosted vector store or a self-managed alternative.
  • How do I keep results fresh?
    Add a streaming ingestion pipeline and scheduled re-indexing.
  • Can I mix keyword and semantic search?
    Yes. Hybrid retrieval often improves precision for enterprise content.
  • How do I reduce hallucinations?
    Use citations, rerankers, and stricter context windows.
  • Does this support multi-tenant deployments?
    Yes. Partition indexes and enforce tenant-aware access controls.
  • Can this run on-prem?
    Yes. Swap LLM, vector DB, and storage modules to match your environment.