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Workflow

RAG Content Ingestion Pipeline

Convert messy docs into searchable, cited knowledge chunks for AI systems.

1 min read

/ quick answer

Build a repeatable ingestion workflow that cleans, chunks, embeds, labels, and refreshes sources before retrieval. Convert messy docs into searchable, cited knowledge chunks for AI systems.

Convert messy docs into searchable, cited knowledge chunks for AI systems. The problem it solves: RAG quality collapses when source documents are duplicated, stale, poorly chunked, or missing metadata. Build a repeatable ingestion workflow that cleans, chunks, embeds, labels, and refreshes sources before retrieval. It runs in 5 steps, starting with collect sources and assign canonical ownership. This workflow node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Problem
RAG quality collapses when source documents are duplicated, stale, poorly chunked, or missing metadata.
Solution
Build a repeatable ingestion workflow that cleans, chunks, embeds, labels, and refreshes sources before retrieval.
Steps
  1. 01Collect sources and assign canonical ownership.
  2. 02Remove duplicates, outdated files, and low-quality drafts.
  3. 03Chunk by semantic section with source URL, owner, date, and permissions.
  4. 04Embed chunks into a vector database and run retrieval tests.
  5. 05Schedule refreshes and flag stale content automatically.
Tools Used
Prompts Used
Variations
  • Separate public docs from internal-only knowledge.
  • Add a content-owner approval queue.
Related Dictionary
/ frequently asked

What does the RAG Content Ingestion Pipeline workflow do?

Build a repeatable ingestion workflow that cleans, chunks, embeds, labels, and refreshes sources before retrieval.

What problem does RAG Content Ingestion Pipeline solve?

RAG quality collapses when source documents are duplicated, stale, poorly chunked, or missing metadata.

How many steps does RAG Content Ingestion Pipeline take?

5 steps. It starts with collect sources and assign canonical ownership. and ends with schedule refreshes and flag stale content automatically..

Which tools does RAG Content Ingestion Pipeline need?

It uses rag-starter-stack, knowledge-graph-stack — each linked below with its own node.

↳ connected nodes
Dictionary↳ linked
RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
Dictionary↳ linked
Semantic Search
Finding information by meaning rather than exact keyword match.
Dictionary↳ linked
Vector Database
A database optimized for similarity search over embeddings.
Dictionary↳ linked
Structured Output
Forcing AI responses into predictable schemas that software can use.
Tool Stack↳ linked
RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
Tool Stack↳ linked
Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.
Prompt↳ linked
Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
Comparison↳ linked
Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
Use Case↳ linked
Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.
Dictionary↳ linked
Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
Dictionary↳ linked
Embedding
A numerical vector representation of text, image or audio that captures meaning for similarity search.
Dictionary↳ linked
Context Window
The maximum amount of text (in tokens) an LLM can consider in a single call.
Dictionary↳ linked
Reranking
A second-pass model that reorders retrieved chunks by true relevance to the query.
Dictionary↳ linked
Chunking
Splitting documents into retrievable pieces before embedding them for RAG.
Comparison↳ linked
RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
Tool Stack↳ linked
AI Support Agent Stack
Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
Workflow↳ linked
AI Voice Agent Patient Intake
This workflow details using an AI voice agent to automate initial patient intake processes in healthcare, including collecting demographic information, symptom pre-screening, and scheduling appointments. It streamlines administrative tasks and improves patient flow.
Workflow↳ linked
PII Data Redaction Workflow
This workflow outlines the systematic process for identifying, extracting, and redacting Personally Identifiable Information (PII) from unstructured and structured data sources to ensure data privacy and compliance.