The complete Reltio product documentation corpus — 3,206 topics — optimized for AI consumption and available as structured Markdown. No authentication required.
Reltio is a cloud-native master data management (MDM) platform for managing, governing, and analyzing enterprise data.
Reltio AI-Ready Documentation gives you Reltio's entire product documentation corpus in a format that AI tools can consume directly. The files in this repository are sourced from Reltio's documentation platform — not scraped from HTML — and are updated every Wednesday and Friday.
You can drop these files into any RAG pipeline, AI agent, coding assistant, or LLM with a single URL. No scraping. No authentication. No custom ETL.
Note: These files are too large to preview in the GitHub web interface. See Why you can't read these files on GitHub for details and How to use these files to get started.
curl -L -o docs.md \
https://raw.githubusercontent.com/reltio-ai/reltio-ai-ready-docs/main/docs.mdThat's it — you now have the full Reltio documentation corpus locally. See How to use these files for integration examples.
The complete Reltio documentation corpus compiled into a single Markdown file. It covers 3,206 topics across all Reltio products and documentation categories:
- Reltio MDM — entity modeling, matching, merging, survivorship, data quality, APIs, and connectors
- Reltio I360 — customer intelligence, analytics, and identity resolution
- Administration — security, access control, audit trails, and environment management
- Integration — REST APIs, webhooks, Kafka, and third-party connectors
- Troubleshooting — diagnostics, common errors, and performance tuning
Source: The content comes directly from Reltio's documentation platform, not from crawling docs.reltio.com. Tables, code blocks, step sequences, and cross-reference URLs are accurately converted. This means you get complete, consistent content — not a best-effort scrape.
Use this file when you want to give an AI tool access to the full Reltio documentation corpus.
A structured index of every published Reltio topic. For each topic, the index provides:
| Field | Description |
|---|---|
| Hierarchy path | Full product area → category → topic path |
| Keywords | Topic-specific search terms |
| Summary | Short description of the topic's content |
| URL | Direct link to the topic on docs.reltio.com |
| Cross-references | Related topics and their paths |
Use this file alongside docs.md in RAG pipelines to significantly improve retrieval accuracy. The index gives AI models structural context about where each topic sits in the documentation hierarchy — not just flat text — which reduces irrelevant results and improves grounding. Chunking by topic hierarchy preserves parent-child context that flat text chunking loses, leading to more relevant retrieval results.
GitHub's web interface has file size limits for preview and rendering:
| File | Size | GitHub behavior |
|---|---|---|
docs.md |
~11 MB | "Sorry, this file is too large to display." |
index.md |
~3 MB | Shown as plain text; Markdown not rendered |
Both files exceed GitHub's 512 KB Markdown rendering limit, and docs.md exceeds the 5 MB display limit entirely.
This is expected behavior. These files aren't designed to be read in a browser — they're designed to be consumed programmatically by AI tools, RAG pipelines, and developer tooling.
To use the files, download them using the raw URL (see How to use these files).
# Download the documentation corpus
curl -L -o docs.md \
https://raw.githubusercontent.com/reltio-ai/reltio-ai-ready-docs/main/docs.md
# Download the contextual retrieval index
curl -L -o index.md \
https://raw.githubusercontent.com/reltio-ai/reltio-ai-ready-docs/main/index.mdgit clone https://github.com/reltio-ai/reltio-ai-ready-docs.gitPoint your vector store or retrieval pipeline at docs.md. Use index.md to chunk by topic hierarchy for more accurate retrieval.
LangChain
from langchain.document_loaders import TextLoader
loader = TextLoader("docs.md")
documents = loader.load()LlamaIndex
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(input_files=["docs.md"]).load_data()AWS Bedrock Knowledge Bases
Upload docs.md as a data source in your S3 bucket, then sync it to your Knowledge Base.
Azure AI Search
Index docs.md as a Blob Storage document using the built-in Markdown chunking skill.
Add docs.md to your project context so your coding assistant has full knowledge of Reltio's APIs, configuration model, and best practices.
Claude Code
Inside an interactive Claude Code session, reference the file directly:
@docs.md How do I configure entity matching in Reltio?
GitHub Copilot / Cursor / Windsurf
Add docs.md to your workspace or project rules context.
For models with large context windows (Claude, Gemini 1.5+), you can pass docs.md directly as part of your prompt:
import anthropic
with open("docs.md", "r") as f:
docs = f.read()
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-sonnet-4-5-20250620",
max_tokens=4096,
messages=[
{
"role": "user",
"content": f"<reltio_docs>\n{docs}\n</reltio_docs>\n\nHow do I configure entity matching in Reltio?"
}
]
)
print(response.content[0].text)The structured Markdown format is designed to be compatible with support platforms and AI bots that accept external knowledge sources. Refer to your platform's documentation for specific ingestion options.
| Use case | Files to use | Notes |
|---|---|---|
| RAG pipeline / AI assistant | docs.md + index.md |
Use index.md for better retrieval accuracy |
| AI coding assistant context | docs.md |
Works with Claude Code, Copilot, Cursor, Codex |
| Support bot / AI assistant | docs.md |
Check your platform's docs for ingestion options |
| Onboarding and training | docs.md |
Query conversationally with any LLM |
| LLM direct context | docs.md |
Best with large context window models |
| Custom GPT / AI agent | docs.md + index.md |
Upload both files for best results |
This repository is updated every Wednesday and Friday from Reltio's documentation platform.
| Time (UTC) | US Pacific | US Eastern | India (IST) | Europe (CET) |
|---|---|---|---|---|
| 17:30 | 10:30 AM | 1:30 PM | 11:00 PM | 6:30 PM |
The files reflect the latest published documentation as of the sync date shown in the badge at the top of this page. If you're maintaining a local copy, run git pull or re-download the files on Wednesdays and Fridays to stay current. Each sync updates the "Last synced" badge, which serves as the version indicator for this repository.
These files contain published product documentation only. The following content is not included:
- API schemas (Swagger / OpenAPI specifications) — see the Reltio Developer Portal
- SDK source code or code samples — see the Reltio Developer Portal
- Support articles — see the Reltio Support Portal
- Release notes in full detail — see Release Notes on docs.reltio.com
- Content in languages other than English
© Reltio, Inc. All rights reserved.
The content in this repository is Reltio's published product documentation, made available for use with Reltio products and services. You may use these files to build AI tools, integrations, and workflows that consume Reltio documentation. You may not republish or redistribute the content as your own.