Explore RAGFlow on DeepWiki

An AI-generated, always-up-to-date knowledge base for understanding RAGFlow's codebase — designed for developers doing secondary development or deep-diving into RAGFlow's internals.

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:::caution NOTE The RAGFlow content on DeepWiki is maintained by DeepWiki, not by the RAGFlow team. It may lag behind the latest official release. Always refer to the official RAGFlow documentation and source code for the most up-to-date information. :::

What is DeepWiki?

DeepWiki is an AI-powered tool that automatically reads a GitHub repository's source code, tests, and documentation to produce a structured, interactive wiki. It maps out architecture diagrams, module relationships, data flows, and design rationale — all without requiring manual documentation work.

The RAGFlow DeepWiki page

The RAGFlow project is indexed at:

https://deepwiki.com/infiniflow/ragflow

Target audience

This resource is primarily intended for:

• Secondary developers who want to extend or customize RAGFlow (e.g., add a new document parser, integrate a new LLM provider, or modify the retrieval pipeline).
• Contributors who need to understand how a specific module fits into the overall architecture before filing a PR.
• Researchers and engineers who want to study RAGFlow's internal design principles — chunking strategies, embedding pipelines, graph-based retrieval, and agent orchestration.

:::tip NOTE For general usage of RAGFlow (configuring knowledge bases, running chat, etc.), the Guides section is a better starting point. :::

What you can find on DeepWiki

Topic	What to look for
Overall architecture	High-level component diagram showing how api/, rag/, deepdoc/, agent/, and web/ relate to each other
Document ingestion pipeline	How files flow from upload → parsing (deepdoc/) → chunking → embedding → storage
Retrieval pipeline	How queries are processed, how hybrid search (keyword + vector) works, and how reranking is applied
Agent framework	How agent/ orchestrates multi-step reasoning, tool calling, and memory
LLM / Embedding abstractions	How rag/llm/ wraps different model providers behind a unified interface
API layer	How api/apps/ Blueprint routes map to internal service calls

Using DeepWiki alongside local development

When you are making changes to the codebase, DeepWiki can help you quickly answer questions such as:

• "Where is the entry point for task execution?"
• "Which class handles PDF page segmentation?"
• "How does the knowledge graph retrieval differ from the dense vector path?"

You can also ask DeepWiki questions in natural language using its built-in chat interface — it will ground its answers in the actual source code.

Keeping the wiki current

DeepWiki re-indexes the repository automatically when the upstream main branch is updated. If you notice the indexed content lagging behind a recent release, you can trigger a manual re-index from the DeepWiki page.

Related resources

• Launch service from source — set up a local RAGFlow development environment.
• Build RAGFlow Docker image — build a custom image after code changes.
• Contribution guidelines — how to file a PR once you understand the codebase.