RAGFlow Architecture Diagram: How the Codebase Works

Project Overview

RAGFlow is a sophisticated system designed to facilitate Retrieval Augmented Generation (RAG) applications by providing advanced, multilingual text analysis capabilities. Its core business problem is the efficient and precise preprocessing of diverse text data into structured, linguistically rich formats suitable for RAG engines. This involves complex tokenization, normalization, linguistic annotation (e.g., Named Entity Recognition, Part-of-Speech tagging), and conversion, all orchestrated through a high-performance C++ engine exposed to a Go backend and managed via a React frontend. Professional developers primarily interact with RAGFlow to configure text processing pipelines for various document types, ensuring optimal data preparation for downstream RAG models to retrieve relevant information effectively.

Category

ai-system

Difficulty

advanced

Tech Stack

Go

Author

infiniflow

Tags

rag, self-hosted, document-parsing, agents

RAGFlow is part of our RAG architecture, self-hosted app architecture and LLM framework architecture collections, where similar codebases are broken down side by side. Or browse the full architecture gallery.

How RAGFlow Works

RAGFlow is a sophisticated system designed to facilitate Retrieval Augmented Generation (RAG) applications by providing advanced, multilingual text analysis capabilities. Its core business problem is the efficient and precise preprocessing of diverse text data into structured, linguistically rich formats suitable for RAG engines. This involves complex tokenization, normalization, linguistic annotation (e.g., Named Entity Recognition, Part-of-Speech tagging), and conversion, all orchestrated through a high-performance C++ engine exposed to a Go backend and managed via a React frontend. Professional developers primarily interact with RAGFlow to configure text processing pipelines for various document types, ensuring optimal data preparation for downstream RAG models to retrieve relevant information effectively.

Data Flow

The primary data flow for domain-specific information starts with raw text content (e.g., a document uploaded via the React frontend). This raw text is sent to the Go backend, which acts as an orchestrator. The Go backend, leveraging CGO, passes the text to the high-performance C++ NLP engine (`rag_analyzer.cpp`). This engine performs intensive linguistic analysis: first, basic tokenization via `tokenizer.cpp`, followed by advanced processing like language-specific normalization (`openccxx.h`), lemmatization (`wordnet_lemmatizer.cpp`), stemming (`stemmer.h`), Named Entity Recognition (`thinc_ner.h`), and Part-of-Speech tagging/parsing (`thinc_parser.h`). Efficient data structures like `dart_trie.h` are used for dictionary lookups. The C++ engine returns a rich, structured representation of the text (e.g., token streams with associated linguistic annotations, identified entities, chunk boundaries) back to the Go backend. The Go backend then stores this processed data, along with various `parser_config.go` and `skill_search_config.go` configurations, in its persistent data store (e.g., a SQL database) via its DAO layer. The React frontend consumes this processed data and configuration information through RESTful APIs exposed by the Go backend, allowing users to visualize analysis results, configure pipelines, and manage knowledge retrieval settings. Updates to configurations follow a similar path from frontend to Go backend to database.

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Key Modules & Components

• Multilingual Text Preprocessing Engine: Implements the high-performance C++ core responsible for fundamental text segmentation, character classification, and language-specific normalization across various languages, including complex CJK processing. It forms the foundational layer for converting raw text into structured Term units suitable for RAG.
  Key files: internal/binding/cpp/rag_analyzer.h, internal/binding/cpp/rag_analyzer.cpp, internal/binding/cpp/tokenizer.h
• Advanced Linguistic Feature Extraction: Provides specialized C++ components for enriching text with deep linguistic annotations. This includes Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and lemmatization, leveraging external libraries like Thinc (for spaCy models) and WordNet, as well as Chinese character set conversion. These features add semantic context crucial for sophisticated RAG applications.
  Key files: internal/binding/cpp/thinc_ner.h, internal/binding/cpp/thinc_parser.h, internal/binding/cpp/wordnet_lemmatizer.h
• C++ to Go Interoperability Layer: Manages the Foreign Function Interface (FFI) using CGO, creating a robust bridge between the Go backend and the performance-optimized C++ NLP engine. This module exposes the C++ RAGAnalyzer functionality through a C-compatible API, enabling seamless integration and high-throughput text processing from Go.
  Key files: internal/binding/cpp/rag_analyzer_c_api.h, internal/binding/cpp/rag_analyzer_c_api.cpp
• RAG Pipeline Configuration Service: Handles the creation, persistence, and management of text analysis pipeline configurations (e.g., ParserConfig) within the Go backend. It provides utility functions for deep copying and merging these configurations, allowing professional developers to define and customize document preprocessing workflows for RAG.
  Key files: internal/common/parser_config.go, internal/dao/parser_config_migration.go
• Skill Search Configuration Management: Implements the Data Access Object (DAO) pattern in the Go backend for persisting and managing 'Skill Search Configurations.' This module defines how specific knowledge 'skills' are indexed and retrieved, offering CRUD operations for fine-tuning RAG retrieval parameters in the underlying database.
  Key files: internal/dao/skill_search_config.go
• RAG Workflow User Interface: Delivers the interactive React frontend application, serving as the primary user interface for configuring RAG analysis pipelines, uploading documents, and visualizing processed text analysis results. It orchestrates user interactions and communicates with the Go backend via RESTful APIs.
  Key files: web/src/app.tsx, web/src/main.tsx
• Frontend Development & Build System: Encompasses the tooling and configurations required for developing, building, and bundling the React frontend application. This includes dependency management (npm), build tooling (Vite), and TypeScript compiler settings, ensuring an efficient and consistent frontend development lifecycle.
  Key files: web/package.json, web/vite.config.ts, web/tsconfig.json
• Core Deployment & Project Infrastructure: Defines the foundational infrastructure and deployment strategy for the RAGFlow application. This includes Dockerization for containerized deployment, C++ build automation, and root-level documentation, providing the necessary framework for setup and operation.
  Key files: Dockerfile, README.md, internal/binding/cpp/Makefile

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