ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Master RAGFlow application debugging with 8 essential techniques. Learn to use centralized logging, control verbosity, and attach remote debuggers for efficient micro-service troubleshooting.
How RAGFlow Supports Querying Heterogeneous Data Sources: Architecture and ImplementationDiscover how RAGFlow unifies heterogeneous data sources with its connector abstraction and unified doc-store for seamless cross-source querying without complex logic. Explore architecture and implementation.
How RAGFlow Implements Python and JavaScript Code Execution for AI AgentsDiscover how RAGFlow securely executes Python and JavaScript code for AI agents using a novel three-layer sandbox architecture. Learn about its unified CodeExec component.
How RAGFlow Manages Document Metadata for Efficient Retrieval and IndexingDiscover how RAGFlow manages document metadata for efficient retrieval and indexing. Leverage per-tenant search indices for millisecond-level filtering alongside vector search.
Scaling RAGFlow for Large Volumes of Documents and Queries: Architecture and Best PracticesDiscover how to scale RAGFlow for massive document volumes and high query loads. Explore architecture, best practices, and key considerations for enterprise-grade performance.
How RAGFlow Handles Authentication and Authorization for API EndpointsLearn how RAGFlow secures its API endpoints with a two layer authentication and authorization mechanism involving UUID tokens and login required decorators.
How to Use the RAGFlow Python SDK to Build Custom RAG ApplicationsBuild custom RAG applications with the RAGFlow Python SDK. Programmatically manage datasets, parse docs, and create chat sessions effortlessly without HTTP boilerplate.
How RAGFlow Parses Multi-Modal Information from PDFs and DOCX Files: A Deep Dive into the DeepDoc ParserDiscover how RAGFlow's DeepDoc parser extracts text, tables, and figures from PDFs and DOCX files for advanced RAG pipelines. Learn about OCR, layout, and table detection for unified data representation.
How Cross-Language Query Support Works in RAGFlow: A Technical Deep DiveDiscover how RAGFlow implements cross-language query support. Learn how LLM translation and prompt templates enable seamless retrieval across multilingual documents. Read the technical deep dive.
How RAGFlow Manages State and Memory for Its AI Agents: A Deep Dive into the Document-Store ArchitectureExplore RAGFlow's document-level memory architecture for AI agents. Discover how per-user indices and hybrid retrieval manage conversational state across sessions efficiently.
How RAGFlow Handles Data Synchronization from Confluence, S3, Notion, Discord, and Google DriveDiscover how RAGFlow synchronizes data from Confluence, S3, Notion, Discord, and Google Drive using its unified SyncBase abstraction and configurable batch processing for seamless updates.
RAGFlow Agent Tools: Complete Catalog and Extension GuideExplore RAGFlow agent tools: discover built-in tools like Wikipedia, Google Search & more, and learn how to extend RAGFlow by adding new tools to its agent tools directory.
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