- Deep Knowledge Extraction: Extracts meaningful insights even from complex, multi-format documents.
- Template-Based Chunking: Offers intelligent, explainable chunking options tailored to your specific data types.
- Grounded Citations: Reduces hallucinations by providing traceable citations and visual text chunking for human verification.
- Heterogeneous Data Support: Seamlessly processes Word docs, Excel sheets, slides, images, and even scanned copies.
There documentation is amazing as well: https://ragflow.io/docs
Agentic RAG: Moving Beyond Simple Retrieval
While standard RAG focuses on simple retrieval, RAGFlow v0.8.0 introduces an agent mechanism that uses a graph-based task orchestration framework. This allows the system to perform complex reasoning steps like query intent classification and query rewriting before the final retrieval occurs.
| Feature | Traditional RAG | RAGFlow (Agentic) |
|---|---|---|
| Data Processing | Basic text splitting | Deep document understanding |
| Error Handling | High hallucination risk | Grounded, traceable citations |
| Workflow | Linear retrieval | Graph-based task orchestration |
Automated and Effortless Workflows
The platform provides a no-code workflow editor on the front end, making it easy to orchestrate complex search technologies. This streamlined approach is vital when scaling LLM applications from prototype to production.
// Conceptual RAGFlow Orchestration Flow 1. Query Intent Classification 2. Query Rewriting (if ambiguous) 3. Multi-recall retrieval 4. Fused Re-ranking 5. Final Context Generation
Large Language Models are only as intelligent as the context provided to them. When dealing with complex, unstructured enterprise data, traditional retrieval methods often struggle to find the exact information needed, leading to frequent hallucinations and unreliable outputs.
To bridge this gap, developers are turning to RAGFlow, a powerful open-source engine designed to create a superior context layer for LLMs by fusing advanced RAG with sophisticated Agent capabilities.
A ‘Quality In, Quality Out’ Approach
RAGFlow operates on the principle that high-fidelity AI systems require deep document understanding. It doesn’t just scrape text; it extracts knowledge from complicated, unstructured formats to ensure your model has access to the most accurate data possible.
- Deep Knowledge Extraction: Extracts meaningful insights even from complex, multi-format documents.
- Template-Based Chunking: Offers intelligent, explainable chunking options tailored to your specific data types.
- Grounded Citations: Reduces hallucinations by providing traceable citations and visual text chunking for human verification.
- Heterogeneous Data Support: Seamlessly processes Word docs, Excel sheets, slides, images, and even scanned copies.
Agentic RAG: Moving Beyond Simple Retrieval
While standard RAG focuses on simple retrieval, RAGFlow v0.8.0 introduces an agent mechanism that uses a graph-based task orchestration framework. This allows the system to perform complex reasoning steps like query intent classification and query rewriting before the final retrieval occurs.
| Feature | Traditional RAG | RAGFlow (Agentic) |
|---|---|---|
| Data Processing | Basic text splitting | Deep document understanding |
| Error Handling | High hallucination risk | Grounded, traceable citations |
| Workflow | Linear retrieval | Graph-based task orchestration |
Automated and Effortless Workflows
The platform provides a no-code workflow editor on the front end, making it easy to orchestrate complex search technologies. This streamlined approach is vital when scaling LLM applications from prototype to production.
// Conceptual RAGFlow Orchestration Flow 1. Query Intent Classification 2. Query Rewriting (if ambiguous) 3. Multi-recall retrieval 4. Fused Re-ranking 5. Final Context Generation
Most of RAGFlow’s chat assistants and Agents are based on datasets. Each of RAGFlow’s datasets serves as a knowledge source, parsing files uploaded from your local machine and file references generated in RAGFlow’s File system into the real ‘knowledge’ for future AI chats.
In an AI-powered chat, you can configure a chat assistant or an agent to respond using knowledge retrieved from multiple specified datasets (datasets), provided that they employ the same embedding model. In situations where you prefer information from certain dataset(s) to take precedence or to be retrieved first, you can use RAGFlow’s page rank feature to increase the ranking of chunks from these datasets. For example, if you have configured a chat assistant to draw from two datasets, dataset A for 2024 news and dataset B for 2023 news, but wish to prioritize news from year 2024, this feature is particularly useful.
To enhance multi-hop question-answering, RAGFlow adds a knowledge graph construction step between data extraction and indexing, as illustrated below. This step creates additional chunks from existing ones generated by your specified chunking method.
RAGFlow’s Memory module is built to save everything, including conversation that happens while an Agent is working. It keeps the raw logs of conversations, like what a user says and what the AI says back. It also saves extra information created during the chat, like summaries or notes the AI makes about the interaction. Its main jobs are to make conversations flow smoothly from one to the next, to allow the AI to remember personal details about a user, and to let the AI learn from all its past talks.
RAGFlow supports deploying models locally using Ollama, Xinference, IPEX-LLM, vLLM ，SGLang , GPUStack or jina. If you have locally deployed models to leverage or wish to enable GPU or CUDA for inference acceleration, you can bind Ollama or Xinference into RAGFlow and use either of them as a local “server” for interacting with your local models.
RAGFlow seamlessly integrates with Ollama and Xinference, without the need for further environment configurations. You can use them to deploy two types of local models in RAGFlow: chat models and embedding models. This tool looks simply incredible, give them a spin if you’re looking for a RAG solution.