Open Source End-to-End RAG Solution RAGFlow

Introduction

Simply put: for many Q&A systems represented by individuals and small to medium enterprises, there is indeed no need to use RAG. However, these long-context LLMs have either already addressed or are in the process of addressing one of the two major problems faced during the development of RAG, thus their relationship with RAG is one of cooperation rather than replacement. These two major issues are:

• Issues with LLM itself

• Issues with RAG

For RAG, the most basic capabilities of LLM include:

• Summarization ability

• Translation ability

Controllability (whether it listens)

Yes, you read that right, these three seemingly unexciting aspects are precisely what many LLMs have not done well. If these capabilities are not unlocked, the so-called logical reasoning and the decision-making systems of various agents are castles in the air. Therefore, with the upgrade of long-context LLMs, especially the enhancement of their ability to find needles in a haystack with long contexts, one of the problems faced in the implementation of RAG — the issues from LLM itself — has been significantly alleviated. The other major issue comes from the RAG system itself, which includes:

• Database issues. We have repeatedly emphasized the importance of multi-route retrieval for RAG in our past public articles. Even the simplest knowledge base struggles to perform well without multi-route retrieval. Therefore, the database of the RAG system needs to possess multi-route retrieval capability, rather than just a simple vector database.

• Data issues. This point may not be obvious to many friends who are starting with RAG, as using existing open-source software stacks, including various vector databases, RAG orchestration tools like LangChain, LlamaIndex, etc., along with a nice UI, can easily get an RAG system up and running. Similar orchestration tools have already gathered tens of thousands of stars on GitHub, yet none of these tools have effectively solved the problem of the data itself, leading to complex format documents entering the database in a chaotic manner, inevitably resulting in Garbage In Garbage Out.

The above two points are the main reasons why the current RAG remains superficial, especially failing to unlock more enterprise scenarios. Therefore, we are pleased to see that while the capabilities of LLM are continuously evolving, it is also necessary for us to specifically address the challenges of RAG itself: we provide a dedicated RAG database, Infinity, to alleviate the first point above, and we also offer a specialized RAG tool to tackle the second point, allowing RAG to gradually be utilized by more enterprises and individuals, unlocking more scenarios. This is the background of RAGFlow’s launch.

First of all, RAGFlow is a complete RAG solution that allows users to upload and manage their documents, which can be of any type, such as PDF, Word, PPT, Excel, and of course TXT. After intelligent parsing, the data enters the database in the correct format, allowing users to ask questions about their uploaded documents using any large model. In other words, it includes the following complete end-to-end process.

Secondly, the biggest feature of RAGFlow is its diversified intelligent document processing, ensuring that user data transitions from Garbage In Garbage Out to Quality In Quality Out. To achieve this, RAGFlow does not use any open-source RAG middleware, including LangChain, LlamaIndex, etc., but has completely redeveloped a set of intelligent document understanding systems, and based on this, constructed a RAG task orchestration system.

Main Features

? Quality In, Quality Out

• Based on deep document understanding^[2], able to extract insights from various complex formats of unstructured data. • Effectively completes needle-in-haystack tests in a truly infinite context (tokens).

? Template-based Text Slicing

• Not only intelligent but also controllable and explainable. • Multiple text templates are available.

? Evidence-based, Minimizing Hallucination

• The text slicing process is visualized and supports manual adjustments. • Evidence-based: answers provide key reference snapshots and support traceability.

? Compatible with Various Heterogeneous Data Sources

• Supports a rich variety of file types, including Word documents, PPT, Excel spreadsheets, TXT files, images, PDFs, photocopies, structured data, web pages, etc.

? Worry-free, Automated RAG Workflow

• Fully optimized RAG workflows can support various ecosystems from personal applications to large enterprises. • Both large language models (LLM) and vector models are configurable. • Based on multi-route retrieval and fusion re-sorting. • Provides an easy-to-use API for seamless integration into various enterprise systems.

Open Source Address

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