EasyDoc Intelligent Document Analysis: Let you answer your RAG correctly and accurately
Solve the document parsing problem of RAG system. EasyDoc intelligent document parsing engine makes the answer more accurate and comprehensive.
Core content:
1. Common pain points and challenges of RAG system document parsing
2. Core functions and advantages of EasyDoc intelligent parsing engine
3. How EasyDoc improves the accuracy and user experience of RAG system
Dear developers, today we are going to talk about a pain point that every RAG system will encounter: the dilemma of document parsing. Imagine that you spent a lot of time building a RAG system, but because of the low-quality input of document parsing, the Q&A results are full of irrelevant content, and the user experience is greatly reduced. Have you ever encountered such an embarrassment: the user asks for a certain data, but the system cannot give an accurate answer because of the failure of table parsing?
We all know that the core advantage of the RAG system is to use massive document libraries to provide smarter context and fewer illusions. But the reality is that the low-quality input of document parsing directly affects the accuracy of the output. Traditional parsing tools simply extract text and ignore structure, tables and pictures, resulting in confusing data and low efficiency.
Basic OCR can give you text, simple parsing tools may give you simpler Markdown, but in the end you get:
Poor chunking : Fixed size or paragraph-based chunking destroys semantic context and leads to retrieval of irrelevant content. Lost Hierarchy : “Chapter 3, Section 2, Point 5” becomes meaningless text that the LLM cannot use to pinpoint or understand the context. “Blind spots” of tables and images : Critical data locked in tables or graphs is either lost or becomes unreadable text gibberish. Multimodal RAG becomes a castle in the air. Endless pre-processing : You spend more time cleaning your data than actually building your RAG application.
EasyDoc is an intelligent parsing engine designed for RAG
A few days ago, a friend recommended me to try EasyDoc, saying that it is an intelligent document parsing engine built specifically for the AI era, and it is currently in the promotion period, providing developers with a very generous free trial quota:
Lite and Pro modes come with a $10 trial fee, which allows you to parse thousands of pages of documents for free . Premium mode comes with 500 pages of free quota .
At that time, my friend showed me an example of using EasyDoc to parse an industry report and then using RAG to do knowledge questions and answers. It can be seen that the key information of the charts in the industry report can be captured and associated with the context. The effect can be said to be quite satisfactory.
How EasyDoc improves RAG accuracy
EasyDoc's core features directly address RAG's data quality bottlenecks:
1. Intelligent content segmentation : Say goodbye to simple segmentation. EasyDoc uses semantic understanding to identify logical content blocks (paragraphs, list items, table cells). This means you will get cleaner, more semantically relevant blocks, leading to higher retrieval accuracy.
2. Deep hierarchical structure analysis : EasyDoc reconstructs the document structure and provides a clear tree structure. Each block contains its parent_id, allowing you to track its exact location and context. This is very helpful for accurately locating sources in RAG answers and implementing context-aware retrieval strategies !
3. True table and image understanding (Premium mode) : This is where the real power lies. EasyDoc does more than just capture tables/images. It understands them, extracts structured data (such as rows/columns) and provides semantic descriptions (see the vlm_understanding field in the JSON output). This unlocks true multimodal RAGs , allowing your system to understand all content, not just text. It even handles cross-page table merging .
EasyDoc can convert documents in a variety of input formats (PDF, Word, PPT, TXT, etc.) into clean, structured JSON, which is optimized for use with LLM, especially for RAG.
Easy to call: API built for developers
EasyDoc provides simple and direct API access and offers multiple modes to meet your RAG needs:
? Lite Mode : A quick start for basic text extraction. Good for prototyping or simple plain text RAGs.
curl --location --request POST 'https://api.easydoc.sh/api/v1/parse' \
--header 'api-key: <your-api-key>' \
--form 'file=@"<your-file-path>"' \
--form 'mode="lite"'
? Pro Mode : Ideal for most RAG scenarios. Captures full text as well as key document hierarchy (parent_id). Great for improving search relevance and source tracking.
curl --location --request POST 'https://api.easydoc.sh/api/v1/parse' \
--header 'api-key: <your-api-key>' \
--form 'file=@"<your-file-path>"' \
--form 'mode="pro"'
? Premium Mode : Fully functional mode. Unlocks deep table/image understanding (vlm_understanding), suitable for advanced, multi-modal RAG applications.
curl --location --request POST 'https://api.easydoc.sh/api/v1/parse' \
--header 'api-key: <your-api-key>' \
--form 'file=@"<your-file-path>"' \
--form 'mode="premium"'
