# [Bug]: Chunking for CSV Files Merges Rows - RAG Performance Impact
Hey guys, I've run into a pretty frustrating issue with chunking CSV files in RAGFlow, and I wanted to share what's going on and hopefully get some insights or solutions. Basically, the chunking process seems to be merging multiple rows together, leading to a significantly reduced number of chunks and, as a result, hurting the performance of my RAG (Retrieval-Augmented Generation) system. Let's dive into the details.
## The Problem: Chunking Gone Wrong
**_The core of the problem lies in how the chunking is behaving when dealing with CSV files_**. Instead of breaking down the CSV into smaller, more manageable chunks, it's lumping a ton of content into just a few chunks. This is a major issue because RAG systems rely on having well-defined, relatively small chunks to retrieve relevant information effectively. When you have massive chunks, the system struggles to pinpoint the exact pieces of information it needs, which leads to less accurate and less relevant results. Imagine trying to find a specific sentence in a 500-page book versus finding it in a single paragraph; the difference in efficiency is massive!
I was working with a CSV file containing around 3500 rows. Using the default chunking settings (general chunking, delimiter = \n, chunksize = 512), the system spat out only 21 chunks. That's a crazy low number! To put it in perspective, this means each chunk, on average, contained a huge amount of data (over 160 rows each!), which is far from ideal for RAG. I tried tweaking the `chunksize` parameter, experimenting with values as low as 2 to try to force smaller chunks, but **_nada, nothing changed._** The output remained stubbornly stuck with these massive chunks. I even tried switching to the "table" chunking strategy, thinking that might help, but that didn't make any difference either. This definitely feels like something is broken, and it's a big headache for anyone relying on RAGFlow for CSV data.
## The Expected Behavior: What Used to Be
This issue is especially frustrating because the behavior I'm seeing now is a complete departure from how things used to work. In v19.1, using the same default chunking settings and, crucially, the same exact CSV file, the system generated roughly 1700 chunks. That's a huge difference! It clearly showed an effort to keep each chunk relatively small, often containing just one or two rows from the CSV. This meant that the RAG system could efficiently retrieve the specific information it needed. It's like having a well-indexed library versus a disorganized storage unit.
I had read in other posts that the default parsing strategy for tables was to keep each row as a chunk, which would make perfect sense. The older behavior in v19.1 aligns with this expectation and worked great. I can only assume that something changed in how the chunking process handles CSV files between versions, and, honestly, it's a significant regression in functionality. Now, **_my RAG pipeline's performance is significantly degraded because of the lack of proper chunking._** It makes it difficult to get accurate and reliable results, as the system cannot easily access the specific data it needs. This regression is significantly affecting the user experience.
## Steps to Reproduce and Additional Info
To reproduce the issue, you essentially need to upload a CSV file and apply the default chunking settings in the RAGFlow environment. The attached image illustrates the output of the current chunking process, showing the severely reduced number of chunks compared to the number of rows in the original CSV file. This visual really highlights the core issue, the chunks are just too big.
The following steps outline how to reproduce this:
1. **Upload a CSV file:** Start by uploading a CSV file to the RAGFlow workspace. The size and content don't seem to matter too much, but a file with multiple rows is, of course, required for demonstration.
2. **Apply default chunking settings:** Make sure you're using the default chunking settings, which should include general chunking, a delimiter of `\n` (newline), and a `chunksize` of 512. This should be easily reproducible.
3. **Observe the output:** Analyze the output after the chunking process is complete. The output should display the total number of generated chunks. Pay close attention to the chunk count. Ideally, it should be close to, or at least related to the number of rows in the original CSV. If the number of chunks is significantly smaller than the number of rows, then you are experiencing the bug.
I hope this detailed report helps in identifying and fixing the issue! It's a critical problem for anyone using RAGFlow with CSV files, and I'm sure many users are facing the same challenges. I'm looking forward to a solution or a workaround to get things back on track.
## Seeking a Solution and Further Investigation
*I'm eager to know if anyone else has encountered this problem.* Are there specific versions where chunking for CSV files functions correctly? Has anyone discovered a workaround or a custom chunking strategy that effectively addresses this issue? I am thinking about writing a custom chunking strategy to bypass this issue, but I would have to perform a lot of testing to make sure it works.
*It would be helpful to know the core mechanics of chunking,* particularly the logic applied to CSV files. Understanding the code would help me determine if it is a configuration problem, an implementation error, or an internal problem. If you have any insights, please share them! It would speed up the troubleshooting process.
*I would like to know if there are any specific parameter settings that I can experiment with* that may help me reduce the chunk size. I tried modifying chunk size, but it was ineffective. Are there any other parameters that can influence the chunking process for CSV files? For example, are there settings related to row splitting or special handling for CSV delimiters? The more information available, the better.
I'll be following any updates on this bug and provide additional information as needed. Thanks for your time and effort in addressing this issue. If anyone knows of a workaround, please let me know!