Best way to ingest and vectorize PDFs

Help me Build my Workflow
,
1

Hi everyone,

I am using n8n for the first time. I am creating a local RAG chat agent at my company to retrieve information from our internal knowledge base.

I have deployed n8n locally through the self hosted starter kit, along with Qdrant, Postgre, and Open WebUI that I added to the docker compose file.

To start with, I am trying to load a few PDF documents, parse, split and vectorize them. Those PDF files are stored on my computer.

Below is the first workflow I created, which seems to be the most intuitive one. I am using the « Read/Write Files from Disk » node to read all PDFs documents on my local folder, then the « Extract from File (From PDF) » to parse them. The « Default Data Loader » « Type of Data » parameter is set to « JSON ». Doing it this way, all the documents metadata are considered as text and vectorized, as shown in the screenshot below.

Capture d’écran 2025-07-25 à 14.52.41
Capture d’écran 2025-07-25 à 14.52.412860×1490 507 KB

I then tried to get rid of the « Extract from File (From PDF) » node and setting the « Default Data Loader » « Type of Data » parameter to « Binary ». This other workflow is shown below.
I also added a metadata option to have a « title » metadata with the « fileName » output of the « Read/Write Files from Disk » taken as value (see first screenshot below).
Doing so, my metadata are no longer considered as text and thus vectorized. I can see my « title » metadata as a first level metadata, as shown in the second screenshot below. But I still have a lot of other metadata fields and subfields. I would prefer to have the hand on which metadata I keep exactly.

Capture d’écran 2025-07-25 à 14.58.09
Capture d’écran 2025-07-25 à 14.58.092860×1490 474 KB

Capture d’écran 2025-07-25 à 14.57.35
Capture d’écran 2025-07-25 à 14.57.352860×1490 484 KB

I would like to know which workflow/solution is best in my case to ingest and vectorize my PDF documents. I would have said the first one intuitively but I don’t want to have metadata as vectors. What is the « Extract from PDF » used for then?

Is the second workflow better? And how can I tidy up this metadata mess?

Thanks a lot in advance

Cheers

Antoine

2

I find the way to get the text first to be cleaner and more space efficient, especially with large datasets, you can still set the metadata manually if you need to with the other approach, I would probably stick with feeding text to the data loader instead of a binary.

3

Thanks for your reply @jabbson!

When you say that I can set metadata manually with the other approach if needed, do you mean that it is exclusive. I can’t set metadata the way I want with the first approach?

4

No it is not exclusive, can totally still grab metadata from the file automatically, and then add more manually.

5

You mean in the Default Data Loader node right?

And what about the fact that metadata are considered as text content and vectorized, as show in my first screenshot above? Are you just fine with it or is there a way to get rid of them?

6

You can avoid it by selecting “load specific data” in the loader, instead of load all data.

1 Like