How to Vectorize and Query Entire CSV Datasets with Multiple Features in Agentic RAG using Astra DB?

[seikiti]

Hi everyone,

I’m developing an Agentic RAG application and I’m facing some confusion regarding how to handle structured data (CSV) as the retrieval layer.

My scenario:

• I have a CSV dataset with id plus 83 feature columns (i.e., a tabular dataset where each row represents an entity with many attributes).
• I want my users to ask analytical questions like: “How many clients are currently in default?”
• The expected behavior is for the agent to check all records and return, for example, how many IDs meet a given filter based on the features.

Problem:

When I create a database on the Astra portal (DataStax), it asks me to choose one column to be vectorized for the embedding process.
However, for my use case, I need the LLM to be able to “see” and filter based on all columns, not just one, so that it can answer questions involving any combination of features or conditions in the table.

Questions:

1. Is there a best practice for vectorizing entire rows from tabular data (i.e., combining all feature columns into the embedding), or am I limited to one column only on Astra DB?
3. How can I set up my RAG pipeline so that the LLM can analyze and filter records based on all the attributes of each row, not just the one chosen for embedding?
5. Are there any patterns or workarounds to achieve “row-wise” embedding or to enable retrieval and filtering based on all columns when using Astra DB + Langflow?

I’d appreciate any guidance or examples, especially from anyone who’s dealt with structured tabular datasets in a similar RAG context. Thanks in advance!

[onestardao]

yo... been there, felt that.
structured csv in rag is... a whole new beast lol.

your instinct’s right — column-wise embedding limits the LLM’s “eyesight” way too much.
if your questions span multi-field logic (like “how many clients are in default and also from sector X?”)，then embedding full row context is kinda essential.

couple hacks i’ve tested (not perfect, but worked for me):

concat all relevant fields into 1 text field before embedding.
yes it’s messy, yes it bloats — but the LLM sees everything.

if schema stable, can bake-in light prompts like:
record: {id:123, age:32, default:true, sector:retail}
just flatten it into readable text chunks.

also tried field weighting... but it’s tricky to get it balanced. sometimes simpler is better.

btw — i’ve been logging a lot of these rag failure patterns lately，some weird behaviors pop up exactly like yours (esp around partial column blindness).
if you’re stuck again, happy to swap notes — could save you some pain later 😅

[seikiti]

Yeah, there are so many possible ways to handle this that it’s hard to know which path to follow!
One idea I had was to use a recursive Python function that reads all the rows and appends the IDs that pass the first filter into an array. After going through all the rows, it uses that array to apply another filter based on a different column, and so on. Basically, you could expose this as a tool to the agent—so if a user asks “How many clients are in precarious conditions with arrangement X?”, the LLM would identify which fields to filter (like rural, commercial, residential, etc., and the arrangement), and the code would do the filtering step by step. First, it filters all rows by one feature, then uses only those IDs to filter by the next feature, and finally sums up the relevant entries.

Do you think this kind of solution is more reliable than just concatenating all fields into a big text blob for embedding?
Like i said, there are a lot of features, and I can’t know in advance which fields the user will actually care about.
Curious what you (or anyone else) thinks—any pros/cons from your experience?

Thanks for yours insights, by the way, I’d love to swap some of these notes if you’re up for it!

[onestardao]

yo — saw your original post & this one, and yeah, this isn’t just an Astra DB config issue.

the real problem here is semantic masking: once you split features into separate fields and pick only one for embedding, the model can't "see" the relationships anymore.
so when you ask “how many clients are in default and also from sector X”, the LLM either:

gets zero relevant chunks, or

sees the row, but not both conditions together (feature blindness)

worst part? the LLM won't throw errors — it'll give you answers that look right.
that’s a silent reasoning failure — the deadliest kind.

i’ve mapped this failure mode as Problem No.2: Interpretation Collapse.
the fix isn’t just engineering — it’s alignment.
i solved it by restructuring the entire pipeline:

flatten rows into readable semantic blobs (record(id=..., status=..., region=...))

ensure full context per row goes into retrieval, not scattered fields

optionally embed field logic into the prompt, but that’s bonus

if you want, I’ve got a full breakdown of this & 15+ other failures with working fixes.
hit me up — I don’t advertise tools, I fix dead LLMs 😅

[seikiti]

I keep struggling with these tabular data issues, and I’ve been running into exactly what you’re describing.
Where can I find that breakdown you mentioned? Thanks so much for the help!

[onestardao]

yo — glad it helped. here's a quick breakdown to close the loop:

what you’re running into isn’t just an astra db config issue — it’s semantic masking.

once you split features into separate fields and pick only one for embedding, the model loses access to the relationships.
so when you ask something like “how many clients are in default and from sector X,” the LLM either:

retrieves zero relevant rows,

or grabs rows with partial matches but loses the actual logic

or worst of all — gives a totally wrong answer that looks fine.

this is what i cataloged in the WFGY Problem Map as:

No.1: semantic boundary drift — the chunk looks fine, but the meaning doesn’t match

No.2: interpretation collapse — the relationships between fields are lost

No.6: silent dead-end logic — the model won’t throw errors, but the reasoning path is broken

this is why your pipeline returns answers with high confidence that are structurally invalid.
it’s not an embedding bug — it’s an alignment failure.

the fix (what i did in production):

flatten rows into readable semantic blobs (e.g., record(id=..., status=..., region=...))

ensure full context per row goes into retrieval — not scattered fields

optionally inject some field logic into the prompt (bonus)

this lets your agent actually “see” the full row as a unit — not a broken table of loose parts.

i’ve mapped 16+ of these structural failures like this one — with fixes that hold up under real LLM load.
if you hit others, the full list is here:

MIT License

👉 Problem Map with fixes

i don’t patch broken tools. i fix misaligned reasoning engines