Is a computed column an edge function?

No. An edge function runs on a request and does not cache per row by default. A computed column stores the result with the row and recomputes when dependencies change. Pixeltable vs Supabase covers Neon/Supabase functions.

Does every insert call the model again for old rows?

No. Unchanged rows keep their cached values. A new or edited source column recomputes dependents of that row. That is incremental computation.

Can the LLM see an image or a document?

Yes, if the provider UDF accepts that type. Text is chat_completions on a string. Gemini generate_content accepts an image and a prompt as contents. Use the UDF that matches the modality.

How to

How do I add computed columns with LLM calls in Python?

A computed column is a value produced by an expression or an LLM call when the row or its dependencies change, then cached. A declarative data pipeline is that schema: you name the columns, and the engine decides which rows run. Adding an LLM call is an assignment on the class, not a job you schedule around a table.

Updated 2026-10-14 · Part of How do I ... in Python?

Before you start

About 10 min.

Python 3.11+
pip install -U 'pixeltable[serve]'
pxt init in the project root
An OpenAI API key for gpt-4o-mini

1. Start with the stored field the model will read

A computed column is an expression over other columns. The source text (or image, or document) is stored. The model output is derived.

import pixeltable as pxt
from pixeltable.functions.openai import chat_completions

TableModel = pxt.model_base()

class Docs(TableModel, name='docs'):
    text: pxt.String

2. Assign the LLM call on the class

Computed fields are assignments, not annotations. chat_completions returns a response object; .choices[0].message.content is the string you cache. Insert a row and that expression runs. Edit the text and dependents of that row recompute.

class Docs(TableModel, name='docs'):
    text: pxt.String
    summary = chat_completions(
        messages=[{'role': 'user', 'content': text}],
        model='gpt-4o-mini',
    ).choices[0].message.content

3. Chain another column on the cached output

A declarative data pipeline is a graph of these assignments. A second call can classify the summary. Unchanged rows keep both caches. You do not enqueue each step.

class Docs(TableModel, name='docs'):
    text: pxt.String
    summary = chat_completions(
        messages=[{'role': 'user', 'content': text}],
        model='gpt-4o-mini',
    ).choices[0].message.content
    label = chat_completions(
        messages=[{
            'role': 'user',
            'content': 'Classify as bug, feature, or question. Reply with one word.\n\n' + summary,
        }],
        model='gpt-4o-mini',
    ).choices[0].message.content

# pxt schema update app.py docs -f

Computed LLM columns versus a job around the table

Computed LLM columns versus a job around the table
	Computed column	A scheduled job
You write	An assignment on the class	A worker that reads and writes rows
Runs when	The row or a dependency changes	A cron tick or a queue
Cache	Per row, with lineage	A column you overwrite by hand
Failure	That row, visible	A retry of the batch

Where Pixeltable fits

In Pixeltable a computed column is an assignment on TableModel. chat_completions is one expression. Insert a row and the LLM runs; unchanged rows stay cached. That is a declarative data pipeline, not an Airflow task or a LangChain chain with no store. See Pixeltable vs LangChain and Pixeltable vs Supabase (edge functions are not this cache).

import pixeltable as pxt
from pixeltable.functions.openai import chat_completions

TableModel = pxt.model_base()

class Docs(TableModel, name='docs'):
    text: pxt.String
    summary = chat_completions(
        messages=[{'role': 'user', 'content': text}],
        model='gpt-4o-mini',
    ).choices[0].message.content

Questions

Is a computed column an edge function?: No. An edge function runs on a request and does not cache per row by default. A computed column stores the result with the row and recomputes when dependencies change. Pixeltable vs Supabase covers Neon/Supabase functions.
Does every insert call the model again for old rows?: No. Unchanged rows keep their cached values. A new or edited source column recomputes dependents of that row. That is incremental computation.
Can the LLM see an image or a document?: Yes, if the provider UDF accepts that type. Text is chat_completions on a string. Gemini generate_content accepts an image and a prompt as contents. Use the UDF that matches the modality.