> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
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# Use a table pipeline as a reusable function
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| Scenario |
Pipeline |
Consumers |
| Research agent |
Web search → LLM analysis → Summary |
Report generator, Dashboard |
| Data enrichment |
Lookup → Transform → Validate |
Orders table, Analytics |
| Content moderation |
Vision analysis → Text check → Score |
User uploads, Comments |
`, `
| title |
summary |
| Climate Report |
Last year was the hottest on record, with global temperatures rising
by 1.2 degrees Celsius above pre-industrial levels due to ongoing
greenhouse gas emissions and a strong El Nino pattern, prompting calls
for urgent action to reduce carbon emissions. |
| Tech Merger |
Two major semiconductor companies are merging in a \$50 billion deal
that will give them a 30% share of the global chip market, pending
regulatory review in several countries over the next 18 months. |
`, `
| Pattern |
Use when |
| Table UDF |
You need to run a multi-step pipeline (LLM → process → LLM) |
@pxt.query |
You need to retrieve/search existing data |
@pxt.udf |
You need a pure Python transformation |
`];
Convert a table with computed columns into a callable function for
multi-agent workflows and pipeline composition.
## Problem
You have a table that runs a complex pipeline—LLM calls, tool use,
post-processing—and you want to reuse that entire pipeline from other
tables. Copy-pasting computed column definitions is error-prone and hard
to maintain.
## Solution
**What’s in this recipe:**
* Create an “agent” table with computed columns
* Convert the table to a callable UDF with
`pxt.udf(table, return_value=...)`
* Use the table UDF in other tables’ computed columns
You wrap an entire table pipeline as a function. When you call this
function from another table, it inserts a row into the agent table, runs
all computed columns, and returns the specified output column.
### Setup
```python theme={null}
%pip install -qU pixeltable openai
```
```python theme={null}
import getpass
import os
if 'OPENAI_API_KEY' not in os.environ:
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key: ')
```
```python theme={null}
import pixeltable as pxt
from pixeltable.functions.openai import chat_completions
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('table_udf_demo', force=True)
pxt.create_dir('table_udf_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'table\_udf\_demo'.
\
### Create an agent table with computed columns
You create a table that encapsulates a complete pipeline. This example
builds a summarization agent:
```python theme={null}
# Create the agent table with input column
summarizer = pxt.create_table(
'table_udf_demo/summarizer', {'text': pxt.String}
)
```
Created table 'summarizer'.
```python theme={null}
# Add the LLM call as a computed column
summarizer.add_computed_column(
response=chat_completions(
messages=[
{
'role': 'user',
'content': 'Summarize this in one sentence:\n\n'
+ summarizer.text,
}
],
model='gpt-4o-mini',
)
)
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Extract the summary text
summarizer.add_computed_column(
summary=summarizer.response.choices[0].message.content
)
```
Added 0 column values with 0 errors.
No rows affected.
### Convert the table to a UDF
You use `pxt.udf(table, return_value=...)` to convert the table into a
callable function. The `return_value` specifies which column to return:
```python theme={null}
# Convert the summarizer table into a callable UDF
summarize = pxt.udf(summarizer, return_value=summarizer.summary)
```
### Use the table UDF in another table
You can now use `summarize()` as a computed column in any other table:
```python theme={null}
# Create a table that uses the summarizer
articles = pxt.create_table(
'table_udf_demo/articles',
{'title': pxt.String, 'content': pxt.String},
)
```
Created table 'articles'.
```python theme={null}
# Add the table UDF as a computed column
articles.add_computed_column(summary=summarize(text=articles.content))
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Insert articles - summaries are generated automatically
articles.insert(
[
{
'title': 'Climate Report',
'content': 'Global temperatures rose by 1.2 degrees Celsius above pre-industrial levels last year, marking the hottest year on record. Scientists attribute this to continued greenhouse gas emissions and a strong El Nino pattern. The report calls for immediate action to reduce carbon emissions.',
},
{
'title': 'Tech Merger',
'content': 'Two major semiconductor companies announced a merger valued at $50 billion. The combined entity will control 30% of the global chip market. Regulators in multiple countries will review the deal over the next 18 months.',
},
]
)
```
Inserting rows into \`articles\`: 2 rows \[00:00, 196.58 rows/s]
Inserted 2 rows with 0 errors.
2 rows inserted, 6 values computed.
```python theme={null}
# View results
articles.select(articles.title, articles.summary).collect()
```
## Explanation
**How table UDFs work:**
Consumer table row → Table UDF called → Agent table inserts row →
Computed columns run → Return value extracted → Consumer gets result
**When to use table UDFs vs `@pxt.query`:**
**Key benefits:**
* **Encapsulation**: Hide complex pipeline details behind a simple
function call
* **Reusability**: Use the same agent from multiple consumer tables
* **Persistence**: All intermediate results are stored in the agent
table for debugging
* **Composition**: Chain agents together for multi-stage workflows
## See also
* [Look up structured
data](/howto/cookbooks/agents/pattern-data-lookup) -
Simple key-based lookups with `retrieval_udf`
* [Build a RAG
pipeline](/howto/cookbooks/agents/pattern-rag-pipeline) -
Retrieval with `@pxt.query`
* [Use tool calling with
LLMs](/howto/cookbooks/agents/llm-tool-calling) -
Add tools to agent tables
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