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# Look up structured data with retrieval UDFs
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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 = [`
| Use case |
Lookup key |
Returns |
| Customer support |
customer_id |
Contact info, order history |
| Product search |
sku or product_name |
Price, inventory, specs |
| Financial analysis |
ticker or date |
Stock prices, transactions |
| Inventory check |
warehouse_id, product_id |
Stock levels |
`, `
| sku |
name |
price |
category |
| LAPTOP-001 |
MacBook Pro 14" |
1999. |
electronics |
| LAPTOP-002 |
ThinkPad X1 |
1499. |
electronics |
| PHONE-001 |
iPhone 15 Pro |
999. |
electronics |
| CHAIR-001 |
Ergonomic Office Chair |
449. |
furniture |
| DESK-001 |
Standing Desk |
699. |
furniture |
`, `
| get_by_category |
| [{"sku": "LAPTOP-001", "name": "MacBook Pro 14\"", "price": 1999.,
"category": "electronics"}, {"sku": "LAPTOP-002", "name": "ThinkPad X1",
"price": 1499., "category": "electronics"}, {"sku": "PHONE-001", "name":
"iPhone 15 Pro", "price": 999., "category": "electronics"}] |
`, `
| order_id |
product_sku |
quantity |
product_info |
| ORD-001 |
LAPTOP-001 |
2 |
[{"sku": "LAPTOP-001", "name": "MacBook Pro 14\"", "price": 1999.,
"category": "electronics"}] |
| ORD-002 |
PHONE-001 |
1 |
[{"sku": "PHONE-001", "name": "iPhone 15 Pro", "price": 999.,
"category": "electronics"}] |
| ORD-003 |
CHAIR-001 |
4 |
[{"sku": "CHAIR-001", "name": "Ergonomic Office Chair", "price":
449., "category": "furniture"}] |
`, `
| Parameter |
Description |
table |
The table to query |
name |
Function name (defaults to table name) |
description |
Description for LLM tool use |
parameters |
Columns to use as lookup keys |
limit |
Max rows to return (None = all) |
`, `
| Pattern |
Example |
| Data enrichment |
Join order SKUs to product details |
| LLM tool calling |
Let agents query databases |
| Reference lookups |
Convert codes to descriptions |
| Cross-table joins |
Link related records |
`];
Create lookup functions that query tables by key—for customer records,
product catalogs, or financial data.
## Problem
You have structured data—customer records, product catalogs, financial
data—and need to look up rows by key values. Common scenarios:
## Solution
**What’s in this recipe:**
* Create lookup functions from tables with `retrieval_udf`
* Query by single or multiple keys
* Use lookups in computed columns for data enrichment
Use `pxt.retrieval_udf(table)` to automatically create a function that
queries the table by its columns.
### Setup
```python theme={null}
%pip install -qU pixeltable
```
```python theme={null}
import pixeltable as pxt
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('lookup_demo', force=True)
pxt.create_dir('lookup_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'lookup\_demo'.
\
### Create a product catalog table
```python theme={null}
# Create a product catalog
products = pxt.create_table(
'lookup_demo/products',
{
'sku': pxt.String,
'name': pxt.String,
'price': pxt.Float,
'category': pxt.String,
},
)
products.insert(
[
{
'sku': 'LAPTOP-001',
'name': 'MacBook Pro 14"',
'price': 1999.00,
'category': 'electronics',
},
{
'sku': 'LAPTOP-002',
'name': 'ThinkPad X1',
'price': 1499.00,
'category': 'electronics',
},
{
'sku': 'PHONE-001',
'name': 'iPhone 15 Pro',
'price': 999.00,
'category': 'electronics',
},
{
'sku': 'CHAIR-001',
'name': 'Ergonomic Office Chair',
'price': 449.00,
'category': 'furniture',
},
{
'sku': 'DESK-001',
'name': 'Standing Desk',
'price': 699.00,
'category': 'furniture',
},
]
)
products.collect()
```
Created table 'products'.
Inserting rows into \`products\`: 5 rows \[00:00, 502.31 rows/s]
Inserted 5 rows with 0 errors.
### Create a lookup function with retrieval\_udf
```python theme={null}
# Create a lookup function that searches by SKU
get_product = pxt.retrieval_udf(
products,
name='get_product',
description='Look up a product by its SKU code',
parameters=['sku'], # Only use SKU as the lookup key
limit=1, # Return at most 1 result
)
# Check the function signature
```
```python theme={null}
# Look up a product by SKU
result = products.select(get_product(sku='LAPTOP-001')).limit(1).collect()
```
### Look up by category (multiple results)
```python theme={null}
# Create a category lookup (returns multiple products)
get_by_category = pxt.retrieval_udf(
products,
name='get_by_category',
description='Get all products in a category',
parameters=['category'],
limit=10, # Return up to 10 products
)
# Find all electronics
products.select(get_by_category(category='electronics')).limit(
1
).collect()
```
### Use lookups for data enrichment
```python theme={null}
# Create an orders table
orders = pxt.create_table(
'lookup_demo/orders',
{
'order_id': pxt.String,
'product_sku': pxt.String,
'quantity': pxt.Int,
},
)
orders.insert(
[
{
'order_id': 'ORD-001',
'product_sku': 'LAPTOP-001',
'quantity': 2,
},
{
'order_id': 'ORD-002',
'product_sku': 'PHONE-001',
'quantity': 1,
},
{
'order_id': 'ORD-003',
'product_sku': 'CHAIR-001',
'quantity': 4,
},
]
)
```
Created table 'orders'.
Inserting rows into \`orders\`: 3 rows \[00:00, 1186.28 rows/s]
Inserted 3 rows with 0 errors.
3 rows inserted, 6 values computed.
```python theme={null}
# Add a computed column that enriches orders with product details
orders.add_computed_column(
product_info=get_product(sku=orders.product_sku)
)
# View enriched orders
orders.select(
orders.order_id,
orders.product_sku,
orders.quantity,
orders.product_info,
).collect()
```
Added 3 column values with 0 errors.
## Explanation
**`retrieval_udf` parameters:**
**Use cases:**
**Tips:**
* Use `limit=1` for unique key lookups
* Specify only needed columns in `parameters` for cleaner APIs
* Add descriptions for LLM tool integration
## See also
* [Use tool calling with
LLMs](/howto/cookbooks/agents/llm-tool-calling) -
Use retrieval UDFs as LLM tools
* [Build a RAG
pipeline](/howto/cookbooks/agents/pattern-rag-pipeline) -
Semantic search with `@pxt.query`
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