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# Create text embeddings with OpenAI
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 = [`
| Use case |
Input |
Output |
| Document search |
Articles |
Find related articles |
| Product matching |
Descriptions |
Find similar products |
| FAQ retrieval |
Questions |
Match to answers |
`, `
| title |
content |
sim |
| Machine Learning |
Machine learning is a subset of AI that enables systems to learn
from data. |
0.415 |
| Data Science |
Data science combines statistics, programming, and domain expertise
to extract insights from data. |
0.256 |
| Web Development |
Web development involves building websites and web applications
using HTML, CSS, and JavaScript. |
0.205 |
`, `
| Model |
Dimensions |
Use case |
text-embedding-3-small |
1536 |
Cost-effective, good quality |
text-embedding-3-large |
3072 |
Higher accuracy |
text-embedding-ada-002 |
1536 |
Legacy model |
`, `
| Metric |
Best for |
cosine |
Text similarity (default) |
ip |
Inner product |
l2 |
Euclidean distance |
`];
Generate vector embeddings for text data to enable semantic search and
similarity matching.
## Problem
You need to convert text into vector embeddings for:
* Semantic search (find similar documents)
* RAG pipelines (retrieve relevant context)
* Clustering and classification
## Solution
**What’s in this recipe:**
* Generate embeddings with OpenAI’s models
* Store embeddings as computed columns
* Use embeddings for similarity queries
You add an embedding column that automatically generates vectors for new
rows. The embeddings are cached and only recomputed when the source text
changes.
### 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 embeddings
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('embed_demo', force=True)
pxt.create_dir('embed_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'embed\_demo'.
\
### Create table with embedding column
```python theme={null}
# Create table for documents
docs = pxt.create_table(
'embed_demo/documents', {'title': pxt.String, 'content': pxt.String}
)
```
Created table 'documents'.
```python theme={null}
# Add embedding column using OpenAI's text-embedding-3-small
docs.add_computed_column(
embedding=embeddings(docs.content, model='text-embedding-3-small')
)
```
Added 0 column values with 0 errors.
No rows affected.
### Insert documents
```python theme={null}
# Insert sample documents
sample_docs = [
{
'title': 'Python Basics',
'content': 'Python is a high-level programming language known for its clear syntax and readability.',
},
{
'title': 'Machine Learning',
'content': 'Machine learning is a subset of AI that enables systems to learn from data.',
},
{
'title': 'Web Development',
'content': 'Web development involves building websites and web applications using HTML, CSS, and JavaScript.',
},
{
'title': 'Data Science',
'content': 'Data science combines statistics, programming, and domain expertise to extract insights from data.',
},
{
'title': 'Cloud Computing',
'content': 'Cloud computing provides on-demand computing resources over the internet.',
},
]
docs.insert(sample_docs)
```
Inserting rows into \`documents\`: 5 rows \[00:00, 553.22 rows/s]
Inserted 5 rows with 0 errors.
5 rows inserted, 15 values computed.
```python theme={null}
# View documents with embeddings (showing first 5 dimensions)
result = docs.select(docs.title, docs.embedding).collect()
```
### Query by similarity
Find documents similar to a query by creating an embedding index:
```python theme={null}
# Add embedding index for semantic search
docs.add_embedding_index(
column='content',
string_embed=embeddings.using(model='text-embedding-3-small'),
)
```
```python theme={null}
# Search for similar documents
sim = docs.content.similarity(
string='artificial intelligence applications'
)
results = (
docs.where(sim > 0.2)
.order_by(sim, asc=False)
.limit(3)
.select(docs.title, docs.content, sim=sim)
)
results.collect()
```
## Explanation
**OpenAI embedding models:**
**Similarity metrics:**
**Key benefits of computed embedding columns:**
* Embeddings are generated automatically on insert
* Results are cached—no re-computation on subsequent queries
* Index enables fast similarity search at scale
## See also
* [Semantic text
search](/howto/cookbooks/search/search-semantic-text) -
Full semantic search patterns
* [Chunk documents for
RAG](/howto/cookbooks/text/doc-chunk-for-rag) -
Prepare documents for retrieval
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