---
title: "Embeddings"
description: "Enable AI-powered semantic search by converting text data into vector representations that understand meaning and context"
---

## What are Embeddings?

Embeddings are numerical vector representations of text that capture semantic meaning. When you create an embedding on a column:

- **Text is converted** into high-dimensional vectors using an embedding model
- **Vectors are stored** alongside your original data
- **Similar content** gets similar vector representations
- **Semantic search** becomes possible using natural language queries

## Creating Embeddings

```python
from timbal.platform.embeddings import create_embedding

# Create an embedding on a text column
await create_embedding(
    org_id="your-org-id",
    kb_id="your-kb-id",
    name="product_descriptions",  # Name for this embedding
    table_name="Products",        # Table containing the text
    column_name="description",    # Column to create embeddings for
    model="text-embedding-3-small",  # Embedding model to use
    with_gin_index=True          # Create index for better performance
)
```

You can create multiple embeddings on different columns or even the same column with different models.

<Warning>
- **Embeddings can only be created on text columns** - they convert text into vector representations
- **Embedding names must be unique across your entire knowledge base** - two different tables cannot have embeddings with the same name
- **Only create embeddings on columns you want to search semantically** - unnecessary embeddings waste storage and processing time
</Warning>

### Available Embedding Models

First, check what models are available for your organization:

```python
from timbal.platform.embeddings import list_embedding_models

# Get available embedding models
models = await list_embedding_models(org_id="your-org-id")

print("Available embedding models:")
for model in models:
    print(f"  - {model}")
```

Common embedding models include:

- **`text-embedding-3-small`** - OpenAI's efficient model, good for most use cases
- **`text-embedding-3-large`** - OpenAI's most powerful model, higher accuracy
- **`text-embedding-ada-002`** - OpenAI's previous generation model
- Additional models may be available depending on your organization setup

<Tip>
Check out [semantic search](/knowledge-bases/tables#semantic-search) to learn how to use embeddings for search.
</Tip>
