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LanceDB will automatically vectorize the data both at ingestion and query time. All you need to do is specify which model to use. We support popular embedding models like OpenAI, Hugging Face, Sentence Transformers, CLIP, and more.

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Step 1: Import Required Libraries

First, import the necessary LanceDB components: 
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
  • lancedb: The main database connection and operations
  • LanceModel: Pydantic model for defining table schemas
  • Vector: Field type for storing vector embeddings
  • get_registry(): Access to the embedding function registry. It has all the supported as well as custom embedding functions registered by the user

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Step 2: Connect to LanceDB

Establish a connection to your LanceDB OSS directory or Enterprise cluster: 
# Enter your LanceDB connection URI for OSS or Enterprise here
db = lancedb.connect(...)

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Step 3: Initialize the Embedding Function

Choose and configure your embedding model: 
model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", )
This creates a Sentence Transformers embedding function using the BGE model. You can:
  • Change "sentence-transformers" to other providers like "openai", "cohere", etc.
  • Modify the model name for different embedding models
  • Set device="cuda" for GPU acceleration if available

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Step 4: Define Your Schema

Create a Pydantic model that defines your table structure: 
class Words(LanceModel):
    text: str = model.SourceField()  
    vector: Vector(model.ndims()) = model.VectorField()
  • SourceField(): This field will be embedded
  • VectorField(): This stores the embeddings
  • model.ndims(): Sets vector dimensions for your model

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Step 5: Create Table and Ingest Data

Create a table with your schema and add data: 
table = db.create_table("words", schema=Words)
table.add([
    {"text": "hello world"},
    {"text": "goodbye world"}
])
The table.add() call automatically:
  • Takes the text from each document
  • Generates embeddings using your chosen model
  • Stores both the original text and the vector embeddings

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Step 6: Query with Automatic Embedding

Note: On LanceDB Enterprise, automatic query embedding is not supported. You need to pass the embedding vector directly. Search your data using natural language queries: 
query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)
The search process:
  1. Automatically converts your query text to embeddings
  2. Finds the most similar vectors in your table
  3. Returns the matching documents

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Examples

LanceDB currently supports the via SDKs in Python, Typescript and Rust. Below are some examples of generating and querying embeddings when using the embedding registry.