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LanceDB is an open-source, high-performance vector database. It allows you to store data objects and perform similarity searches over them. This destination helps you load data into LanceDB from dlt resources.

Setup Guideโ€‹

Choosing a Model Providerโ€‹

First, you need to decide which embedding model provider to use. You can find all supported providers by visiting the official LanceDB docs.

Install dlt with LanceDBโ€‹

To use LanceDB as a destination, make sure dlt is installed with the lancedb extra:

pip install "dlt[lancedb]"

the lancedb extra only installs dlt and lancedb. You will need to install your model provider's SDK.

You can find which libraries you need to also referring to the LanceDB docs.

Configure the destinationโ€‹

Configure the destination in the dlt secrets file located at ~/.dlt/secrets.toml by default. Add the following section:

embedding_model_provider = "cohere"
embedding_model = "embed-english-v3.0"
uri = ".lancedb"
api_key = "api_key" # API key to connect to LanceDB Cloud. Leave out if you are using LanceDB OSS.
embedding_model_provider_api_key = "embedding_model_provider_api_key" # Not needed for providers that don't need authentication (ollama, sentence-transformers).
  • The uri specifies the location of your LanceDB instance. It defaults to a local, on-disk instance if not provided.
  • The api_key is your api key for LanceDB Cloud connections. If you're using LanceDB OSS, you don't need to supply this key.
  • The embedding_model_provider specifies the embedding provider used for generating embeddings. The default is cohere.
  • The embedding_model specifies the model used by the embedding provider for generating embeddings. Check with the embedding provider which options are available. Reference
  • The embedding_model_provider_api_key is the API key for the embedding model provider used to generate embeddings. If you're using a provider that doesn't need authentication, say ollama, you don't need to supply this key.
Available Model Providers
  • "gemini-text"
  • "bedrock-text"
  • "cohere"
  • "gte-text"
  • "imagebind"
  • "instructor"
  • "open-clip"
  • "openai"
  • "sentence-transformers"
  • "huggingface"
  • "colbert"

Define your data sourceโ€‹

For example:

import dlt
from dlt.destinations.adapters import lancedb_adapter

movies = [
"id": 1,
"title": "Blade Runner",
"year": 1982,
"id": 2,
"title": "Ghost in the Shell",
"year": 1995,
"id": 3,
"title": "The Matrix",
"year": 1999,

Create a pipeline:โ€‹

pipeline = dlt.pipeline(

Run the pipeline:โ€‹

info =

The data is now loaded into LanceDB.

To use vector search after loading, you must specify which fields LanceDB should generate embeddings for. Do this by wrapping the data (or dlt resource) with the lancedb_adapter function.

Using an Adapter to Specify Columns to Vectoriseโ€‹

Out of the box, LanceDB will act as a normal database. To use LanceDB's embedding facilities, you'll need to specify which fields you'd like to embed in your dlt resource.

The lancedb_adapter is a helper function that configures the resource for the LanceDB destination:

lancedb_adapter(data, embed)

It accepts the following arguments:

  • data: a dlt resource object, or a Python data structure (e.g. a list of dictionaries).
  • embed: a name of the field or a list of names to generate embeddings for.

Returns: dlt resource object that you can pass to the


embed=["title", "description"],

Bear in mind that you can't use an adapter on a dlt source, only a dlt resource.

Write dispositionโ€‹

All write dispositions are supported by the LanceDB destination.


The replace disposition replaces the data in the destination with the data from the resource.

info =


The merge write disposition merges the data from the resource with the data at the destination based on a unique identifier.


This is the default disposition. It will append the data to the existing data in the destination.

Additional Destination Optionsโ€‹

  • dataset_separator: The character used to separate the dataset name from table names. Defaults to "___".
  • vector_field_name: The name of the special field to store vector embeddings. Defaults to "vector__".
  • id_field_name: The name of the special field used for deduplication and merging. Defaults to "id__".
  • max_retries: The maximum number of retries for embedding operations. Set to 0 to disable retries. Defaults to 3.

dbt supportโ€‹

The LanceDB destination doesn't support dbt integration.

Syncing of dlt stateโ€‹

The LanceDB destination supports syncing of the dlt state.

Current Limitationsโ€‹

Adding new fields to an existing LanceDB table requires loading the entire table data into memory as a PyArrow table. This is because PyArrow tables are immutable, so adding fields requires creating a new table with the updated schema.

For huge tables, this may impact performance and memory usage since the full table must be loaded into memory to add the new fields. Keep these considerations in mind when working with large datasets and monitor memory usage if adding fields to sizable existing tables.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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