How to Use LanceDB for Vector Search in 2026

Introduction

LanceDB is an open-source vector database built in Rust for optimal performance and seamless integration with Python applications. Unlike cloud-based options like Pinecone or Weaviate, LanceDB is embedded: it runs locally without a dedicated server, making it ideal for AI prototypes, edge apps, or resource-constrained environments.

Why use it in 2026? With the rise of LLMs and embeddings (like those from Hugging Face), semantic searches are essential. LanceDB shines in speed (ANN searches in milliseconds) and simplicity: no rigid schema, SQL-like support, and native integration with LangChain or LlamaIndex. This beginner tutorial guides you step-by-step to create a DB, insert vectors, and query. By the end, you'll handle real embeddings for similarity search. Real gains: slash cloud costs by 100% while boosting speed 10x.

Prerequisites

Python 3.10 or higher installed
pip up to date (python -m pip install --upgrade pip)
Basic Python knowledge (lists, dictionaries)
An editor like VS Code or Jupyter Notebook (optional but recommended)

Installing LanceDB

terminal

pip install lancedb
pip install numpy
pip install sentence-transformers

These commands install the LanceDB Python SDK, NumPy for vectors, and sentence-transformers for generating real embeddings. LanceDB is pure Rust under the hood, so it's blazing fast with minimal dependencies. Avoid pip install --user in production—use virtual environments instead.

Creating the Database

Let's start with the basics: connect to a LanceDB database. It can be in-memory (ephemeral, for testing) or on-disk (persistent). Think of it like SQLite for vectors, but optimized for cosine or Euclidean similarity.

Connecting and Creating a Table

create_db.py

import lancedb
from lancedb.embeddings import get_registry

# Connexion à une DB sur disque (créée si inexistante)
db = lancedb.connect("./ma_base_lancedb")

# Schéma simple : vecteur de dim 384 + métadonnées
schema = {
    "vector": "float[384]",
    "item": "string",
    "price": "float"
}

table = db.create_table("produits", schema=schema)
print("Table 'produits' créée avec succès !")

This code creates a persistent DB and a table with a vector schema (384 dims, standard for all-MiniLM). The embeddings registry is ready for later use. Pitfall: always specify exact dimensions, or inserts will fail.

Inserting Vector Data

Now, let's populate the table. LanceDB accepts Pandas DataFrames or lists of dicts. We'll generate simple embeddings using a lightweight model.

Generating and Inserting Embeddings

insert_data.py

import lancedb
import numpy as np
from sentence_transformers import SentenceTransformer

# Modèle d'embedding lightweight
model = SentenceTransformer('all-MiniLM-L6-v2')

db = lancedb.connect("./ma_base_lancedb")
table = db.open_table("produits")

# Données texte
textes = [
    "iPhone 15 Pro Max bleu",
    "Samsung Galaxy S24 Ultra",
    "MacBook Air M3",
    "iPad Pro 2024",
    "Google Pixel 8"
]

# Générer embeddings
embeddings = model.encode(textes).tolist()

# Données à insérer
data = [
    {"vector": emb, "item": texte, "price": 1200.0},
    {"vector": embeddings[1], "item": textes[1], "price": 1100.0},
    {"vector": embeddings[2], "item": textes[2], "price": 1300.0},
    {"vector": embeddings[3], "item": textes[3], "price": 900.0},
    {"vector": embeddings[4], "item": textes[4], "price": 800.0}
]

table.add(data)
print("5 produits insérés avec embeddings !")
print(table.to_pandas())

We encode texts into 384D vectors, then insert via add(). Embeddings capture semantics (e.g., 'iPhone' close to 'iPad'). Tip: batch inserts for >10k items; don't reuse the same vector.

Basic Vector Search

The heart of LanceDB: ANN (Approximate Nearest Neighbors) search. Query by vector or text (with auto-embedding).

Simple Similarity Query

query_base.py

import lancedb
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
db = lancedb.connect("./ma_base_lancedb")
table = db.open_table("produits")

# Query texte : auto-embedding
query = "smartphone Apple haut de gamme"
results = table.search(query).limit(3).to_pandas()

print("Top 3 résultats :")
print(results[['item', 'price', '_distance']])

LanceDB automatically embeds the query and finds the closest matches (cosine metric by default). _distance measures similarity (0=identical). Use .limit() for performance; add thresholds with select(['*']).where("_distance < 0.3").

Advanced Search with Filters

Add SQL-like filters to refine results: price < 1000, etc. LanceDB seamlessly combines vectors and metadata.

Filtered Query with Custom Metric

query_avancee.py

import lancedb

db = lancedb.connect("./ma_base_lancedb")
table = db.open_table("produits")

# Query avec filtre SQL et métrique L2
query_vector = [0.1] * 384  # Vecteur exemple (remplacez par vrai embedding)
results = (
    table
    .search(query_vector, search_type="L2")  # Distance euclidienne
    .where("price < 1000")  # Filtre metadata
    .limit(2)
    .to_pandas()
)

print("Résultats filtrés :")
print(results)

.where() applies SQL predicates on metadata before vector search. Choose cosine, L2, or dot. Performance boost: automatic indexing; avoid unindexed queries on large datasets.

Updating and Deleting

update_delete.py

import lancedb

db = lancedb.connect("./ma_base_lancedb")
table = db.open_table("produits")

# Mise à jour par ID (premier item)
table.update(
    where="price > 1100",
    values={"price": 1150.0}
)

# Suppression
n_suppr = table.delete(where="price < 900")
print(f"{n_suppr} lignes supprimées.")

# Vérif
table.to_pandas().head()

Updates and deletes use SQL where clauses. LanceDB automatically regenerates indexes. Pitfall: back up before bulk ops; IDs are auto-generated if not provided.

Best Practices

Use virtual environments: venv to isolate LanceDB and dependencies.
Fixed dimensions: Match your model (e.g., 384 for MiniLM, 1536 for OpenAI).
Batch inserts: For >1000 items, use Pandas DataFrame for 5x speed.
FALANN index: Enable for >1M vectors via create_index(metric_type="L2").
SSD storage: LanceDB is I/O-intensive; avoid HDD for live queries.

Common Errors to Avoid

Dimension mismatch: Vector dim mismatch error? Check schema vs. embeddings.
Forgotten connection: Always db.open_table() after connect().
Queries without limit: Risk of OOM on large datasets; cap at 100 max.
Unloaded model: Load SentenceTransformer once globally, not per query.

Next Steps

Dive deeper into advanced embeddings with Hugging Face. Integrate with LangChain: pip install langchain-lancedb. Official docs: LanceDB Docs. Check out our Learni courses on vector databases to scale to production.