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LanceDB

LanceDB v0.1.1

Build LanceDB pipelines

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Onboarding example

Create a LanceDB table, add sample data, use a model to build embeddings, and run a vector search. Show the table and results.
Sources & dates

Developer declarations; not independently tested. Listing last checked Nov 6, 2026. Package observed Nov 6, 2026.

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Description

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Write, review, debug, and document LanceDB pipelines in Python and TypeScript that work across local LanceDB OSS tables and remote LanceDB Enterprise tables, with idiomatic query/search patterns and performance defaults for ingestion, indexing, filtering, and diagnostics.

Language: English · Automatically detected from descriptions.

Matches for “vector”

Exact text from the indicated source. A mention alone does not establish support for your task.

Publisher keywords · listing

lancedb vector-search full-text-search hybrid-search python typescript pipelines

What changed

Earlier changes · 2 more observation days on this page

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Plugin package101 files · 283 KBBrowse files →
Skill instructions
lancedb9.58 KB

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---
name: lancedb
description: Use when writing, reviewing, debugging, or documenting LanceDB pipelines in Python or TypeScript, especially code that should work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables. Helps avoid non-portable full-table materialization, choose idiomatic query/search patterns, apply LanceDB performance defaults for ingestion, indexing, filtering, and diagnostics, and resolve connections to the remote server for Enterprise-only operations such as jobs.
---

# Building LanceDB Pipelines

Use this skill to produce LanceDB pipelines that are portable between local and remote tables (for LanceDB Enterprise/Cloud) and idiomatic for the selected SDK.

## LanceDB Table Modes

LanceDB has two common execution modes:

- **Local table**: embedded, open source, in-process LanceDB. The client opens data from a local path or object storage URI and executes queries in the application process.
- **Remote table**: LanceDB Enterprise/Cloud table opened through a `db://...` URI. The data may be very large, commonly backed by object storage, and queried through a remote service.

Do NOT assume local-only table helpers exist on remote tables. If the user asks for LanceDB Enterprise, Cloud, `db://...`, production remote access, or a remote table, focus on the remote table path: use `search()` / `query()`, keep reads bounded with `select()` and `limit()`, and avoid table-level full materialization APIs.

## Workflow

1. Identify the SDK: Python, TypeScript, or both.
2. Identify the table mode: local/embedded OSS, remote Enterprise/Cloud, or portable across both. If the user says "LanceDB Enterprise", choose the remote table path. If the task involves jobs in any way (listing, inspecting, creating, or canceling jobs), it is always the remote path and requires a remote server connection — see "Connecting to the LanceDB remote server" below before doing anything else.
3. Read the matching topic reference before writing or changing code:
   - Column metadata authoring (both SDKs): `references/column_metadata.md`
   - Branch operations (both SDKs): `references/branch_ops.md`
   - Remote server connection resolution (jobs, raw REST): `references/remote_connect.md`
   - Job operations REST API (list/describe/cancel/query_events): `references/remote_jobs.md`

   There is no bundled per-language guide. For exact method names, signatures, and options, look them up in the canonical sources instead of relying on memory:
   - Python: `docs/src/python/python.md` (the hand-maintained API reference) and the source under `python/python/lancedb/` when working inside the LanceDB repo; otherwise <https://lancedb.github.io/lancedb/python/python/>.
   - TypeScript: the generated typedoc under `docs/src/js/` and the source under `nodejs/lancedb/` when working inside the LanceDB repo; otherwise <https://lancedb.github.io/lancedb/js/globals/>.
4. Apply the SDK invariants in "Per-SDK Invariants" below. Read `column_metadata.md` when the task is documenting, tagging, classifying, or grouping table columns (field descriptions, `lancedb:tag:*` tags, logical column families). Read `branch_ops.md` when the task involves branch lifecycle (list/create/delete), writing to a non-main branch, or verifying a change stayed off main. Read `remote_connect.md` when the task involves jobs or direct REST access to an Enterprise deployment, and `remote_jobs.md` for the job REST methods themselves (list, describe, cancel, query_events).
5. For Python schemas, favor Pydantic models and validate records before writing. Use PyArrow schemas when Arrow-native, streaming, or highly dynamic data makes them materially better suited.
6. Prefer `search()` or `query()` builders with explicit `select()` and `limit()` for reads.
7. Avoid table-level full materialization in remote or portable code. This is the main local-vs-remote read pitfall.
8. After a successful embedded OSS ingestion, call `table.optimize()`. Do not call it for Enterprise/Cloud; remo

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Package details

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Package author
LanceDB
Keywords
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Declared capabilities

  • Developer Tools

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Package observed Nov 6, 2026.

Technical details
First seen
Sep 30, 2026 · 22:02 UTC
Last seen
Nov 6, 2026 · 06:00 UTC
Collection status
Collected

plugins_6a8f5be7126c819180b85cde4cf21163

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