lancedb/.agents/skills/lancedb/references/python/api_reference.md

Python API Reference

Quick method reference for Python LanceDB code. Cross-check source for non-trivial claims.

Connect

import lancedb

db = lancedb.connect("./camelot-db")         # local/OSS
db = lancedb.connect("db://my-db", api_key=api_key, region=region)  # remote

Place the local database directory next to the script/entrypoint that opens it (i.e. resolve the path relative to the script, Path(__file__).parent / "camelot-db"), not buried under a shared data/ folder. The Lance dataset is the database, not a data file — keeping it beside its code makes ownership obvious and paths stable regardless of the working directory the script is launched from.

Do not name the directory lancedb (e.g. ./lancedb, ./data/lancedb). It collides with the imported lancedb package name, which is confusing to read and easy to shadow in scripts. Give it a name derived from the repo or dataset with a clear prefix/suffix — for example ./<dataset>-db, ./<repo>_lancedb, or ./vectordb.

Async:

db = await lancedb.connect_async("./camelot-db")

Table Reads

Task	Preferred API
Vector search	table.search(query_vector).limit(k)
Full scan with filters/projection (sync)	table.search().where(...).select(...).limit(...)
Full scan with filters/projection (async)	table.query().where(...).select(...).limit(...)
Filter	.where("col > 10")
Projection	.select(["id", "text"])
Bound result count	.limit(20)
Collect bounded result as Python objects (default, no extra deps)	.to_list() on query/search result
Collect bounded result as Arrow (default, pyarrow always available)	.to_arrow() on query/search result
Collect bounded result as pandas (only if project uses pandas)	.to_pandas() on query/search result
Collect bounded result as Polars (only if project uses polars)	.to_polars() on query/search result

Sync vs Async Scan API

The plain-scan entry point differs between the sync and async clients. Verified against lancedb 0.34.0 — re-check if the pinned version changes:

• Sync (lancedb.connect(...)): the table has no .query() method. Use .search() with no argument for a plain scan; it returns a query builder that supports .where(), .select(), .limit(), and the .to_list() / .to_arrow() / .to_pandas() / .to_polars() collectors.

  rows = table.search().where("status = 'ready'").select(["id", "text"]).limit(20).to_list()
  

• Async (lancedb.connect_async(...)): the table has both .query() and .search(). Use .query() for a plain scan.

  rows = await async_table.query().where("status = 'ready'").select(["id", "text"]).limit(20).to_list()
  

Do not call table.query() on a sync table — it raises AttributeError.

Local vs Remote Table Methods

API	Local table	Remote table	Agent guidance
table.search(...)	Yes	Yes	Preferred read path (sync + async)
table.query()	Async only	Async only	Sync scan path is table.search(); .query() is the async scan builder
table.to_pandas()	Yes	No / unsafe for portability	Avoid in portable code
table.to_arrow()	Yes	No / unsafe for portability	Avoid in portable code
table.to_polars()	Yes	No / unsafe for portability	Avoid in portable code
table.to_lance()	Yes	No	Local/OSS escape hatch only

Indexes

Use create_index(...) for vector indexes and modern index configs. Use scalar indexes for filtered or merge keys.

Common calls:

table.create_index("vector")
table.create_scalar_index("status")
table.create_fts_index("text")

Check source docs before specifying advanced index config names or parameters.

Filtering And Recall Knobs

table.search(query_vector).where("status = 'ready'")  # pre-filter by default
table.search(query_vector).where("status = 'ready'", prefilter=False)
table.search(query_vector).limit(10).refine_factor(20)
table.search(query_vector).limit(10).nprobes(50)

Use post-filtering only when fewer than limit results are acceptable.

Diagnostics

print(table.search(query_vector).where("year > 2000").limit(10).analyze_plan())
print(table.index_stats("vector_idx"))

Use these before changing indexes or search tuning.

Maintenance

table.optimize()

Call this after every successful local/OSS ingestion. It handles compaction, cleanup of old versions according to retention, and index optimization. Do not add this for LanceDB Enterprise/Cloud remote tables; Enterprise handles compaction and cleanup automatically from cluster configuration.