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85d870b397
The server now serializes an index's `created_at` as an RFC 3339 string
(e.g. `"2026-06-18T21:37:36.637Z"`), but the client deserializer only
accepted a unix timestamp in milliseconds. This caused `list_indices` to
fail with:
```
Failed to parse list_indices response: invalid type: string "2026-06-18T21:37:36.637Z", expected a unix timestamp in milliseconds
```
This PR replaces the fixed millisecond deserializer with a custom one
that accepts both an RFC 3339 string (current server) and a
unix-millisecond integer (legacy deployments), so the client works
against any server version.
It also improves the `IndexConfig` repr in the Python bindings.
Previously it printed only three fields (`Index(FTS, columns=["text"],
name="text_idx")`), hiding the metadata that `list_indices` returns. It
now renders every populated field, omitting any that are `None`. Each
value is valid Python — integer counts use `_` thousands separators and
`created_at` uses the `datetime` repr — so values round-trip. The real
repr is a single line; it's wrapped here for readability:
```python
>>> table.list_indices()
[IndexConfig(
name="text_idx",
index_type="FTS",
columns=["text"],
index_uuid="aefd3e00-2f95-4bdc-92ac-06de84442bf1",
type_url="/lance.table.InvertedIndexDetails",
created_at=datetime.datetime(2026, 6, 18, 21, 37, 36, 637000, tzinfo=datetime.timezone.utc),
num_indexed_rows=2,
size_bytes=3_669,
num_segments=1,
index_version=1,
index_details={
'lance_tokenizer': None,
'base_tokenizer': 'simple',
'language': 'English',
'with_position': False,
'max_token_length': 40,
'lower_case': True,
'stem': True,
'remove_stop_words': True,
'custom_stop_words': None,
'ascii_folding': True,
'min_ngram_length': 3,
'max_ngram_length': 3,
'prefix_only': False,
},
)]
```
Fixes #3556
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
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