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docs: clear the mkdocs warning backlog so --strict passes

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`mkdocs build` emitted 61 warnings on main, and rendering the previously
undocumented classes in this PR pushed that to 158. That backlog is what
blocks turning on strict mode (#3707), so clear it here rather than leave
it worse than we found it.

Most of it was one systematic false positive: griffe cannot see the
generated `__init__` of a pydantic dataclass, so every documented
parameter looked unknown. `warn_unknown_params` turns that check off.

The rest were real docstring bugs, in 15 docstrings:

* Prose trailing a `Parameters` section is read as parameter names, which
  invented parameters called `The`, `you` and `To`. Moved into `Notes` or
  the summary.
* numpydoc only reads a type when the colon has spaces around it. Where
  the documented name is a pydantic attribute rather than a signature
  parameter, griffe has no signature to fall back on and the type was
  dropped. Affects nine embedding classes.
* `num_partitions, default sqrt(num_rows)` and friends parse as a list of
  names, rendering a bogus `default` parameter.
* One parameter indented five spaces instead of four.

`nodejs/CONTRIBUTING.md` links to the repo-root CONTRIBUTING.md, which
does not resolve once typedoc copies the file into `docs/src/js/_media/`;
an absolute URL works from both places.

`mkdocs build --strict` now exits 0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
Will Jones
2026-07-29 14:15:20 -07:00
parent ac2b689cdb
commit ed6be12ad6
14 changed files with 77 additions and 64 deletions
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python/python/lancedb/embeddings/bedrock.py
+5 -5
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@@ -21,20 +21,20 @@ class BedRockText(TextEmbeddingFunction):
"""
Parameters
----------
name: str, default "amazon.titan-embed-text-v1"
name : str, default "amazon.titan-embed-text-v1"
The model ID of the bedrock model to use. Supported models for are:
- amazon.titan-embed-text-v1
- cohere.embed-english-v3
- cohere.embed-multilingual-v3
region: str, default "us-east-1"
region : str, default "us-east-1"
Optional name of the AWS Region in which the service should be called.
profile_name: str, default None
profile_name : str, default None
Optional name of the AWS profile to use for calling the Bedrock service.
If not specified, the default profile will be used.
assumed_role: str, default None
assumed_role : str, default None
Optional ARN of an AWS IAM role to assume for calling the Bedrock service.
If not specified, the current active credentials will be used.
role_session_name: str, default "lancedb-embeddings"
role_session_name : str, default "lancedb-embeddings"
Optional name of the AWS IAM role session to use for calling the Bedrock
service. If not specified, "lancedb-embeddings" name will be used.
python/python/lancedb/embeddings/cohere.py
+5 -3
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@@ -22,7 +22,7 @@ class CohereEmbeddingFunction(TextEmbeddingFunction):
Parameters
----------
name: str, default "embed-multilingual-v2.0"
name : str, default "embed-multilingual-v2.0"
The name of the model to use. List of acceptable models:
* embed-english-v3.0
@@ -33,12 +33,14 @@ class CohereEmbeddingFunction(TextEmbeddingFunction):
* embed-english-light-v2.0
* embed-multilingual-v2.0
source_input_type: str, default "search_document"
source_input_type : str, default "search_document"
The input type for the source column in the database
query_input_type: str, default "search_query"
query_input_type : str, default "search_query"
The input type for the query column in the database
Notes
-----
Cohere supports following input types:
| Input Type | Description |
python/python/lancedb/embeddings/colpali.py
+2 -2
View File
@@ -44,7 +44,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
The token pooling strategy to use, by default "hierarchical".
- "hierarchical": Progressively pools tokens to reduce sequence length.
- "lambda": A simpler pooling that uses a custom `pooling_func`.
pooling_func: typing.Callable, optional
pooling_func : typing.Callable, optional
A function to use for pooling when `pooling_strategy` is "lambda".
pool_factor : int
Factor to reduce sequence length if token pooling is enabled (default 2).
@@ -52,7 +52,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
Quantization configuration for the model. (default None, bitsandbytes needed)
batch_size : int
Batch size for processing inputs (default 2).
offload_folder: str, optional
offload_folder : str, optional
Folder to offload model weights if using CPU offloading (default None). This is
useful for large models that do not fit in memory.
"""
python/python/lancedb/embeddings/gemini_text.py
+3 -3
View File
@@ -48,16 +48,16 @@ class GeminiText(TextEmbeddingFunction):
Parameters
----------
name: str, default "gemini-embedding-001"
name : str, default "gemini-embedding-001"
The name of the model to use. Supported models include:
- "gemini-embedding-001" (768 dimensions)
Note: The legacy "models/embedding-001" format is also supported but
"gemini-embedding-001" is recommended.
query_task_type: str, default "retrieval_query"
query_task_type : str, default "retrieval_query"
Sets the task type for the queries.
source_task_type: str, default "retrieval_document"
source_task_type : str, default "retrieval_document"
Sets the task type for ingestion.
Examples
python/python/lancedb/embeddings/gte.py
+4 -4
View File
@@ -26,13 +26,13 @@ class GteEmbeddings(TextEmbeddingFunction):
Parameters
----------
name: str, default "thenlper/gte-large"
name : str, default "thenlper/gte-large"
The name of the model to use.
device: str, default "cpu"
device : str, default "cpu"
Sets the device type for the model.
normalize: str, default "True"
normalize : str, default "True"
Controls normalize param in encode function for the transformer.
mlx: bool, default False
mlx : bool, default False
Controls which model to use. False for gte-large,True for the mlx version.
Examples
python/python/lancedb/embeddings/instructor.py
+8 -8
View File
@@ -35,23 +35,23 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
Parameters
----------
name: str
name : str
The name of the model to use. Available models are listed at
https://github.com/xlang-ai/instructor-embedding#model-list;
The default model is hkunlp/instructor-base
batch_size: int, default 32
batch_size : int, default 32
The batch size to use when generating embeddings
device: str, default "cpu"
device : str, default "cpu"
The device to use when generating embeddings
show_progress_bar: bool, default True
show_progress_bar : bool, default True
Whether to show a progress bar when generating embeddings
normalize_embeddings: bool, default True
normalize_embeddings : bool, default True
Whether to normalize the embeddings
quantize: bool, default False
quantize : bool, default False
Whether to quantize the model
source_instruction: str, default "represent the document for retrieval"
source_instruction : str, default "represent the document for retrieval"
The instruction for the source column
query_instruction: str, default "represent the document for retrieving the most
query_instruction : str, default "represent the document for retrieving the most
similar documents"
The instruction for the query
python/python/lancedb/embeddings/jinaai.py
+2 -2
View File
@@ -40,10 +40,10 @@ class JinaEmbeddings(EmbeddingFunction):
Parameters
----------
name: str, default "jina-clip-v1". Note that some models support both image
name : str, default "jina-clip-v1". Note that some models support both image
and text embeddings and some just text embedding
api_key: str, default None
api_key : str, default None
The api key to access Jina API. If you pass None, you can set JINA_API_KEY
environment variable
python/python/lancedb/embeddings/sentence_transformers.py
+4 -4
View File
@@ -21,13 +21,13 @@ class SentenceTransformerEmbeddings(TextEmbeddingFunction):
Parameters
----------
name: str, default "all-MiniLM-L6-v2"
name : str, default "all-MiniLM-L6-v2"
The name of the model to use.
device: str, default "cpu"
device : str, default "cpu"
The device to use for the model
normalize: bool, default True
normalize : bool, default True
Whether to normalize the embeddings
trust_remote_code: bool, default True
trust_remote_code : bool, default True
Whether to trust the remote code
"""
python/python/lancedb/embeddings/voyageai.py
+2 -2
View File
@@ -167,7 +167,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
Parameters
----------
name: str
name : str
The name of the model to use. List of acceptable models:
* voyage-4 (1024 dims, general-purpose and multilingual retrieval)
@@ -185,7 +185,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
* voyage-law-2
* voyage-code-2
output_dimension: int, optional
output_dimension : int, optional
The output dimension for models that support flexible dimensions.
Currently only voyage-multimodal-3.5 supports this feature.
Valid options: 256, 512, 1024 (default), 2048.
python/python/lancedb/index.py
+23 -23
View File
@@ -219,7 +219,7 @@ class HnswPq:
distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
l2 norm is 1), then dot distance is equivalent to the cosine distance.
num_partitions, default sqrt(num_rows)
num_partitions: int, default sqrt(num_rows)
The number of IVF partitions to create.
@@ -228,7 +228,7 @@ class HnswPq:
will require too much memory. Each partition becomes its own HNSW graph, so
setting this value higher reduces the peak memory use of training.
num_sub_vectors, default is vector dimension / 16
num_sub_vectors: int, default is vector dimension / 16
Number of sub-vectors of PQ.
@@ -244,13 +244,13 @@ class HnswPq:
If the dimension is not visible by 8 then we use 1 subvector. This is not
ideal and will likely result in poor performance.
num_bits: int, default 8
num_bits: int, default 8
Number of bits to encode each sub-vector.
This value controls how much the sub-vectors are compressed. The more bits
the more accurate the index but the slower search. Only 4 and 8 are supported.
max_iterations, default 50
max_iterations: int, default 50
Max iterations to train kmeans.
@@ -263,7 +263,7 @@ class HnswPq:
those cases it is unlikely that setting this larger will lead to the index
converging anyways.
sample_rate, default 256
sample_rate: int, default 256
The rate used to calculate the number of training vectors for kmeans.
@@ -279,14 +279,14 @@ class HnswPq:
Increasing this value might improve the quality of the index but in
most cases the default should be sufficient.
m, default 20
m: int, default 20
The number of neighbors to select for each vector in the HNSW graph.
This value controls the tradeoff between search speed and accuracy.
The higher the value the more accurate the search but the slower it will be.
ef_construction, default 300
ef_construction: int, default 300
The number of candidates to evaluate during the construction of the HNSW graph.
@@ -297,7 +297,7 @@ class HnswPq:
This value should be set to a value that is not less than `ef` in the
search phase.
target_partition_size, default is 1,048,576
target_partition_size: int, default is 1,048,576
The target size of each partition.
@@ -351,7 +351,7 @@ class HnswSq:
distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
l2 norm is 1), then dot distance is equivalent to the cosine distance.
num_partitions, default sqrt(num_rows)
num_partitions: int, default sqrt(num_rows)
The number of IVF partitions to create.
@@ -360,7 +360,7 @@ class HnswSq:
will require too much memory. Each partition becomes its own HNSW graph, so
setting this value higher reduces the peak memory use of training.
max_iterations, default 50
max_iterations: int, default 50
Max iterations to train kmeans.
@@ -373,7 +373,7 @@ class HnswSq:
In those cases it is unlikely that setting this larger will lead to
the index converging anyways.
sample_rate, default 256
sample_rate: int, default 256
The rate used to calculate the number of training vectors for kmeans.
@@ -389,14 +389,14 @@ class HnswSq:
Increasing this value might improve the quality of the index but in
most cases the default should be sufficient.
m, default 20
m: int, default 20
The number of neighbors to select for each vector in the HNSW graph.
This value controls the tradeoff between search speed and accuracy.
The higher the value the more accurate the search but the slower it will be.
ef_construction, default 300
ef_construction: int, default 300
The number of candidates to evaluate during the construction of the HNSW graph.
@@ -407,7 +407,7 @@ class HnswSq:
This value should be set to a value that is not less than `ef` in the search
phase.
target_partition_size, default is 1,048,576
target_partition_size: int, default is 1,048,576
The target size of each partition.
@@ -460,7 +460,7 @@ class HnswFlat:
distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
l2 norm is 1), then dot distance is equivalent to the cosine distance.
num_partitions, default sqrt(num_rows)
num_partitions: int, default sqrt(num_rows)
The number of IVF partitions to create.
@@ -470,18 +470,18 @@ class HnswFlat:
graph, so setting this value higher reduces the peak memory use of
training.
max_iterations, default 50
max_iterations: int, default 50
Max iterations to train kmeans.
When training an IVF index we use kmeans to calculate the partitions.
This parameter controls how many iterations of kmeans to run.
sample_rate, default 256
sample_rate: int, default 256
The rate used to calculate the number of training vectors for kmeans.
m, default 20
m: int, default 20
The number of neighbors to select for each vector in the HNSW graph.
@@ -489,7 +489,7 @@ class HnswFlat:
The higher the value the more accurate the search but the slower it
will be.
ef_construction, default 300
ef_construction: int, default 300
The number of candidates to evaluate during the construction of the HNSW
graph.
@@ -501,7 +501,7 @@ class HnswFlat:
than 500. This value should be set to a value that is not less than `ef`
in the search phase.
target_partition_size, default is 1,048,576
target_partition_size: int, default is 1,048,576
The target size of each partition.
"""
@@ -605,7 +605,7 @@ class IvfFlat:
The default value is 256.
target_partition_size, default is 8192
target_partition_size: int, default is 8192
The target size of each partition.
@@ -769,7 +769,7 @@ class IvfPq:
The default value is 256.
target_partition_size, default is 8192
target_partition_size: int, default is 8192
The target size of each partition.
@@ -830,7 +830,7 @@ class IvfRq:
sample_rate: int, default 256
Controls the number of training vectors: sample_rate * num_partitions.
target_partition_size, default is 8192
target_partition_size: int, default is 8192
Target size of each partition.
"""
python/python/lancedb/table.py
+12 -6
View File
@@ -1832,6 +1832,8 @@ class Table(ABC):
retrain: bool, default False
This parameter is no longer used and is deprecated.
Notes
-----
The frequency an application should call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
@@ -1986,15 +1988,14 @@ class Table(ABC):
change permanent you can use the `[Self::restore]` method.
Any operation that modifies the table will fail while the table is in a checked
out state.
out state. To return the table to a normal state use
`[Self::checkout_latest]`.
Parameters
----------
version: int | str,
The version to check out. A version number (`int`) or a tag
(`str`) can be provided.
To return the table to a normal state use `[Self::checkout_latest]`
"""
@abstractmethod
@@ -3813,6 +3814,8 @@ class LanceTable(Table):
retrain: bool, default False
This parameter is no longer used and is deprecated.
Notes
-----
The frequency an application should call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
@@ -5774,15 +5777,14 @@ class AsyncTable:
change permanent you can use the `[Self::restore]` method.
Any operation that modifies the table will fail while the table is in a checked
out state.
out state. To return the table to a normal state use
`[Self::checkout_latest]`.
Parameters
----------
version: int | str,
The version to check out. A version number (`int`) or a tag
(`str`) can be provided.
To return the table to a normal state use `[Self::checkout_latest]`
"""
try:
await self._inner.checkout(version)
@@ -5966,6 +5968,8 @@ class AsyncTable:
retrain: bool, default False
This parameter is no longer used and is deprecated.
Notes
-----
The frequency an application should call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
@@ -6346,6 +6350,8 @@ class Branches:
dry_run: bool, default False
When True, only preview. When False, attempt the merge.
Notes
-----
A rejected merge returns ``status="rejected"`` instead of raising.
"""
return LOOP.run(self._table.branches.merge(from_branch, dry_run))