mirror of
https://github.com/lancedb/lancedb.git
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ci: add spell checking (#4148)
Adds [typos](https://github.com/crate-ci/typos) as a CI check and pre-commit hook, the same way Lance does it, so misspellings like the ones fixed in #4146 get caught automatically going forward. This also fixes the misspellings `typos` found across the repo (Rust, Python, TypeScript source, comments, and generated docs), and adds a small `.typos.toml` with `extend-words` entries for terms that are correct but look like typos: `AKS` (Azure Kubernetes Service), `RabitQ` (a real quantization algorithm name), `mmaped` (the actual name of a `candle-core` API we call), and `Writeable` (from Python's `_typeshed.WriteableBuffer`). Third-party license files are excluded. Fixes #4147 Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
co-authored by
Claude Sonnet 5
parent
577fb48376
commit
1da5876870
@@ -0,0 +1,20 @@
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name: Typo checker
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on:
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push:
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branches:
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- main
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pull_request:
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permissions:
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contents: read
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jobs:
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run:
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name: Spell Check with Typos
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runs-on: ubuntu-latest
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steps:
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- name: Check out code
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uses: actions/checkout@v6
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- name: Check spelling of the entire repository
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uses: crate-ci/typos@6802cc60d4e7f78b9d5454f6cf3935c042d5e1e3 # v1.26.0
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@@ -10,6 +10,10 @@ repos:
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rev: v0.9.9
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hooks:
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- id: ruff
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- repo: https://github.com/crate-ci/typos
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rev: v1.26.0
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hooks:
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- id: typos
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# - repo: https://github.com/RobertCraigie/pyright-python
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# rev: v1.1.395
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# hooks:
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+19
@@ -0,0 +1,19 @@
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[default]
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extend-ignore-re = ["(?Rm)^.*(#|//)\\s*spellchecker:disable-line$"]
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[default.extend-words]
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# Azure Kubernetes Service, mentioned in rust/lancedb/src/remote/oauth.rs.
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AKS = "AKS"
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# RabitQ is the name of a vector quantization algorithm, not a typo of "Rabbit".
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Rabit = "Rabit"
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# `VarBuilder::from_mmaped_safetensors` is the real (if oddly-spelled) name of
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# the candle-core API we call in rust/lancedb/src/embeddings/sentence_transformers.rs.
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mmaped = "mmaped"
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# `WriteableBuffer` is the real name of a type from Python's `_typeshed` stubs,
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# used in python/python/lancedb/_blob.py.
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Writeable = "Writeable"
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[files]
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extend-exclude = [
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"*_THIRD_PARTY_LICENSES.*",
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]
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+1
-1
@@ -155,7 +155,7 @@ paths:
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vector:
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type: FixedSizeList
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description: |
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The targetted vector to search for. Required.
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The targeted vector to search for. Required.
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vector_column:
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type: string
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description: |
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@@ -141,7 +141,7 @@ Currently this causes multiple copies of the row to be created
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but that behavior is subject to change.
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An optional condition may be specified. If it is, then only
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matched rows that satisfy the condtion will be updated. Any
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matched rows that satisfy the condition will be updated. Any
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rows that do not satisfy the condition will be left as they
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are. Failing to satisfy the condition does not cause a
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"matched row" to become a "not matched" row.
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@@ -1266,7 +1266,7 @@ value is 0")
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Note: if your condition is something like "some_id_column == 7" and
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you are updating many rows (with different ids) then you will get
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better performance with a single [`merge_insert`] call instead of
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repeatedly calilng this method.
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repeatedly calling this method.
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##### Parameters
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@@ -118,7 +118,7 @@ Number of sub-vectors of PQ.
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This value controls how much the vector is compressed during the quantization step.
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The more sub vectors there are the less the vector is compressed. The default is
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the dimension of the vector divided by 16. If the dimension is not evenly divisible
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by 16 we use the dimension divded by 8.
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by 16 we use the dimension divided by 8.
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The above two cases are highly preferred. Having 8 or 16 values per subvector allows
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us to use efficient SIMD instructions.
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@@ -16,7 +16,7 @@ optional config: Index;
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Advanced index configuration
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This option allows you to specify a specfic index to create and also
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This option allows you to specify a specific index to create and also
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allows you to pass in configuration for training the index.
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See the static methods on Index for details on the various index types.
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@@ -112,7 +112,7 @@ Number of sub-vectors of PQ.
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This value controls how much the vector is compressed during the quantization step.
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The more sub vectors there are the less the vector is compressed. The default is
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the dimension of the vector divided by 16. If the dimension is not evenly divisible
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by 16 we use the dimension divded by 8.
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by 16 we use the dimension divided by 8.
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The above two cases are highly preferred. Having 8 or 16 values per subvector allows
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us to use efficient SIMD instructions.
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@@ -3252,7 +3252,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
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const db = await connect(tmpDir.name);
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const data = [
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{ text: "fa", vector: [0.1, 0.2, 0.3] },
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{ text: "fo", vector: [0.4, 0.5, 0.6] },
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{ text: "fo", vector: [0.4, 0.5, 0.6] }, // spellchecker:disable-line
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{ text: "fob", vector: [0.4, 0.5, 0.6] },
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{ text: "focus", vector: [0.4, 0.5, 0.6] },
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{ text: "foo", vector: [0.4, 0.5, 0.6] },
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@@ -3277,7 +3277,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
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const resultSet = new Set(fuzzyResults.map((r) => r.text));
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expect(resultSet.has("foo")).toBe(true);
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expect(resultSet.has("fob")).toBe(true);
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expect(resultSet.has("fo")).toBe(true);
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expect(resultSet.has("fo")).toBe(true); // spellchecker:disable-line
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expect(resultSet.has("food")).toBe(true);
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const prefixResults = await table
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@@ -600,7 +600,7 @@ function makeVector(
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}
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if (values.length === 0) {
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throw Error(
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"makeVector requires at least one value or the type must be specfied",
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"makeVector requires at least one value or the type must be specified",
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);
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}
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const sampleValue = values.find((val) => val !== null && val !== undefined);
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@@ -858,7 +858,7 @@ async function applyEmbeddings<T>(
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* customized by the `embeddingDataType` property of the embedding function.
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*
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* If a schema is provided in `makeTableOptions` then it should include the
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* embedding columns. If no schema is provded then embedding columns will
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* embedding columns. If no schema is provided then embedding columns will
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* be placed at the end of the table, after all of the input columns.
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*/
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export async function convertToTable(
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@@ -26,7 +26,7 @@ export interface IvfPqOptions {
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* This value controls how much the vector is compressed during the quantization step.
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* The more sub vectors there are the less the vector is compressed. The default is
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* the dimension of the vector divided by 16. If the dimension is not evenly divisible
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* by 16 we use the dimension divded by 8.
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* by 16 we use the dimension divided by 8.
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*
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* The above two cases are highly preferred. Having 8 or 16 values per subvector allows
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* us to use efficient SIMD instructions.
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@@ -228,7 +228,7 @@ export interface HnswPqOptions {
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* This value controls how much the vector is compressed during the quantization step.
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* The more sub vectors there are the less the vector is compressed. The default is
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* the dimension of the vector divided by 16. If the dimension is not evenly divisible
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* by 16 we use the dimension divded by 8.
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* by 16 we use the dimension divided by 8.
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*
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* The above two cases are highly preferred. Having 8 or 16 values per subvector allows
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* us to use efficient SIMD instructions.
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@@ -825,7 +825,7 @@ export interface IndexOptions {
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/**
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* Advanced index configuration
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*
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* This option allows you to specify a specfic index to create and also
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* This option allows you to specify a specific index to create and also
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* allows you to pass in configuration for training the index.
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*
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* See the static methods on Index for details on the various index types.
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@@ -27,7 +27,7 @@ export class MergeInsertBuilder {
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* but that behavior is subject to change.
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*
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* An optional condition may be specified. If it is, then only
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* matched rows that satisfy the condtion will be updated. Any
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* matched rows that satisfy the condition will be updated. Any
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* rows that do not satisfy the condition will be left as they
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* are. Failing to satisfy the condition does not cause a
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* "matched row" to become a "not matched" row.
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@@ -3,7 +3,7 @@
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// The utilities in this file help sanitize data from the user's arrow
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// library into the types expected by vectordb's arrow library. Node
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// generally allows for mulitple versions of the same library (and sometimes
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// generally allows for multiple versions of the same library (and sometimes
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// even multiple copies of the same version) to be installed at the same
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// time. However, arrow-js uses instanceof which expected that the input
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// comes from the exact same library instance. This is not always the case
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@@ -313,7 +313,7 @@ export abstract class Table {
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* Note: if your condition is something like "some_id_column == 7" and
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* you are updating many rows (with different ids) then you will get
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* better performance with a single [`merge_insert`] call instead of
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* repeatedly calilng this method.
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* repeatedly calling this method.
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* @param {Map<string, string> | Record<string, string>} updates - the
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* columns to update
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* @returns {Promise<UpdateResult>} A promise that resolves to an object
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@@ -60,7 +60,7 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
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import lancedb
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from lancedb.pydantic import LanceModel, Vector
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from lancedb.embeddings import get_registry, InstuctorEmbeddingFunction
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from lancedb.embeddings import get_registry, InstructorEmbeddingFunction
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instructor = get_registry().get("instructor").create(
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source_instruction="represent the document for retrieval",
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@@ -5638,7 +5638,7 @@ class AsyncTable:
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if fill_value is None:
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fill_value = 0.0
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# _santitize_data is an old code path, but we will use it until the
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# _sanitize_data is an old code path, but we will use it until the
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# new code path is ready.
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if mode == "overwrite":
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# For overwrite, apply the same preprocessing as create_table
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@@ -327,8 +327,8 @@ def test_embedding_function_with_pandas(tmp_path):
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) -> List[np.array]:
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return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
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registery = get_registry()
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func = registery.get("mock-embedding").create()
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registry = get_registry()
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func = registry.get("mock-embedding").create()
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class TestSchema(LanceModel):
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text: str = func.SourceField()
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@@ -394,9 +394,9 @@ def test_multiple_embeddings_for_pandas(tmp_path):
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) -> List[np.array]:
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return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
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registery = get_registry()
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func1 = registery.get("mock-embedding").create()
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func2 = registery.get("mock-embedding2").create()
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registry = get_registry()
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func1 = registry.get("mock-embedding").create()
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func2 = registry.get("mock-embedding2").create()
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class TestSchema(LanceModel):
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text: str = func1.SourceField()
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@@ -1011,8 +1011,13 @@ def test_fts_ngram(mem_db: DBConnection):
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assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
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results = (
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table.search("nce", query_type="fts").limit(10).to_list()
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) # spellchecker:disable-line
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table.search(
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"nce", # spellchecker:disable-line
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query_type="fts",
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)
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.limit(10)
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.to_list()
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)
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assert len(results) == 2
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assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
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@@ -1034,8 +1039,13 @@ def test_fts_ngram(mem_db: DBConnection):
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assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
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results = (
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table.search("nce", query_type="fts").limit(10).to_list()
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) # spellchecker:disable-line
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table.search(
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"nce", # spellchecker:disable-line
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query_type="fts",
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)
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.limit(10)
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.to_list()
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)
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assert len(results) == 0
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results = table.search("la", query_type="fts").limit(10).to_list()
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@@ -81,7 +81,7 @@ def get_test_table(tmp_path):
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"but his son was mortal",
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"there hasn't been a good battlefield game since 2142",
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"I wish they would make another one",
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"campains are not as good as they used to be",
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"campaigns are not as good as they used to be",
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"Multiplayer and open world games have destroyed the single player experience",
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"Maybe the future is console games",
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"I don't know",
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@@ -3354,7 +3354,7 @@ def test_empty_query(mem_db: DBConnection):
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# None is the same as default
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df = table.search().select(["id"]).limit(None).to_arrow()
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assert df.num_rows == 100
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# invalid limist is the same as None, wihch is the same as default
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# invalid limist is the same as None, which is the same as default
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df = table.search().select(["id"]).limit(-1).to_arrow()
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assert df.num_rows == 100
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# valid limit should work
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+1
-1
@@ -334,7 +334,7 @@ pub struct PyQueryRequest {
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pub column: Option<String>,
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pub query_vector: Option<PyQueryVectors>,
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pub minimum_nprobes: Option<usize>,
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// None means user did not set it and default shoud be used (currenty 20)
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// None means user did not set it and default should be used (currently 20)
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// Some(0) means user set it to None and there is no limit
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pub maximum_nprobes: Option<usize>,
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pub lower_bound: Option<f32>,
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@@ -163,7 +163,7 @@ pub struct PolarsDataFrameRecordBatchReader {
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impl PolarsDataFrameRecordBatchReader {
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/// Creates a new `PolarsDataFrameRecordBatchReader` from a given Polars DataFrame.
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/// If the input dataframe does not have aligned chunks, this function undergoes
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/// the costly operation of reallocating each series as a single contigous chunk.
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/// the costly operation of reallocating each series as a single contiguous chunk.
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pub fn new(mut df: DataFrame) -> Result<Self> {
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df.align_chunks();
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let arrow_schema =
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@@ -827,7 +827,7 @@ impl Connection {
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pub struct ConnectRequest {
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/// Database URI
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///
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/// ### Accpeted URI formats
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/// ### Accepted URI formats
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///
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/// - `/path/to/database` - local database on file system.
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/// - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud object store
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@@ -512,7 +512,7 @@ impl ListingDatabase {
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// iter thru the query params and extract the commit store param
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let mut engine = None;
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let mut mirrored_store = None;
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let mut filtered_querys = vec![];
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let mut filtered_queries = vec![];
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// WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
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// THE API WILL CHANGE
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@@ -528,13 +528,13 @@ impl ListingDatabase {
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mirrored_store = Some(value.to_string());
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} else {
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// to owned so we can modify the url
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filtered_querys.push((key.to_string(), value.to_string()));
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filtered_queries.push((key.to_string(), value.to_string()));
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}
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}
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// Filter out the commit store query param -- it's a lancedb param
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url.query_pairs_mut().clear();
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url.query_pairs_mut().extend_pairs(filtered_querys);
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url.query_pairs_mut().extend_pairs(filtered_queries);
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// Take a copy of the query string so we can propagate it to lance.
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// `query_pairs_mut()` leaves the URL with `Some("")` even when no
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// pairs survive (or none existed in the first place), so an empty
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@@ -896,11 +896,11 @@ impl Database for ListingDatabase {
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}
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async fn read_consistency(&self) -> Result<ReadConsistency> {
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if let Some(read_consistency_inverval) = self.read_consistency_interval {
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if read_consistency_inverval.is_zero() {
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if let Some(interval) = self.read_consistency_interval {
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if interval.is_zero() {
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Ok(ReadConsistency::Strong)
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} else {
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Ok(ReadConsistency::Eventual(read_consistency_inverval))
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Ok(ReadConsistency::Eventual(interval))
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}
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} else {
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Ok(ReadConsistency::Manual)
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@@ -3043,15 +3043,15 @@ mod tests {
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/// across platforms — see the `file://` test below).
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fn capture_query_like_connect(input_uri: &str) -> Option<String> {
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let mut url = url::Url::parse(input_uri).unwrap();
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let mut filtered_querys = Vec::new();
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let mut filtered_queries = Vec::new();
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for (key, value) in url.query_pairs() {
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if key == ENGINE || key == MIRRORED_STORE {
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continue;
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}
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filtered_querys.push((key.to_string(), value.to_string()));
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filtered_queries.push((key.to_string(), value.to_string()));
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}
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url.query_pairs_mut().clear();
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url.query_pairs_mut().extend_pairs(filtered_querys);
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url.query_pairs_mut().extend_pairs(filtered_queries);
|
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url.query().filter(|q| !q.is_empty()).map(|s| s.to_string())
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}
|
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|
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|
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@@ -251,11 +251,11 @@ impl Database for LanceNamespaceDatabase {
|
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}
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|
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async fn read_consistency(&self) -> Result<ReadConsistency> {
|
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if let Some(read_consistency_inverval) = self.read_consistency_interval {
|
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if read_consistency_inverval.is_zero() {
|
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if let Some(interval) = self.read_consistency_interval {
|
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if interval.is_zero() {
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Ok(ReadConsistency::Strong)
|
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} else {
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Ok(ReadConsistency::Eventual(read_consistency_inverval))
|
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Ok(ReadConsistency::Eventual(interval))
|
||||
}
|
||||
} else {
|
||||
Ok(ReadConsistency::Manual)
|
||||
|
||||
@@ -125,7 +125,7 @@ macro_rules! impl_pq_params_setter {
|
||||
/// This value controls how much the vector is compressed during the quantization step.
|
||||
/// The more sub vectors there are the less the vector is compressed. The default is
|
||||
/// the dimension of the vector divided by 16. If the dimension is not evenly divisible
|
||||
/// by 16 we use the dimension divded by 8.
|
||||
/// by 16 we use the dimension divided by 8.
|
||||
///
|
||||
/// The above two cases are highly preferred. Having 8 or 16 values per subvector allows
|
||||
/// us to use efficient SIMD instructions.
|
||||
|
||||
@@ -1299,7 +1299,7 @@ impl VectorQuery {
|
||||
/// This can be useful when there is a narrow filter to allow these queries to
|
||||
/// spend more time searching and avoid potential false negatives.
|
||||
///
|
||||
/// Set to None to search all partitions, if needed, to satsify the limit
|
||||
/// Set to None to search all partitions, if needed, to satisfy the limit
|
||||
pub fn maximum_nprobes(mut self, maximum_nprobes: Option<usize>) -> Result<Self> {
|
||||
if let Some(maximum_nprobes) = maximum_nprobes {
|
||||
if maximum_nprobes == 0 {
|
||||
|
||||
@@ -240,7 +240,7 @@ enum BadVectorHandling {
|
||||
/// An error is returned
|
||||
#[default]
|
||||
Error,
|
||||
/// The offending row is droppped
|
||||
/// The offending row is dropped
|
||||
Drop,
|
||||
/// The invalid/missing items are replaced by fill_value
|
||||
Fill(f32),
|
||||
@@ -1326,7 +1326,7 @@ impl Table {
|
||||
/// Note: if your condition is something like "some_id_column == 7" and
|
||||
/// you are updating many rows (with different ids) then you will get
|
||||
/// better performance with a single [`merge_insert`] call instead of
|
||||
/// repeatedly calilng this method.
|
||||
/// repeatedly calling this method.
|
||||
pub fn update(&self) -> UpdateBuilder {
|
||||
UpdateBuilder::new(self.inner.clone())
|
||||
}
|
||||
|
||||
@@ -52,7 +52,7 @@ enum ConsistencyMode {
|
||||
/// refresh_window = min(3s, TTL/4)
|
||||
///
|
||||
/// | t < TTL - refresh_window | t < TTL | t >= TTL |
|
||||
/// | Return value | Background refresh & return value | syncronous refresh |
|
||||
/// | Return value | Background refresh & return value | synchronous refresh |
|
||||
Eventual(BackgroundCache<Arc<Dataset>, Error>),
|
||||
}
|
||||
|
||||
|
||||
@@ -103,7 +103,7 @@ impl MergeInsertBuilder {
|
||||
/// but that behavior is subject to change.
|
||||
///
|
||||
/// An optional condition may be specified. If it is, then only
|
||||
/// matched rows that satisfy the condtion will be updated. Any
|
||||
/// matched rows that satisfy the condition will be updated. Any
|
||||
/// rows that do not satisfy the condition will be left as they
|
||||
/// are. Failing to satisfy the condition does not cause a
|
||||
/// "matched row" to become a "not matched" row.
|
||||
|
||||
@@ -904,7 +904,7 @@ fn unsharded_shard_id() -> Uuid {
|
||||
|
||||
/// Build a [`ShardWriterConfig`] from the persisted `writer_config_defaults`.
|
||||
///
|
||||
/// Unknown or unparseable keys are ignored; absent keys keep the
|
||||
/// Unknown or unparsable keys are ignored; absent keys keep the
|
||||
/// [`ShardWriterConfig`] default. The shard id is set by `mem_wal_writer`.
|
||||
fn shard_writer_config_from_defaults(defaults: &HashMap<String, String>) -> ShardWriterConfig {
|
||||
let mut config = ShardWriterConfig::default().with_shard_spec_id(SHARDING_SPEC_ID);
|
||||
|
||||
Reference in New Issue
Block a user