[python] Bump version: 0.2.6 → 0.3.0

Bump to lance 0.8.1 for both javascript and python sdk

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.5
+current_version = 0.2.6
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True

.github/workflows/node.yml

@@ -9,6 +9,7 @@ on:
       - node/**
       - rust/ffi/node/**
       - .github/workflows/node.yml
+      - docker-compose.yml
 
 env:
   # Disable full debug symbol generation to speed up CI build and keep memory down
@@ -133,7 +134,7 @@ jobs:
         cache: 'npm'
         cache-dependency-path: node/package-lock.json
     - name: start local stack
-      run: docker compose -f ../docker-compose.yml up -d 
+      run: docker compose -f ../docker-compose.yml up -d --wait
     - name: create s3
       run: aws s3 mb s3://lancedb-integtest --endpoint $AWS_ENDPOINT
     - name: create ddb

.github/workflows/python.yml

@@ -38,7 +38,7 @@ jobs:
     - name: isort
       run: isort --check --diff --quiet .
     - name: Run tests
-      run: pytest -x -v --durations=30 tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
     - name: doctest
       run: pytest --doctest-modules lancedb
   mac:
@@ -65,4 +65,34 @@ jobs:
     - name: Black
       run: black --check --diff --no-color --quiet .
     - name: Run tests
-      run: pytest -x -v --durations=30 tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
+  pydantic1x:
+    timeout-minutes: 30
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: python
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.9
+    - name: Install lancedb
+      run: |
+        pip install "pydantic<2"
+        pip install -e .[tests]
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+        pip install pytest pytest-mock black isort
+    - name: Black
+      run: black --check --diff --no-color --quiet .
+    - name: isort
+      run: isort --check --diff --quiet .
+    - name: Run tests
+      run: pytest -m "not slow" -x -v --durations=30 tests
+    - name: doctest
+      run: pytest --doctest-modules lancedb

Cargo.toml

@@ -5,8 +5,8 @@ exclude = ["python"]
 resolver = "2"
 
 [workspace.dependencies]
-lance = { "version" = "=0.7.4", "features" = ["dynamodb"] }
-lance-linalg = { "version" = "=0.7.4" }
+lance = { "version" = "=0.8.1", "features" = ["dynamodb"] }
+lance-linalg = { "version" = "=0.8.1" }
 # Note that this one does not include pyarrow
 arrow = { version = "43.0.0", optional = false }
 arrow-array = "43.0"

docker-compose.yml

@@ -13,3 +13,6 @@ services:
       - AWS_SECRET_ACCESS_KEY=SECRETKEY
     healthcheck:
       test: [ "CMD", "curl", "-f", "http://localhost:4566/health" ]
+      interval: 5s
+      retries: 3
+      start_period: 10s

docs/src/ann_indexes.md

@@ -154,28 +154,28 @@ You can select the columns returned by the query using a select clause.
 
 ## FAQ
 
-### When is it necessary to create an ANN vector index.
+### When is it necessary to create an ANN vector index?
 
-`LanceDB` has manually tuned SIMD code for computing vector distances.
-In our benchmarks, computing 100K pairs of 1K dimension vectors only take less than 20ms.
-For small dataset (<100K rows) or the applications which can accept 100ms latency, vector indices are usually not necessary.
+`LanceDB` has manually-tuned SIMD code for computing vector distances.
+In our benchmarks, computing 100K pairs of 1K dimension vectors takes **less than 20ms**.
+For small datasets (< 100K rows) or applications that can accept 100ms latency, vector indices are usually not necessary.
 
 For large-scale or higher dimension vectors, it is beneficial to create vector index.
 
-### How big is my index, and how many memory will it take.
+### How big is my index, and how many memory will it take?
 
-In LanceDB, all vector indices are disk-based, meaning that when responding to a vector query, only the relevant pages from the index file are loaded from disk and cached in memory. Additionally, each sub-vector is usually encoded into 1 byte PQ code.
+In LanceDB, all vector indices are **disk-based**, meaning that when responding to a vector query, only the relevant pages from the index file are loaded from disk and cached in memory. Additionally, each sub-vector is usually encoded into 1 byte PQ code.
 
 For example, with a 1024-dimension dataset, if we choose `num_sub_vectors=64`, each sub-vector has `1024 / 64 = 16` float32 numbers.
 Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` times of space reduction.
 
-### How to choose `num_partitions` and `num_sub_vectors` for `IVF_PQ` index.
+### How to choose `num_partitions` and `num_sub_vectors` for `IVF_PQ` index?
 
 `num_partitions` is used to decide how many partitions the first level `IVF` index uses.
 Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
 On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows lead to a good latency / recall.
 
-`num_sub_vectors` decides how many Product Quantization code to generate on each vector. Because
-Product Quantization is a lossy compression of the original vector, the more `num_sub_vectors` usually results to
-less space distortion, and thus yield better accuracy. However, similarly, more `num_sub_vectors` causes heavier I/O and
-more PQ computation, thus, higher latency. `dimension / num_sub_vectors` should be aligned with 8 for better SIMD efficiency.
+`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. Because
+PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
+less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and
+more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.

docs/src/basic.md

@@ -123,9 +123,15 @@ After a table has been created, you can always add more data to it using
 
 === "Python"
       ```python
-      df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
-                        {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}])
-      tbl.add(df)
+
+      # Option 1: Add a list of dicts to a table
+      data = [{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+            {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}]
+      tbl.add(data)
+
+      # Option 2: Add a pandas DataFrame to a table
+      df = pd.DataFrame(data)
+      tbl.add(data)
       ```
 
 === "Javascript"

docs/src/fts.md

@@ -6,17 +6,19 @@ to make this available for JS as well.
 
 ## Installation
 
-To use full text search, you must install optional dependency tantivy-py:
+To use full text search, you must install the dependency `tantivy-py`:
 
-# tantivy 0.19.2
-pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+# tantivy 0.20.1
+```sh
+pip install tantivy==0.20.1
+```
 
 
 ## Quickstart
 
 Assume:
 1. `table` is a LanceDB Table
-2. `text` is the name of the Table column that we want to index
+2. `text` is the name of the `Table` column that we want to index
 
 For example,
 

docs/src/guides/tables.md

@@ -42,7 +42,7 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     import pandas as pd
 
     data = pd.DataFrame({
-        "vector": [[1.1, 1.2], [0.2, 1.8]],
+        "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
         "lat": [45.5, 40.1],
         "long": [-122.7, -74.1]
     })
@@ -56,7 +56,7 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
 
     ```python
     custom_schema = pa.schema([
-    pa.field("vector", pa.list_(pa.float32(), 2)),
+    pa.field("vector", pa.list_(pa.float32(), 4)),
     pa.field("lat", pa.float32()),
     pa.field("long", pa.float32())
     ])
@@ -70,8 +70,8 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     ```python
     table = pa.Table.from_arrays(
             [
-                pa.array([[3.1, 4.1], [5.9, 26.5]],
-                        pa.list_(pa.float32(), 2)),
+                pa.array([[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                        pa.list_(pa.float32(), 4)),
                 pa.array(["foo", "bar"]),
                 pa.array([10.0, 20.0]),
             ],
@@ -84,7 +84,17 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     ```
 
     ### From Pydantic Models
-    LanceDB supports to create Apache Arrow Schema from a Pydantic BaseModel via pydantic_to_schema() method.
+    When you create an empty table without data, you must specify the table schema.
+    LanceDB supports creating tables by specifying a pyarrow schema or a specialized
+    pydantic model called `LanceModel`.
+
+    For example, the following Content model specifies a table with 5 columns:
+    movie_id, vector, genres, title, and imdb_id. When you create a table, you can
+    pass the class as the value of the `schema` parameter to `create_table`.
+    The `vector` column is a `Vector` type, which is a specialized pydantic type that
+    can be configured with the vector dimensions. It is also important to note that
+    LanceDB only understands subclasses of `lancedb.pydantic.LanceModel`
+    (which itself derives from `pydantic.BaseModel`).
 
     ```python
     from lancedb.pydantic import Vector, LanceModel
@@ -121,8 +131,8 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
         for i in range(5):
             yield pa.RecordBatch.from_arrays(
                 [
-                    pa.array([[3.1, 4.1], [5.9, 26.5]],
-                            pa.list_(pa.float32(), 2)),
+                    pa.array([[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
+                            pa.list_(pa.float32(), 4)),
                     pa.array(["foo", "bar"]),
                     pa.array([10.0, 20.0]),
                 ],
@@ -130,7 +140,7 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
             )
 
     schema = pa.schema([
-        pa.field("vector", pa.list_(pa.float32(), 2)),
+        pa.field("vector", pa.list_(pa.float32(), 4)),
         pa.field("item", pa.utf8()),
         pa.field("price", pa.float32()),
     ])

docs/src/python/python.md

@@ -26,15 +26,19 @@ pip install lancedb
 
 ## Embeddings
 
-::: lancedb.embeddings.with_embeddings
-
 ::: lancedb.embeddings.functions.EmbeddingFunctionRegistry
 
-::: lancedb.embeddings.functions.EmbeddingFunctionModel
+::: lancedb.embeddings.functions.EmbeddingFunction
 
-::: lancedb.embeddings.functions.TextEmbeddingFunctionModel
+::: lancedb.embeddings.functions.TextEmbeddingFunction
 
-::: lancedb.embeddings.functions.SentenceTransformerEmbeddingFunction
+::: lancedb.embeddings.functions.SentenceTransformerEmbeddings
+
+::: lancedb.embeddings.functions.OpenAIEmbeddings
+
+::: lancedb.embeddings.functions.OpenClipEmbeddings
+
+::: lancedb.embeddings.with_embeddings
 
 ## Context
 

docs/src/search.md

@@ -25,8 +25,8 @@ Currently, we support the following metrics:
 
 ### Flat Search
 
-If LanceDB does not create a vector index, LanceDB would need to scan (`Flat Search`) the entire vector column
-and compute the distance for each vector in order to find the closest matches.
+If you do not create a vector index, LanceDB would need to exhaustively scan the entire vector column (via `Flat Search`)
+and compute the distance for *every* vector in order to find the closest matches. This is effectively a KNN search.
 
 
 <!-- Setup Code
@@ -110,7 +110,7 @@ as well.
 
 To accelerate vector retrievals, it is common to build vector indices.
 A vector index is a data structure specifically designed to efficiently organize and
-search vector data based on their similarity or distance metrics.
+search vector data based on their similarity via the chosen distance metric.
 By constructing a vector index, you can reduce the search space and avoid the need
 for brute-force scanning of the entire vector column.
 

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.2.5",
+  "version": "0.2.6",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.2.5",
+      "version": "0.2.6",
       "cpu": [
         "x64",
         "arm64"
@@ -53,11 +53,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.2.5",
-        "@lancedb/vectordb-darwin-x64": "0.2.5",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.2.5",
-        "@lancedb/vectordb-linux-x64-gnu": "0.2.5",
-        "@lancedb/vectordb-win32-x64-msvc": "0.2.5"
+        "@lancedb/vectordb-darwin-arm64": "0.2.6",
+        "@lancedb/vectordb-darwin-x64": "0.2.6",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.2.6",
+        "@lancedb/vectordb-linux-x64-gnu": "0.2.6",
+        "@lancedb/vectordb-win32-x64-msvc": "0.2.6"
       }
     },
     "node_modules/@apache-arrow/ts": {
@@ -317,9 +317,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.5.tgz",
-      "integrity": "sha512-V4206SajkMN3o+bBFBAYJq5emlrjevitP0g8RFfVlmj/LS38i8k4uvSe1bICQ2amUrYkL/Jw4ktYn19NRfTU+g==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.6.tgz",
+      "integrity": "sha512-9KCUvDmhVMuGIhleib/Gq43QhrRXjy2QJz21S85HDwL3DTH4J9n00A0V6eyLTBUyctnvMTcp3XZijosYUy1A8Q==",
       "cpu": [
         "arm64"
       ],
@@ -329,9 +329,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.5.tgz",
-      "integrity": "sha512-orePizgXCbTJbDJ4bMMnYh/4OgmWDBbHShNxHKQobcX+NgWTexmR0lV1WNOG+DtczBiGH422e3gHJ+xhTO13vg==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.6.tgz",
+      "integrity": "sha512-WCYRFV9w13STgVYn4WSYne39mp+g8ET6TgMLvSSQBYJKp3xEggpSCtACetaDfmNpkml9DK/b5R95Jc7PBbmYgA==",
       "cpu": [
         "x64"
       ],
@@ -341,9 +341,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.5.tgz",
-      "integrity": "sha512-xIMNwsFGOHeY9EUWCHhUAcA2sCHZ5Lim0sc42uuUOeWayyH+HeR6ZWReptDQRuAoJHqQeag9qcqteE0AZPDTEw==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.6.tgz",
+      "integrity": "sha512-SE9OUgsOT6dG1q9v3nFr9ew+kwPTA4ktvNiHiyQstNz9BniuLNldF/Wtxzk/Z7DhbkPci4MfkR6RdsPTHBatHg==",
       "cpu": [
         "arm64"
       ],
@@ -353,9 +353,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.5.tgz",
-      "integrity": "sha512-Qr8dbHavtE+Zfd45kEORJQe01kRWhMF703gk8zhtZhskDUBCfqm3ap22JIux58tASxVcBqY8EtUFojfYGnQVvA==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.6.tgz",
+      "integrity": "sha512-hvUsRQbaJiQnSjjKHIRhJM/eObJOqDJUXcpzz1fWw/MMSoy/CFaQwf9Uen2IWTgcngGkJAkeEKG7N5GxQxVbBQ==",
       "cpu": [
         "x64"
       ],
@@ -365,9 +365,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.5.tgz",
-      "integrity": "sha512-jTqkR9HRfbjxhUrlTfveNkJ78tlpVXeNn3BS4wBm4VIsPd75jminKBRYtrlQCWyHusqrUQedKny4hhG1CuNUkg==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.6.tgz",
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       "cpu": [
         "x64"
       ],
@@ -4869,33 +4869,33 @@
       }
     },
     "@lancedb/vectordb-darwin-arm64": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.5.tgz",
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+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.6.tgz",
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       "optional": true
     },
     "@lancedb/vectordb-darwin-x64": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.5.tgz",
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+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.6.tgz",
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       "optional": true
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+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.6.tgz",
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       "optional": true
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-      "version": "0.2.5",
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+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.6.tgz",
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       "optional": true
     },
     "@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.2.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.5.tgz",
-      "integrity": "sha512-jTqkR9HRfbjxhUrlTfveNkJ78tlpVXeNn3BS4wBm4VIsPd75jminKBRYtrlQCWyHusqrUQedKny4hhG1CuNUkg==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.6.tgz",
+      "integrity": "sha512-XPIzbBPt28nsAa7INuyvYMZyJ78bgLfxjSyazlydzO10orIBHvR+sjcrdnCK4l48YmvPXcSYnKxlKMa1oUeIWQ==",
       "optional": true
     },
     "@neon-rs/cli": {

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.2.5",
+  "version": "0.2.6",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -81,10 +81,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.2.5",
-    "@lancedb/vectordb-darwin-x64": "0.2.5",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.2.5",
-    "@lancedb/vectordb-linux-x64-gnu": "0.2.5",
-    "@lancedb/vectordb-win32-x64-msvc": "0.2.5"
+    "@lancedb/vectordb-darwin-arm64": "0.2.6",
+    "@lancedb/vectordb-darwin-x64": "0.2.6",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.2.6",
+    "@lancedb/vectordb-linux-x64-gnu": "0.2.6",
+    "@lancedb/vectordb-win32-x64-msvc": "0.2.6"
   }
 }

node/src/integration_test/test.ts

@@ -24,7 +24,7 @@ chai.use(chaiAsPromised)
 
 describe('LanceDB AWS Integration test', function () {
   it('s3+ddb schema is processed correctly', async function () {
-    this.timeout(5000)
+    this.timeout(15000)
 
     // WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
     // THE API WILL CHANGE

node/src/test/test.ts

@@ -19,7 +19,7 @@ import * as chaiAsPromised from 'chai-as-promised'
 
 import * as lancedb from '../index'
 import { type AwsCredentials, type EmbeddingFunction, MetricType, Query, WriteMode, DefaultWriteOptions, isWriteOptions } from '../index'
-import { Field, Int32, makeVector, Schema, Utf8, Table as ArrowTable, vectorFromArray } from 'apache-arrow'
+import { FixedSizeList, Field, Int32, makeVector, Schema, Utf8, Table as ArrowTable, vectorFromArray, Float32 } from 'apache-arrow'
 
 const expect = chai.expect
 const assert = chai.assert
@@ -258,6 +258,36 @@ describe('LanceDB client', function () {
     })
   })
 
+  describe('when searching an empty dataset', function () {
+    it('should not fail', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      const result = await table.search(Array(128).fill(0.1)).execute()
+      assert.isEmpty(result)
+    })
+  })
+
+  describe('when searching an empty-after-delete dataset', function () {
+    it('should not fail', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      await table.add([{ vector: Array(128).fill(0.1) }])
+      await table.delete('vector IS NOT NULL')
+      const result = await table.search(Array(128).fill(0.1)).execute()
+      assert.isEmpty(result)
+    })
+  })
+
   describe('when creating a vector index', function () {
     it('overwrite all records in a table', async function () {
       const uri = await createTestDB(32, 300)

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.4
+current_version = 0.3.0
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True

python/lancedb/__init__.py

@@ -11,12 +11,15 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import importlib.metadata
 from typing import Optional
 
 from .db import URI, DBConnection, LanceDBConnection
 from .remote.db import RemoteDBConnection
 from .schema import vector
 
+__version__ = importlib.metadata.version("lancedb")
+
 
 def connect(
     uri: URI,

python/lancedb/conftest.py

@@ -1,9 +1,9 @@
 import os
 
-import pyarrow as pa
+import numpy as np
 import pytest
 
-from lancedb.embeddings import EmbeddingFunctionModel, EmbeddingFunctionRegistry
+from .embeddings import EmbeddingFunctionRegistry, TextEmbeddingFunction
 
 # import lancedb so we don't have to in every example
 
@@ -22,17 +22,19 @@ def doctest_setup(monkeypatch, tmpdir):
 registry = EmbeddingFunctionRegistry.get_instance()
 
 
-@registry.register()
-class MockEmbeddingFunction(EmbeddingFunctionModel):
-    def __call__(self, data):
-        if isinstance(data, str):
-            data = [data]
-        elif isinstance(data, pa.ChunkedArray):
-            data = data.combine_chunks().to_pylist()
-        elif isinstance(data, pa.Array):
-            data = data.to_pylist()
+@registry.register("test")
+class MockTextEmbeddingFunction(TextEmbeddingFunction):
+    """
+    Return the hash of the first 10 characters
+    """
 
-        return [self.embed(row) for row in data]
+    def generate_embeddings(self, texts):
+        return [self._compute_one_embedding(row) for row in texts]
 
-    def embed(self, row):
-        return [float(hash(c)) for c in row[:10]]
+    def _compute_one_embedding(self, row):
+        emb = np.array([float(hash(c)) for c in row[:10]])
+        emb /= np.linalg.norm(emb)
+        return emb
+
+    def ndims(self):
+        return 10

python/lancedb/db.py

@@ -22,7 +22,7 @@ import pyarrow as pa
 from pyarrow import fs
 
 from .common import DATA, URI
-from .embeddings import EmbeddingFunctionModel
+from .embeddings import EmbeddingFunctionConfig
 from .pydantic import LanceModel
 from .table import LanceTable, Table
 from .util import fs_from_uri, get_uri_location, get_uri_scheme
@@ -290,7 +290,7 @@ class LanceDBConnection(DBConnection):
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
-        embedding_functions: Optional[List[EmbeddingFunctionModel]] = None,
+        embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
     ) -> LanceTable:
         """Create a table in the database.
 

python/lancedb/embeddings/__init__.py

@@ -13,10 +13,12 @@
 
 
 from .functions import (
-    REGISTRY,
-    EmbeddingFunctionModel,
+    EmbeddingFunction,
+    EmbeddingFunctionConfig,
     EmbeddingFunctionRegistry,
-    SentenceTransformerEmbeddingFunction,
-    TextEmbeddingFunctionModel,
+    OpenAIEmbeddings,
+    OpenClipEmbeddings,
+    SentenceTransformerEmbeddings,
+    TextEmbeddingFunction,
 )
 from .utils import with_embeddings

python/lancedb/embeddings/functions.py

@@ -10,43 +10,79 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+import concurrent.futures
+import importlib
+import io
 import json
+import os
+import socket
+import urllib.error
+import urllib.parse as urlparse
+import urllib.request
 from abc import ABC, abstractmethod
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Union
 
 import numpy as np
 import pyarrow as pa
 from cachetools import cached
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, PrivateAttr
+from tqdm import tqdm
 
 
 class EmbeddingFunctionRegistry:
     """
     This is a singleton class used to register embedding functions
-    and fetch them by name. It also handles serializing and deserializing
+    and fetch them by name. It also handles serializing and deserializing.
+    You can implement your own embedding function by subclassing EmbeddingFunction
+    or TextEmbeddingFunction and registering it with the registry.
+
+    Examples
+    --------
+    >>> registry = EmbeddingFunctionRegistry.get_instance()
+    >>> @registry.register("my-embedding-function")
+    ... class MyEmbeddingFunction(EmbeddingFunction):
+    ...     def ndims(self) -> int:
+    ...         return 128
+    ...
+    ...     def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
+    ...         return self.compute_source_embeddings(query, *args, **kwargs)
+    ...
+    ...     def compute_source_embeddings(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
+    ...         return [np.random.rand(self.ndims()) for _ in range(len(texts))]
+    ...
+    >>> registry.get("my-embedding-function")
+    <class 'lancedb.embeddings.functions.MyEmbeddingFunction'>
     """
 
     @classmethod
     def get_instance(cls):
-        return REGISTRY
+        return __REGISTRY__
 
     def __init__(self):
         self._functions = {}
 
-    def register(self):
+    def register(self, alias: str = None):
         """
         This creates a decorator that can be used to register
-        an EmbeddingFunctionModel.
+        an EmbeddingFunction.
+
+        Parameters
+        ----------
+        alias : Optional[str]
+            a human friendly name for the embedding function. If not
+            provided, the class name will be used.
         """
 
         # This is a decorator for a class that inherits from BaseModel
         # It adds the class to the registry
         def decorator(cls):
-            if not issubclass(cls, EmbeddingFunctionModel):
-                raise TypeError("Must be a subclass of EmbeddingFunctionModel")
+            if not issubclass(cls, EmbeddingFunction):
+                raise TypeError("Must be a subclass of EmbeddingFunction")
             if cls.__name__ in self._functions:
                 raise KeyError(f"{cls.__name__} was already registered")
-            self._functions[cls.__name__] = cls
+            key = alias or cls.__name__
+            self._functions[key] = cls
+            cls.__embedding_function_registry_alias__ = alias
             return cls
 
         return decorator
@@ -57,13 +93,22 @@ class EmbeddingFunctionRegistry:
         """
         self._functions = {}
 
-    def load(self, name: str):
+    def get(self, name: str):
         """
         Fetch an embedding function class by name
+
+        Parameters
+        ----------
+        name : str
+            The name of the embedding function to fetch
+            Either the alias or the class name if no alias was provided
+            during registration
         """
         return self._functions[name]
 
-    def parse_functions(self, metadata: Optional[dict]) -> dict:
+    def parse_functions(
+        self, metadata: Optional[Dict[bytes, bytes]]
+    ) -> Dict[str, "EmbeddingFunctionConfig"]:
         """
         Parse the metadata from an arrow table and
         return a mapping of the vector column to the
@@ -71,9 +116,9 @@ class EmbeddingFunctionRegistry:
 
         Parameters
         ----------
-        metadata : Optional[dict]
+        metadata : Optional[Dict[bytes, bytes]]
             The metadata from an arrow table. Note that
-            the keys and values are bytes.
+            the keys and values are bytes (pyarrow api)
 
         Returns
         -------
@@ -86,68 +131,94 @@ class EmbeddingFunctionRegistry:
             return {}
         serialized = metadata[b"embedding_functions"]
         raw_list = json.loads(serialized.decode("utf-8"))
-        functions = {}
-        for obj in raw_list:
-            model = self.load(obj["schema"]["title"])
-            functions[obj["model"]["vector_column"]] = model(**obj["model"])
-        return functions
+        return {
+            obj["vector_column"]: EmbeddingFunctionConfig(
+                vector_column=obj["vector_column"],
+                source_column=obj["source_column"],
+                function=self.get(obj["name"])(**obj["model"]),
+            )
+            for obj in raw_list
+        }
 
-    def function_to_metadata(self, func):
+    def function_to_metadata(self, conf: "EmbeddingFunctionConfig"):
         """
         Convert the given embedding function and source / vector column configs
         into a config dictionary that can be serialized into arrow metadata
         """
-        schema = func.model_json_schema()
-        json_data = func.model_dump()
+        func = conf.function
+        name = getattr(
+            func, "__embedding_function_registry_alias__", func.__class__.__name__
+        )
+        json_data = func.safe_model_dump()
         return {
-            "schema": schema,
+            "name": name,
             "model": json_data,
+            "source_column": conf.source_column,
+            "vector_column": conf.vector_column,
         }
 
     def get_table_metadata(self, func_list):
         """
-        Convert a list of embedding functions and source / vector column configs
+        Convert a list of embedding functions and source / vector configs
         into a config dictionary that can be serialized into arrow metadata
         """
+        if func_list is None or len(func_list) == 0:
+            return None
         json_data = [self.function_to_metadata(func) for func in func_list]
-        # Note that metadata dictionary values must be bytes so we need to json dump then utf8 encode
+        # Note that metadata dictionary values must be bytes
+        # so we need to json dump then utf8 encode
         metadata = json.dumps(json_data, indent=2).encode("utf-8")
         return {"embedding_functions": metadata}
 
 
-REGISTRY = EmbeddingFunctionRegistry()
-
-
-class EmbeddingFunctionModel(BaseModel, ABC):
-    """
-    A callable ABC for embedding functions
-    """
-
-    source_column: Optional[str]
-    vector_column: str
-
-    @abstractmethod
-    def __call__(self, *args, **kwargs) -> List[np.array]:
-        pass
+# Global instance
+__REGISTRY__ = EmbeddingFunctionRegistry()
 
 
 TEXT = Union[str, List[str], pa.Array, pa.ChunkedArray, np.ndarray]
+IMAGES = Union[
+    str, bytes, List[str], List[bytes], pa.Array, pa.ChunkedArray, np.ndarray
+]
 
 
-class TextEmbeddingFunctionModel(EmbeddingFunctionModel):
+class EmbeddingFunction(BaseModel, ABC):
     """
-    A callable ABC for embedding functions that take text as input
+    An ABC for embedding functions.
+
+    All concrete embedding functions must implement the following:
+    1. compute_query_embeddings() which takes a query and returns a list of embeddings
+    2. get_source_embeddings() which returns a list of embeddings for the source column
+    For text data, the two will be the same. For multi-modal data, the source column
+    might be images and the vector column might be text.
+    3. ndims method which returns the number of dimensions of the vector column
     """
 
-    def __call__(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
-        texts = self.sanitize_input(texts)
-        return self.generate_embeddings(texts)
+    _ndims: int = PrivateAttr()
+
+    @classmethod
+    def create(cls, **kwargs):
+        """
+        Create an instance of the embedding function
+        """
+        return cls(**kwargs)
+
+    @abstractmethod
+    def compute_query_embeddings(self, *args, **kwargs) -> List[np.array]:
+        """
+        Compute the embeddings for a given user query
+        """
+        pass
+
+    @abstractmethod
+    def compute_source_embeddings(self, *args, **kwargs) -> List[np.array]:
+        """
+        Compute the embeddings for the source column in the database
+        """
+        pass
 
     def sanitize_input(self, texts: TEXT) -> Union[List[str], np.ndarray]:
         """
-        Sanitize the input to the embedding function. This is called
-        before generate_embeddings() and is useful for stripping
-        whitespace, lowercasing, etc.
+        Sanitize the input to the embedding function.
         """
         if isinstance(texts, str):
             texts = [texts]
@@ -157,6 +228,78 @@ class TextEmbeddingFunctionModel(EmbeddingFunctionModel):
             texts = texts.combine_chunks().to_pylist()
         return texts
 
+    @classmethod
+    def safe_import(cls, module: str, mitigation=None):
+        """
+        Import the specified module. If the module is not installed,
+        raise an ImportError with a helpful message.
+
+        Parameters
+        ----------
+        module : str
+            The name of the module to import
+        mitigation : Optional[str]
+            The package(s) to install to mitigate the error.
+            If not provided then the module name will be used.
+        """
+        try:
+            return importlib.import_module(module)
+        except ImportError:
+            raise ImportError(f"Please install {mitigation or module}")
+
+    def safe_model_dump(self):
+        from ..pydantic import PYDANTIC_VERSION
+
+        if PYDANTIC_VERSION.major < 2:
+            return dict(self)
+        return self.model_dump()
+
+    @abstractmethod
+    def ndims(self):
+        """
+        Return the dimensions of the vector column
+        """
+        pass
+
+    def SourceField(self, **kwargs):
+        """
+        Creates a pydantic Field that can automatically annotate
+        the source column for this embedding function
+        """
+        return Field(json_schema_extra={"source_column_for": self}, **kwargs)
+
+    def VectorField(self, **kwargs):
+        """
+        Creates a pydantic Field that can automatically annotate
+        the target vector column for this embedding function
+        """
+        return Field(json_schema_extra={"vector_column_for": self}, **kwargs)
+
+
+class EmbeddingFunctionConfig(BaseModel):
+    """
+    This model encapsulates the configuration for a embedding function
+    in a lancedb table. It holds the embedding function, the source column,
+    and the vector column
+    """
+
+    vector_column: str
+    source_column: str
+    function: EmbeddingFunction
+
+
+class TextEmbeddingFunction(EmbeddingFunction):
+    """
+    A callable ABC for embedding functions that take text as input
+    """
+
+    def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
+        return self.compute_source_embeddings(query, *args, **kwargs)
+
+    def compute_source_embeddings(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
+        texts = self.sanitize_input(texts)
+        return self.generate_embeddings(texts)
+
     @abstractmethod
     def generate_embeddings(
         self, texts: Union[List[str], np.ndarray]
@@ -167,15 +310,25 @@ class TextEmbeddingFunctionModel(EmbeddingFunctionModel):
         pass
 
 
-@REGISTRY.register()
-class SentenceTransformerEmbeddingFunction(TextEmbeddingFunctionModel):
+# @EmbeddingFunctionRegistry.get_instance().register(name) doesn't work in 3.8
+register = lambda name: EmbeddingFunctionRegistry.get_instance().register(name)
+
+
+@register("sentence-transformers")
+class SentenceTransformerEmbeddings(TextEmbeddingFunction):
     """
     An embedding function that uses the sentence-transformers library
+
+    https://huggingface.co/sentence-transformers
     """
 
     name: str = "all-MiniLM-L6-v2"
     device: str = "cpu"
-    normalize: bool = False
+    normalize: bool = True
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._ndims = None
 
     @property
     def embedding_model(self):
@@ -186,6 +339,11 @@ class SentenceTransformerEmbeddingFunction(TextEmbeddingFunctionModel):
         """
         return self.__class__.get_embedding_model(self.name, self.device)
 
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = len(self.generate_embeddings("foo")[0])
+        return self._ndims
+
     def generate_embeddings(
         self, texts: Union[List[str], np.ndarray]
     ) -> List[np.array]:
@@ -220,9 +378,201 @@ class SentenceTransformerEmbeddingFunction(TextEmbeddingFunctionModel):
 
         TODO: use lru_cache instead with a reasonable/configurable maxsize
         """
-        try:
-            from sentence_transformers import SentenceTransformer
+        sentence_transformers = cls.safe_import(
+            "sentence_transformers", "sentence-transformers"
+        )
+        return sentence_transformers.SentenceTransformer(name, device=device)
 
-            return SentenceTransformer(name, device=device)
-        except ImportError:
-            raise ValueError("Please install sentence_transformers")
+
+@register("openai")
+class OpenAIEmbeddings(TextEmbeddingFunction):
+    """
+    An embedding function that uses the OpenAI API
+
+    https://platform.openai.com/docs/guides/embeddings
+    """
+
+    name: str = "text-embedding-ada-002"
+
+    def ndims(self):
+        # TODO don't hardcode this
+        return 1536
+
+    def generate_embeddings(
+        self, texts: Union[List[str], np.ndarray]
+    ) -> List[np.array]:
+        """
+        Get the embeddings for the given texts
+
+        Parameters
+        ----------
+        texts: list[str] or np.ndarray (of str)
+            The texts to embed
+        """
+        # TODO retry, rate limit, token limit
+        openai = self.safe_import("openai")
+        rs = openai.Embedding.create(input=texts, model=self.name)["data"]
+        return [v["embedding"] for v in rs]
+
+
+@register("open-clip")
+class OpenClipEmbeddings(EmbeddingFunction):
+    """
+    An embedding function that uses the OpenClip API
+    For multi-modal text-to-image search
+
+    https://github.com/mlfoundations/open_clip
+    """
+
+    name: str = "ViT-B-32"
+    pretrained: str = "laion2b_s34b_b79k"
+    device: str = "cpu"
+    batch_size: int = 64
+    normalize: bool = True
+    _model = PrivateAttr()
+    _preprocess = PrivateAttr()
+    _tokenizer = PrivateAttr()
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        open_clip = self.safe_import("open_clip", "open-clip")
+        model, _, preprocess = open_clip.create_model_and_transforms(
+            self.name, pretrained=self.pretrained
+        )
+        model.to(self.device)
+        self._model, self._preprocess = model, preprocess
+        self._tokenizer = open_clip.get_tokenizer(self.name)
+        self._ndims = None
+
+    def ndims(self):
+        if self._ndims is None:
+            self._ndims = self.generate_text_embeddings("foo").shape[0]
+        return self._ndims
+
+    def compute_query_embeddings(
+        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
+    ) -> List[np.ndarray]:
+        """
+        Compute the embeddings for a given user query
+
+        Parameters
+        ----------
+        query : Union[str, PIL.Image.Image]
+            The query to embed. A query can be either text or an image.
+        """
+        if isinstance(query, str):
+            return [self.generate_text_embeddings(query)]
+        else:
+            PIL = self.safe_import("PIL", "pillow")
+            if isinstance(query, PIL.Image.Image):
+                return [self.generate_image_embedding(query)]
+            else:
+                raise TypeError("OpenClip supports str or PIL Image as query")
+
+    def generate_text_embeddings(self, text: str) -> np.ndarray:
+        torch = self.safe_import("torch")
+        text = self.sanitize_input(text)
+        text = self._tokenizer(text)
+        text.to(self.device)
+        with torch.no_grad():
+            text_features = self._model.encode_text(text.to(self.device))
+            if self.normalize:
+                text_features /= text_features.norm(dim=-1, keepdim=True)
+            return text_features.cpu().numpy().squeeze()
+
+    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+        """
+        Sanitize the input to the embedding function.
+        """
+        if isinstance(images, (str, bytes)):
+            images = [images]
+        elif isinstance(images, pa.Array):
+            images = images.to_pylist()
+        elif isinstance(images, pa.ChunkedArray):
+            images = images.combine_chunks().to_pylist()
+        return images
+
+    def compute_source_embeddings(
+        self, images: IMAGES, *args, **kwargs
+    ) -> List[np.array]:
+        """
+        Get the embeddings for the given images
+        """
+        images = self.sanitize_input(images)
+        embeddings = []
+        for i in range(0, len(images), self.batch_size):
+            j = min(i + self.batch_size, len(images))
+            batch = images[i:j]
+            embeddings.extend(self._parallel_get(batch))
+        return embeddings
+
+    def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
+        """
+        Issue concurrent requests to retrieve the image data
+        """
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = [
+                executor.submit(self.generate_image_embedding, image)
+                for image in images
+            ]
+            return [future.result() for future in tqdm(futures)]
+
+    def generate_image_embedding(
+        self, image: Union[str, bytes, "PIL.Image.Image"]
+    ) -> np.ndarray:
+        """
+        Generate the embedding for a single image
+
+        Parameters
+        ----------
+        image : Union[str, bytes, PIL.Image.Image]
+            The image to embed. If the image is a str, it is treated as a uri.
+            If the image is bytes, it is treated as the raw image bytes.
+        """
+        torch = self.safe_import("torch")
+        # TODO handle retry and errors for https
+        image = self._to_pil(image)
+        image = self._preprocess(image).unsqueeze(0)
+        with torch.no_grad():
+            return self._encode_and_normalize_image(image)
+
+    def _to_pil(self, image: Union[str, bytes]):
+        PIL = self.safe_import("PIL", "pillow")
+        if isinstance(image, bytes):
+            return PIL.Image.open(io.BytesIO(image))
+        if isinstance(image, PIL.Image.Image):
+            return image
+        elif isinstance(image, str):
+            parsed = urlparse.urlparse(image)
+            # TODO handle drive letter on windows.
+            if parsed.scheme == "file":
+                return PIL.Image.open(parsed.path)
+            elif parsed.scheme == "":
+                return PIL.Image.open(image if os.name == "nt" else parsed.path)
+            elif parsed.scheme.startswith("http"):
+                return PIL.Image.open(io.BytesIO(url_retrieve(image)))
+            else:
+                raise NotImplementedError("Only local and http(s) urls are supported")
+
+    def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
+        """
+        encode a single image tensor and optionally normalize the output
+        """
+        image_features = self._model.encode_image(image_tensor.to(self.device))
+        if self.normalize:
+            image_features /= image_features.norm(dim=-1, keepdim=True)
+        return image_features.cpu().numpy().squeeze()
+
+
+def url_retrieve(url: str):
+    """
+    Parameters
+    ----------
+    url: str
+        URL to download from
+    """
+    try:
+        with urllib.request.urlopen(url) as conn:
+            return conn.read()
+    except (socket.gaierror, urllib.error.URLError) as err:
+        raise ConnectionError("could not download {} due to {}".format(url, err))

python/lancedb/pydantic.py

@@ -26,6 +26,8 @@ import pyarrow as pa
 import pydantic
 import semver
 
+from .embeddings import EmbeddingFunctionRegistry
+
 PYDANTIC_VERSION = semver.Version.parse(pydantic.__version__)
 try:
     from pydantic_core import CoreSchema, core_schema
@@ -126,7 +128,7 @@ def Vector(
         def validate(cls, v):
             if not isinstance(v, (list, range, np.ndarray)) or len(v) != dim:
                 raise TypeError("A list of numbers or numpy.ndarray is needed")
-            return v
+            return cls(v)
 
         if PYDANTIC_VERSION < (2, 0):
 
@@ -236,27 +238,18 @@ def pydantic_to_schema(model: Type[pydantic.BaseModel]) -> pa.Schema:
     >>> from typing import List, Optional
     >>> import pydantic
     >>> from lancedb.pydantic import pydantic_to_schema
-    ...
-    >>> class InnerModel(pydantic.BaseModel):
-    ...     a: str
-    ...     b: Optional[float]
-    >>>
     >>> class FooModel(pydantic.BaseModel):
     ...     id: int
-    ...     s: Optional[str] = None
+    ...     s: str
     ...     vec: List[float]
     ...     li: List[int]
-    ...     inner: InnerModel
+    ...
     >>> schema = pydantic_to_schema(FooModel)
     >>> assert schema == pa.schema([
     ...     pa.field("id", pa.int64(), False),
-    ...     pa.field("s", pa.utf8(), True),
+    ...     pa.field("s", pa.utf8(), False),
     ...     pa.field("vec", pa.list_(pa.float64()), False),
     ...     pa.field("li", pa.list_(pa.int64()), False),
-    ...     pa.field("inner", pa.struct([
-    ...         pa.field("a", pa.utf8(), False),
-    ...         pa.field("b", pa.float64(), True),
-    ...     ]), False),
     ... ])
     """
     fields = _pydantic_model_to_fields(model)
@@ -290,13 +283,58 @@ class LanceModel(pydantic.BaseModel):
         """
         Get the Arrow Schema for this model.
         """
-        return pydantic_to_schema(cls)
+        schema = pydantic_to_schema(cls)
+        functions = cls.parse_embedding_functions()
+        if len(functions) > 0:
+            metadata = EmbeddingFunctionRegistry.get_instance().get_table_metadata(
+                functions
+            )
+            schema = schema.with_metadata(metadata)
+        return schema
 
     @classmethod
     def field_names(cls) -> List[str]:
         """
         Get the field names of this model.
         """
+        return list(cls.safe_get_fields().keys())
+
+    @classmethod
+    def safe_get_fields(cls):
         if PYDANTIC_VERSION.major < 2:
-            return list(cls.__fields__.keys())
-        return list(cls.model_fields.keys())
+            return cls.__fields__
+        return cls.model_fields
+
+    @classmethod
+    def parse_embedding_functions(cls) -> List["EmbeddingFunctionConfig"]:
+        """
+        Parse the embedding functions from this model.
+        """
+        from .embeddings import EmbeddingFunctionConfig
+
+        vec_and_function = []
+        for name, field_info in cls.safe_get_fields().items():
+            func = get_extras(field_info, "vector_column_for")
+            if func is not None:
+                vec_and_function.append([name, func])
+
+        configs = []
+        for vec, func in vec_and_function:
+            for source, field_info in cls.safe_get_fields().items():
+                src_func = get_extras(field_info, "source_column_for")
+                if src_func == func:
+                    configs.append(
+                        EmbeddingFunctionConfig(
+                            source_column=source, vector_column=vec, function=func
+                        )
+                    )
+        return configs
+
+
+def get_extras(field_info: pydantic.fields.FieldInfo, key: str) -> Any:
+    """
+    Get the extra metadata from a Pydantic FieldInfo.
+    """
+    if PYDANTIC_VERSION.major >= 2:
+        return (field_info.json_schema_extra or {}).get(key)
+    return (field_info.field_info.extra or {}).get("json_schema_extra", {}).get(key)

python/lancedb/query.py

@@ -38,6 +38,9 @@ class Query(pydantic.BaseModel):
     # sql filter to refine the query with
     filter: Optional[str] = None
 
+    # if True then apply the filter before vector search
+    prefilter: bool = False
+
     # top k results to return
     k: int
 
@@ -60,13 +63,15 @@ class LanceQueryBuilder(ABC):
     def create(
         cls,
         table: "lancedb.table.Table",
-        query: Optional[Union[np.ndarray, str]],
+        query: Optional[Union[np.ndarray, str, "PIL.Image.Image"]],
         query_type: str,
         vector_column_name: str,
     ) -> LanceQueryBuilder:
         if query is None:
             return LanceEmptyQueryBuilder(table)
 
+        # convert "auto" query_type to "vector" or "fts"
+        # and convert the query to vector if needed
         query, query_type = cls._resolve_query(
             table, query, query_type, vector_column_name
         )
@@ -90,30 +95,27 @@ class LanceQueryBuilder(ABC):
         # otherwise raise TypeError
         if query_type == "fts":
             if not isinstance(query, str):
-                raise TypeError(
-                    f"Query type is 'fts' but query is not a string: {type(query)}"
-                )
+                raise TypeError(f"'fts' queries must be a string: {type(query)}")
             return query, query_type
         elif query_type == "vector":
-            # If query_type is vector, then query must be a list or np.ndarray.
-            # otherwise raise TypeError
             if not isinstance(query, (list, np.ndarray)):
-                raise TypeError(
-                    f"Query type is 'vector' but query is not a list or np.ndarray: {type(query)}"
-                )
+                conf = table.embedding_functions.get(vector_column_name)
+                if conf is not None:
+                    query = conf.function.compute_query_embeddings(query)[0]
+                else:
+                    msg = f"No embedding function for {vector_column_name}"
+                    raise ValueError(msg)
             return query, query_type
         elif query_type == "auto":
             if isinstance(query, (list, np.ndarray)):
                 return query, "vector"
-            elif isinstance(query, str):
-                func = table.embedding_functions.get(vector_column_name, None)
-                if func is not None:
-                    query = func(query)[0]
+            else:
+                conf = table.embedding_functions.get(vector_column_name)
+                if conf is not None:
+                    query = conf.function.compute_query_embeddings(query)[0]
                     return query, "vector"
                 else:
                     return query, "fts"
-            else:
-                raise TypeError("Query must be a list, np.ndarray, or str")
         else:
             raise ValueError(
                 f"Invalid query_type, must be 'vector', 'fts', or 'auto': {query_type}"
@@ -163,7 +165,7 @@ class LanceQueryBuilder(ABC):
             for row in self.to_arrow().to_pylist()
         ]
 
-    def limit(self, limit: int) -> LanceVectorQueryBuilder:
+    def limit(self, limit: int) -> LanceQueryBuilder:
         """Set the maximum number of results to return.
 
         Parameters
@@ -173,13 +175,13 @@ class LanceQueryBuilder(ABC):
 
         Returns
         -------
-        LanceVectorQueryBuilder
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._limit = limit
         return self
 
-    def select(self, columns: list) -> LanceVectorQueryBuilder:
+    def select(self, columns: list) -> LanceQueryBuilder:
         """Set the columns to return.
 
         Parameters
@@ -189,13 +191,13 @@ class LanceQueryBuilder(ABC):
 
         Returns
         -------
-        LanceVectorQueryBuilder
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._columns = columns
         return self
 
-    def where(self, where: str) -> LanceVectorQueryBuilder:
+    def where(self, where) -> LanceQueryBuilder:
         """Set the where clause.
 
         Parameters
@@ -205,7 +207,7 @@ class LanceQueryBuilder(ABC):
 
         Returns
         -------
-        LanceVectorQueryBuilder
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._where = where
@@ -238,7 +240,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
     def __init__(
         self,
         table: "lancedb.table.Table",
-        query: Union[np.ndarray, list],
+        query: Union[np.ndarray, list, "PIL.Image.Image"],
         vector_column: str = VECTOR_COLUMN_NAME,
     ):
         super().__init__(table)
@@ -247,6 +249,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._nprobes = 20
         self._refine_factor = None
         self._vector_column = vector_column
+        self._prefilter = False
 
     def metric(self, metric: Literal["L2", "cosine"]) -> LanceVectorQueryBuilder:
         """Set the distance metric to use.
@@ -321,6 +324,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         query = Query(
             vector=vector,
             filter=self._where,
+            prefilter=self._prefilter,
             k=self._limit,
             metric=self._metric,
             columns=self._columns,
@@ -330,6 +334,30 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         )
         return self._table._execute_query(query)
 
+    def where(self, where: str, prefilter: bool = False) -> LanceVectorQueryBuilder:
+        """Set the where clause.
+
+        Parameters
+        ----------
+        where: str
+            The where clause.
+        prefilter: bool, default False
+            If True, apply the filter before vector search, otherwise the
+            filter is applied on the result of vector search.
+            This feature is **EXPERIMENTAL** and may be removed and modified
+            without warning in the future. Currently this is only supported
+            in OSS and can only be used with a table that does not have an ANN
+            index.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        self._where = where
+        self._prefilter = prefilter
+        return self
+
 
 class LanceFtsQueryBuilder(LanceQueryBuilder):
     def __init__(self, table: "lancedb.table.Table", query: str):

python/lancedb/remote/__init__.py

@@ -14,7 +14,7 @@
 import abc
 from typing import List, Optional
 
-import attr
+import attrs
 import pyarrow as pa
 from pydantic import BaseModel
 
@@ -44,7 +44,7 @@ class VectorQuery(BaseModel):
     refine_factor: Optional[int] = None
 
 
-@attr.define
+@attrs.define
 class VectorQueryResult:
     # for now the response is directly seralized into a pandas dataframe
     tbl: pa.Table

python/lancedb/remote/client.py

@@ -16,7 +16,7 @@ import functools
 from typing import Any, Callable, Dict, Optional, Union
 
 import aiohttp
-import attr
+import attrs
 import pyarrow as pa
 from pydantic import BaseModel
 
@@ -43,14 +43,14 @@ async def _read_ipc(resp: aiohttp.ClientResponse) -> pa.Table:
         return reader.read_all()
 
 
-@attr.define(slots=False)
+@attrs.define(slots=False)
 class RestfulLanceDBClient:
     db_name: str
     region: str
     api_key: Credential
-    host_override: Optional[str] = attr.field(default=None)
+    host_override: Optional[str] = attrs.field(default=None)
 
-    closed: bool = attr.field(default=False, init=False)
+    closed: bool = attrs.field(default=False, init=False)
 
     @functools.cached_property
     def session(self) -> aiohttp.ClientSession:

python/lancedb/remote/db.py

@@ -18,10 +18,9 @@ from urllib.parse import urlparse
 
 import pyarrow as pa
 
-from lancedb.common import DATA
-from lancedb.db import DBConnection
-from lancedb.table import Table, _sanitize_data
-
+from ..common import DATA
+from ..db import DBConnection
+from ..table import Table, _sanitize_data
 from .arrow import to_ipc_binary
 from .client import ARROW_STREAM_CONTENT_TYPE, RestfulLanceDBClient
 

python/lancedb/remote/table.py

@@ -98,6 +98,8 @@ class RemoteTable(Table):
         return LanceVectorQueryBuilder(self, query, vector_column_name)
 
     def _execute_query(self, query: Query) -> pa.Table:
+        if query.prefilter:
+            raise NotImplementedError("Cloud support for prefiltering is coming soon")
         result = self._conn._client.query(self._name, query)
         return self._conn._loop.run_until_complete(result).to_arrow()
 

python/lancedb/table.py

@@ -28,7 +28,8 @@ from lance.dataset import ReaderLike
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
-from .embeddings import EmbeddingFunctionModel, EmbeddingFunctionRegistry
+from .embeddings import EmbeddingFunctionRegistry
+from .embeddings.functions import EmbeddingFunctionConfig
 from .pydantic import LanceModel
 from .query import LanceQueryBuilder, Query
 from .util import fs_from_uri, safe_import_pandas
@@ -81,15 +82,16 @@ def _append_vector_col(data: pa.Table, metadata: dict, schema: Optional[pa.Schem
     vector column to the table.
     """
     functions = EmbeddingFunctionRegistry.get_instance().parse_functions(metadata)
-    for vector_col, func in functions.items():
-        if vector_col not in data.column_names:
-            col_data = func(data[func.source_column])
+    for vector_column, conf in functions.items():
+        func = conf.function
+        if vector_column not in data.column_names:
+            col_data = func.compute_source_embeddings(data[conf.source_column])
             if schema is not None:
-                dtype = schema.field(vector_col).type
+                dtype = schema.field(vector_column).type
             else:
                 dtype = pa.list_(pa.float32(), len(col_data[0]))
             data = data.append_column(
-                pa.field(vector_col, type=dtype), pa.array(col_data, type=dtype)
+                pa.field(vector_column, type=dtype), pa.array(col_data, type=dtype)
             )
     return data
 
@@ -230,7 +232,7 @@ class Table(ABC):
     @abstractmethod
     def search(
         self,
-        query: Optional[Union[VEC, str]] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image"]] = None,
         vector_column_name: str = VECTOR_COLUMN_NAME,
         query_type: str = "auto",
     ) -> LanceQueryBuilder:
@@ -239,7 +241,7 @@ class Table(ABC):
 
         Parameters
         ----------
-        query: str, list, np.ndarray, default None
+        query: str, list, np.ndarray, PIL.Image.Image, default None
             The query to search for. If None then
             the select/where/limit clauses are applied to filter
             the table
@@ -249,6 +251,8 @@ class Table(ABC):
             "vector", "fts", or "auto"
             If "auto" then the query type is inferred from the query;
             If `query` is a list/np.ndarray then the query type is "vector";
+            If `query` is a PIL.Image.Image then either do vector search
+            or raise an error if no corresponding embedding function is found.
             If `query` is a string, then the query type is "vector" if the
             table has embedding functions else the query type is "fts"
 
@@ -524,6 +528,9 @@ class LanceTable(Table):
         fill_value: float = 0.0,
     ):
         """Add data to the table.
+        If vector columns are missing and the table
+        has embedding functions, then the vector columns
+        are automatically computed and added.
 
         Parameters
         ----------
@@ -617,12 +624,6 @@ class LanceTable(Table):
         )
         self._reset_dataset()
 
-    def _get_embedding_function_for_source_col(self, column_name: str):
-        for k, v in self.embedding_functions.items():
-            if v.source_column == column_name:
-                return v
-        return None
-
     @cached_property
     def embedding_functions(self) -> dict:
         """
@@ -640,7 +641,7 @@ class LanceTable(Table):
 
     def search(
         self,
-        query: Optional[Union[VEC, str]] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image"]] = None,
         vector_column_name: str = VECTOR_COLUMN_NAME,
         query_type: str = "auto",
     ) -> LanceQueryBuilder:
@@ -649,7 +650,7 @@ class LanceTable(Table):
 
         Parameters
         ----------
-        query: str, list, np.ndarray, or None
+        query: str, list, np.ndarray, a PIL Image or None
             The query to search for. If None then
             the select/where/limit clauses are applied to filter
             the table
@@ -658,9 +659,11 @@ class LanceTable(Table):
         query_type: str, default "auto"
             "vector", "fts", or "auto"
             If "auto" then the query type is inferred from the query;
-            If the query is a list/np.ndarray then the query type is "vector";
+            If `query` is a list/np.ndarray then the query type is "vector";
+            If `query` is a PIL.Image.Image then either do vector search
+            or raise an error if no corresponding embedding function is found.
             If the query is a string, then the query type is "vector" if the
-            table has embedding functions else the query type is "fts"
+            table has embedding functions, else the query type is "fts"
 
         Returns
         -------
@@ -684,7 +687,7 @@ class LanceTable(Table):
         mode="create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
-        embedding_functions: List[EmbeddingFunctionModel] = None,
+        embedding_functions: List[EmbeddingFunctionConfig] = None,
     ):
         """
         Create a new table.
@@ -727,10 +730,16 @@ class LanceTable(Table):
         """
         tbl = LanceTable(db, name)
         if inspect.isclass(schema) and issubclass(schema, LanceModel):
+            # convert LanceModel to pyarrow schema
+            # note that it's possible this contains
+            # embedding function metadata already
             schema = schema.to_arrow_schema()
 
         metadata = None
         if embedding_functions is not None:
+            # If we passed in embedding functions explicitly
+            # then we'll override any schema metadata that
+            # may was implicitly specified by the LanceModel schema
             registry = EmbeddingFunctionRegistry.get_instance()
             metadata = registry.get_table_metadata(embedding_functions)
 
@@ -835,9 +844,16 @@ class LanceTable(Table):
 
     def _execute_query(self, query: Query) -> pa.Table:
         ds = self.to_lance()
+        if query.prefilter:
+            for idx in ds.list_indices():
+                if query.vector_column in idx["fields"]:
+                    raise NotImplementedError(
+                        "Prefiltering for indexed vector column is coming soon."
+                    )
         return ds.to_table(
             columns=query.columns,
             filter=query.filter,
+            prefilter=query.prefilter,
             nearest={
                 "column": query.vector_column,
                 "q": query.vector,

python/lancedb/util.py

@@ -70,7 +70,11 @@ def fs_from_uri(uri: str) -> Tuple[pa_fs.FileSystem, str]:
     Get a PyArrow FileSystem from a URI, handling extra environment variables.
     """
     if get_uri_scheme(uri) == "s3":
-        fs = pa_fs.S3FileSystem(endpoint_override=os.environ.get("AWS_ENDPOINT"))
+        fs = pa_fs.S3FileSystem(
+            endpoint_override=os.environ.get("AWS_ENDPOINT"),
+            request_timeout=30,
+            connect_timeout=30,
+        )
         path = get_uri_location(uri)
         return fs, path
 

python/pyproject.toml

@@ -1,14 +1,14 @@
 [project]
 name = "lancedb"
-version = "0.2.4"
+version = "0.3.0"
 dependencies = [
-    "pylance==0.7.4",
-    "ratelimiter",
-    "retry",
-    "tqdm",
+    "pylance==0.8.1",
+    "ratelimiter~=1.0",
+    "retry>=0.9.2",
+    "tqdm>=4.1.0",
     "aiohttp",
-    "pydantic",
-    "attr",
+    "pydantic>=1.10",
+    "attrs>=21.3.0",
     "semver>=3.0",
     "cachetools"
 ]
@@ -44,9 +44,11 @@ classifiers = [
 repository = "https://github.com/lancedb/lancedb"
 
 [project.optional-dependencies]
-tests = ["pandas>=1.4", "pytest", "pytest-mock", "pytest-asyncio"]
+tests = ["pandas>=1.4", "pytest", "pytest-mock", "pytest-asyncio", "requests"]
 dev = ["ruff", "pre-commit", "black"]
 docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
+clip = ["torch", "pillow", "open-clip"]
+embeddings = ["openai", "sentence-transformers", "torch", "pillow", "open-clip"]
 
 [build-system]
 requires = ["setuptools", "wheel"]
@@ -54,3 +56,10 @@ build-backend = "setuptools.build_meta"
 
 [tool.isort]
 profile = "black"
+
+[tool.pytest.ini_options]
+addopts = "--strict-markers"
+markers = [
+    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
+    "asyncio"
+]

python/tests/test_db.py

@@ -136,11 +136,9 @@ def test_ingest_iterator(tmp_path):
     def run_tests(schema):
         db = lancedb.connect(tmp_path)
         tbl = db.create_table("table2", make_batches(), schema=schema, mode="overwrite")
-
         tbl.to_pandas()
         assert tbl.search([3.1, 4.1]).limit(1).to_df()["_distance"][0] == 0.0
         assert tbl.search([5.9, 26.5]).limit(1).to_df()["_distance"][0] == 0.0
-
         tbl_len = len(tbl)
         tbl.add(make_batches())
         assert tbl_len == 50

python/tests/test_embeddings.py

@@ -16,8 +16,12 @@ import lance
 import numpy as np
 import pyarrow as pa
 
-from lancedb.conftest import MockEmbeddingFunction
-from lancedb.embeddings import EmbeddingFunctionRegistry, with_embeddings
+from lancedb.conftest import MockTextEmbeddingFunction
+from lancedb.embeddings import (
+    EmbeddingFunctionConfig,
+    EmbeddingFunctionRegistry,
+    with_embeddings,
+)
 
 
 def mock_embed_func(input_data):
@@ -54,8 +58,12 @@ def test_embedding_function(tmp_path):
             "vector": [np.random.randn(10), np.random.randn(10)],
         }
     )
-    func = MockEmbeddingFunction(source_column="text", vector_column="vector")
-    metadata = registry.get_table_metadata([func])
+    conf = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="vector",
+        function=MockTextEmbeddingFunction(),
+    )
+    metadata = registry.get_table_metadata([conf])
     table = table.replace_schema_metadata(metadata)
 
     # Write it to disk
@@ -65,14 +73,13 @@ def test_embedding_function(tmp_path):
     ds = lance.dataset(tmp_path / "test.lance")
 
     # can we get the serialized version back out?
-    functions = registry.parse_functions(ds.schema.metadata)
+    configs = registry.parse_functions(ds.schema.metadata)
 
-    func = functions["vector"]
-    actual = func("hello world")
+    conf = configs["vector"]
+    func = conf.function
+    actual = func.compute_query_embeddings("hello world")
 
-    # We create an instance
-    expected_func = MockEmbeddingFunction(source_column="text", vector_column="vector")
     # And we make sure we can call it
-    expected = expected_func("hello world")
+    expected = func.compute_query_embeddings("hello world")
 
     assert np.allclose(actual, expected)

python/tests/test_embeddings_slow.py

@@ -0,0 +1,125 @@
+#  Copyright (c) 2023. LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import io
+
+import numpy as np
+import pandas as pd
+import pytest
+import requests
+
+import lancedb
+from lancedb.embeddings import EmbeddingFunctionRegistry
+from lancedb.pydantic import LanceModel, Vector
+
+# These are integration tests for embedding functions.
+# They are slow because they require downloading models
+# or connection to external api
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("alias", ["sentence-transformers", "openai"])
+def test_sentence_transformer(alias, tmp_path):
+    db = lancedb.connect(tmp_path)
+    registry = EmbeddingFunctionRegistry.get_instance()
+    func = registry.get(alias).create()
+
+    class Words(LanceModel):
+        text: str = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("words", schema=Words)
+    table.add(
+        pd.DataFrame(
+            {
+                "text": [
+                    "hello world",
+                    "goodbye world",
+                    "fizz",
+                    "buzz",
+                    "foo",
+                    "bar",
+                    "baz",
+                ]
+            }
+        )
+    )
+
+    query = "greetings"
+    actual = table.search(query).limit(1).to_pydantic(Words)[0]
+
+    vec = func.compute_query_embeddings(query)[0]
+    expected = table.search(vec).limit(1).to_pydantic(Words)[0]
+    assert actual.text == expected.text
+    assert actual.text == "hello world"
+
+
+@pytest.mark.slow
+def test_openclip(tmp_path):
+    from PIL import Image
+
+    db = lancedb.connect(tmp_path)
+    registry = EmbeddingFunctionRegistry.get_instance()
+    func = registry.get("open-clip").create()
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+        vec_from_bytes: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("images", schema=Images)
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    # get each uri as bytes
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+
+    # text search
+    actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0]
+    assert actual.label == "dog"
+    frombytes = (
+        table.search("man's best friend", vector_column_name="vec_from_bytes")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    assert actual.label == frombytes.label
+    assert np.allclose(actual.vector, frombytes.vector)
+
+    # image search
+    query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
+    image_bytes = requests.get(query_image_uri).content
+    query_image = Image.open(io.BytesIO(image_bytes))
+    actual = table.search(query_image).limit(1).to_pydantic(Images)[0]
+    assert actual.label == "dog"
+    other = (
+        table.search(query_image, vector_column_name="vec_from_bytes")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    assert actual.label == other.label
+
+    arrow_table = table.search().select(["vector", "vec_from_bytes"]).to_arrow()
+    assert np.allclose(
+        arrow_table["vector"].combine_chunks().values.to_numpy(),
+        arrow_table["vec_from_bytes"].combine_chunks().values.to_numpy(),
+    )

python/tests/test_query.py

@@ -38,6 +38,7 @@ class MockTable:
         return ds.to_table(
             columns=query.columns,
             filter=query.filter,
+            prefilter=query.prefilter,
             nearest={
                 "column": query.vector_column,
                 "q": query.vector,
@@ -97,6 +98,25 @@ def test_query_builder_with_filter(table):
     assert all(df["vector"].values[0] == [3, 4])
 
 
+def test_query_builder_with_prefilter(table):
+    df = (
+        LanceVectorQueryBuilder(table, [0, 0], "vector")
+        .where("id = 2")
+        .limit(1)
+        .to_df()
+    )
+    assert len(df) == 0
+
+    df = (
+        LanceVectorQueryBuilder(table, [0, 0], "vector")
+        .where("id = 2", prefilter=True)
+        .limit(1)
+        .to_df()
+    )
+    assert df["id"].values[0] == 2
+    assert all(df["vector"].values[0] == [3, 4])
+
+
 def test_query_builder_with_metric(table):
     query = [4, 8]
     vector_column_name = "vector"

python/tests/test_remote_client.py

@@ -11,7 +11,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import attr
+import attrs
 import numpy as np
 import pandas as pd
 import pyarrow as pa
@@ -21,10 +21,10 @@ from aiohttp import web
 from lancedb.remote.client import RestfulLanceDBClient, VectorQuery
 
 
-@attr.define
+@attrs.define
 class MockLanceDBServer:
-    runner: web.AppRunner = attr.field(init=False)
-    site: web.TCPSite = attr.field(init=False)
+    runner: web.AppRunner = attrs.field(init=False)
+    site: web.TCPSite = attrs.field(init=False)
 
     async def query_handler(self, request: web.Request) -> web.Response:
         table_name = request.match_info["table_name"]

python/tests/test_table.py

@@ -22,8 +22,9 @@ import pandas as pd
 import pyarrow as pa
 import pytest
 
-from lancedb.conftest import MockEmbeddingFunction
+from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.db import LanceDBConnection
+from lancedb.embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
 from lancedb.pydantic import LanceModel, Vector
 from lancedb.table import LanceTable
 
@@ -356,20 +357,23 @@ def test_create_with_embedding_function(db):
         text: str
         vector: Vector(10)
 
-    func = MockEmbeddingFunction(source_column="text", vector_column="vector")
+    func = MockTextEmbeddingFunction()
     texts = ["hello world", "goodbye world", "foo bar baz fizz buzz"]
-    df = pd.DataFrame({"text": texts, "vector": func(texts)})
+    df = pd.DataFrame({"text": texts, "vector": func.compute_source_embeddings(texts)})
 
+    conf = EmbeddingFunctionConfig(
+        source_column="text", vector_column="vector", function=func
+    )
     table = LanceTable.create(
         db,
         "my_table",
         schema=MyTable,
-        embedding_functions=[func],
+        embedding_functions=[conf],
     )
     table.add(df)
 
     query_str = "hi how are you?"
-    query_vector = func(query_str)[0]
+    query_vector = func.compute_query_embeddings(query_str)[0]
     expected = table.search(query_vector).limit(2).to_arrow()
 
     actual = table.search(query_str).limit(2).to_arrow()
@@ -377,17 +381,13 @@ def test_create_with_embedding_function(db):
 
 
 def test_add_with_embedding_function(db):
-    class MyTable(LanceModel):
-        text: str
-        vector: Vector(10)
+    emb = EmbeddingFunctionRegistry.get_instance().get("test")()
 
-    func = MockEmbeddingFunction(source_column="text", vector_column="vector")
-    table = LanceTable.create(
-        db,
-        "my_table",
-        schema=MyTable,
-        embedding_functions=[func],
-    )
+    class MyTable(LanceModel):
+        text: str = emb.SourceField()
+        vector: Vector(emb.ndims()) = emb.VectorField()
+
+    table = LanceTable.create(db, "my_table", schema=MyTable)
 
     texts = ["hello world", "goodbye world", "foo bar baz fizz buzz"]
     df = pd.DataFrame({"text": texts})
@@ -397,7 +397,7 @@ def test_add_with_embedding_function(db):
     table.add([{"text": t} for t in texts])
 
     query_str = "hi how are you?"
-    query_vector = func(query_str)[0]
+    query_vector = emb.compute_query_embeddings(query_str)[0]
     expected = table.search(query_vector).limit(2).to_arrow()
 
     actual = table.search(query_str).limit(2).to_arrow()

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.2.5"
+version = "0.2.6"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"

rust/ffi/node/src/index/vector.rs

@@ -12,8 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use lance::index::vector::ivf::IvfBuildParams;
-use lance::index::vector::pq::PQBuildParams;
+use lance::index::vector::{ivf::IvfBuildParams, pq::PQBuildParams};
 use lance_linalg::distance::MetricType;
 use neon::context::FunctionContext;
 use neon::prelude::*;
@@ -79,11 +78,9 @@ fn get_index_params_builder(
 
             num_partitions.map(|np| {
                 let max_iters = max_iters.unwrap_or(50);
-                let ivf_params = IvfBuildParams {
-                    num_partitions: np,
-                    max_iters,
-                    centroids: None,
-                };
+                let mut ivf_params = IvfBuildParams::default();
+                ivf_params.num_partitions = np;
+                ivf_params.max_iters = max_iters;
                 index_builder.ivf_params(ivf_params)
             });
 

rust/vectordb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb"
-version = "0.2.5"
+version = "0.2.6"
 edition = "2021"
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"

rust/vectordb/src/database.rs

@@ -20,7 +20,7 @@ use lance::dataset::WriteParams;
 use lance::io::object_store::ObjectStore;
 use snafu::prelude::*;
 
-use crate::error::{CreateDirSnafu, Error, InvalidTableNameSnafu, Result};
+use crate::error::{CreateDirSnafu, InvalidTableNameSnafu, Result};
 use crate::table::{ReadParams, Table};
 
 pub const LANCE_FILE_EXTENSION: &str = "lance";
@@ -36,17 +36,6 @@ pub struct Database {
 const LANCE_EXTENSION: &str = "lance";
 const ENGINE: &str = "engine";
 
-/// Parse a url, if it's not a valid url, assume it's a local file
-/// and try to parse with file:// appended
-fn parse_url(url: &str) -> Result<url::Url> {
-    match url::Url::parse(url) {
-        Ok(url) => Ok(url),
-        Err(_) => url::Url::parse(format!("file://{}", url).as_str()).map_err(|e| Error::Lance {
-            message: format!("Failed to parse uri: {}", e),
-        }),
-    }
-}
-
 /// A connection to LanceDB
 impl Database {
     /// Connects to LanceDB
@@ -59,25 +48,11 @@ impl Database {
     ///
     /// * A [Database] object.
     pub async fn connect(uri: &str) -> Result<Database> {
-        // For a native (using lance directly) connection
-        // The DB doesn't use any uri parameters, but lance does
-        // So we need to parse the uri, extract the query string, and progate it to lance
-        let mut url = parse_url(uri)?;
-
-        // special handling for windows
-        if url.scheme().len() == 1 && cfg!(windows) {
-            let (object_store, base_path) = ObjectStore::from_uri(uri).await?;
-            if object_store.is_local() {
-                Self::try_create_dir(uri).context(CreateDirSnafu { path: uri })?;
-            }
-            return Ok(Database {
-                uri: uri.to_string(),
-                query_string: None,
-                base_path,
-                object_store,
-            });
-        }
+        let parse_res = url::Url::parse(uri);
 
+        match parse_res {
+            Ok(url) if url.scheme().len() == 1 && cfg!(windows) => Self::open_path(uri).await,
+            Ok(mut url) => {
                 // iter thru the query params and extract the commit store param
                 let mut engine = None;
                 let mut filtered_querys = vec![];
@@ -128,6 +103,22 @@ impl Database {
                     object_store,
                 })
             }
+            Err(_) => Self::open_path(uri).await,
+        }
+    }
+
+    async fn open_path(path: &str) -> Result<Database> {
+        let (object_store, base_path) = ObjectStore::from_uri(path).await?;
+        if object_store.is_local() {
+            Self::try_create_dir(path).context(CreateDirSnafu { path: path })?;
+        }
+        Ok(Self {
+            uri: path.to_string(),
+            query_string: None,
+            base_path,
+            object_store,
+        })
+    }
 
     /// Try to create a local directory to store the lancedb dataset
     fn try_create_dir(path: &str) -> core::result::Result<(), std::io::Error> {
@@ -240,6 +231,7 @@ impl Database {
 #[cfg(test)]
 mod tests {
     use std::fs::create_dir_all;
+
     use tempfile::tempdir;
 
     use crate::database::Database;
@@ -250,15 +242,29 @@ mod tests {
         let uri = tmp_dir.path().to_str().unwrap();
         let db = Database::connect(uri).await.unwrap();
 
-        // file:// scheme should be automatically appended if not specified
-        // windows path come with drive letter, so file:// won't be appended
-        let expected = if cfg!(windows) {
-            uri.to_string()
-        } else {
-            format!("file://{}", uri)
-        };
+        assert_eq!(db.uri, uri);
+    }
 
-        assert_eq!(db.uri, expected);
+    #[cfg(not(windows))]
+    #[tokio::test]
+    async fn test_connect_relative() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = std::fs::canonicalize(tmp_dir.path().to_str().unwrap()).unwrap();
+
+        let mut relative_anacestors = vec![];
+        let current_dir = std::env::current_dir().unwrap();
+        let mut ancestors = current_dir.ancestors();
+        while let Some(_) = ancestors.next() {
+            relative_anacestors.push("..");
+        }
+        let relative_root = std::path::PathBuf::from(relative_anacestors.join("/"));
+        let relative_uri = relative_root.join(&uri);
+
+        let db = Database::connect(relative_uri.to_str().unwrap())
+            .await
+            .unwrap();
+
+        assert_eq!(db.uri, relative_uri.to_str().unwrap().to_string());
     }
 
     #[tokio::test]

rust/vectordb/src/table.rs

@@ -190,9 +190,8 @@ impl Table {
     pub async fn create_index(&mut self, index_builder: &impl VectorIndexBuilder) -> Result<()> {
         use lance::index::DatasetIndexExt;
 
-        let dataset = self
-            .dataset
-            .create_index(
+        let mut dataset = self.dataset.as_ref().clone();
+        dataset.create_index(
                 &[index_builder
                     .get_column()
                     .unwrap_or(VECTOR_COLUMN_NAME.to_string())