[python] Bump version: 0.2.5 → 0.2.6

We have experimental support for prefiltering (without ANN) in pylance.
This means that we can now apply a filter BEFORE vector search is
performed. This can be done via the `.where(filter_string,
prefilter=True)` kwargs of the query.

Limitations:
- When connecting to LanceDB cloud, `prefilter=True` will raise
NotImplemented
- When an ANN index is present, `prefilter=True` will raise
NotImplemented
- This option is not available for full text search query
- This option is not available for empty search query (just
filter/project)

Additional changes in this PR:
- Bump pylance version to v0.8.0 which supports the experimental
prefiltering.

---------

Co-authored-by: Chang She <chang@lancedb.com>

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.4
+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
@@ -107,3 +108,56 @@ jobs:
     - name: Test
       run: |
         npm run test
+  aws-integtest:
+    timeout-minutes: 45
+    runs-on: "ubuntu-22.04"
+    defaults:
+      run:
+        shell: bash
+        working-directory: node
+    env:
+      AWS_ACCESS_KEY_ID: ACCESSKEY
+      AWS_SECRET_ACCESS_KEY: SECRETKEY
+      AWS_DEFAULT_REGION: us-west-2
+      # this one is for s3
+      AWS_ENDPOINT: http://localhost:4566
+      # this one is for dynamodb
+      DYNAMODB_ENDPOINT: http://localhost:4566
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        lfs: true
+    - uses: actions/setup-node@v3
+      with:
+        node-version: 18
+        cache: 'npm'
+        cache-dependency-path: node/package-lock.json
+    - name: start local stack
+      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
+      run: |
+        aws dynamodb create-table \
+          --table-name lancedb-integtest \
+          --attribute-definitions '[{"AttributeName": "base_uri", "AttributeType": "S"}, {"AttributeName": "version", "AttributeType": "N"}]' \
+          --key-schema '[{"AttributeName": "base_uri", "KeyType": "HASH"}, {"AttributeName": "version", "KeyType": "RANGE"}]' \
+          --provisioned-throughput '{"ReadCapacityUnits": 10, "WriteCapacityUnits": 10}' \
+          --endpoint-url $DYNAMODB_ENDPOINT
+    - uses: Swatinem/rust-cache@v2
+    - name: Install dependencies
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler libssl-dev
+    - name: Build
+      run: |
+        npm ci
+        npm run tsc
+        npm run build
+        npm run pack-build
+        npm install --no-save ./dist/lancedb-vectordb-*.tgz
+        # Remove index.node to test with dependency installed
+        rm index.node
+    - name: Test
+      run: npm run integration-test

.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

@@ -1,16 +1,25 @@
 [workspace]
-members = [
-    "rust/vectordb",
-    "rust/ffi/node"
-]
+members = ["rust/ffi/node", "rust/vectordb"]
+# Python package needs to be built by maturin.
+exclude = ["python"]
 resolver = "2"
 
 [workspace.dependencies]
-lance = "=0.6.5"
+lance = { "version" = "=0.7.5", "features" = ["dynamodb"] }
+lance-linalg = { "version" = "=0.7.5" }
+# Note that this one does not include pyarrow
+arrow = { version = "43.0.0", optional = false }
 arrow-array = "43.0"
 arrow-data = "43.0"
-arrow-schema = "43.0"
 arrow-ipc = "43.0"
-half = { "version" = "=2.2.1", default-features = false }
+arrow-ord = "43.0"
+arrow-schema = "43.0"
+arrow-arith = "43.0"
+arrow-cast = "43.0"
+half = { "version" = "=2.2.1", default-features = false, features = [
+    "num-traits"
+] }
+log = "0.4"
 object_store = "0.6.1"
 snafu = "0.7.4"
+url = "2"

docker-compose.yml

@@ -0,0 +1,18 @@
+version: "3.9"
+services:
+  localstack:
+    image: localstack/localstack:0.14
+    ports:
+      - 4566:4566
+    environment:
+      - SERVICES=s3,dynamodb
+      - DEBUG=1
+      - LS_LOG=trace
+      - DOCKER_HOST=unix:///var/run/docker.sock
+      - AWS_ACCESS_KEY_ID=ACCESSKEY
+      - AWS_SECRET_ACCESS_KEY=SECRETKEY
+    healthcheck:
+      test: [ "CMD", "curl", "-f", "http://localhost:4566/health" ]
+      interval: 5s
+      retries: 3
+      start_period: 10s

docs/mkdocs.yml

@@ -67,6 +67,11 @@ nav:
 - Home:
   - 🏢 Home: index.md
   - 💡 Basics: basic.md
+  - 📚 Guides:
+    - Tables: guides/tables.md
+    - Vector Search: search.md
+    - SQL filters: sql.md
+    - Indexing: ann_indexes.md
   - 🧬 Embeddings: embedding.md
   - 🔍 Python full-text search: fts.md
   - 🔌 Integrations:
@@ -91,12 +96,12 @@ nav:
     - Serverless Website Chatbot: examples/serverless_website_chatbot.md
     - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
     - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
-  - 📚 Guides:
-    - Tables: guides/tables.md
-    - Vector Search: search.md
-    - SQL filters: sql.md
-    - Indexing: ann_indexes.md
 - Basics: basic.md
+- Guides:
+  - Tables: guides/tables.md
+  - Vector Search: search.md
+  - SQL filters: sql.md
+  - Indexing: ann_indexes.md
 - Embeddings: embedding.md
 - Python full-text search: fts.md
 - Integrations:
@@ -121,12 +126,6 @@ nav:
   - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
   - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
   - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
-
-- Guides:
-  - Tables: guides/tables.md
-  - Vector Search: search.md
-  - SQL filters: sql.md
-  - Indexing: ann_indexes.md
 - API references:
   - Python API: python/python.md
   - Javascript API: javascript/modules.md

docs/src/guides/tables.md

@@ -49,11 +49,11 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
 
     db.create_table("table2", data)
 
-    db["table2"].head() 
+    db["table2"].head()
     ```
     !!! info "Note"
         Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.
-  
+
     ```python
     custom_schema = pa.schema([
     pa.field("vector", pa.list_(pa.float32(), 2)),
@@ -66,7 +66,7 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
 
     ### From PyArrow Tables
     You can also create LanceDB tables directly from pyarrow tables
-    
+
     ```python
     table = pa.Table.from_arrays(
             [
@@ -84,18 +84,28 @@ 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
+    from lancedb.pydantic import Vector, LanceModel
 
     class Content(LanceModel):
         movie_id: int
-        vector: vector(128)
+        vector: Vector(128)
         genres: str
         title: str
         imdb_id: int
-            
+
         @property
         def imdb_url(self) -> str:
             return f"https://www.imdb.com/title/tt{self.imdb_id}"
@@ -103,7 +113,7 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     import pyarrow as pa
     db = lancedb.connect("~/.lancedb")
     table_name = "movielens_small"
-    table = db.create_table(table_name, schema=Content.to_arrow_schema())
+    table = db.create_table(table_name, schema=Content)
     ```
 
     ### Using Iterators / Writing Large Datasets
@@ -113,7 +123,7 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     LanceDB additionally supports pyarrow's `RecordBatch` Iterators or other generators producing supported data types.
 
     Here's an example using using `RecordBatch` iterator for creating tables.
-    
+
     ```python
     import pyarrow as pa
 
@@ -142,11 +152,11 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
 
     ## Creating Empty Table
     You can also create empty tables in python. Initialize it with schema and later ingest data into it.
-    
+
     ```python
     import lancedb
     import pyarrow as pa
-  
+
     schema = pa.schema(
       [
           pa.field("vector", pa.list_(pa.float32(), 2)),
@@ -168,8 +178,8 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     from lancedb.pydantic import LanceModel, vector
 
     class Model(LanceModel):
-          vector: vector(2)
-    
+          vector: Vector(2)
+
     tbl = db.create_table("table5", schema=Model.to_arrow_schema())
     ```
 
@@ -249,7 +259,7 @@ After a table has been created, you can always add more data to it using
     You can also add a large dataset batch in one go using Iterator of any supported data types.
 
     ### Adding to table using Iterator
-    
+
     ```python
     import pandas as pd
 
@@ -261,10 +271,10 @@ After a table has been created, you can always add more data to it using
                     "item": ["foo", "bar"],
                     "price": [10.0, 20.0],
                 })
-    
+
     tbl.add(make_batches())
     ```
-  
+
     The other arguments accepted:
 
     | Name | Type | Description | Default |
@@ -274,7 +284,7 @@ After a table has been created, you can always add more data to it using
     | on_bad_vectors | str | What to do if any of the vectors are not the same size or contains NaNs. One of "error", "drop", "fill". | drop |
     | fill value | float | The value to use when filling vectors: Only used if on_bad_vectors="fill". | 0.0 |
 
-  
+
 === "Javascript/Typescript"
 
     ```javascript
@@ -312,7 +322,7 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
     #   x      vector
     # 0  1  [1.0, 2.0]
     # 1  3  [5.0, 6.0]
-    ```  
+    ```
 
     ### Delete from a list of values
 
@@ -325,7 +335,7 @@ Use the `delete()` method on tables to delete rows from a table. To choose which
     #   x      vector
     # 0  3  [5.0, 6.0]
     ```
-  
+
 === "Javascript/Typescript"
 
     ```javascript

docs/src/index.md

@@ -1,6 +1,6 @@
 # LanceDB
 
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.
 
 ![Illustration](/lancedb/assets/ecosystem-illustration.png)
 

docs/src/notebooks/tables_guide.ipynb

@@ -249,11 +249,11 @@
     }
    ],
    "source": [
-    "from lancedb.pydantic import vector, LanceModel\n",
+    "from lancedb.pydantic import Vector, LanceModel\n",
     "\n",
     "class Content(LanceModel):\n",
     "    movie_id: int\n",
-    "    vector: vector(128)\n",
+    "    vector: Vector(128)\n",
     "    genres: str\n",
     "    title: str\n",
     "    imdb_id: int\n",
@@ -359,7 +359,7 @@
     "import pandas as pd\n",
     "\n",
     "class PydanticSchema(LanceModel):\n",
-    "    vector: vector(2)\n",
+    "    vector: Vector(2)\n",
     "    item: str\n",
     "    price: float\n",
     "\n",
@@ -394,10 +394,10 @@
    "outputs": [],
    "source": [
     "import lancedb\n",
-    "from lancedb.pydantic import LanceModel, vector\n",
+    "from lancedb.pydantic import LanceModel, Vector\n",
     "\n",
     "class Model(LanceModel):\n",
-    "      vector: vector(2)\n",
+    "      vector: Vector(2)\n",
     "\n",
     "tbl = db.create_table(\"table6\", schema=Model.to_arrow_schema())"
    ]

docs/src/python/pydantic.md

@@ -13,10 +13,10 @@ via [pydantic_to_schema()](python.md##lancedb.pydantic.pydantic_to_schema) metho
 
 ## Vector Field
 
-LanceDB provides a [`vector(dim)`](python.md#lancedb.pydantic.vector) method to define a
+LanceDB provides a [`Vector(dim)`](python.md#lancedb.pydantic.Vector) method to define a
 vector Field in a Pydantic Model.
 
-::: lancedb.pydantic.vector
+::: lancedb.pydantic.Vector
 
 ## Type Conversion
 
@@ -33,4 +33,4 @@ Current supported type conversions:
 | `str`               | `pyarrow.utf8()`    |
 | `list`              | `pyarrow.List`    |
 | `BaseModel`         | `pyarrow.Struct`    |
-| `vector(n)`         | `pyarrow.FixedSizeList(float32, n)` |
+| `Vector(n)`         | `pyarrow.FixedSizeList(float32, n)` |

docs/src/python/python.md

@@ -26,9 +26,19 @@ pip install lancedb
 
 ## Embeddings
 
-::: lancedb.embeddings.with_embeddings
+::: lancedb.embeddings.functions.EmbeddingFunctionRegistry
 
-::: lancedb.embeddings.EmbeddingFunction
+::: lancedb.embeddings.functions.EmbeddingFunction
+
+::: lancedb.embeddings.functions.TextEmbeddingFunction
+
+::: lancedb.embeddings.functions.SentenceTransformerEmbeddings
+
+::: lancedb.embeddings.functions.OpenAIEmbeddings
+
+::: lancedb.embeddings.functions.OpenClipEmbeddings
+
+::: lancedb.embeddings.with_embeddings
 
 ## Context
 

docs/test/md_testing.py

@@ -8,7 +8,8 @@ excluded_globs = [
     "../src/embedding.md",
     "../src/examples/*.md",
     "../src/integrations/voxel51.md",
-    "../src/guides/tables.md"
+    "../src/guides/tables.md",
+    "../src/python/duckdb.md",
 ]
 
 python_prefix = "py"

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.2.4",
+  "version": "0.2.6",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.2.4",
+      "version": "0.2.6",
       "cpu": [
         "x64",
         "arm64"
@@ -31,6 +31,7 @@
         "@types/node": "^18.16.2",
         "@types/sinon": "^10.0.15",
         "@types/temp": "^0.9.1",
+        "@types/uuid": "^9.0.3",
         "@typescript-eslint/eslint-plugin": "^5.59.1",
         "cargo-cp-artifact": "^0.1",
         "chai": "^4.3.7",
@@ -48,14 +49,15 @@
         "ts-node-dev": "^2.0.0",
         "typedoc": "^0.24.7",
         "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "*"
+        "typescript": "*",
+        "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.2.4",
-        "@lancedb/vectordb-darwin-x64": "0.2.4",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.2.4",
-        "@lancedb/vectordb-linux-x64-gnu": "0.2.4",
-        "@lancedb/vectordb-win32-x64-msvc": "0.2.4"
+        "@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": {
@@ -315,9 +317,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.4.tgz",
-      "integrity": "sha512-MqiZXamHYEOfguPsHWLBQ56IabIN6Az8u2Hx8LCyXcxW9gcyJZMSAfJc+CcA4KYHKotv0KsVBhgxZ3kaZQQyiw==",
+      "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"
       ],
@@ -327,9 +329,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.4.tgz",
-      "integrity": "sha512-DzL+mw5WhKDwXdEFlPh8M9zSDhGnfks7NvEh6ZqKbU6znH206YB7g3OA4WfFyV579IIEQ8jd4v/XDthNzQKuSA==",
+      "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"
       ],
@@ -339,9 +341,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.4.tgz",
-      "integrity": "sha512-LP1nNfIpFxCgcCMlIQdseDX9dZU27TNhCL41xar8euqcetY5uKvi0YqhiVlpNO85Ss1FRQBgQ/GtnOM6Bo7oBQ==",
+      "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"
       ],
@@ -351,9 +353,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.4.tgz",
-      "integrity": "sha512-m4RhOI5JJWPU9Ip2LlRIzXu4mwIv9M//OyAuTLiLKRm8726jQHhYi5VFUEtNzqY0o0p6pS0b3XbifYQ+cyJn3Q==",
+      "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"
       ],
@@ -363,9 +365,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.4.tgz",
-      "integrity": "sha512-lMF/2e3YkKWnTYv0R7cUCfjMkAqepNaHSc/dvJzCNsFVEhfDsFdScQFLToARs5GGxnq4fOf+MKpaHg/W6QTxiA==",
+      "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==",
       "cpu": [
         "x64"
       ],
@@ -596,6 +598,12 @@
         "@types/node": "*"
       }
     },
+    "node_modules/@types/uuid": {
+      "version": "9.0.3",
+      "resolved": "https://registry.npmjs.org/@types/uuid/-/uuid-9.0.3.tgz",
+      "integrity": "sha512-taHQQH/3ZyI3zP8M/puluDEIEvtQHVYcC6y3N8ijFtAd28+Ey/G4sg1u2gB01S8MwybLOKAp9/yCMu/uR5l3Ug==",
+      "dev": true
+    },
     "node_modules/@typescript-eslint/eslint-plugin": {
       "version": "5.59.1",
       "resolved": "https://registry.npmjs.org/@typescript-eslint/eslint-plugin/-/eslint-plugin-5.59.1.tgz",
@@ -4451,6 +4459,15 @@
         "punycode": "^2.1.0"
       }
     },
+    "node_modules/uuid": {
+      "version": "9.0.0",
+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.0.tgz",
+      "integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg==",
+      "dev": true,
+      "bin": {
+        "uuid": "dist/bin/uuid"
+      }
+    },
     "node_modules/v8-compile-cache-lib": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz",
@@ -4852,33 +4869,33 @@
       }
     },
     "@lancedb/vectordb-darwin-arm64": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.4.tgz",
-      "integrity": "sha512-MqiZXamHYEOfguPsHWLBQ56IabIN6Az8u2Hx8LCyXcxW9gcyJZMSAfJc+CcA4KYHKotv0KsVBhgxZ3kaZQQyiw==",
+      "version": "0.2.6",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.6.tgz",
+      "integrity": "sha512-9KCUvDmhVMuGIhleib/Gq43QhrRXjy2QJz21S85HDwL3DTH4J9n00A0V6eyLTBUyctnvMTcp3XZijosYUy1A8Q==",
       "optional": true
     },
     "@lancedb/vectordb-darwin-x64": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.4.tgz",
-      "integrity": "sha512-DzL+mw5WhKDwXdEFlPh8M9zSDhGnfks7NvEh6ZqKbU6znH206YB7g3OA4WfFyV579IIEQ8jd4v/XDthNzQKuSA==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.4.tgz",
-      "integrity": "sha512-LP1nNfIpFxCgcCMlIQdseDX9dZU27TNhCL41xar8euqcetY5uKvi0YqhiVlpNO85Ss1FRQBgQ/GtnOM6Bo7oBQ==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.4.tgz",
-      "integrity": "sha512-m4RhOI5JJWPU9Ip2LlRIzXu4mwIv9M//OyAuTLiLKRm8726jQHhYi5VFUEtNzqY0o0p6pS0b3XbifYQ+cyJn3Q==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.2.4",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.4.tgz",
-      "integrity": "sha512-lMF/2e3YkKWnTYv0R7cUCfjMkAqepNaHSc/dvJzCNsFVEhfDsFdScQFLToARs5GGxnq4fOf+MKpaHg/W6QTxiA==",
+      "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": {
@@ -5093,6 +5110,12 @@
         "@types/node": "*"
       }
     },
+    "@types/uuid": {
+      "version": "9.0.3",
+      "resolved": "https://registry.npmjs.org/@types/uuid/-/uuid-9.0.3.tgz",
+      "integrity": "sha512-taHQQH/3ZyI3zP8M/puluDEIEvtQHVYcC6y3N8ijFtAd28+Ey/G4sg1u2gB01S8MwybLOKAp9/yCMu/uR5l3Ug==",
+      "dev": true
+    },
     "@typescript-eslint/eslint-plugin": {
       "version": "5.59.1",
       "resolved": "https://registry.npmjs.org/@typescript-eslint/eslint-plugin/-/eslint-plugin-5.59.1.tgz",
@@ -7844,6 +7867,12 @@
         "punycode": "^2.1.0"
       }
     },
+    "uuid": {
+      "version": "9.0.0",
+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.0.tgz",
+      "integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg==",
+      "dev": true
+    },
     "v8-compile-cache-lib": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.2.4",
+  "version": "0.2.6",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -9,6 +9,7 @@
     "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
     "test": "npm run tsc && mocha -recursive dist/test",
+    "integration-test": "npm run tsc && mocha -recursive dist/integration_test",
     "lint": "eslint native.js src --ext .js,.ts",
     "clean": "rm -rf node_modules *.node dist/",
     "pack-build": "neon pack-build",
@@ -34,6 +35,7 @@
     "@types/node": "^18.16.2",
     "@types/sinon": "^10.0.15",
     "@types/temp": "^0.9.1",
+    "@types/uuid": "^9.0.3",
     "@typescript-eslint/eslint-plugin": "^5.59.1",
     "cargo-cp-artifact": "^0.1",
     "chai": "^4.3.7",
@@ -51,7 +53,8 @@
     "ts-node-dev": "^2.0.0",
     "typedoc": "^0.24.7",
     "typedoc-plugin-markdown": "^3.15.3",
-    "typescript": "*"
+    "typescript": "*",
+    "uuid": "^9.0.0"
   },
   "dependencies": {
     "@apache-arrow/ts": "^12.0.0",
@@ -78,10 +81,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.2.4",
-    "@lancedb/vectordb-darwin-x64": "0.2.4",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.2.4",
-    "@lancedb/vectordb-linux-x64-gnu": "0.2.4",
-    "@lancedb/vectordb-win32-x64-msvc": "0.2.4"
+    "@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

@@ -0,0 +1,43 @@
+// Copyright 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 { describe } from 'mocha'
+import * as chai from 'chai'
+import * as chaiAsPromised from 'chai-as-promised'
+import { v4 as uuidv4 } from 'uuid'
+
+import * as lancedb from '../index'
+
+const assert = chai.assert
+chai.use(chaiAsPromised)
+
+describe('LanceDB AWS Integration test', function () {
+  it('s3+ddb schema is processed correctly', async function () {
+    this.timeout(15000)
+
+    // WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
+    // THE API WILL CHANGE
+    const conn = await lancedb.connect('s3://lancedb-integtest?engine=ddb&ddbTableName=lancedb-integtest')
+    const data = [{ vector: Array(128).fill(1.0) }]
+
+    const tableName = uuidv4()
+    let table = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
+
+    const futs = [table.add(data), table.add(data), table.add(data), table.add(data), table.add(data)]
+    await Promise.allSettled(futs)
+
+    table = await conn.openTable(tableName)
+    assert.equal(await table.countRows(), 6)
+  })
+})

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 = 0.2.2
+current_version = 0.2.6
 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,
@@ -31,9 +34,13 @@ def connect(
     ----------
     uri: str or Path
         The uri of the database.
-    api_token: str, optional
+    api_key: str, optional
         If presented, connect to LanceDB cloud.
         Otherwise, connect to a database on file system or cloud storage.
+    region: str, default "us-west-2"
+        The region to use for LanceDB Cloud.
+    host_override: str, optional
+        The override url for LanceDB Cloud.
 
     Examples
     --------

python/lancedb/conftest.py

@@ -1,7 +1,10 @@
 import os
 
+import numpy as np
 import pytest
 
+from .embeddings import EmbeddingFunctionRegistry, TextEmbeddingFunction
+
 # import lancedb so we don't have to in every example
 
 
@@ -14,3 +17,24 @@ def doctest_setup(monkeypatch, tmpdir):
     monkeypatch.setitem(os.environ, "COLUMNS", "80")
     # Work in a temporary directory
     monkeypatch.chdir(tmpdir)
+
+
+registry = EmbeddingFunctionRegistry.get_instance()
+
+
+@registry.register("test")
+class MockTextEmbeddingFunction(TextEmbeddingFunction):
+    """
+    Return the hash of the first 10 characters
+    """
+
+    def generate_embeddings(self, texts):
+        return [self._compute_one_embedding(row) for row in texts]
+
+    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

@@ -16,12 +16,13 @@ from __future__ import annotations
 import os
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Optional
+from typing import List, Optional, Union
 
 import pyarrow as pa
 from pyarrow import fs
 
 from .common import DATA, URI
+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
@@ -40,7 +41,7 @@ class DBConnection(ABC):
         self,
         name: str,
         data: Optional[DATA] = None,
-        schema: Optional[pa.Schema, LanceModel] = None,
+        schema: Optional[Union[pa.Schema, LanceModel]] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
@@ -285,10 +286,11 @@ class LanceDBConnection(DBConnection):
         self,
         name: str,
         data: Optional[DATA] = None,
-        schema: Optional[pa.Schema, LanceModel] = None,
+        schema: Optional[Union[pa.Schema, LanceModel]] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
+        embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
     ) -> LanceTable:
         """Create a table in the database.
 
@@ -307,6 +309,7 @@ class LanceDBConnection(DBConnection):
             mode=mode,
             on_bad_vectors=on_bad_vectors,
             fill_value=fill_value,
+            embedding_functions=embedding_functions,
         )
         return tbl
 

python/lancedb/embeddings/__init__.py

@@ -0,0 +1,24 @@
+#  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.
+
+
+from .functions import (
+    EmbeddingFunction,
+    EmbeddingFunctionConfig,
+    EmbeddingFunctionRegistry,
+    OpenAIEmbeddings,
+    OpenClipEmbeddings,
+    SentenceTransformerEmbeddings,
+    TextEmbeddingFunction,
+)
+from .utils import with_embeddings

python/lancedb/embeddings/functions.py

@@ -0,0 +1,578 @@
+#  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 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 Dict, List, Optional, Union
+
+import numpy as np
+import pyarrow as pa
+from cachetools import cached
+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.
+    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__
+
+    def __init__(self):
+        self._functions = {}
+
+    def register(self, alias: str = None):
+        """
+        This creates a decorator that can be used to register
+        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, EmbeddingFunction):
+                raise TypeError("Must be a subclass of EmbeddingFunction")
+            if cls.__name__ in self._functions:
+                raise KeyError(f"{cls.__name__} was already registered")
+            key = alias or cls.__name__
+            self._functions[key] = cls
+            cls.__embedding_function_registry_alias__ = alias
+            return cls
+
+        return decorator
+
+    def reset(self):
+        """
+        Reset the registry to its initial state
+        """
+        self._functions = {}
+
+    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[bytes, bytes]]
+    ) -> Dict[str, "EmbeddingFunctionConfig"]:
+        """
+        Parse the metadata from an arrow table and
+        return a mapping of the vector column to the
+        embedding function and source column
+
+        Parameters
+        ----------
+        metadata : Optional[Dict[bytes, bytes]]
+            The metadata from an arrow table. Note that
+            the keys and values are bytes (pyarrow api)
+
+        Returns
+        -------
+        functions : dict
+            A mapping of vector column name to embedding function.
+            An empty dict is returned if input is None or does not
+            contain b"embedding_functions".
+        """
+        if metadata is None or b"embedding_functions" not in metadata:
+            return {}
+        serialized = metadata[b"embedding_functions"]
+        raw_list = json.loads(serialized.decode("utf-8"))
+        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, conf: "EmbeddingFunctionConfig"):
+        """
+        Convert the given embedding function and source / vector column configs
+        into a config dictionary that can be serialized into arrow metadata
+        """
+        func = conf.function
+        name = getattr(
+            func, "__embedding_function_registry_alias__", func.__class__.__name__
+        )
+        json_data = func.safe_model_dump()
+        return {
+            "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 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
+        metadata = json.dumps(json_data, indent=2).encode("utf-8")
+        return {"embedding_functions": metadata}
+
+
+# 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 EmbeddingFunction(BaseModel, ABC):
+    """
+    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
+    """
+
+    _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.
+        """
+        if isinstance(texts, str):
+            texts = [texts]
+        elif isinstance(texts, pa.Array):
+            texts = texts.to_pylist()
+        elif isinstance(texts, pa.ChunkedArray):
+            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]
+    ) -> List[np.array]:
+        """
+        Generate the embeddings for the given texts
+        """
+        pass
+
+
+# @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 = True
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._ndims = None
+
+    @property
+    def embedding_model(self):
+        """
+        Get the sentence-transformers embedding model specified by the
+        name and device. This is cached so that the model is only loaded
+        once per process.
+        """
+        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]:
+        """
+        Get the embeddings for the given texts
+
+        Parameters
+        ----------
+        texts: list[str] or np.ndarray (of str)
+            The texts to embed
+        """
+        return self.embedding_model.encode(
+            list(texts),
+            convert_to_numpy=True,
+            normalize_embeddings=self.normalize,
+        ).tolist()
+
+    @classmethod
+    @cached(cache={})
+    def get_embedding_model(cls, name, device):
+        """
+        Get the sentence-transformers embedding model specified by the
+        name and device. This is cached so that the model is only loaded
+        once per process.
+
+        Parameters
+        ----------
+        name : str
+            The name of the model to load
+        device : str
+            The device to load the model on
+
+        TODO: use lru_cache instead with a reasonable/configurable maxsize
+        """
+        sentence_transformers = cls.safe_import(
+            "sentence_transformers", "sentence-transformers"
+        )
+        return sentence_transformers.SentenceTransformer(name, device=device)
+
+
+@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/embeddings/utils.py

@@ -1,4 +1,4 @@
-#  Copyright 2023 LanceDB Developers
+#  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.
@@ -20,7 +20,7 @@ import pyarrow as pa
 from lance.vector import vec_to_table
 from retry import retry
 
-from .util import safe_import_pandas
+from ..util import safe_import_pandas
 
 pd = safe_import_pandas()
 DATA = Union[pa.Table, "pd.DataFrame"]
@@ -58,7 +58,7 @@ def with_embeddings(
     pa.Table
         The input table with a new column called "vector" containing the embeddings.
     """
-    func = EmbeddingFunction(func)
+    func = FunctionWrapper(func)
     if wrap_api:
         func = func.retry().rate_limit()
     func = func.batch_size(batch_size)
@@ -71,7 +71,11 @@ def with_embeddings(
     return data.append_column("vector", table["vector"])
 
 
-class EmbeddingFunction:
+class FunctionWrapper:
+    """
+    A wrapper for embedding functions that adds rate limiting, retries, and batching.
+    """
+
     def __init__(self, func: Callable):
         self.func = func
         self.rate_limiter_kwargs = {}

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
@@ -46,7 +48,19 @@ class FixedSizeListMixin(ABC):
         raise NotImplementedError
 
 
-def vector(
+def vector(dim: int, value_type: pa.DataType = pa.float32()):
+    # TODO: remove in future release
+    from warnings import warn
+
+    warn(
+        "lancedb.pydantic.vector() is deprecated, use lancedb.pydantic.Vector instead."
+        "This function will be removed in future release",
+        DeprecationWarning,
+    )
+    return Vector(dim, value_type)
+
+
+def Vector(
     dim: int, value_type: pa.DataType = pa.float32()
 ) -> Type[FixedSizeListMixin]:
     """Pydantic Vector Type.
@@ -65,12 +79,12 @@ def vector(
     --------
 
     >>> import pydantic
-    >>> from lancedb.pydantic import vector
+    >>> from lancedb.pydantic import Vector
     ...
     >>> class MyModel(pydantic.BaseModel):
     ...     id: int
     ...     url: str
-    ...     embeddings: vector(768)
+    ...     embeddings: Vector(768)
     >>> schema = pydantic_to_schema(MyModel)
     >>> assert schema == pa.schema([
     ...     pa.field("id", pa.int64(), False),
@@ -114,7 +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):
 
@@ -224,27 +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)
@@ -258,11 +263,11 @@ class LanceModel(pydantic.BaseModel):
     Examples
     --------
     >>> import lancedb
-    >>> from lancedb.pydantic import LanceModel, vector
+    >>> from lancedb.pydantic import LanceModel, Vector
     >>>
     >>> class TestModel(LanceModel):
     ...     name: str
-    ...     vector: vector(2)
+    ...     vector: Vector(2)
     ...
     >>> db = lancedb.connect("/tmp")
     >>> table = db.create_table("test", schema=TestModel.to_arrow_schema())
@@ -278,13 +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

@@ -13,6 +13,7 @@
 
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
 from typing import List, Literal, Optional, Type, Union
 
 import numpy as np
@@ -37,6 +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
 
@@ -54,44 +58,112 @@ class Query(pydantic.BaseModel):
     refine_factor: Optional[int] = None
 
 
-class LanceQueryBuilder:
-    """
-    A builder for nearest neighbor queries for LanceDB.
-
-    Examples
-    --------
-    >>> import lancedb
-    >>> data = [{"vector": [1.1, 1.2], "b": 2},
-    ...         {"vector": [0.5, 1.3], "b": 4},
-    ...         {"vector": [0.4, 0.4], "b": 6},
-    ...         {"vector": [0.4, 0.4], "b": 10}]
-    >>> db = lancedb.connect("./.lancedb")
-    >>> table = db.create_table("my_table", data=data)
-    >>> (table.search([0.4, 0.4])
-    ...       .metric("cosine")
-    ...       .where("b < 10")
-    ...       .select(["b"])
-    ...       .limit(2)
-    ...       .to_df())
-       b      vector  _distance
-    0  6  [0.4, 0.4]        0.0
-    """
-
-    def __init__(
-        self,
+class LanceQueryBuilder(ABC):
+    @classmethod
+    def create(
+        cls,
         table: "lancedb.table.Table",
-        query: Union[np.ndarray, str],
-        vector_column: str = VECTOR_COLUMN_NAME,
-    ):
-        self._metric = "L2"
-        self._nprobes = 20
-        self._refine_factor = None
+        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
+        )
+
+        if isinstance(query, str):
+            # fts
+            return LanceFtsQueryBuilder(table, query)
+
+        if isinstance(query, list):
+            query = np.array(query, dtype=np.float32)
+        elif isinstance(query, np.ndarray):
+            query = query.astype(np.float32)
+        else:
+            raise TypeError(f"Unsupported query type: {type(query)}")
+
+        return LanceVectorQueryBuilder(table, query, vector_column_name)
+
+    @classmethod
+    def _resolve_query(cls, table, query, query_type, vector_column_name):
+        # If query_type is fts, then query must be a string.
+        # otherwise raise TypeError
+        if query_type == "fts":
+            if not isinstance(query, str):
+                raise TypeError(f"'fts' queries must be a string: {type(query)}")
+            return query, query_type
+        elif query_type == "vector":
+            if not isinstance(query, (list, np.ndarray)):
+                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"
+            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 ValueError(
+                f"Invalid query_type, must be 'vector', 'fts', or 'auto': {query_type}"
+            )
+
+    def __init__(self, table: "lancedb.table.Table"):
         self._table = table
-        self._query = query
         self._limit = 10
         self._columns = None
         self._where = None
-        self._vector_column = vector_column
+
+    def to_df(self) -> "pd.DataFrame":
+        """
+        Execute the query and return the results as a pandas DataFrame.
+        In addition to the selected columns, LanceDB also returns a vector
+        and also the "_distance" column which is the distance between the query
+        vector and the returned vector.
+        """
+        return self.to_arrow().to_pandas()
+
+    @abstractmethod
+    def to_arrow(self) -> pa.Table:
+        """
+        Execute the query and return the results as an
+        [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).
+
+        In addition to the selected columns, LanceDB also returns a vector
+        and also the "_distance" column which is the distance between the query
+        vector and the returned vectors.
+        """
+        raise NotImplementedError
+
+    def to_pydantic(self, model: Type[LanceModel]) -> List[LanceModel]:
+        """Return the table as a list of pydantic models.
+
+        Parameters
+        ----------
+        model: Type[LanceModel]
+            The pydantic model to use.
+
+        Returns
+        -------
+        List[LanceModel]
+        """
+        return [
+            model(**{k: v for k, v in row.items() if k in model.field_names()})
+            for row in self.to_arrow().to_pylist()
+        ]
 
     def limit(self, limit: int) -> LanceQueryBuilder:
         """Set the maximum number of results to return.
@@ -125,7 +197,7 @@ class LanceQueryBuilder:
         self._columns = columns
         return self
 
-    def where(self, where: str) -> LanceQueryBuilder:
+    def where(self, where) -> LanceQueryBuilder:
         """Set the where clause.
 
         Parameters
@@ -141,7 +213,45 @@ class LanceQueryBuilder:
         self._where = where
         return self
 
-    def metric(self, metric: Literal["L2", "cosine"]) -> LanceQueryBuilder:
+
+class LanceVectorQueryBuilder(LanceQueryBuilder):
+    """
+    A builder for nearest neighbor queries for LanceDB.
+
+    Examples
+    --------
+    >>> import lancedb
+    >>> data = [{"vector": [1.1, 1.2], "b": 2},
+    ...         {"vector": [0.5, 1.3], "b": 4},
+    ...         {"vector": [0.4, 0.4], "b": 6},
+    ...         {"vector": [0.4, 0.4], "b": 10}]
+    >>> db = lancedb.connect("./.lancedb")
+    >>> table = db.create_table("my_table", data=data)
+    >>> (table.search([0.4, 0.4])
+    ...       .metric("cosine")
+    ...       .where("b < 10")
+    ...       .select(["b"])
+    ...       .limit(2)
+    ...       .to_df())
+       b      vector  _distance
+    0  6  [0.4, 0.4]        0.0
+    """
+
+    def __init__(
+        self,
+        table: "lancedb.table.Table",
+        query: Union[np.ndarray, list, "PIL.Image.Image"],
+        vector_column: str = VECTOR_COLUMN_NAME,
+    ):
+        super().__init__(table)
+        self._query = query
+        self._metric = "L2"
+        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.
 
         Parameters
@@ -151,13 +261,13 @@ class LanceQueryBuilder:
 
         Returns
         -------
-        LanceQueryBuilder
+        LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
         self._metric = metric
         return self
 
-    def nprobes(self, nprobes: int) -> LanceQueryBuilder:
+    def nprobes(self, nprobes: int) -> LanceVectorQueryBuilder:
         """Set the number of probes to use.
 
         Higher values will yield better recall (more likely to find vectors if
@@ -173,13 +283,13 @@ class LanceQueryBuilder:
 
         Returns
         -------
-        LanceQueryBuilder
+        LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
         self._nprobes = nprobes
         return self
 
-    def refine_factor(self, refine_factor: int) -> LanceQueryBuilder:
+    def refine_factor(self, refine_factor: int) -> LanceVectorQueryBuilder:
         """Set the refine factor to use, increasing the number of vectors sampled.
 
         As an example, a refine factor of 2 will sample 2x as many vectors as
@@ -195,22 +305,12 @@ class LanceQueryBuilder:
 
         Returns
         -------
-        LanceQueryBuilder
+        LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
         self._refine_factor = refine_factor
         return self
 
-    def to_df(self) -> "pd.DataFrame":
-        """
-        Execute the query and return the results as a pandas DataFrame.
-        In addition to the selected columns, LanceDB also returns a vector
-        and also the "_distance" column which is the distance between the query
-        vector and the returned vector.
-        """
-
-        return self.to_arrow().to_pandas()
-
     def to_arrow(self) -> pa.Table:
         """
         Execute the query and return the results as an
@@ -224,6 +324,7 @@ class LanceQueryBuilder:
         query = Query(
             vector=vector,
             filter=self._where,
+            prefilter=self._prefilter,
             k=self._limit,
             metric=self._metric,
             columns=self._columns,
@@ -233,25 +334,36 @@ class LanceQueryBuilder:
         )
         return self._table._execute_query(query)
 
-    def to_pydantic(self, model: Type[LanceModel]) -> List[LanceModel]:
-        """Return the table as a list of pydantic models.
+    def where(self, where: str, prefilter: bool = False) -> LanceVectorQueryBuilder:
+        """Set the where clause.
 
         Parameters
         ----------
-        model: Type[LanceModel]
-            The pydantic model to use.
+        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
         -------
-        List[LanceModel]
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
         """
-        return [
-            model(**{k: v for k, v in row.items() if k in model.field_names()})
-            for row in self.to_arrow().to_pylist()
-        ]
+        self._where = where
+        self._prefilter = prefilter
+        return self
 
 
 class LanceFtsQueryBuilder(LanceQueryBuilder):
+    def __init__(self, table: "lancedb.table.Table", query: str):
+        super().__init__(table)
+        self._query = query
+
     def to_arrow(self) -> pa.Table:
         try:
             import tantivy
@@ -275,3 +387,13 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
         output_tbl = self._table.to_lance().take(row_ids, columns=self._columns)
         output_tbl = output_tbl.append_column("score", scores)
         return output_tbl
+
+
+class LanceEmptyQueryBuilder(LanceQueryBuilder):
+    def to_arrow(self) -> pa.Table:
+        ds = self._table.to_lance()
+        return ds.to_table(
+            columns=self._columns,
+            filter=self._where,
+            limit=self._limit,
+        )

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

@@ -20,7 +20,7 @@ from lance import json_to_schema
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 
-from ..query import LanceQueryBuilder
+from ..query import LanceVectorQueryBuilder
 from ..table import Query, Table, _sanitize_data
 from .arrow import to_ipc_binary
 from .client import ARROW_STREAM_CONTENT_TYPE
@@ -73,7 +73,11 @@ class RemoteTable(Table):
         fill_value: float = 0.0,
     ) -> int:
         data = _sanitize_data(
-            data, self.schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
+            data,
+            self.schema,
+            metadata=None,
+            on_bad_vectors=on_bad_vectors,
+            fill_value=fill_value,
         )
         payload = to_ipc_binary(data)
 
@@ -89,11 +93,13 @@ class RemoteTable(Table):
         )
 
     def search(
-        self, query: Union[VEC, str], vector_column: str = VECTOR_COLUMN_NAME
-    ) -> LanceQueryBuilder:
-        return LanceQueryBuilder(self, query, vector_column)
+        self, query: Union[VEC, str], vector_column_name: str = VECTOR_COLUMN_NAME
+    ) -> LanceVectorQueryBuilder:
+        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

@@ -17,7 +17,7 @@ import inspect
 import os
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import Iterable, List, Optional, Union
+from typing import Any, Iterable, List, Optional, Union
 
 import lance
 import numpy as np
@@ -28,54 +28,89 @@ from lance.dataset import ReaderLike
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
+from .embeddings import EmbeddingFunctionRegistry
+from .embeddings.functions import EmbeddingFunctionConfig
 from .pydantic import LanceModel
-from .query import LanceFtsQueryBuilder, LanceQueryBuilder, Query
+from .query import LanceQueryBuilder, Query
 from .util import fs_from_uri, safe_import_pandas
 
 pd = safe_import_pandas()
 
 
-def _sanitize_data(data, schema, on_bad_vectors, fill_value):
+def _sanitize_data(
+    data,
+    schema: Optional[pa.Schema],
+    metadata: Optional[dict],
+    on_bad_vectors: str,
+    fill_value: Any,
+):
     if isinstance(data, list):
         # convert to list of dict if data is a bunch of LanceModels
         if isinstance(data[0], LanceModel):
             schema = data[0].__class__.to_arrow_schema()
             data = [dict(d) for d in data]
         data = pa.Table.from_pylist(data)
-        data = _sanitize_schema(
-            data, schema=schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
-        )
-    if isinstance(data, dict):
+    elif isinstance(data, dict):
         data = vec_to_table(data)
-    if pd is not None and isinstance(data, pd.DataFrame):
+    elif pd is not None and isinstance(data, pd.DataFrame):
         data = pa.Table.from_pandas(data, preserve_index=False)
+        # Do not serialize Pandas metadata
+        meta = data.schema.metadata if data.schema.metadata is not None else {}
+        meta = {k: v for k, v in meta.items() if k != b"pandas"}
+        data = data.replace_schema_metadata(meta)
+
+    if isinstance(data, pa.Table):
+        if metadata:
+            data = _append_vector_col(data, metadata, schema)
+            metadata.update(data.schema.metadata or {})
+            data = data.replace_schema_metadata(metadata)
         data = _sanitize_schema(
             data, schema=schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
         )
-        # Do not serialize Pandas metadata
-        metadata = data.schema.metadata if data.schema.metadata is not None else {}
-        metadata = {k: v for k, v in metadata.items() if k != b"pandas"}
-        schema = data.schema.with_metadata(metadata)
-        data = pa.Table.from_arrays(data.columns, schema=schema)
-    if isinstance(data, Iterable):
-        data = _to_record_batch_generator(data, schema, on_bad_vectors, fill_value)
-    if not isinstance(data, (pa.Table, Iterable)):
+    elif isinstance(data, Iterable):
+        data = _to_record_batch_generator(
+            data, schema, metadata, on_bad_vectors, fill_value
+        )
+    else:
         raise TypeError(f"Unsupported data type: {type(data)}")
     return data
 
 
-def _to_record_batch_generator(data: Iterable, schema, on_bad_vectors, fill_value):
+def _append_vector_col(data: pa.Table, metadata: dict, schema: Optional[pa.Schema]):
+    """
+    Use the embedding function to automatically embed the source column and add the
+    vector column to the table.
+    """
+    functions = EmbeddingFunctionRegistry.get_instance().parse_functions(metadata)
+    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_column).type
+            else:
+                dtype = pa.list_(pa.float32(), len(col_data[0]))
+            data = data.append_column(
+                pa.field(vector_column, type=dtype), pa.array(col_data, type=dtype)
+            )
+    return data
+
+
+def _to_record_batch_generator(
+    data: Iterable, schema, metadata, on_bad_vectors, fill_value
+):
     for batch in data:
         if not isinstance(batch, pa.RecordBatch):
-            table = _sanitize_data(batch, schema, on_bad_vectors, fill_value)
+            table = _sanitize_data(batch, schema, metadata, on_bad_vectors, fill_value)
             for batch in table.to_batches():
                 yield batch
-        yield batch
+        else:
+            yield batch
 
 
 class Table(ABC):
     """
-    A [Table](Table) is a collection of Records in a LanceDB [Database](Database).
+    A Table is a collection of Records in a LanceDB Database.
 
     Examples
     --------
@@ -196,17 +231,30 @@ class Table(ABC):
 
     @abstractmethod
     def search(
-        self, query: Union[VEC, str], vector_column: str = VECTOR_COLUMN_NAME
+        self,
+        query: Optional[Union[VEC, str, "PIL.Image.Image"]] = None,
+        vector_column_name: str = VECTOR_COLUMN_NAME,
+        query_type: str = "auto",
     ) -> LanceQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector.
 
         Parameters
         ----------
-        query: list, np.ndarray
-            The query vector.
-        vector_column: str, default "vector"
+        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
+        vector_column_name: str, default "vector"
             The name of the vector column to search.
+        query_type: str, default "auto"
+            "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"
 
         Returns
         -------
@@ -325,14 +373,14 @@ class LanceTable(Table):
         >>> db = lancedb.connect("./.lancedb")
         >>> table = db.create_table("my_table", [{"vector": [1.1, 0.9], "type": "vector"}])
         >>> table.version
-        1
+        2
         >>> table.to_pandas()
                vector    type
         0  [1.1, 0.9]  vector
         >>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
         >>> table.version
-        2
-        >>> table.checkout(1)
+        3
+        >>> table.checkout(2)
         >>> table.to_pandas()
                vector    type
         0  [1.1, 0.9]  vector
@@ -361,19 +409,19 @@ class LanceTable(Table):
         >>> db = lancedb.connect("./.lancedb")
         >>> table = db.create_table("my_table", [{"vector": [1.1, 0.9], "type": "vector"}])
         >>> table.version
-        1
+        2
         >>> table.to_pandas()
                vector    type
         0  [1.1, 0.9]  vector
         >>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
         >>> table.version
-        2
-        >>> table.restore(1)
+        3
+        >>> table.restore(2)
         >>> table.to_pandas()
                vector    type
         0  [1.1, 0.9]  vector
         >>> len(table.list_versions())
-        3
+        4
         """
         max_ver = max([v["version"] for v in self._dataset.versions()])
         if version is None:
@@ -480,6 +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
         ----------
@@ -501,7 +552,11 @@ class LanceTable(Table):
         """
         # TODO: manage table listing and metadata separately
         data = _sanitize_data(
-            data, self.schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
+            data,
+            self.schema,
+            metadata=self.schema.metadata,
+            on_bad_vectors=on_bad_vectors,
+            fill_value=fill_value,
         )
         lance.write_dataset(data, self._dataset_uri, schema=self.schema, mode=mode)
         self._reset_dataset()
@@ -511,7 +566,7 @@ class LanceTable(Table):
         other_table: Union[LanceTable, ReaderLike],
         left_on: str,
         right_on: Optional[str] = None,
-        schema: Optional[pa.Schema, LanceModel] = None,
+        schema: Optional[Union[pa.Schema, LanceModel]] = None,
     ):
         """Merge another table into this table.
 
@@ -569,18 +624,46 @@ class LanceTable(Table):
         )
         self._reset_dataset()
 
+    @cached_property
+    def embedding_functions(self) -> dict:
+        """
+        Get the embedding functions for the table
+
+        Returns
+        -------
+        funcs: dict
+            A mapping of the vector column to the embedding function
+            or empty dict if not configured.
+        """
+        return EmbeddingFunctionRegistry.get_instance().parse_functions(
+            self.schema.metadata
+        )
+
     def search(
-        self, query: Union[VEC, str], vector_column_name=VECTOR_COLUMN_NAME
+        self,
+        query: Optional[Union[VEC, str, "PIL.Image.Image"]] = None,
+        vector_column_name: str = VECTOR_COLUMN_NAME,
+        query_type: str = "auto",
     ) -> LanceQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector.
 
         Parameters
         ----------
-        query: list, np.ndarray
-            The query vector.
+        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
         vector_column_name: str, default "vector"
             The name of the vector column to search.
+        query_type: str, default "auto"
+            "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 the query is a string, then the query type is "vector" if the
+            table has embedding functions, else the query type is "fts"
 
         Returns
         -------
@@ -590,17 +673,9 @@ class LanceTable(Table):
             and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
-        if isinstance(query, str):
-            # fts
-            return LanceFtsQueryBuilder(self, query, vector_column_name)
-
-        if isinstance(query, list):
-            query = np.array(query)
-        if isinstance(query, np.ndarray):
-            query = query.astype(np.float32)
-        else:
-            raise TypeError(f"Unsupported query type: {type(query)}")
-        return LanceQueryBuilder(self, query, vector_column_name)
+        return LanceQueryBuilder.create(
+            self, query, query_type, vector_column_name=vector_column_name
+        )
 
     @classmethod
     def create(
@@ -612,6 +687,7 @@ class LanceTable(Table):
         mode="create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
+        embedding_functions: List[EmbeddingFunctionConfig] = None,
     ):
         """
         Create a new table.
@@ -649,20 +725,58 @@ class LanceTable(Table):
             One of "error", "drop", "fill".
         fill_value: float, default 0.
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
+        embedding_functions: list of EmbeddingFunctionModel, default None
+            The embedding functions to use when creating the 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)
+
         if data is not None:
             data = _sanitize_data(
-                data, schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
+                data,
+                schema,
+                metadata=metadata,
+                on_bad_vectors=on_bad_vectors,
+                fill_value=fill_value,
             )
-        else:
-            if schema is None:
+
+        if schema is None:
+            if data is None:
                 raise ValueError("Either data or schema must be provided")
-            data = pa.Table.from_pylist([], schema=schema)
-        lance.write_dataset(data, tbl._dataset_uri, schema=schema, mode=mode)
-        return LanceTable(db, name)
+            elif hasattr(data, "schema"):
+                schema = data.schema
+            elif isinstance(data, Iterable):
+                if metadata:
+                    raise TypeError(
+                        (
+                            "Persistent embedding functions not yet "
+                            "supported for generator data input"
+                        )
+                    )
+
+        if metadata:
+            schema = schema.with_metadata(metadata)
+
+        empty = pa.Table.from_pylist([], schema=schema)
+        lance.write_dataset(empty, tbl._dataset_uri, schema=schema, mode=mode)
+        table = LanceTable(db, name)
+
+        if data is not None:
+            table.add(data)
+
+        return table
 
     @classmethod
     def open(cls, db, name):
@@ -678,11 +792,68 @@ class LanceTable(Table):
     def delete(self, where: str):
         self._dataset.delete(where)
 
+    def update(self, where: str, values: dict):
+        """
+        EXPERIMENTAL: Update rows in the table (not threadsafe).
+
+        This can be used to update zero to all rows depending on how many
+        rows match the where clause.
+
+        Parameters
+        ----------
+        where: str
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
+        values: dict
+            The values to update. The keys are the column names and the values
+            are the values to set.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> import pandas as pd
+        >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
+        >>> db = lancedb.connect("./.lancedb")
+        >>> table = db.create_table("my_table", data)
+        >>> table.to_pandas()
+           x      vector
+        0  1  [1.0, 2.0]
+        1  2  [3.0, 4.0]
+        2  3  [5.0, 6.0]
+        >>> table.update(where="x = 2", values={"vector": [10, 10]})
+        >>> table.to_pandas()
+           x        vector
+        0  1    [1.0, 2.0]
+        1  3    [5.0, 6.0]
+        2  2  [10.0, 10.0]
+
+        """
+        orig_data = self._dataset.to_table(filter=where).combine_chunks()
+        if len(orig_data) == 0:
+            return
+        for col, val in values.items():
+            i = orig_data.column_names.index(col)
+            if i < 0:
+                raise ValueError(f"Column {col} does not exist")
+            orig_data = orig_data.set_column(
+                i, col, pa.array([val] * len(orig_data), type=orig_data[col].type)
+            )
+        self.delete(where)
+        self.add(orig_data, mode="append")
+        self._reset_dataset()
+
     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,
@@ -720,22 +891,38 @@ def _sanitize_schema(
             return data
         # cast the columns to the expected types
         data = data.combine_chunks()
-        data = _sanitize_vector_column(
+        for field in schema:
+            # TODO: we're making an assumption that fixed size list of 10 or more
+            # is a vector column. This is definitely a bit hacky.
+            likely_vector_col = (
+                pa.types.is_fixed_size_list(field.type)
+                and pa.types.is_float32(field.type.value_type)
+                and field.type.list_size >= 10
+            )
+            is_default_vector_col = field.name == VECTOR_COLUMN_NAME
+            if field.name in data.column_names and (
+                likely_vector_col or is_default_vector_col
+            ):
+                data = _sanitize_vector_column(
+                    data,
+                    vector_column_name=field.name,
+                    on_bad_vectors=on_bad_vectors,
+                    fill_value=fill_value,
+                )
+        return pa.Table.from_arrays(
+            [data[name] for name in schema.names], schema=schema
+        )
+
+    # just check the vector column
+    if VECTOR_COLUMN_NAME in data.column_names:
+        return _sanitize_vector_column(
             data,
             vector_column_name=VECTOR_COLUMN_NAME,
             on_bad_vectors=on_bad_vectors,
             fill_value=fill_value,
         )
-        return pa.Table.from_arrays(
-            [data[name] for name in schema.names], schema=schema
-        )
-    # just check the vector column
-    return _sanitize_vector_column(
-        data,
-        vector_column_name=VECTOR_COLUMN_NAME,
-        on_bad_vectors=on_bad_vectors,
-        fill_value=fill_value,
-    )
+
+    return data
 
 
 def _sanitize_vector_column(
@@ -759,8 +946,6 @@ def _sanitize_vector_column(
     fill_value: float, default 0.0
         The value to use when filling vectors. Only used if on_bad_vectors="fill".
     """
-    if vector_column_name not in data.column_names:
-        raise ValueError(f"Missing vector column: {vector_column_name}")
     # ChunkedArray is annoying to work with, so we combine chunks here
     vec_arr = data[vector_column_name].combine_chunks()
     if pa.types.is_list(data[vector_column_name].type):

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

@@ -1,15 +1,16 @@
 [project]
 name = "lancedb"
-version = "0.2.2"
+version = "0.2.6"
 dependencies = [
-    "pylance==0.6.5",
-    "ratelimiter",
-    "retry",
-    "tqdm",
+    "pylance==0.8.0",
+    "ratelimiter~=1.0",
+    "retry>=0.9.2",
+    "tqdm>=4.1.0",
     "aiohttp",
-    "pydantic",
-    "attr",
-    "semver>=3.0"
+    "pydantic>=1.10",
+    "attrs>=21.3.0",
+    "semver>=3.0",
+    "cachetools"
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -43,9 +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"]
@@ -53,3 +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

@@ -17,7 +17,7 @@ import pyarrow as pa
 import pytest
 
 import lancedb
-from lancedb.pydantic import LanceModel, vector
+from lancedb.pydantic import LanceModel, Vector
 
 
 def test_basic(tmp_path):
@@ -79,7 +79,7 @@ def test_ingest_pd(tmp_path):
 
 def test_ingest_iterator(tmp_path):
     class PydanticSchema(LanceModel):
-        vector: vector(2)
+        vector: Vector(2)
         item: str
         price: float
 
@@ -136,15 +136,14 @@ 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
         assert len(tbl) == tbl_len * 2
-        assert len(tbl.list_versions()) == 2
+        assert len(tbl.list_versions()) == 3
         db.drop_database()
 
     run_tests(arrow_schema)

python/tests/test_embeddings.py

@@ -12,10 +12,16 @@
 #  limitations under the License.
 import sys
 
+import lance
 import numpy as np
 import pyarrow as pa
 
-from lancedb.embeddings import with_embeddings
+from lancedb.conftest import MockTextEmbeddingFunction
+from lancedb.embeddings import (
+    EmbeddingFunctionConfig,
+    EmbeddingFunctionRegistry,
+    with_embeddings,
+)
 
 
 def mock_embed_func(input_data):
@@ -40,3 +46,40 @@ def test_with_embeddings():
         assert data.column_names == ["text", "price", "vector"]
         assert data.column("text").to_pylist() == ["foo", "bar"]
         assert data.column("price").to_pylist() == [10.0, 20.0]
+
+
+def test_embedding_function(tmp_path):
+    registry = EmbeddingFunctionRegistry.get_instance()
+
+    # let's create a table
+    table = pa.table(
+        {
+            "text": pa.array(["hello world", "goodbye world"]),
+            "vector": [np.random.randn(10), np.random.randn(10)],
+        }
+    )
+    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
+    lance.write_dataset(table, tmp_path / "test.lance")
+
+    # Load this back
+    ds = lance.dataset(tmp_path / "test.lance")
+
+    # can we get the serialized version back out?
+    configs = registry.parse_functions(ds.schema.metadata)
+
+    conf = configs["vector"]
+    func = conf.function
+    actual = func.compute_query_embeddings("hello world")
+
+    # And we make sure we can call it
+    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_pydantic.py

@@ -19,8 +19,9 @@ from typing import List, Optional
 import pyarrow as pa
 import pydantic
 import pytest
+from pydantic import Field
 
-from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, pydantic_to_schema, vector
+from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, Vector, pydantic_to_schema
 
 
 @pytest.mark.skipif(
@@ -107,7 +108,7 @@ def test_pydantic_to_arrow_py38():
 
 def test_fixed_size_list_field():
     class TestModel(pydantic.BaseModel):
-        vec: vector(16)
+        vec: Vector(16)
         li: List[int]
 
     data = TestModel(vec=list(range(16)), li=[1, 2, 3])
@@ -154,7 +155,7 @@ def test_fixed_size_list_field():
 
 def test_fixed_size_list_validation():
     class TestModel(pydantic.BaseModel):
-        vec: vector(8)
+        vec: Vector(8)
 
     with pytest.raises(pydantic.ValidationError):
         TestModel(vec=range(9))
@@ -167,9 +168,12 @@ def test_fixed_size_list_validation():
 
 def test_lance_model():
     class TestModel(LanceModel):
-        vec: vector(16)
-        li: List[int]
+        vector: Vector(16) = Field(default=[0.0] * 16)
+        li: List[int] = Field(default=[1, 2, 3])
 
     schema = pydantic_to_schema(TestModel)
     assert schema == TestModel.to_arrow_schema()
-    assert TestModel.field_names() == ["vec", "li"]
+    assert TestModel.field_names() == ["vector", "li"]
+
+    t = TestModel()
+    assert t == TestModel(vec=[0.0] * 16, li=[1, 2, 3])

python/tests/test_query.py

@@ -20,8 +20,8 @@ import pyarrow as pa
 import pytest
 
 from lancedb.db import LanceDBConnection
-from lancedb.pydantic import LanceModel, vector
-from lancedb.query import LanceQueryBuilder, Query
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.query import LanceVectorQueryBuilder, Query
 from lancedb.table import LanceTable
 
 
@@ -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,
@@ -67,12 +68,12 @@ def table(tmp_path) -> MockTable:
 
 def test_cast(table):
     class TestModel(LanceModel):
-        vector: vector(2)
+        vector: Vector(2)
         id: int
         str_field: str
         float_field: float
 
-    q = LanceQueryBuilder(table, [0, 0], "vector").limit(1)
+    q = LanceVectorQueryBuilder(table, [0, 0], "vector").limit(1)
     results = q.to_pydantic(TestModel)
     assert len(results) == 1
     r0 = results[0]
@@ -84,13 +85,34 @@ def test_cast(table):
 
 
 def test_query_builder(table):
-    df = LanceQueryBuilder(table, [0, 0], "vector").limit(1).select(["id"]).to_df()
+    df = (
+        LanceVectorQueryBuilder(table, [0, 0], "vector").limit(1).select(["id"]).to_df()
+    )
     assert df["id"].values[0] == 1
     assert all(df["vector"].values[0] == [1, 2])
 
 
 def test_query_builder_with_filter(table):
-    df = LanceQueryBuilder(table, [0, 0], "vector").where("id = 2").to_df()
+    df = LanceVectorQueryBuilder(table, [0, 0], "vector").where("id = 2").to_df()
+    assert df["id"].values[0] == 2
+    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])
 
@@ -98,12 +120,14 @@ def test_query_builder_with_filter(table):
 def test_query_builder_with_metric(table):
     query = [4, 8]
     vector_column_name = "vector"
-    df_default = LanceQueryBuilder(table, query, vector_column_name).to_df()
-    df_l2 = LanceQueryBuilder(table, query, vector_column_name).metric("L2").to_df()
+    df_default = LanceVectorQueryBuilder(table, query, vector_column_name).to_df()
+    df_l2 = (
+        LanceVectorQueryBuilder(table, query, vector_column_name).metric("L2").to_df()
+    )
     tm.assert_frame_equal(df_default, df_l2)
 
     df_cosine = (
-        LanceQueryBuilder(table, query, vector_column_name)
+        LanceVectorQueryBuilder(table, query, vector_column_name)
         .metric("cosine")
         .limit(1)
         .to_df()
@@ -120,7 +144,7 @@ def test_query_builder_with_different_vector_column():
     query = [4, 8]
     vector_column_name = "foo_vector"
     builder = (
-        LanceQueryBuilder(table, query, vector_column_name)
+        LanceVectorQueryBuilder(table, query, vector_column_name)
         .metric("cosine")
         .where("b < 10")
         .select(["b"])

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 +22,10 @@ import pandas as pd
 import pyarrow as pa
 import pytest
 
+from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.db import LanceDBConnection
-from lancedb.pydantic import LanceModel, vector
+from lancedb.embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
+from lancedb.pydantic import LanceModel, Vector
 from lancedb.table import LanceTable
 
 
@@ -139,7 +141,7 @@ def test_add(db):
 
 def test_add_pydantic_model(db):
     class TestModel(LanceModel):
-        vector: vector(16)
+        vector: Vector(16)
         li: List[int]
 
     data = TestModel(vector=list(range(16)), li=[1, 2, 3])
@@ -178,16 +180,16 @@ def test_versioning(db):
         ],
     )
 
-    assert len(table.list_versions()) == 1
-    assert table.version == 1
-
-    table.add([{"vector": [6.3, 100.5], "item": "new", "price": 30.0}])
     assert len(table.list_versions()) == 2
     assert table.version == 2
+
+    table.add([{"vector": [6.3, 100.5], "item": "new", "price": 30.0}])
+    assert len(table.list_versions()) == 3
+    assert table.version == 3
     assert len(table) == 3
 
-    table.checkout(1)
-    assert table.version == 1
+    table.checkout(2)
+    assert table.version == 2
     assert len(table) == 2
 
 
@@ -278,21 +280,21 @@ def test_restore(db):
         data=[{"vector": [1.1, 0.9], "type": "vector"}],
     )
     table.add([{"vector": [0.5, 0.2], "type": "vector"}])
-    table.restore(1)
-    assert len(table.list_versions()) == 3
+    table.restore(2)
+    assert len(table.list_versions()) == 4
     assert len(table) == 1
 
     expected = table.to_arrow()
-    table.checkout(1)
+    table.checkout(2)
     table.restore()
-    assert len(table.list_versions()) == 4
+    assert len(table.list_versions()) == 5
     assert table.to_arrow() == expected
 
-    table.restore(4)  # latest version should be no-op
-    assert len(table.list_versions()) == 4
+    table.restore(5)  # latest version should be no-op
+    assert len(table.list_versions()) == 5
 
     with pytest.raises(ValueError):
-        table.restore(5)
+        table.restore(6)
 
     with pytest.raises(ValueError):
         table.restore(0)
@@ -306,7 +308,7 @@ def test_merge(db, tmp_path):
     )
     other_table = pa.table({"document": ["foo", "bar"], "id": [0, 1]})
     table.merge(other_table, left_on="id")
-    assert len(table.list_versions()) == 2
+    assert len(table.list_versions()) == 3
     expected = pa.table(
         {"vector": [[1.1, 0.9], [1.2, 1.9]], "id": [0, 1], "document": ["foo", "bar"]},
         schema=table.schema,
@@ -316,3 +318,126 @@ def test_merge(db, tmp_path):
     other_dataset = lance.write_dataset(other_table, tmp_path / "other_table.lance")
     table.restore(1)
     table.merge(other_dataset, left_on="id")
+
+
+def test_delete(db):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[{"vector": [1.1, 0.9], "id": 0}, {"vector": [1.2, 1.9], "id": 1}],
+    )
+    assert len(table) == 2
+    assert len(table.list_versions()) == 2
+    table.delete("id=0")
+    assert len(table.list_versions()) == 3
+    assert table.version == 3
+    assert len(table) == 1
+    assert table.to_pandas()["id"].tolist() == [1]
+
+
+def test_update(db):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[{"vector": [1.1, 0.9], "id": 0}, {"vector": [1.2, 1.9], "id": 1}],
+    )
+    assert len(table) == 2
+    assert len(table.list_versions()) == 2
+    table.update(where="id=0", values={"vector": [1.1, 1.1]})
+    assert len(table.list_versions()) == 4
+    assert table.version == 4
+    assert len(table) == 2
+    v = table.to_arrow()["vector"].combine_chunks()
+    v = v.values.to_numpy().reshape(2, 2)
+    assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
+
+
+def test_create_with_embedding_function(db):
+    class MyTable(LanceModel):
+        text: str
+        vector: Vector(10)
+
+    func = MockTextEmbeddingFunction()
+    texts = ["hello world", "goodbye world", "foo bar baz fizz buzz"]
+    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=[conf],
+    )
+    table.add(df)
+
+    query_str = "hi how are you?"
+    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()
+    assert actual == expected
+
+
+def test_add_with_embedding_function(db):
+    emb = EmbeddingFunctionRegistry.get_instance().get("test")()
+
+    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})
+    table.add(df)
+
+    texts = ["the quick brown fox", "jumped over the lazy dog"]
+    table.add([{"text": t} for t in texts])
+
+    query_str = "hi how are you?"
+    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()
+    assert actual == expected
+
+
+def test_multiple_vector_columns(db):
+    class MyTable(LanceModel):
+        text: str
+        vector1: Vector(10)
+        vector2: Vector(10)
+
+    table = LanceTable.create(
+        db,
+        "my_table",
+        schema=MyTable,
+    )
+
+    v1 = np.random.randn(10)
+    v2 = np.random.randn(10)
+    data = [
+        {"vector1": v1, "vector2": v2, "text": "foo"},
+        {"vector1": v2, "vector2": v1, "text": "bar"},
+    ]
+    df = pd.DataFrame(data)
+    table.add(df)
+
+    q = np.random.randn(10)
+    result1 = table.search(q, vector_column_name="vector1").limit(1).to_df()
+    result2 = table.search(q, vector_column_name="vector2").limit(1).to_df()
+
+    assert result1["text"].iloc[0] != result2["text"].iloc[0]
+
+
+def test_empty_query(db):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[{"text": "foo", "id": 0}, {"text": "bar", "id": 1}],
+    )
+    df = table.search().select(["id"]).where("text='bar'").limit(1).to_df()
+    val = df.id.iloc[0]
+    assert val == 1

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.2.4"
+version = "0.2.6"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"
@@ -18,6 +18,7 @@ once_cell = "1"
 futures = "0.3"
 half = { workspace = true }
 lance = { workspace = true }
+lance-linalg = { workspace = true }
 vectordb = { path = "../../vectordb" }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 neon = {version = "0.10.1", default-features = false, features = ["channel-api", "napi-6", "promise-api", "task-api"] }

rust/ffi/node/src/arrow.rs

@@ -28,7 +28,9 @@ fn validate_vector_column(record_batch: &RecordBatch) -> Result<()> {
     record_batch
         .column_by_name(VECTOR_COLUMN_NAME)
         .map(|_| ())
-        .context(MissingColumnSnafu { name: VECTOR_COLUMN_NAME })
+        .context(MissingColumnSnafu {
+            name: VECTOR_COLUMN_NAME,
+        })
 }
 
 pub(crate) fn arrow_buffer_to_record_batch(slice: &[u8]) -> Result<(Vec<RecordBatch>, SchemaRef)> {

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

@@ -14,7 +14,7 @@
 
 use lance::index::vector::ivf::IvfBuildParams;
 use lance::index::vector::pq::PQBuildParams;
-use lance::index::vector::MetricType;
+use lance_linalg::distance::MetricType;
 use neon::context::FunctionContext;
 use neon::prelude::*;
 use std::convert::TryFrom;

rust/ffi/node/src/lib.rs

@@ -183,11 +183,9 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let aws_region = get_aws_region(&mut cx, 4)?;
 
     let params = ReadParams {
-        store_options: Some(ObjectStoreParams {
-            aws_credentials: aws_creds,
-            aws_region,
-            ..ObjectStoreParams::default()
-        }),
+        store_options: Some(ObjectStoreParams::with_aws_credentials(
+            aws_creds, aws_region,
+        )),
         ..ReadParams::default()
     };
 

rust/ffi/node/src/query.rs

@@ -3,7 +3,7 @@ use std::ops::Deref;
 
 use arrow_array::Float32Array;
 use futures::{TryFutureExt, TryStreamExt};
-use lance::index::vector::MetricType;
+use lance_linalg::distance::MetricType;
 use neon::context::FunctionContext;
 use neon::handle::Handle;
 use neon::prelude::*;

rust/ffi/node/src/table.rs

@@ -43,7 +43,8 @@ impl JsTable {
             .downcast_or_throw::<JsBox<JsDatabase>, _>(&mut cx)?;
         let table_name = cx.argument::<JsString>(0)?.value(&mut cx);
         let buffer = cx.argument::<JsBuffer>(1)?;
-        let (batches, schema) = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
+        let (batches, schema) =
+            arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
 
         // Write mode
         let mode = match cx.argument::<JsString>(2)?.value(&mut cx).as_str() {
@@ -65,11 +66,9 @@ impl JsTable {
         let aws_region = get_aws_region(&mut cx, 6)?;
 
         let params = WriteParams {
-            store_params: Some(ObjectStoreParams {
-                aws_credentials: aws_creds,
-                aws_region,
-                ..ObjectStoreParams::default()
-            }),
+            store_params: Some(ObjectStoreParams::with_aws_credentials(
+                aws_creds, aws_region,
+            )),
             mode: mode,
             ..WriteParams::default()
         };
@@ -92,7 +91,8 @@ impl JsTable {
         let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
         let buffer = cx.argument::<JsBuffer>(0)?;
         let write_mode = cx.argument::<JsString>(1)?.value(&mut cx);
-        let (batches, schema) = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
+        let (batches, schema) =
+            arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
         let rt = runtime(&mut cx)?;
         let channel = cx.channel();
         let mut table = js_table.table.clone();
@@ -108,11 +108,9 @@ impl JsTable {
         let aws_region = get_aws_region(&mut cx, 5)?;
 
         let params = WriteParams {
-            store_params: Some(ObjectStoreParams {
-                aws_credentials: aws_creds,
-                aws_region,
-                ..ObjectStoreParams::default()
-            }),
+            store_params: Some(ObjectStoreParams::with_aws_credentials(
+                aws_creds, aws_region,
+            )),
             mode: write_mode,
             ..WriteParams::default()
         };

rust/vectordb/Cargo.toml

@@ -1,21 +1,30 @@
 [package]
 name = "vectordb"
-version = "0.2.4"
+version = "0.2.6"
 edition = "2021"
-description = "Serverless, low-latency vector database for AI applications"
+description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 repository = "https://github.com/lancedb/lancedb"
+keywords = ["lancedb", "lance", "database", "search"]
+categories = ["database-implementations"]
 
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 [dependencies]
+arrow = { workspace = true }
 arrow-array = { workspace = true }
 arrow-data = { workspace = true }
 arrow-schema = { workspace = true }
+arrow-ord = { workspace = true }
+arrow-cast = { workspace = true }
 object_store = { workspace = true }
 snafu = { workspace = true }
 half = { workspace = true }
 lance = { workspace = true }
+lance-linalg = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
+log = { workspace = true }
+num-traits = "0"
+url = { workspace = true }
 
 [dev-dependencies]
 tempfile = "3.5.0"

rust/vectordb/README.md

@@ -0,0 +1,3 @@
+# LanceDB Rust
+
+Rust client for LanceDB, a serverless vector database. Read more at: https://lancedb.com/

rust/vectordb/src/arrow.rs

@@ -0,0 +1,15 @@
+// Copyright 2023 Lance 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.
+
+pub use lance::arrow::*;

rust/vectordb/src/data.rs

@@ -0,0 +1,18 @@
+// Copyright 2023 Lance 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.
+
+//! Data types, schema coercion, and data cleaning and etc.
+
+pub mod inspect;
+pub mod sanitize;

rust/vectordb/src/data/inspect.rs

@@ -0,0 +1,180 @@
+// Copyright 2023 Lance 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.
+
+use std::collections::HashMap;
+
+use arrow::compute::kernels::{aggregate::bool_and, length::length};
+use arrow_array::{
+    cast::AsArray,
+    types::{ArrowPrimitiveType, Int32Type, Int64Type},
+    Array, GenericListArray, OffsetSizeTrait, RecordBatchReader,
+};
+use arrow_ord::comparison::eq_dyn_scalar;
+use arrow_schema::DataType;
+use num_traits::{ToPrimitive, Zero};
+
+use crate::error::{Error, Result};
+
+pub(crate) fn infer_dimension<T: ArrowPrimitiveType>(
+    list_arr: &GenericListArray<T::Native>,
+) -> Result<Option<T::Native>>
+where
+    T::Native: OffsetSizeTrait + ToPrimitive,
+{
+    let len_arr = length(list_arr)?;
+    if len_arr.is_empty() {
+        return Ok(Some(Zero::zero()));
+    }
+
+    let dim = len_arr.as_primitive::<T>().value(0);
+    if bool_and(&eq_dyn_scalar(len_arr.as_primitive::<T>(), dim)?) != Some(true) {
+        Ok(None)
+    } else {
+        Ok(Some(dim))
+    }
+}
+
+/// Infer the vector columns from a dataset.
+///
+/// Parameters
+/// ----------
+/// - reader: RecordBatchReader
+/// - strict: if set true, only fixed_size_list<float> is considered as vector column. If set to false,
+///           a list<float> column with same length is also considered as vector column.
+pub fn infer_vector_columns(
+    reader: impl RecordBatchReader + Send,
+    strict: bool,
+) -> Result<Vec<String>> {
+    let mut columns = vec![];
+
+    let mut columns_to_infer: HashMap<String, Option<i64>> = HashMap::new();
+    for field in reader.schema().fields() {
+        match field.data_type() {
+            DataType::FixedSizeList(sub_field, _) if sub_field.data_type().is_floating() => {
+                columns.push(field.name().to_string());
+            }
+            DataType::List(sub_field) if sub_field.data_type().is_floating() && !strict => {
+                columns_to_infer.insert(field.name().to_string(), None);
+            }
+            DataType::LargeList(sub_field) if sub_field.data_type().is_floating() && !strict => {
+                columns_to_infer.insert(field.name().to_string(), None);
+            }
+            _ => {}
+        }
+    }
+    for batch in reader {
+        let batch = batch?;
+        let col_names = columns_to_infer.keys().cloned().collect::<Vec<_>>();
+        for col_name in col_names {
+            let col = batch.column_by_name(&col_name).ok_or(Error::Schema {
+                message: format!("Column {} not found", col_name),
+            })?;
+            if let Some(dim) = match *col.data_type() {
+                DataType::List(_) => {
+                    infer_dimension::<Int32Type>(col.as_list::<i32>())?.map(|d| d as i64)
+                }
+                DataType::LargeList(_) => infer_dimension::<Int64Type>(col.as_list::<i64>())?,
+                _ => {
+                    return Err(Error::Schema {
+                        message: format!("Column {} is not a list", col_name),
+                    })
+                }
+            } {
+                if let Some(Some(prev_dim)) = columns_to_infer.get(&col_name) {
+                    if prev_dim != &dim {
+                        columns_to_infer.remove(&col_name);
+                    }
+                } else {
+                    columns_to_infer.insert(col_name, Some(dim));
+                }
+            } else {
+                columns_to_infer.remove(&col_name);
+            }
+        }
+    }
+    columns.extend(columns_to_infer.keys().cloned());
+    Ok(columns)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    use arrow_array::{
+        types::{Float32Type, Float64Type},
+        FixedSizeListArray, Float32Array, ListArray, RecordBatch, RecordBatchIterator, StringArray,
+    };
+    use arrow_schema::{DataType, Field, Schema};
+    use std::{sync::Arc, vec};
+
+    #[test]
+    fn test_infer_vector_columns() {
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("f", DataType::Float32, false),
+            Field::new("s", DataType::Utf8, false),
+            Field::new(
+                "l1",
+                DataType::List(Arc::new(Field::new("item", DataType::Float32, true))),
+                false,
+            ),
+            Field::new(
+                "l2",
+                DataType::List(Arc::new(Field::new("item", DataType::Float64, true))),
+                false,
+            ),
+            Field::new(
+                "fl",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 32),
+                true,
+            ),
+        ]));
+
+        let batch = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Float32Array::from(vec![1.0, 2.0, 3.0])),
+                Arc::new(StringArray::from(vec!["a", "b", "c"])),
+                Arc::new(ListArray::from_iter_primitive::<Float32Type, _, _>(
+                    (0..3).map(|_| Some(vec![Some(1.0), Some(2.0), Some(3.0), Some(4.0)])),
+                )),
+                // Var-length list
+                Arc::new(ListArray::from_iter_primitive::<Float64Type, _, _>(vec![
+                    Some(vec![Some(1.0_f64)]),
+                    Some(vec![Some(2.0_f64), Some(3.0_f64)]),
+                    Some(vec![Some(4.0_f64), Some(5.0_f64), Some(6.0_f64)]),
+                ])),
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                        vec![
+                            Some(vec![Some(1.0); 32]),
+                            Some(vec![Some(2.0); 32]),
+                            Some(vec![Some(3.0); 32]),
+                        ],
+                        32,
+                    ),
+                ),
+            ],
+        )
+        .unwrap();
+        let reader =
+            RecordBatchIterator::new(vec![batch.clone()].into_iter().map(Ok), schema.clone());
+
+        let cols = infer_vector_columns(reader, false).unwrap();
+        assert_eq!(cols, vec!["fl", "l1"]);
+
+        let reader = RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema);
+        let cols = infer_vector_columns(reader, true).unwrap();
+        assert_eq!(cols, vec!["fl"]);
+    }
+}

rust/vectordb/src/data/sanitize.rs

@@ -0,0 +1,284 @@
+// Copyright 2023 Lance 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.
+
+use std::{iter::repeat_with, sync::Arc};
+
+use arrow_array::{
+    cast::AsArray,
+    types::{Float16Type, Float32Type, Float64Type, Int32Type, Int64Type},
+    Array, ArrowNumericType, FixedSizeListArray, PrimitiveArray, RecordBatch, RecordBatchIterator,
+    RecordBatchReader,
+};
+use arrow_cast::{can_cast_types, cast};
+use arrow_schema::{ArrowError, DataType, Field, Schema};
+use half::f16;
+use lance::arrow::{DataTypeExt, FixedSizeListArrayExt};
+use log::warn;
+use num_traits::cast::AsPrimitive;
+
+use super::inspect::infer_dimension;
+use crate::error::Result;
+
+fn cast_array<I: ArrowNumericType, O: ArrowNumericType>(
+    arr: &PrimitiveArray<I>,
+) -> Arc<PrimitiveArray<O>>
+where
+    I::Native: AsPrimitive<O::Native>,
+{
+    Arc::new(PrimitiveArray::<O>::from_iter_values(
+        arr.values().iter().map(|v| (*v).as_()),
+    ))
+}
+
+fn cast_float_array<I: ArrowNumericType>(
+    arr: &PrimitiveArray<I>,
+    dt: &DataType,
+) -> std::result::Result<Arc<dyn Array>, ArrowError>
+where
+    I::Native: AsPrimitive<f64> + AsPrimitive<f32> + AsPrimitive<f16>,
+{
+    match dt {
+        DataType::Float16 => Ok(cast_array::<I, Float16Type>(arr)),
+        DataType::Float32 => Ok(cast_array::<I, Float32Type>(arr)),
+        DataType::Float64 => Ok(cast_array::<I, Float64Type>(arr)),
+        _ => Err(ArrowError::SchemaError(format!(
+            "Incompatible change field: unable to coerce {:?} to {:?}",
+            arr.data_type(),
+            dt
+        ))),
+    }
+}
+
+fn coerce_array(
+    array: &Arc<dyn Array>,
+    field: &Field,
+) -> std::result::Result<Arc<dyn Array>, ArrowError> {
+    if array.data_type() == field.data_type() {
+        return Ok(array.clone());
+    }
+    match (array.data_type(), field.data_type()) {
+        // Normal cast-able types.
+        (adt, dt) if can_cast_types(adt, dt) => cast(&array, dt),
+        // Casting between f16/f32/f64 can be lossy.
+        (adt, dt) if (adt.is_floating() || dt.is_floating()) => {
+            if adt.byte_width() > dt.byte_width() {
+                warn!(
+                    "Coercing field {} {:?} to {:?} might lose precision",
+                    field.name(),
+                    adt,
+                    dt
+                );
+            }
+            match adt {
+                DataType::Float16 => cast_float_array(array.as_primitive::<Float16Type>(), dt),
+                DataType::Float32 => cast_float_array(array.as_primitive::<Float32Type>(), dt),
+                DataType::Float64 => cast_float_array(array.as_primitive::<Float64Type>(), dt),
+                _ => unreachable!(),
+            }
+        }
+        (adt, DataType::FixedSizeList(exp_field, exp_dim)) => match adt {
+            // Cast a float fixed size array with same dimension to the expected type.
+            DataType::FixedSizeList(_, dim) if dim == exp_dim => {
+                let actual_sub = array.as_fixed_size_list();
+                let values = coerce_array(actual_sub.values(), exp_field)?;
+                Ok(Arc::new(FixedSizeListArray::try_new_from_values(
+                    values.clone(),
+                    *dim,
+                )?) as Arc<dyn Array>)
+            }
+            DataType::List(_) | DataType::LargeList(_) => {
+                let Some(dim) = (match adt {
+                    DataType::List(_) => infer_dimension::<Int32Type>(array.as_list::<i32>())
+                        .map_err(|e| {
+                            ArrowError::SchemaError(format!(
+                                "failed to infer dimension from list: {}",
+                                e
+                            ))
+                        })?
+                        .map(|d| d as i64),
+                    DataType::LargeList(_) => infer_dimension::<Int64Type>(array.as_list::<i64>())
+                        .map_err(|e| {
+                            ArrowError::SchemaError(format!(
+                                "failed to infer dimension from large list: {}",
+                                e
+                            ))
+                        })?,
+                    _ => unreachable!(),
+                }) else {
+                    return Err(ArrowError::SchemaError(format!(
+                        "Incompatible coerce fixed size list: unable to coerce {:?} from {:?}",
+                        field,
+                        array.data_type()
+                    )));
+                };
+
+                if dim != *exp_dim as i64 {
+                    return Err(ArrowError::SchemaError(format!(
+                        "Incompatible coerce fixed size list: expected dimension {} but got {}",
+                        exp_dim, dim
+                    )));
+                }
+
+                let values = coerce_array(array, exp_field)?;
+                Ok(Arc::new(FixedSizeListArray::try_new_from_values(
+                    values.clone(),
+                    *exp_dim,
+                )?) as Arc<dyn Array>)
+            }
+            _ => Err(ArrowError::SchemaError(format!(
+                "Incompatible coerce fixed size list: unable to coerce {:?} from {:?}",
+                field,
+                array.data_type()
+            )))?,
+        },
+        _ => Err(ArrowError::SchemaError(format!(
+            "Incompatible change field {}: unable to coerce {:?} to {:?}",
+            field.name(),
+            array.data_type(),
+            field.data_type()
+        )))?,
+    }
+}
+
+fn coerce_schema_batch(
+    batch: RecordBatch,
+    schema: Arc<Schema>,
+) -> std::result::Result<RecordBatch, ArrowError> {
+    if batch.schema() == schema {
+        return Ok(batch);
+    }
+    let columns = schema
+        .fields()
+        .iter()
+        .map(|field| {
+            batch
+                .column_by_name(field.name())
+                .ok_or_else(|| {
+                    ArrowError::SchemaError(format!("Column {} not found", field.name()))
+                })
+                .and_then(|c| coerce_array(c, field))
+        })
+        .collect::<std::result::Result<Vec<_>, ArrowError>>()?;
+    RecordBatch::try_new(schema, columns)
+}
+
+/// Coerce the reader (input data) to match the given [Schema].
+///
+pub fn coerce_schema(
+    reader: impl RecordBatchReader + Send + 'static,
+    schema: Arc<Schema>,
+) -> Result<Box<dyn RecordBatchReader + Send>> {
+    if reader.schema() == schema {
+        return Ok(Box::new(RecordBatchIterator::new(reader, schema)));
+    }
+    let s = schema.clone();
+    let batches = reader
+        .zip(repeat_with(move || s.clone()))
+        .map(|(batch, s)| coerce_schema_batch(batch?, s));
+    Ok(Box::new(RecordBatchIterator::new(batches, schema)))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    use std::sync::Arc;
+
+    use arrow_array::{
+        FixedSizeListArray, Float16Array, Float32Array, Float64Array, Int32Array, Int8Array,
+        RecordBatch, RecordBatchIterator, StringArray,
+    };
+    use arrow_schema::Field;
+    use half::f16;
+    use lance::arrow::FixedSizeListArrayExt;
+
+    #[test]
+    fn test_coerce_list_to_fixed_size_list() {
+        let schema = Arc::new(Schema::new(vec![
+            Field::new(
+                "fl",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 64),
+                true,
+            ),
+            Field::new("s", DataType::Utf8, true),
+            Field::new("f", DataType::Float16, true),
+            Field::new("i", DataType::Int32, true),
+        ]));
+
+        let batch = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(
+                    FixedSizeListArray::try_new_from_values(
+                        Float32Array::from_iter_values((0..256).map(|v| v as f32)),
+                        64,
+                    )
+                    .unwrap(),
+                ),
+                Arc::new(StringArray::from(vec![
+                    Some("hello"),
+                    Some("world"),
+                    Some("from"),
+                    Some("lance"),
+                ])),
+                Arc::new(Float16Array::from_iter_values(
+                    (0..4).map(|v| f16::from_f32(v as f32)),
+                )),
+                Arc::new(Int32Array::from_iter_values(0..4)),
+            ],
+        )
+        .unwrap();
+        let reader =
+            RecordBatchIterator::new(vec![batch.clone()].into_iter().map(Ok), schema.clone());
+
+        let expected_schema = Arc::new(Schema::new(vec![
+            Field::new(
+                "fl",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float16, true)), 64),
+                true,
+            ),
+            Field::new("s", DataType::Utf8, true),
+            Field::new("f", DataType::Float64, true),
+            Field::new("i", DataType::Int8, true),
+        ]));
+        let stream = coerce_schema(reader, expected_schema.clone()).unwrap();
+        let batches = stream.collect::<Vec<_>>();
+        assert_eq!(batches.len(), 1);
+        let batch = batches[0].as_ref().unwrap();
+        assert_eq!(batch.schema(), expected_schema);
+
+        let expected = RecordBatch::try_new(
+            expected_schema,
+            vec![
+                Arc::new(
+                    FixedSizeListArray::try_new_from_values(
+                        Float16Array::from_iter_values((0..256).map(|v| f16::from_f32(v as f32))),
+                        64,
+                    )
+                    .unwrap(),
+                ),
+                Arc::new(StringArray::from(vec![
+                    Some("hello"),
+                    Some("world"),
+                    Some("from"),
+                    Some("lance"),
+                ])),
+                Arc::new(Float64Array::from_iter_values((0..4).map(|v| v as f64))),
+                Arc::new(Int8Array::from_iter_values(0..4)),
+            ],
+        )
+        .unwrap();
+        assert_eq!(batch, &expected);
+    }
+}

rust/vectordb/src/database.rs

@@ -27,12 +27,14 @@ pub const LANCE_FILE_EXTENSION: &str = "lance";
 
 pub struct Database {
     object_store: ObjectStore,
+    query_string: Option<String>,
 
     pub(crate) uri: String,
     pub(crate) base_path: object_store::path::Path,
 }
 
 const LANCE_EXTENSION: &str = "lance";
+const ENGINE: &str = "engine";
 
 /// A connection to LanceDB
 impl Database {
@@ -46,12 +48,73 @@ impl Database {
     ///
     /// * A [Database] object.
     pub async fn connect(uri: &str) -> Result<Database> {
-        let (object_store, base_path) = ObjectStore::from_uri(uri).await?;
-        if object_store.is_local() {
-            Self::try_create_dir(uri).context(CreateDirSnafu { path: uri })?;
+        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![];
+
+                // WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
+                // THE API WILL CHANGE
+                for (key, value) in url.query_pairs() {
+                    if key == ENGINE {
+                        engine = Some(value.to_string());
+                    } else {
+                        // to owned so we can modify the url
+                        filtered_querys.push((key.to_string(), value.to_string()));
+                    }
+                }
+
+                // Filter out the commit store query param -- it's a lancedb param
+                url.query_pairs_mut().clear();
+                url.query_pairs_mut().extend_pairs(filtered_querys);
+                // Take a copy of the query string so we can propagate it to lance
+                let query_string = url.query().map(|s| s.to_string());
+                // clear the query string so we can use the url as the base uri
+                // use .set_query(None) instead of .set_query("") because the latter
+                // will add a trailing '?' to the url
+                url.set_query(None);
+
+                let table_base_uri = if let Some(store) = engine {
+                    static WARN_ONCE: std::sync::Once = std::sync::Once::new();
+                    WARN_ONCE.call_once(|| {
+                        log::warn!("Specifing engine is not a publicly supported feature in lancedb yet. THE API WILL CHANGE");
+                    });
+                    let old_scheme = url.scheme().to_string();
+                    let new_scheme = format!("{}+{}", old_scheme, store);
+                    url.to_string().replacen(&old_scheme, &new_scheme, 1)
+                } else {
+                    url.to_string()
+                };
+
+                let plain_uri = url.to_string();
+                let (object_store, base_path) = ObjectStore::from_uri(&plain_uri).await?;
+                if object_store.is_local() {
+                    Self::try_create_dir(&plain_uri).context(CreateDirSnafu { path: plain_uri })?;
+                }
+
+                Ok(Database {
+                    uri: table_base_uri,
+                    query_string,
+                    base_path,
+                    object_store,
+                })
+            }
+            Err(_) => Self::open_path(uri).await,
         }
-        Ok(Database {
-            uri: uri.to_string(),
+    }
+
+    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,
         })
@@ -149,17 +212,26 @@ impl Database {
         let path = Path::new(&self.uri);
         let table_uri = path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
 
-        let uri = table_uri
+        let mut uri = table_uri
             .as_path()
             .to_str()
-            .context(InvalidTableNameSnafu { name })?;
-        Ok(uri.to_string())
+            .context(InvalidTableNameSnafu { name })?
+            .to_string();
+
+        // If there are query string set on the connection, propagate to lance
+        if let Some(query) = self.query_string.as_ref() {
+            uri.push('?');
+            uri.push_str(query.as_str());
+        }
+
+        Ok(uri)
     }
 }
 
 #[cfg(test)]
 mod tests {
     use std::fs::create_dir_all;
+
     use tempfile::tempdir;
 
     use crate::database::Database;
@@ -173,6 +245,28 @@ mod tests {
         assert_eq!(db.uri, uri);
     }
 
+    #[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]
     async fn test_table_names() {
         let tmp_dir = tempdir().unwrap();

rust/vectordb/src/error.rs

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+use arrow_schema::ArrowError;
 use snafu::Snafu;
 
 #[derive(Debug, Snafu)]
@@ -32,10 +33,20 @@ pub enum Error {
     Store { message: String },
     #[snafu(display("LanceDBError: {message}"))]
     Lance { message: String },
+    #[snafu(display("LanceDB Schema Error: {message}"))]
+    Schema { message: String },
 }
 
 pub type Result<T> = std::result::Result<T, Error>;
 
+impl From<ArrowError> for Error {
+    fn from(e: ArrowError) -> Self {
+        Self::Lance {
+            message: e.to_string(),
+        }
+    }
+}
+
 impl From<lance::Error> for Error {
     fn from(e: lance::Error) -> Self {
         Self::Lance {

rust/vectordb/src/index/vector.rs

@@ -14,7 +14,8 @@
 
 use lance::index::vector::ivf::IvfBuildParams;
 use lance::index::vector::pq::PQBuildParams;
-use lance::index::vector::{MetricType, VectorIndexParams};
+use lance::index::vector::VectorIndexParams;
+use lance_linalg::distance::MetricType;
 
 pub trait VectorIndexBuilder {
     fn get_column(&self) -> Option<String>;
@@ -107,9 +108,11 @@ impl VectorIndexBuilder for IvfPQIndexBuilder {
 
 #[cfg(test)]
 mod tests {
+    use super::*;
+
     use lance::index::vector::ivf::IvfBuildParams;
     use lance::index::vector::pq::PQBuildParams;
-    use lance::index::vector::{MetricType, StageParams};
+    use lance::index::vector::StageParams;
 
     use crate::index::vector::{IvfPQIndexBuilder, VectorIndexBuilder};
 

rust/vectordb/src/lib.rs

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+pub mod data;
 pub mod database;
 pub mod error;
 pub mod index;

rust/vectordb/src/query.rs

@@ -17,7 +17,7 @@ use std::sync::Arc;
 use arrow_array::Float32Array;
 use lance::dataset::scanner::{DatasetRecordBatchStream, Scanner};
 use lance::dataset::Dataset;
-use lance::index::vector::MetricType;
+use lance_linalg::distance::MetricType;
 
 use crate::error::Result;
 
@@ -164,10 +164,10 @@ impl Query {
 mod tests {
     use std::sync::Arc;
 
+    use super::*;
     use arrow_array::{Float32Array, RecordBatch, RecordBatchIterator, RecordBatchReader};
     use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
     use lance::dataset::Dataset;
-    use lance::index::vector::MetricType;
 
     use crate::query::Query;