tests pass; still need catalog

Closes #639 
Closes #638

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.8
+current_version = 0.4.0
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True

.github/ISSUE_TEMPLATE/bug-node.yml

@@ -0,0 +1,33 @@
+name: Bug Report - Node / Typescript
+description: File a bug report
+title: "bug(node): "
+labels: [bug, typescript]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+  - type: input
+    id: version
+    attributes:
+      label: LanceDB version
+      description: What version of LanceDB are you using? `npm list | grep vectordb`.
+      placeholder: v0.3.2
+    validations:
+      required: false
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: Also tell us, what did you expect to happen?
+    validations:
+      required: true
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Are there known steps to reproduce?
+      description: |
+        Let us know how to reproduce the bug and we may be able to fix it more
+        quickly. This is not required, but it is helpful.
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/bug-python.yml

@@ -0,0 +1,33 @@
+name: Bug Report - Python
+description: File a bug report
+title: "bug(python): "
+labels: [bug, python]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+  - type: input
+    id: version
+    attributes:
+      label: LanceDB version
+      description: What version of LanceDB are you using? `python -c "import lancedb; print(lancedb.__version__)"`.
+      placeholder: v0.3.2
+    validations:
+      required: false
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: Also tell us, what did you expect to happen?
+    validations:
+      required: true
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Are there known steps to reproduce?
+      description: |
+        Let us know how to reproduce the bug and we may be able to fix it more
+        quickly. This is not required, but it is helpful.
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,5 @@
+blank_issues_enabled: true
+contact_links:
+  - name: Discord Community Support
+    url: https://discord.com/invite/zMM32dvNtd
+    about: Please ask and answer questions here.

.github/ISSUE_TEMPLATE/documentation.yml

@@ -0,0 +1,23 @@
+name: 'Documentation improvement'
+description: Report an issue with the documentation.
+labels: [documentation]
+
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: >
+        Describe the issue with the documentation and how it can be fixed or improved.
+    validations:
+      required: true
+
+  - type: input
+    id: link
+    attributes:
+      label: Link
+      description: >
+        Provide a link to the existing documentation, if applicable.
+      placeholder: ex. https://lancedb.github.io/lancedb/guides/tables/...
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/feature.yml

@@ -0,0 +1,31 @@
+name: Feature suggestion
+description: Suggestion a new feature for LanceDB
+title: "Feature: "
+labels: [enhancement]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Share a new idea for a feature or improvement. Be sure to search existing
+        issues first to avoid duplicates.
+  - type: dropdown
+    id: sdk
+    attributes:
+      label: SDK
+      description: Which SDK are you using? This helps us prioritize.
+      options:
+        - Python
+        - Node
+        - Rust
+      default: 0
+    validations:
+      required: false
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: |
+        Describe the feature and why it would be useful. If applicable, consider
+        providing a code example of what it might be like to use the feature.
+    validations:
+      required: true

.github/workflows/npm-publish.yml

@@ -38,13 +38,17 @@ jobs:
             node/vectordb-*.tgz
 
   node-macos:
-    runs-on: macos-13
+    strategy:
+      matrix:
+        config:
+          - arch: x86_64-apple-darwin
+            runner: macos-13
+          - arch: aarch64-apple-darwin
+            # xlarge is implicitly arm64.
+            runner: macos-13-xlarge
+    runs-on: ${{ matrix.config.runner }}
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
-    strategy:
-      fail-fast: false
-      matrix:
-        target: [x86_64-apple-darwin, aarch64-apple-darwin]
     steps:
       - name: Checkout
         uses: actions/checkout@v3
@@ -54,17 +58,15 @@ jobs:
         run: |
           cd node
           npm ci
-      - name: Install rustup target
-        if: ${{ matrix.target == 'aarch64-apple-darwin' }}
-        run: rustup target add aarch64-apple-darwin
       - name: Build MacOS native node modules
-        run: bash ci/build_macos_artifacts.sh ${{ matrix.target }}
+        run: bash ci/build_macos_artifacts.sh ${{ matrix.config.arch }}
       - name: Upload Darwin Artifacts
         uses: actions/upload-artifact@v3
         with:
           name: native-darwin
           path: |
             node/dist/lancedb-vectordb-darwin*.tgz
+         
 
   node-linux:
     name: node-linux (${{ matrix.config.arch}}-unknown-linux-gnu

.github/workflows/python.yml

@@ -44,12 +44,19 @@ jobs:
       run: pytest -m "not slow" -x -v --durations=30 tests
     - name: doctest
       run: pytest --doctest-modules lancedb
-  mac:
+  platform:
+    name: "Platform: ${{ matrix.config.name }}"
     timeout-minutes: 30
     strategy:
       matrix:
-        mac-runner: [ "macos-13", "macos-13-xlarge" ]
-    runs-on: "${{ matrix.mac-runner }}"
+        config: 
+          - name: x86 Mac
+            runner: macos-13
+          - name: Arm Mac
+            runner: macos-13-xlarge
+          - name: x86 Windows
+            runner: windows-latest
+    runs-on: "${{ matrix.config.runner }}"
     defaults:
       run:
         shell: bash
@@ -91,11 +98,7 @@ jobs:
         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 .
+        pip install pytest pytest-mock
     - name: Run tests
       run: pytest -m "not slow" -x -v --durations=30 tests
     - name: doctest

.github/workflows/rust.yml

@@ -24,6 +24,29 @@ env:
   RUST_BACKTRACE: "1"
 
 jobs:
+  lint:
+    timeout-minutes: 30
+    runs-on: ubuntu-22.04
+    defaults:
+      run:
+        shell: bash
+        working-directory: rust
+    steps:
+    - uses: actions/checkout@v3
+      with:
+          fetch-depth: 0
+          lfs: true
+    - uses: Swatinem/rust-cache@v2
+      with:
+        workspaces: rust
+    - name: Install dependencies
+      run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+    - name: Run format
+      run: cargo fmt --all -- --check
+    - name: Run clippy
+      run: cargo clippy --all --all-features -- -D warnings
   linux:
     timeout-minutes: 30
     runs-on: ubuntu-22.04

Cargo.toml

@@ -5,24 +5,24 @@ exclude = ["python"]
 resolver = "2"
 
 [workspace.dependencies]
-lance = { "version" = "=0.8.17", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.8.17" }
-lance-linalg = { "version" = "=0.8.17" }
-lance-testing = { "version" = "=0.8.17" }
+lance = { "version" = "=0.9.1", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.9.1" }
+lance-linalg = { "version" = "=0.9.1" }
+lance-testing = { "version" = "=0.9.1" }
 # Note that this one does not include pyarrow
-arrow = { version = "47.0.0", optional = false }
-arrow-array = "47.0"
-arrow-data = "47.0"
-arrow-ipc = "47.0"
-arrow-ord = "47.0"
-arrow-schema = "47.0"
-arrow-arith = "47.0"
-arrow-cast = "47.0"
+arrow = { version = "49.0.0", optional = false }
+arrow-array = "49.0"
+arrow-data = "49.0"
+arrow-ipc = "49.0"
+arrow-ord = "49.0"
+arrow-schema = "49.0"
+arrow-arith = "49.0"
+arrow-cast = "49.0"
 chrono = "0.4.23"
 half = { "version" = "=2.3.1", default-features = false, features = [
     "num-traits",
 ] }
 log = "0.4"
-object_store = "0.7.1"
+object_store = "0.8.0"
 snafu = "0.7.4"
 url = "2"

README.md

@@ -5,10 +5,11 @@
 
 **Developer-friendly, serverless vector database for AI applications**
 
-<a href="https://lancedb.github.io/lancedb/">Documentation</a> •
-<a href="https://blog.lancedb.com/">Blog</a> •
-<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
-<a href="https://twitter.com/lancedb">Twitter</a>
+<a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
+<a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
+[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/) 
+[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd) 
+[![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
 
 </p>
 

ci/build_macos_artifacts.sh

@@ -1,6 +1,7 @@
 # Builds the macOS artifacts (node binaries).
 # Usage: ./ci/build_macos_artifacts.sh [target]
 # Targets supported: x86_64-apple-darwin aarch64-apple-darwin
+set -e
 
 prebuild_rust() {
     # Building here for the sake of easier debugging.

docs/mkdocs.yml

@@ -98,6 +98,7 @@ nav:
     - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
     - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
     - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+    - Example - Calculate CLIP Embeddings with Roboflow Inference: examples/image_embeddings_roboflow.md
     - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
     - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
   - 🌐 Javascript examples:
@@ -145,7 +146,8 @@ nav:
   - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
   - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
 - API references:
-  - Python API: python/python.md
+  - OSS Python API: python/python.md
+  - SaaS Python API: python/saas-python.md
   - Javascript API: javascript/modules.md
 - LanceDB Cloud↗: https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms
 

docs/requirements.txt

@@ -2,3 +2,4 @@ mkdocs==1.4.2
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.1.3
 mkdocstrings[python]==0.20.0
+pydantic

docs/src/basic.md

@@ -64,18 +64,26 @@ We'll cover the basics of using LanceDB on your local machine in this section.
       tbl = db.create_table("table_from_df", data=df)
       ```
 
+      !!! warning
+
+            If the table already exists, LanceDB will raise an error by default.
+            If you want to overwrite the table, you can pass in `mode="overwrite"`
+            to the `createTable` function.
+
 === "Javascript"
       ```javascript
-      const tb = await db.createTable("my_table",
-                        data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-                              {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      const tb = await db.createTable(
+        "myTable",
+        [{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+         {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
       ```
-      
-!!! warning
 
-      If the table already exists, LanceDB will raise an error by default.
-      If you want to overwrite the table, you can pass in `mode="overwrite"`
-      to the `createTable` function.
+      !!! warning
+
+            If the table already exists, LanceDB will raise an error by default.
+            If you want to overwrite the table, you can pass in `"overwrite"`
+            to the `createTable` function like this: `await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })`
+      
 
 ??? info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in [Lance format](https://www.github.com/lancedb/lance)."
 
@@ -108,7 +116,7 @@ Once created, you can open a table using the following code:
 
 === "Javascript"
       ```javascript
-      const tbl = await db.openTable("my_table");
+      const tbl = await db.openTable("myTable");
       ```
 
       If you forget the name of your table, you can always get a listing of all table names:
@@ -194,10 +202,17 @@ Use the `drop_table()` method on the database to remove a table.
       db.drop_table("my_table")
       ```
 
-This permanently removes the table and is not recoverable, unlike deleting rows.
-By default, if the table does not exist an exception is raised. To suppress this,
-you can pass in `ignore_missing=True`.
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      By default, if the table does not exist an exception is raised. To suppress this,
+      you can pass in `ignore_missing=True`.
 
+=== "JavaScript"
+      ```javascript
+      await db.dropTable('myTable')
+      ```
+
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      If the table does not exist an exception is raised. 
 
 ## What's next
 

docs/src/examples/image_embeddings_roboflow.md

@@ -0,0 +1,165 @@
+# How to Load Image Embeddings into LanceDB
+
+With the rise of Large Multimodal Models (LMMs) such as [GPT-4 Vision](https://blog.roboflow.com/gpt-4-vision/), the need for storing image embeddings is growing. The most effective way to store text and image embeddings is in a vector database such as LanceDB. Vector databases are a special kind of data store that enables efficient search over stored embeddings. 
+
+[CLIP](https://blog.roboflow.com/openai-clip/), a multimodal model developed by OpenAI, is commonly used to calculate image embeddings. These embeddings can then be used with a vector database to build a semantic search engine that you can query using images or text. For example, you could use LanceDB and CLIP embeddings to build a search engine for a database of folders.
+
+In this guide, we are going to show you how to use Roboflow Inference to load image embeddings into LanceDB. Without further ado, let’s get started!
+
+## Step #1: Install Roboflow Inference
+
+[Roboflow Inference](https://inference.roboflow.com) enables you to run state-of-the-art computer vision models with minimal configuration. Inference supports a range of models, from fine-tuned object detection, classification, and segmentation models to foundation models like CLIP. We will use Inference to calculate CLIP image embeddings.
+
+Inference provides a HTTP API through which you can run vision models.
+
+Inference powers the Roboflow hosted API, and is available as an open source utility. In this guide, we are going to run Inference locally, which enables you to calculate CLIP embeddings on your own hardware. We will also show you how to use the hosted Roboflow CLIP API, which is ideal if you need to scale and do not want to manage a system for calculating embeddings.
+
+To get started, first install the Inference CLI:
+
+```
+pip install inference-cli
+```
+
+Next, install Docker. Refer to the official Docker installation instructions for your operating system to get Docker set up. Once Docker is ready, you can start Inference using the following command:
+
+```
+inference server start
+```
+
+An Inference server will start running at ‘http://localhost:9001’.
+
+## Step #2: Set Up a LanceDB Vector Database
+
+Now that we have Inference running, we can set up a LanceDB vector database. You can run LanceDB in JavaScript and Python. For this guide, we will use the Python API. But, you can take the HTTP requests we make below and change them to JavaScript if required.
+
+For this guide, we are going to search the [COCO 128 dataset](https://universe.roboflow.com/team-roboflow/coco-128), which contains a wide range of objects. The variability in objects present in this dataset makes it a good dataset to demonstrate the capabilities of vector search. If you want to use this dataset, you can download [COCO 128 from Roboflow Universe](https://universe.roboflow.com/team-roboflow/coco-128). With that said, you can search whatever folder of images you want.
+
+Once you have a dataset ready, install LanceDB with the following command:
+
+```
+pip install lancedb
+```
+
+We also need to install a specific commit of `tantivy`, a dependency of the LanceDB full text search engine we will use later in this guide:
+
+```
+pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+```
+
+Create a new Python file and add the following code:
+
+```python
+import cv2
+import supervision as sv
+import requests
+
+import lancedb
+
+db = lancedb.connect("./embeddings")
+
+IMAGE_DIR = "images/"
+API_KEY = os.environ.get("ROBOFLOW_API_KEY")
+SERVER_URL = "http://localhost:9001"
+
+results = []
+
+for i, image in enumerate(os.listdir(IMAGE_DIR)):
+    infer_clip_payload = {
+        #Images can be provided as urls or as base64 encoded strings
+        "image": {
+            "type": "base64",
+            "value": base64.b64encode(open(IMAGE_DIR + image, "rb").read()).decode("utf-8"),
+        },
+    }
+
+    res = requests.post(
+        f"{SERVER_URL}/clip/embed_image?api_key={API_KEY}",
+        json=infer_clip_payload,
+    )
+
+    embeddings = res.json()['embeddings']
+
+    print("Calculated embedding for image: ", image)
+
+    image = {"vector": embeddings[0], "name": os.path.join(IMAGE_DIR, image)}
+
+    results.append(image)
+
+tbl = db.create_table("images", data=results)
+
+tbl.create_fts_index("name")
+```
+
+To use the code above, you will need a Roboflow API key. [Learn how to retrieve a Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key). Run the following command to set up your API key in your environment:
+
+```
+export ROBOFLOW_API_KEY=""
+```
+
+Replace the `IMAGE_DIR` value with the folder in which you are storing the images for which you want to calculate embeddings. If you want to use the Roboflow CLIP API to calculate embeddings, replace the `SERVER_URL` value with `https://infer.roboflow.com`.
+
+Run the script above to create a new LanceDB database. This database will be stored on your local machine. The database will be called `embeddings` and the table will be called `images`.
+
+The script above calculates all embeddings for a folder then creates a new table. To add additional images, use the following code:
+
+```python
+def make_batches():
+    for i in range(5):
+        yield [
+                {"vector": [3.1, 4.1], "name": "image1.png"},
+                {"vector": [5.9, 26.5], "name": "image2.png"}
+            ]
+
+tbl = db.open_table("images")
+tbl.add(make_batches())
+```
+
+Replacing the `make_batches()` function with code to load embeddings for images.
+
+## Step #3: Run a Search Query
+
+We are now ready to run a search query. To run a search query, we need a text embedding that represents a text query. We can use this embedding to search our LanceDB database for an entry.
+
+Let’s calculate a text embedding for the query “cat”, then run a search query:
+
+```python
+infer_clip_payload = {
+    "text": "cat",
+}
+
+res = requests.post(
+    f"{SERVER_URL}/clip/embed_text?api_key={API_KEY}",
+    json=infer_clip_payload,
+)
+
+embeddings = res.json()['embeddings']
+
+df = tbl.search(embeddings[0]).limit(3).to_list()
+
+print("Results:")
+
+for i in df:
+    print(i["name"])
+```
+
+This code will search for the three images most closely related to the prompt “cat”. The names of the most similar three images will be printed to the console. Here are the three top results:
+
+```
+dataset/images/train/000000000650_jpg.rf.1b74ba165c5a3513a3211d4a80b69e1c.jpg
+dataset/images/train/000000000138_jpg.rf.af439ef1c55dd8a4e4b142d186b9c957.jpg
+dataset/images/train/000000000165_jpg.rf.eae14d5509bf0c9ceccddbb53a5f0c66.jpg
+```
+
+Let’s open the top image:
+
+![Cat](https://media.roboflow.com/cat_lancedb.jpg)
+
+The top image was a cat. Our search was successful.
+
+## Conclusion
+
+LanceDB is a vector database that you can use to store and efficiently search your image embeddings. You can use Roboflow Inference, a scalable computer vision inference server, to calculate CLIP embeddings that you can store in LanceDB.
+
+You can use Inference and LanceDB together to build a range of applications with image embeddings, from a media search engine to a retrieval-augmented generation pipeline for use with LMMs.
+
+To learn more about Inference and its capabilities, refer to the Inference documentation.

docs/src/guides/tables.md

@@ -201,8 +201,8 @@ A Table is a collection of Records in a LanceDB Database. You can follow along o
     ```javascript
     data
     const tb = await db.createTable("my_table",
-                  data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-                        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      [{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+       {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
     ```
 
     !!! info "Note"

docs/src/python/saas-python.md

@@ -0,0 +1,18 @@
+# LanceDB Python API Reference
+
+## Installation
+
+```shell
+pip install lancedb
+```
+
+## Connection
+
+::: lancedb.connect
+
+::: lancedb.remote.db.RemoteDBConnection
+
+## Table
+
+::: lancedb.remote.table.RemoteTable
+

docs/src/search.md

@@ -118,4 +118,101 @@ However, fast vector search using indices often entails making a trade-off with
 This is why it is often called **Approximate Nearest Neighbors (ANN)** search, while the Flat Search (KNN)
 always returns 100% recall.
 
-See [ANN Index](ann_indexes.md) for more details.
+See [ANN Index](ann_indexes.md) for more details.
+
+
+### Output formats
+
+LanceDB returns results in many different formats commonly used in python.
+Let's create a LanceDB table with a nested schema:
+
+```python
+from datetime import datetime
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+import numpy as np
+from pydantic import BaseModel
+uri = "data/sample-lancedb-nested"
+
+class Metadata(BaseModel):
+    source: str
+    timestamp: datetime
+
+class Document(BaseModel):
+    content: str
+    meta: Metadata
+
+class LanceSchema(LanceModel):
+    id: str
+    vector: Vector(1536)
+    payload: Document
+
+# Let's add 100 sample rows to our dataset
+data = [LanceSchema(
+    id=f"id{i}",
+    vector=np.random.randn(1536),
+    payload=Document(
+        content=f"document{i}", meta=Metadata(source=f"source{i%10}", timestamp=datetime.now())
+    ),
+) for i in range(100)]
+
+tbl = db.create_table("documents", data=data)
+```
+
+#### As a pyarrow table
+
+Using `to_arrow()` we can get the results back as a pyarrow Table.
+This result table has the same columns as the LanceDB table, with 
+the addition of an `_distance` column for vector search or a `score`
+column for full text search.
+
+```python
+tbl.search(np.random.randn(1536)).to_arrow()
+```
+
+#### As a pandas dataframe
+
+You can also get the results as a pandas dataframe.
+
+```python
+tbl.search(np.random.randn(1536)).to_pandas()
+```
+
+While other formats like Arrow/Pydantic/Python dicts have a natural 
+way to handle nested schemas, pandas can only store nested data as a 
+python dict column, which makes it difficult to support nested references.
+So for convenience, you can also tell LanceDB to flatten a nested schema 
+when creating the pandas dataframe. 
+
+```python
+tbl.search(np.random.randn(1536)).to_pandas(flatten=True)
+```
+
+If your table has a deeply nested struct, you can control how many levels
+of nesting to flatten by passing in a positive integer.
+
+```python
+tbl.search(np.random.randn(1536)).to_pandas(flatten=1)
+```
+
+
+#### As a list of python dicts
+
+You can of course return results as a list of python dicts.
+
+```python
+tbl.search(np.random.randn(1536)).to_list()
+```
+
+#### As a list of pydantic models
+
+We can add data using pydantic models, and we can certainly
+retrieve results as pydantic models
+
+```python
+tbl.search(np.random.randn(1536)).to_pydantic(LanceSchema)
+```
+
+Note that in this case the extra `_distance` field is discarded since
+it's not part of the LanceSchema.
+

docs/src/sql.md

@@ -22,7 +22,7 @@ import numpy as np
 uri = "data/sample-lancedb"
 db = lancedb.connect(uri)
 
-data = [{"vector": row, "item": f"item {i}"}
+data = [{"vector": row, "item": f"item {i}", "id": i}
      for i, row in enumerate(np.random.random((10_000, 2)).astype('int'))]
 
 tbl = db.create_table("my_vectors", data=data)
@@ -35,33 +35,25 @@ const db = await vectordb.connect('data/sample-lancedb')
 
 let data = []
 for (let i = 0; i < 10_000; i++) {
-     data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
+     data.push({vector: Array(1536).fill(i), id: i, item: `item ${i}`, strId: `${i}`})
 }
-const tbl = await db.createTable('my_vectors', data)
+const tbl = await db.createTable('myVectors', data)
 ```
 -->
 === "Python"
 
     ```python
     tbl.search([100, 102]) \
-       .where("""(
-        (label IN [10, 20])
-        AND
-        (note.email IS NOT NULL)
-        ) OR NOT note.created
-        """)
-
+       .where("(item IN ('item 0', 'item 2')) AND (id > 10)") \
+       .to_arrow()
     ```
+
 === "Javascript"
 
     ```javascript
-    tbl.search([100, 102])
-       .where(`(
-            (label IN [10, 20])
-            AND
-            (note.email IS NOT NULL)
-        ) OR NOT note.created
-       `)
+    await tbl.search(Array(1536).fill(0))
+       .where("(item IN ('item 0', 'item 2')) AND (id > 10)")
+       .execute()
     ```
 
 
@@ -118,3 +110,22 @@ The mapping from SQL types to Arrow types is:
 
 [^1]: See precision mapping in previous table.
 
+
+## Filtering without Vector Search
+
+You can also filter your data without search.
+
+=== "Python"
+      ```python
+      tbl.search().where("id=10").limit(10).to_arrow()
+      ```
+
+=== "JavaScript"
+      ```javascript
+      await tbl.where('id=10').limit(10).execute()
+      ```
+
+!!! warning
+    If your table is large, this could potentially return a very large
+    amount of data. Please be sure to use a `limit` clause unless
+    you're sure you want to return the whole result set.

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.3.8",
+  "version": "0.4.0",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.3.8",
+      "version": "0.4.0",
       "cpu": [
         "x64",
         "arm64"
@@ -53,11 +53,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.3.8",
-        "@lancedb/vectordb-darwin-x64": "0.3.8",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.3.8",
-        "@lancedb/vectordb-linux-x64-gnu": "0.3.8",
-        "@lancedb/vectordb-win32-x64-msvc": "0.3.8"
+        "@lancedb/vectordb-darwin-arm64": "0.4.0",
+        "@lancedb/vectordb-darwin-x64": "0.4.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.0",
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.0"
       }
     },
     "node_modules/@apache-arrow/ts": {
@@ -316,6 +316,66 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.0.tgz",
+      "integrity": "sha512-cP6zGtBWXEcJHCI4uLNIP5ILtRvexvwmL8Uri1dnHG8dT8g12Ykug3BHO6Wt6wp/xASd2jJRIF/VAJsN9IeP1A==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.0.tgz",
+      "integrity": "sha512-ig0gV5ol1sFe2lb1HOatK0rizyj9I91WbnH79i7OdUl3nAQIcWm70CnxrPLtx0DS2NTGh2kFJbYCWcaUlu6YfA==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.0.tgz",
+      "integrity": "sha512-gMXIDT2kriAPDwWIRKXdaTCNdOeFGEok1S9Y30AOruHXddW1vCIo4JNJIYbBqHnwAeI4wI3ae6GRCFaf1UxO3g==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.0.tgz",
+      "integrity": "sha512-ZQ3lDrDSz1IKdx/mS9Lz08agFO+OD5oSFrrcFNCoT1+H93eS1mCLdmCoEARu3jKbx0tMs38l5J9yXZ2QmJye3w==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.0.tgz",
+      "integrity": "sha512-toNcNwBRE1sdsSf5hr7W8QiqZ33csc/knVEek4CyvYkZHJGh4Z6WI+DJUIASo5wzUez4TX7qUPpRPL9HuaPMCg==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",
@@ -4808,6 +4868,36 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "@lancedb/vectordb-darwin-arm64": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.0.tgz",
+      "integrity": "sha512-cP6zGtBWXEcJHCI4uLNIP5ILtRvexvwmL8Uri1dnHG8dT8g12Ykug3BHO6Wt6wp/xASd2jJRIF/VAJsN9IeP1A==",
+      "optional": true
+    },
+    "@lancedb/vectordb-darwin-x64": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.0.tgz",
+      "integrity": "sha512-ig0gV5ol1sFe2lb1HOatK0rizyj9I91WbnH79i7OdUl3nAQIcWm70CnxrPLtx0DS2NTGh2kFJbYCWcaUlu6YfA==",
+      "optional": true
+    },
+    "@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.0.tgz",
+      "integrity": "sha512-gMXIDT2kriAPDwWIRKXdaTCNdOeFGEok1S9Y30AOruHXddW1vCIo4JNJIYbBqHnwAeI4wI3ae6GRCFaf1UxO3g==",
+      "optional": true
+    },
+    "@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.0.tgz",
+      "integrity": "sha512-ZQ3lDrDSz1IKdx/mS9Lz08agFO+OD5oSFrrcFNCoT1+H93eS1mCLdmCoEARu3jKbx0tMs38l5J9yXZ2QmJye3w==",
+      "optional": true
+    },
+    "@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.0.tgz",
+      "integrity": "sha512-toNcNwBRE1sdsSf5hr7W8QiqZ33csc/knVEek4CyvYkZHJGh4Z6WI+DJUIASo5wzUez4TX7qUPpRPL9HuaPMCg==",
+      "optional": true
+    },
     "@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.3.8",
+  "version": "0.4.0",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -81,10 +81,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.3.8",
-    "@lancedb/vectordb-darwin-x64": "0.3.8",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.3.8",
-    "@lancedb/vectordb-linux-x64-gnu": "0.3.8",
-    "@lancedb/vectordb-win32-x64-msvc": "0.3.8"
+    "@lancedb/vectordb-darwin-arm64": "0.4.0",
+    "@lancedb/vectordb-darwin-x64": "0.4.0",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.4.0",
+    "@lancedb/vectordb-linux-x64-gnu": "0.4.0",
+    "@lancedb/vectordb-win32-x64-msvc": "0.4.0"
   }
 }

node/src/index.ts

@@ -21,9 +21,10 @@ import type { EmbeddingFunction } from './embedding/embedding_function'
 import { RemoteConnection } from './remote'
 import { Query } from './query'
 import { isEmbeddingFunction } from './embedding/embedding_function'
+import { type Literal, toSQL } from './util'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
-const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete, tableCleanupOldVersions, tableCompactFiles, tableListIndices, tableIndexStats } = require('../native.js')
+const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete, tableUpdate, tableCleanupOldVersions, tableCompactFiles, tableListIndices, tableIndexStats } = require('../native.js')
 
 export { Query }
 export type { EmbeddingFunction }
@@ -261,6 +262,39 @@ export interface Table<T = number[]> {
    */
   delete: (filter: string) => Promise<void>
 
+  /**
+   * Update rows in this table.
+   *
+   * This can be used to update a single row, many rows, all rows, or
+   * sometimes no rows (if your predicate matches nothing).
+   *
+   * @param args see {@link UpdateArgs} and {@link UpdateSqlArgs} for more details
+   *
+   * @examples
+   *
+   * ```ts
+   * const con = await lancedb.connect("./.lancedb")
+   * const data = [
+   *    {id: 1, vector: [3, 3], name: 'Ye'},
+   *    {id: 2, vector: [4, 4], name: 'Mike'},
+   * ];
+   * const tbl = await con.createTable("my_table", data)
+   *
+   * await tbl.update({
+   *   filter: "id = 2",
+   *   updates: { vector: [2, 2], name: "Michael" },
+   * })
+   *
+   * let results = await tbl.search([1, 1]).execute();
+   * // Returns [
+   * //   {id: 2, vector: [2, 2], name: 'Michael'}
+   * //   {id: 1, vector: [3, 3], name: 'Ye'}
+   * // ]
+   * ```
+   *
+   */
+  update: (args: UpdateArgs | UpdateSqlArgs) => Promise<void>
+
   /**
    * List the indicies on this table.
    */
@@ -272,6 +306,34 @@ export interface Table<T = number[]> {
   indexStats: (indexUuid: string) => Promise<IndexStats>
 }
 
+export interface UpdateArgs {
+  /**
+   * A filter in the same format used by a sql WHERE clause. The filter may be empty,
+   * in which case all rows will be updated.
+   */
+  where?: string
+
+  /**
+   * A key-value map of updates. The keys are the column names, and the values are the
+   * new values to set
+   */
+  values: Record<string, Literal>
+}
+
+export interface UpdateSqlArgs {
+  /**
+   * A filter in the same format used by a sql WHERE clause. The filter may be empty,
+   * in which case all rows will be updated.
+   */
+  where?: string
+
+  /**
+   * A key-value map of updates. The keys are the column names, and the values are the
+   * new values to set as SQL expressions.
+   */
+  valuesSql: Record<string, string>
+}
+
 export interface VectorIndex {
   columns: string[]
   name: string
@@ -426,6 +488,16 @@ export class LocalTable<T = number[]> implements Table<T> {
     return new Query(query, this._tbl, this._embeddings)
   }
 
+  /**
+   * Creates a filter query to find all rows matching the specified criteria
+   * @param value The filter criteria (like SQL where clause syntax)
+   */
+  filter (value: string): Query<T> {
+    return new Query(undefined, this._tbl, this._embeddings).filter(value)
+  }
+
+  where = this.filter
+
   /**
    * Insert records into this Table.
    *
@@ -481,6 +553,31 @@ export class LocalTable<T = number[]> implements Table<T> {
     return tableDelete.call(this._tbl, filter).then((newTable: any) => { this._tbl = newTable })
   }
 
+  /**
+   * Update rows in this table.
+   *
+   * @param args see {@link UpdateArgs} and {@link UpdateSqlArgs} for more details
+   *
+   * @returns
+   */
+  async update (args: UpdateArgs | UpdateSqlArgs): Promise<void> {
+    let filter: string | null
+    let updates: Record<string, string>
+
+    if ('valuesSql' in args) {
+      filter = args.where ?? null
+      updates = args.valuesSql
+    } else {
+      filter = args.where ?? null
+      updates = {}
+      for (const [key, value] of Object.entries(args.values)) {
+        updates[key] = toSQL(value)
+      }
+    }
+
+    return tableUpdate.call(this._tbl, filter, updates).then((newTable: any) => { this._tbl = newTable })
+  }
+
   /**
    * Clean up old versions of the table, freeing disk space.
    *
@@ -647,6 +744,11 @@ export interface IvfPQIndexConfig {
    */
   replace?: boolean
 
+  /**
+   * Cache size of the index
+   */
+  index_cache_size?: number
+
   type: 'ivf_pq'
 }
 

node/src/query.ts

@@ -23,27 +23,29 @@ const { tableSearch } = require('../native.js')
  * A builder for nearest neighbor queries for LanceDB.
  */
 export class Query<T = number[]> {
-  private readonly _query: T
+  private readonly _query?: T
   private readonly _tbl?: any
   private _queryVector?: number[]
-  private _limit: number
+  private _limit?: number
   private _refineFactor?: number
   private _nprobes: number
   private _select?: string[]
   private _filter?: string
   private _metricType?: MetricType
+  private _prefilter: boolean
   protected readonly _embeddings?: EmbeddingFunction<T>
 
-  constructor (query: T, tbl?: any, embeddings?: EmbeddingFunction<T>) {
+  constructor (query?: T, tbl?: any, embeddings?: EmbeddingFunction<T>) {
     this._tbl = tbl
     this._query = query
-    this._limit = 10
+    this._limit = undefined
     this._nprobes = 20
     this._refineFactor = undefined
     this._select = undefined
     this._filter = undefined
     this._metricType = undefined
     this._embeddings = embeddings
+    this._prefilter = false
   }
 
   /***
@@ -102,14 +104,21 @@ export class Query<T = number[]> {
     return this
   }
 
+  prefilter (value: boolean): Query<T> {
+    this._prefilter = value
+    return this
+  }
+
   /**
      * Execute the query and return the results as an Array of Objects
      */
   async execute<T = Record<string, unknown>> (): Promise<T[]> {
-    if (this._embeddings !== undefined) {
-      this._queryVector = (await this._embeddings.embed([this._query]))[0]
-    } else {
-      this._queryVector = this._query as number[]
+    if (this._query !== undefined) {
+      if (this._embeddings !== undefined) {
+        this._queryVector = (await this._embeddings.embed([this._query]))[0]
+      } else {
+        this._queryVector = this._query as number[]
+      }
     }
 
     const isElectron = this.isElectron()

node/src/remote/client.ts

@@ -38,6 +38,7 @@ export class HttpLancedbClient {
     vector: number[],
     k: number,
     nprobes: number,
+    prefilter: boolean,
     refineFactor?: number,
     columns?: string[],
     filter?: string
@@ -50,7 +51,8 @@ export class HttpLancedbClient {
                 nprobes,
                 refineFactor,
                 columns,
-                filter
+                filter,
+                prefilter
               },
               {
                 headers: {

node/src/remote/index.ts

@@ -16,7 +16,8 @@ import {
   type EmbeddingFunction, type Table, type VectorIndexParams, type Connection,
   type ConnectionOptions, type CreateTableOptions, type VectorIndex,
   type WriteOptions,
-  type IndexStats
+  type IndexStats,
+  type UpdateArgs, type UpdateSqlArgs
 } from '../index'
 import { Query } from '../query'
 
@@ -24,6 +25,7 @@ import { Vector, Table as ArrowTable } from 'apache-arrow'
 import { HttpLancedbClient } from './client'
 import { isEmbeddingFunction } from '../embedding/embedding_function'
 import { createEmptyTable, fromRecordsToStreamBuffer, fromTableToStreamBuffer } from '../arrow'
+import { toSQL } from '../util'
 
 /**
  * Remote connection.
@@ -55,8 +57,8 @@ export class RemoteConnection implements Connection {
     return 'db://' + this._client.uri
   }
 
-  async tableNames (): Promise<string[]> {
-    const response = await this._client.get('/v1/table/')
+  async tableNames (pageToken: string = '', limit: number = 10): Promise<string[]> {
+    const response = await this._client.get('/v1/table/', { limit, page_token: pageToken })
     return response.data.tables
   }
 
@@ -154,6 +156,7 @@ export class RemoteQuery<T = number[]> extends Query<T> {
       queryVector,
       (this as any)._limit,
       (this as any)._nprobes,
+      (this as any)._prefilter,
       (this as any)._refineFactor,
       (this as any)._select,
       (this as any)._filter
@@ -192,6 +195,17 @@ export class RemoteTable<T = number[]> implements Table<T> {
     return this._name
   }
 
+  get schema (): Promise<any> {
+    return this._client.post(`/v1/table/${this._name}/describe/`).then(res => {
+      if (res.status !== 200) {
+        throw new Error(`Server Error, status: ${res.status}, ` +
+        // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
+          `message: ${res.statusText}: ${res.data}`)
+      }
+      return res.data?.schema
+    })
+  }
+
   search (query: T): Query<T> {
     return new RemoteQuery(query, this._client, this._name)//, this._embeddings_new)
   }
@@ -232,8 +246,41 @@ export class RemoteTable<T = number[]> implements Table<T> {
     return data.length
   }
 
-  async createIndex (indexParams: VectorIndexParams): Promise<any> {
-    throw new Error('Not implemented')
+  async createIndex (indexParams: VectorIndexParams): Promise<void> {
+    const unsupportedParams = [
+      'index_name',
+      'num_partitions',
+      'max_iters',
+      'use_opq',
+      'num_sub_vectors',
+      'num_bits',
+      'max_opq_iters',
+      'replace'
+    ]
+    for (const param of unsupportedParams) {
+      // eslint-disable-next-line @typescript-eslint/strict-boolean-expressions
+      if (indexParams[param as keyof VectorIndexParams]) {
+        throw new Error(`${param} is not supported for remote connections`)
+      }
+    }
+
+    const column = indexParams.column ?? 'vector'
+    const indexType = 'vector' // only vector index is supported for remote connections
+    const metricType = indexParams.metric_type ?? 'L2'
+    const indexCacheSize = indexParams ?? null
+
+    const data = {
+      column,
+      index_type: indexType,
+      metric_type: metricType,
+      index_cache_size: indexCacheSize
+    }
+    const res = await this._client.post(`/v1/table/${this._name}/create_index/`, data)
+    if (res.status !== 200) {
+      throw new Error(`Server Error, status: ${res.status}, ` +
+      // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
+        `message: ${res.statusText}: ${res.data}`)
+    }
   }
 
   async countRows (): Promise<number> {
@@ -245,6 +292,26 @@ export class RemoteTable<T = number[]> implements Table<T> {
     await this._client.post(`/v1/table/${this._name}/delete/`, { predicate: filter })
   }
 
+  async update (args: UpdateArgs | UpdateSqlArgs): Promise<void> {
+    let filter: string | null
+    let updates: Record<string, string>
+
+    if ('valuesSql' in args) {
+      filter = args.where ?? null
+      updates = args.valuesSql
+    } else {
+      filter = args.where ?? null
+      updates = {}
+      for (const [key, value] of Object.entries(args.values)) {
+        updates[key] = toSQL(value)
+      }
+    }
+    await this._client.post(`/v1/table/${this._name}/update/`, {
+      predicate: filter,
+      updates: Object.entries(updates).map(([key, value]) => [key, value])
+    })
+  }
+
   async listIndices (): Promise<VectorIndex[]> {
     const results = await this._client.post(`/v1/table/${this._name}/index/list/`)
     return results.data.indexes?.map((index: any) => ({

node/src/test/test.ts

@@ -78,12 +78,31 @@ describe('LanceDB client', function () {
     })
 
     it('limits # of results', async function () {
-      const uri = await createTestDB()
+      const uri = await createTestDB(2, 100)
       const con = await lancedb.connect(uri)
       const table = await con.openTable('vectors')
-      const results = await table.search([0.1, 0.3]).limit(1).execute()
+      let results = await table.search([0.1, 0.3]).limit(1).execute()
       assert.equal(results.length, 1)
       assert.equal(results[0].id, 1)
+
+      // there is a default limit if unspecified
+      results = await table.search([0.1, 0.3]).execute()
+      assert.equal(results.length, 10)
+    })
+
+    it('uses a filter / where clause without vector search', async function () {
+      // eslint-disable-next-line @typescript-eslint/explicit-function-return-type
+      const assertResults = (results: Array<Record<string, unknown>>) => {
+        assert.equal(results.length, 50)
+      }
+
+      const uri = await createTestDB(2, 100)
+      const con = await lancedb.connect(uri)
+      const table = (await con.openTable('vectors')) as LocalTable
+      let results = await table.filter('id % 2 = 0').execute()
+      assertResults(results)
+      results = await table.where('id % 2 = 0').execute()
+      assertResults(results)
     })
 
     it('uses a filter / where clause', async function () {
@@ -102,6 +121,20 @@ describe('LanceDB client', function () {
       assertResults(results)
     })
 
+    it('should correctly process prefilter/postfilter', async function () {
+      const uri = await createTestDB(16, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+      // post filter should return less than the limit
+      let results = await table.search(new Array(16).fill(0.1)).limit(10).filter('id >= 10').prefilter(false).execute()
+      assert.isTrue(results.length < 10)
+
+      // pre filter should return exactly the limit
+      results = await table.search(new Array(16).fill(0.1)).limit(10).filter('id >= 10').prefilter(true).execute()
+      assert.isTrue(results.length === 10)
+    })
+
     it('select only a subset of columns', async function () {
       const uri = await createTestDB()
       const con = await lancedb.connect(uri)
@@ -246,6 +279,46 @@ describe('LanceDB client', function () {
       assert.equal(await table.countRows(), 2)
     })
 
+    it('can update records in the table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.update({ where: 'price = 10', valuesSql: { price: '100' } })
+      const results = await table.search([0.1, 0.2]).execute()
+      assert.equal(results[0].price, 100)
+      assert.equal(results[1].price, 11)
+    })
+
+    it('can update the records using a literal value', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.update({ where: 'price = 10', values: { price: 100 } })
+      const results = await table.search([0.1, 0.2]).execute()
+      assert.equal(results[0].price, 100)
+      assert.equal(results[1].price, 11)
+    })
+
+    it('can update every record in the table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.update({ valuesSql: { price: '100' } })
+      const results = await table.search([0.1, 0.2]).execute()
+
+      assert.equal(results[0].price, 100)
+      assert.equal(results[1].price, 100)
+    })
+
     it('can delete records from a table', async function () {
       const uri = await createTestDB()
       const con = await lancedb.connect(uri)
@@ -528,7 +601,7 @@ describe('Compact and cleanup', function () {
 
     // should have no effect, but this validates the arguments are parsed.
     await table.compactFiles({
-      targetRowsPerFragment: 1024 * 10,
+      targetRowsPerFragment: 102410,
       maxRowsPerGroup: 1024,
       materializeDeletions: true,
       materializeDeletionsThreshold: 0.5,

node/src/test/util.ts

@@ -0,0 +1,45 @@
+// 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 { toSQL } from '../util'
+import * as chai from 'chai'
+
+const expect = chai.expect
+
+describe('toSQL', function () {
+  it('should turn string to SQL expression', function () {
+    expect(toSQL('foo')).to.equal("'foo'")
+  })
+
+  it('should turn number to SQL expression', function () {
+    expect(toSQL(123)).to.equal('123')
+  })
+
+  it('should turn boolean to SQL expression', function () {
+    expect(toSQL(true)).to.equal('TRUE')
+  })
+
+  it('should turn null to SQL expression', function () {
+    expect(toSQL(null)).to.equal('NULL')
+  })
+
+  it('should turn Date to SQL expression', function () {
+    const date = new Date('05 October 2011 14:48 UTC')
+    expect(toSQL(date)).to.equal("'2011-10-05T14:48:00.000Z'")
+  })
+
+  it('should turn array to SQL expression', function () {
+    expect(toSQL(['foo', 'bar', true, 1])).to.equal("['foo', 'bar', TRUE, 1]")
+  })
+})

node/src/util.ts

@@ -0,0 +1,44 @@
+// 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.
+
+export type Literal = string | number | boolean | null | Date | Literal[]
+
+export function toSQL (value: Literal): string {
+  if (typeof value === 'string') {
+    return `'${value}'`
+  }
+
+  if (typeof value === 'number') {
+    return value.toString()
+  }
+
+  if (typeof value === 'boolean') {
+    return value ? 'TRUE' : 'FALSE'
+  }
+
+  if (value === null) {
+    return 'NULL'
+  }
+
+  if (value instanceof Date) {
+    return `'${value.toISOString()}'`
+  }
+
+  if (Array.isArray(value)) {
+    return `[${value.map(toSQL).join(', ')}]`
+  }
+
+  // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
+  throw new Error(`Unsupported value type: ${typeof value} value: (${value})`)
+}

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.4
+current_version = 0.4.0
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True

python/lancedb/__init__.py

@@ -27,7 +27,7 @@ def connect(
     uri: URI,
     *,
     api_key: Optional[str] = None,
-    region: str = "us-west-2",
+    region: str = "us-east-1",
     host_override: Optional[str] = None,
 ) -> DBConnection:
     """Connect to a LanceDB database.
@@ -39,7 +39,7 @@ def connect(
     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"
+    region: str, default "us-east-1"
         The region to use for LanceDB Cloud.
     host_override: str, optional
         The override url for LanceDB Cloud.

python/lancedb/db.py

@@ -23,7 +23,7 @@ from overrides import EnforceOverrides, override
 from pyarrow import fs
 
 from .table import LanceTable, Table
-from .util import fs_from_uri, get_uri_location, get_uri_scheme
+from .util import fs_from_uri, get_uri_location, get_uri_scheme, join_uri
 
 if TYPE_CHECKING:
     from .common import DATA, URI
@@ -288,14 +288,13 @@ class LanceDBConnection(DBConnection):
             A list of table names.
         """
         try:
-            filesystem, path = fs_from_uri(self.uri)
+            filesystem = fs_from_uri(self.uri)[0]
         except pa.ArrowInvalid:
             raise NotImplementedError("Unsupported scheme: " + self.uri)
 
         try:
-            paths = filesystem.get_file_info(
-                fs.FileSelector(get_uri_location(self.uri))
-            )
+            loc = get_uri_location(self.uri)
+            paths = filesystem.get_file_info(fs.FileSelector(loc))
         except FileNotFoundError:
             # It is ok if the file does not exist since it will be created
             paths = []
@@ -373,7 +372,7 @@ class LanceDBConnection(DBConnection):
         """
         try:
             filesystem, path = fs_from_uri(self.uri)
-            table_path = os.path.join(path, name + ".lance")
+            table_path = join_uri(path, name + ".lance")
             filesystem.delete_dir(table_path)
         except FileNotFoundError:
             if not ignore_missing:

python/lancedb/fts.py

@@ -75,8 +75,14 @@ def populate_index(index: tantivy.Index, table: LanceTable, fields: List[str]) -
         The number of rows indexed
     """
     # first check the fields exist and are string or large string type
+    nested = []
     for name in fields:
-        f = table.schema.field(name)  # raises KeyError if not found
+        try:
+            f = table.schema.field(name)  # raises KeyError if not found
+        except KeyError:
+            f = resolve_path(table.schema, name)
+            nested.append(name)
+
         if not pa.types.is_string(f.type) and not pa.types.is_large_string(f.type):
             raise TypeError(f"Field {name} is not a string type")
 
@@ -85,7 +91,16 @@ def populate_index(index: tantivy.Index, table: LanceTable, fields: List[str]) -
     # write data into index
     dataset = table.to_lance()
     row_id = 0
+
+    max_nested_level = 0
+    if len(nested) > 0:
+        max_nested_level = max([len(name.split(".")) for name in nested])
+
     for b in dataset.to_batches(columns=fields):
+        if max_nested_level > 0:
+            b = pa.Table.from_batches([b])
+            for _ in range(max_nested_level - 1):
+                b = b.flatten()
         for i in range(b.num_rows):
             doc = tantivy.Document()
             doc.add_integer("doc_id", row_id)
@@ -98,6 +113,30 @@ def populate_index(index: tantivy.Index, table: LanceTable, fields: List[str]) -
     return row_id
 
 
+def resolve_path(schema, field_name: str) -> pa.Field:
+    """
+    Resolve a nested field path to a list of field names
+
+    Parameters
+    ----------
+    field_name : str
+        The field name to resolve
+
+    Returns
+    -------
+    List[str]
+        The resolved path
+    """
+    path = field_name.split(".")
+    field = schema.field(path.pop(0))
+    for segment in path:
+        if pa.types.is_struct(field.type):
+            field = field.type.field(segment)
+        else:
+            raise KeyError(f"field {field_name} not found in schema {schema}")
+    return field
+
+
 def search_index(
     index: tantivy.Index, query: str, limit: int = 10
 ) -> Tuple[Tuple[int], Tuple[float]]:

python/lancedb/pydantic.py

@@ -348,3 +348,20 @@ def get_extras(field_info: pydantic.fields.FieldInfo, key: str) -> Any:
     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)
+
+
+if PYDANTIC_VERSION.major < 2:
+
+    def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
+        """
+        Convert a Pydantic model to a dictionary.
+        """
+        return model.dict()
+
+else:
+
+    def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
+        """
+        Convert a Pydantic model to a dictionary.
+        """
+        return model.model_dump()

python/lancedb/query.py

@@ -185,14 +185,40 @@ class LanceQueryBuilder(ABC):
         """
         return self.to_pandas()
 
-    def to_pandas(self) -> "pd.DataFrame":
+    def to_pandas(self, flatten: Optional[Union[int, bool]] = None) -> "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.
+
+        Parameters
+        ----------
+        flatten: Optional[Union[int, bool]]
+            If flatten is True, flatten all nested columns.
+            If flatten is an integer, flatten the nested columns up to the
+            specified depth.
+            If unspecified, do not flatten the nested columns.
         """
-        return self.to_arrow().to_pandas()
+        tbl = self.to_arrow()
+        if flatten is True:
+            while True:
+                tbl = tbl.flatten()
+                has_struct = False
+                # loop through all columns to check if there is any struct column
+                if any(pa.types.is_struct(col.type) for col in tbl.schema):
+                    continue
+                else:
+                    break
+        elif isinstance(flatten, int):
+            if flatten <= 0:
+                raise ValueError(
+                    "Please specify a positive integer for flatten or the boolean value `True`"
+                )
+            while flatten > 0:
+                tbl = tbl.flatten()
+                flatten -= 1
+        return tbl.to_pandas()
 
     @abstractmethod
     def to_arrow(self) -> pa.Table:

python/lancedb/remote/__init__.py

@@ -18,6 +18,8 @@ import attrs
 import pyarrow as pa
 from pydantic import BaseModel
 
+from lancedb.common import VECTOR_COLUMN_NAME
+
 __all__ = ["LanceDBClient", "VectorQuery", "VectorQueryResult"]
 
 
@@ -43,6 +45,8 @@ class VectorQuery(BaseModel):
 
     refine_factor: Optional[int] = None
 
+    vector_column: str = VECTOR_COLUMN_NAME
+
 
 @attrs.define
 class VectorQueryResult:

python/lancedb/remote/db.py

@@ -28,6 +28,7 @@ from ..pydantic import LanceModel
 from ..table import Table, _sanitize_data
 from .arrow import to_ipc_binary
 from .client import ARROW_STREAM_CONTENT_TYPE, RestfulLanceDBClient
+from .errors import LanceDBClientError
 
 
 class RemoteDBConnection(DBConnection):
@@ -55,16 +56,20 @@ class RemoteDBConnection(DBConnection):
             self._loop = asyncio.get_event_loop()
 
     def __repr__(self) -> str:
-        return f"RemoveConnect(name={self.db_name})"
+        return f"RemoteConnect(name={self.db_name})"
 
     @override
-    def table_names(self, page_token: Optional[str] = None, limit=10) -> Iterable[str]:
+    def table_names(
+        self, page_token: Optional[str] = None, limit: int = 10
+    ) -> Iterable[str]:
         """List the names of all tables in the database.
 
         Parameters
         ----------
         page_token: str
             The last token to start the new page.
+        limit: int, default 10
+            The maximum number of tables to return for each page.
 
         Returns
         -------
@@ -101,11 +106,12 @@ class RemoteDBConnection(DBConnection):
             self._loop.run_until_complete(
                 self._client.post(f"/v1/table/{name}/describe/")
             )
-        except Exception:
-            logging.error(
-                "Table {name} does not exist."
-                "Please first call db.create_table({name}, data)"
-            )
+        except LanceDBClientError as err:
+            if str(err).startswith("Not found"):
+                logging.error(
+                    f"Table {name} does not exist. "
+                    f"Please first call db.create_table({name}, data)"
+                )
         return RemoteTable(self, name)
 
     @override
@@ -118,6 +124,97 @@ class RemoteDBConnection(DBConnection):
         fill_value: float = 0.0,
         embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
     ) -> Table:
+        """Create a [Table][lancedb.table.Table] in the database.
+
+        Parameters
+        ----------
+        name: str
+            The name of the table.
+        data: The data to initialize the table, *optional*
+            User must provide at least one of `data` or `schema`.
+            Acceptable types are:
+
+            - dict or list-of-dict
+
+            - pandas.DataFrame
+
+            - pyarrow.Table or pyarrow.RecordBatch
+        schema: The schema of the table, *optional*
+            Acceptable types are:
+
+            - pyarrow.Schema
+
+            - [LanceModel][lancedb.pydantic.LanceModel]
+        on_bad_vectors: str, default "error"
+            What to do if any of the vectors are not the same size or contains NaNs.
+            One of "error", "drop", "fill".
+        fill_value: float
+            The value to use when filling vectors. Only used if on_bad_vectors="fill".
+
+        Returns
+        -------
+        LanceTable
+            A reference to the newly created table.
+
+        !!! note
+
+            The vector index won't be created by default.
+            To create the index, call the `create_index` method on the table.
+
+        Examples
+        --------
+
+        Can create with list of tuples or dictionaries:
+
+        >>> import lancedb
+        >>> db = lancedb.connect("db://...", api_key="...", region="...") # doctest: +SKIP
+        >>> data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
+        ...         {"vector": [0.2, 1.8], "lat": 40.1, "long":  -74.1}]
+        >>> db.create_table("my_table", data) # doctest: +SKIP
+        LanceTable(my_table)
+
+        You can also pass a pandas DataFrame:
+
+        >>> import pandas as pd
+        >>> data = pd.DataFrame({
+        ...    "vector": [[1.1, 1.2], [0.2, 1.8]],
+        ...    "lat": [45.5, 40.1],
+        ...    "long": [-122.7, -74.1]
+        ... })
+        >>> db.create_table("table2", data) # doctest: +SKIP
+        LanceTable(table2)
+
+        >>> custom_schema = pa.schema([
+        ...   pa.field("vector", pa.list_(pa.float32(), 2)),
+        ...   pa.field("lat", pa.float32()),
+        ...   pa.field("long", pa.float32())
+        ... ])
+        >>> db.create_table("table3", data, schema = custom_schema) # doctest: +SKIP
+        LanceTable(table3)
+
+        It is also possible to create an table from `[Iterable[pa.RecordBatch]]`:
+
+        >>> import pyarrow as pa
+        >>> def make_batches():
+        ...     for i in range(5):
+        ...         yield pa.RecordBatch.from_arrays(
+        ...             [
+        ...                 pa.array([[3.1, 4.1], [5.9, 26.5]],
+        ...                     pa.list_(pa.float32(), 2)),
+        ...                 pa.array(["foo", "bar"]),
+        ...                 pa.array([10.0, 20.0]),
+        ...             ],
+        ...             ["vector", "item", "price"],
+        ...         )
+        >>> schema=pa.schema([
+        ...     pa.field("vector", pa.list_(pa.float32(), 2)),
+        ...     pa.field("item", pa.utf8()),
+        ...     pa.field("price", pa.float32()),
+        ... ])
+        >>> db.create_table("table4", make_batches(), schema=schema) # doctest: +SKIP
+        LanceTable(table4)
+
+        """
         if data is None and schema is None:
             raise ValueError("Either data or schema must be provided.")
         if embedding_functions is not None:

python/lancedb/remote/table.py

@@ -13,7 +13,7 @@
 
 import uuid
 from functools import cached_property
-from typing import Optional, Union
+from typing import Dict, Optional, Union
 
 import pyarrow as pa
 from lance import json_to_schema
@@ -22,6 +22,7 @@ from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 
 from ..query import LanceVectorQueryBuilder
 from ..table import Query, Table, _sanitize_data
+from ..util import value_to_sql
 from .arrow import to_ipc_binary
 from .client import ARROW_STREAM_CONTENT_TYPE
 from .db import RemoteDBConnection
@@ -37,7 +38,10 @@ class RemoteTable(Table):
 
     @cached_property
     def schema(self) -> pa.Schema:
-        """Return the schema of the table."""
+        """The [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#)
+        of this Table
+
+        """
         resp = self._conn._loop.run_until_complete(
             self._conn._client.post(f"/v1/table/{self._name}/describe/")
         )
@@ -53,24 +57,17 @@ class RemoteTable(Table):
         return resp["version"]
 
     def to_arrow(self) -> pa.Table:
-        """Return the table as an Arrow table."""
+        """to_arrow() is not supported on the LanceDB cloud"""
         raise NotImplementedError("to_arrow() is not supported on the LanceDB cloud")
 
     def to_pandas(self):
-        """Return the table as a Pandas DataFrame.
-
-        Intercept `to_arrow()` for better error message.
-        """
+        """to_pandas() is not supported on the LanceDB cloud"""
         return NotImplementedError("to_pandas() is not supported on the LanceDB cloud")
 
     def create_index(
         self,
         metric="L2",
-        num_partitions=256,
-        num_sub_vectors=96,
         vector_column_name: str = VECTOR_COLUMN_NAME,
-        replace: bool = True,
-        accelerator: Optional[str] = None,
         index_cache_size: Optional[int] = None,
     ):
         """Create an index on the table.
@@ -81,39 +78,28 @@ class RemoteTable(Table):
         ----------
         metric : str
             The metric to use for the index. Default is "L2".
-        num_partitions : int
-            The number of partitions to use for the index. Default is 256.
-        num_sub_vectors : int
-            The number of sub-vectors to use for the index. Default is 96.
         vector_column_name : str
             The name of the vector column. Default is "vector".
-        replace : bool
-            Whether to replace the existing index. Default is True.
-        accelerator : str, optional
-            If set, use the given accelerator to create the index.
-            Default is None. Currently not supported.
-        index_cache_size : int, optional
-            The size of the index cache in number of entries. Default value is 256.
 
         Examples
         --------
-        import lancedb
-        import uuid
-        from lancedb.schema import vector
-        conn = lancedb.connect("db://...", api_key="...", region="...")
-        table_name = uuid.uuid4().hex
-        schema = pa.schema(
-            [
-                    pa.field("id", pa.uint32(), False),
-                    pa.field("vector", vector(128), False),
-                    pa.field("s", pa.string(), False),
-            ]
-        )
-        table = conn.create_table(
-            table_name,
-            schema=schema,
-        )
-        table.create_index()
+        >>> import lancedb
+        >>> import uuid
+        >>> from lancedb.schema import vector
+        >>> db = lancedb.connect("db://...", api_key="...", region="...") # doctest: +SKIP
+        >>> table_name = uuid.uuid4().hex
+        >>> schema = pa.schema(
+        ...     [
+        ...             pa.field("id", pa.uint32(), False),
+        ...            pa.field("vector", vector(128), False),
+        ...             pa.field("s", pa.string(), False),
+        ...     ]
+        ... )
+        >>> table = db.create_table( # doctest: +SKIP
+        ...     table_name, # doctest: +SKIP
+        ...     schema=schema, # doctest: +SKIP
+        ... )
+        >>> table.create_index("L2", "vector") # doctest: +SKIP
         """
         index_type = "vector"
 
@@ -135,6 +121,28 @@ class RemoteTable(Table):
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
     ) -> int:
+        """Add more data to the [Table](Table). It has the same API signature as the OSS version.
+
+        Parameters
+        ----------
+        data: DATA
+            The data to insert into the table. Acceptable types are:
+
+            - dict or list-of-dict
+
+            - pandas.DataFrame
+
+            - pyarrow.Table or pyarrow.RecordBatch
+        mode: str
+            The mode to use when writing the data. Valid values are
+            "append" and "overwrite".
+        on_bad_vectors: str, default "error"
+            What to do if any of the vectors are not the same size or contains NaNs.
+            One of "error", "drop", "fill".
+        fill_value: float, default 0.
+            The value to use when filling vectors. Only used if on_bad_vectors="fill".
+
+        """
         data = _sanitize_data(
             data,
             self.schema,
@@ -158,6 +166,58 @@ class RemoteTable(Table):
     def search(
         self, query: Union[VEC, str], vector_column_name: str = VECTOR_COLUMN_NAME
     ) -> LanceVectorQueryBuilder:
+        """Create a search query to find the nearest neighbors
+        of the given query vector. We currently support [vector search][search]
+
+        All query options are defined in [Query][lancedb.query.Query].
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("db://...", api_key="...", region="...") # doctest: +SKIP
+        >>> data = [
+        ...    {"original_width": 100, "caption": "bar", "vector": [0.1, 2.3, 4.5]},
+        ...    {"original_width": 2000, "caption": "foo",  "vector": [0.5, 3.4, 1.3]},
+        ...    {"original_width": 3000, "caption": "test", "vector": [0.3, 6.2, 2.6]}
+        ... ]
+        >>> table = db.create_table("my_table", data) # doctest: +SKIP
+        >>> query = [0.4, 1.4, 2.4]
+        >>> (table.search(query, vector_column_name="vector") # doctest: +SKIP
+        ...     .where("original_width > 1000", prefilter=True) # doctest: +SKIP
+        ...     .select(["caption", "original_width"]) # doctest: +SKIP
+        ...     .limit(2) # doctest: +SKIP
+        ...     .to_pandas()) # doctest: +SKIP
+          caption  original_width           vector  _distance # doctest: +SKIP
+        0     foo            2000  [0.5, 3.4, 1.3]   5.220000 # doctest: +SKIP
+        1    test            3000  [0.3, 6.2, 2.6]  23.089996 # doctest: +SKIP
+
+        Parameters
+        ----------
+        query: list/np.ndarray/str/PIL.Image.Image, default None
+            The targetted vector to search for.
+
+            - *default None*.
+            Acceptable types are: list, np.ndarray, PIL.Image.Image
+
+            - If None then the select/where/limit clauses are applied to filter
+            the table
+        vector_column_name: str
+            The name of the vector column to search.
+            *default "vector"*
+
+        Returns
+        -------
+        LanceQueryBuilder
+            A query builder object representing the query.
+            Once executed, the query returns
+
+            - selected columns
+
+            - the vector
+
+            - and also the "_distance" column which is the distance between the query
+            vector and the returned vector.
+        """
         return LanceVectorQueryBuilder(self, query, vector_column_name)
 
     def _execute_query(self, query: Query) -> pa.Table:
@@ -165,8 +225,114 @@ class RemoteTable(Table):
         return self._conn._loop.run_until_complete(result).to_arrow()
 
     def delete(self, predicate: str):
-        """Delete rows from the table."""
+        """Delete rows from the table.
+
+        This can be used to delete a single row, many rows, all rows, or
+        sometimes no rows (if your predicate matches nothing).
+
+        Parameters
+        ----------
+        predicate: str
+            The SQL where clause to use when deleting rows.
+
+            - For example, 'x = 2' or 'x IN (1, 2, 3)'.
+
+            The filter must not be empty, or it will error.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> data = [
+        ...    {"x": 1, "vector": [1, 2]},
+        ...    {"x": 2, "vector": [3, 4]},
+        ...    {"x": 3, "vector": [5, 6]}
+        ... ]
+        >>> db = lancedb.connect("db://...", api_key="...", region="...") # doctest: +SKIP
+        >>> table = db.create_table("my_table", data) # doctest: +SKIP
+        >>> table.search([10,10]).to_pandas() # doctest: +SKIP
+           x      vector  _distance # doctest: +SKIP
+        0  3  [5.0, 6.0]       41.0 # doctest: +SKIP
+        1  2  [3.0, 4.0]       85.0 # doctest: +SKIP
+        2  1  [1.0, 2.0]      145.0 # doctest: +SKIP
+        >>> table.delete("x = 2") # doctest: +SKIP
+        >>> table.search([10,10]).to_pandas() # doctest: +SKIP
+           x      vector  _distance # doctest: +SKIP
+        0  3  [5.0, 6.0]       41.0 # doctest: +SKIP
+        1  1  [1.0, 2.0]      145.0 # doctest: +SKIP
+
+        If you have a list of values to delete, you can combine them into a
+        stringified list and use the `IN` operator:
+
+        >>> to_remove = [1, 3] # doctest: +SKIP
+        >>> to_remove = ", ".join([str(v) for v in to_remove]) # doctest: +SKIP
+        >>> table.delete(f"x IN ({to_remove})") # doctest: +SKIP
+        >>> table.search([10,10]).to_pandas() # doctest: +SKIP
+           x      vector  _distance # doctest: +SKIP
+        0  2  [3.0, 4.0]       85.0 # doctest: +SKIP
+        """
         payload = {"predicate": predicate}
         self._conn._loop.run_until_complete(
             self._conn._client.post(f"/v1/table/{self._name}/delete/", data=payload)
         )
+
+    def update(
+        self,
+        where: Optional[str] = None,
+        values: Optional[dict] = None,
+        *,
+        values_sql: Optional[Dict[str, str]] = None,
+    ):
+        """
+        This can be used to update zero to all rows depending on how many
+        rows match the where clause.
+
+        Parameters
+        ----------
+        where: str, optional
+            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, optional
+            The values to update. The keys are the column names and the values
+            are the values to set.
+        values_sql: dict, optional
+            The values to update, expressed as SQL expression strings. These can
+            reference existing columns. For example, {"x": "x + 1"} will increment
+            the x column by 1.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> data = [
+        ...    {"x": 1, "vector": [1, 2]},
+        ...    {"x": 2, "vector": [3, 4]},
+        ...    {"x": 3, "vector": [5, 6]}
+        ... ]
+        >>> db = lancedb.connect("db://...", api_key="...", region="...") # doctest: +SKIP
+        >>> table = db.create_table("my_table", data) # doctest: +SKIP
+        >>> table.to_pandas() # doctest: +SKIP
+           x      vector # doctest: +SKIP
+        0  1  [1.0, 2.0] # doctest: +SKIP
+        1  2  [3.0, 4.0] # doctest: +SKIP
+        2  3  [5.0, 6.0] # doctest: +SKIP
+        >>> table.update(where="x = 2", values={"vector": [10, 10]}) # doctest: +SKIP
+        >>> table.to_pandas() # doctest: +SKIP
+           x        vector # doctest: +SKIP
+        0  1    [1.0, 2.0] # doctest: +SKIP
+        1  3    [5.0, 6.0] # doctest: +SKIP
+        2  2  [10.0, 10.0] # doctest: +SKIP
+
+        """
+        if values is not None and values_sql is not None:
+            raise ValueError("Only one of values or values_sql can be provided")
+        if values is None and values_sql is None:
+            raise ValueError("Either values or values_sql must be provided")
+
+        if values is not None:
+            updates = [[k, value_to_sql(v)] for k, v in values.items()]
+        else:
+            updates = [[k, v] for k, v in values_sql.items()]
+
+        payload = {"predicate": where, "updates": updates}
+        self._conn._loop.run_until_complete(
+            self._conn._client.post(f"/v1/table/{self._name}/update/", data=payload)
+        )

python/lancedb/table.py

@@ -17,20 +17,21 @@ import inspect
 import os
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
 
 import lance
 import numpy as np
 import pyarrow as pa
 import pyarrow.compute as pc
+import pyarrow.fs as pa_fs
 from lance import LanceDataset
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
 from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
-from .pydantic import LanceModel
+from .pydantic import LanceModel, model_to_dict
 from .query import LanceQueryBuilder, Query
-from .util import fs_from_uri, safe_import_pandas
+from .util import fs_from_uri, safe_import_pandas, value_to_sql, join_uri
 from .utils.events import register_event
 
 if TYPE_CHECKING:
@@ -53,8 +54,10 @@ def _sanitize_data(
         # 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 = [model_to_dict(d) for d in data]
+            data = pa.Table.from_pylist(data, schema=schema)
+        else:
+            data = pa.Table.from_pylist(data)
     elif isinstance(data, dict):
         data = vec_to_table(data)
     elif pd is not None and isinstance(data, pd.DataFrame):
@@ -394,14 +397,6 @@ class LanceTable(Table):
         self.name = name
         self._version = version
 
-    def _reset_dataset(self, version=None):
-        try:
-            if "_dataset" in self.__dict__:
-                del self.__dict__["_dataset"]
-            self._version = version
-        except AttributeError:
-            pass
-
     @property
     def schema(self) -> pa.Schema:
         """Return the schema of the table.
@@ -410,16 +405,16 @@ class LanceTable(Table):
         -------
         pa.Schema
             A PyArrow schema object."""
-        return self._dataset.schema
+        return self.to_lance().schema
 
     def list_versions(self):
         """List all versions of the table"""
-        return self._dataset.versions()
+        return self.to_lance().versions()
 
     @property
     def version(self) -> int:
         """Get the current version of the table"""
-        return self._dataset.version
+        return self.to_lance().version
 
     def checkout(self, version: int):
         """Checkout a version of the table. This is an in-place operation.
@@ -452,14 +447,12 @@ class LanceTable(Table):
                vector    type
         0  [1.1, 0.9]  vector
         """
-        max_ver = max([v["version"] for v in self._dataset.versions()])
+        max_ver = max([v["version"] for v in self.to_lance().versions()])
         if version < 1 or version > max_ver:
             raise ValueError(f"Invalid version {version}")
-        self._reset_dataset(version=version)
 
         try:
-            # Accessing the property updates the cached value
-            _ = self._dataset
+            self.to_lance().checkout(version)
         except Exception as e:
             if "not found" in str(e):
                 raise ValueError(
@@ -502,7 +495,7 @@ class LanceTable(Table):
         >>> len(table.list_versions())
         4
         """
-        max_ver = max([v["version"] for v in self._dataset.versions()])
+        max_ver = max([v["version"] for v in self.to_lance().versions()])
         if version is None:
             version = self.version
         elif version < 1 or version > max_ver:
@@ -514,11 +507,10 @@ class LanceTable(Table):
             # no-op if restoring the latest version
             return
 
-        self._dataset.restore()
-        self._reset_dataset()
+        self.to_lance().restore()
 
     def __len__(self):
-        return self._dataset.count_rows()
+        return self.to_lance().count_rows()
 
     def __repr__(self) -> str:
         return f"LanceTable({self.name})"
@@ -528,7 +520,7 @@ class LanceTable(Table):
 
     def head(self, n=5) -> pa.Table:
         """Return the first n rows of the table."""
-        return self._dataset.head(n)
+        return self.to_lance().head(n)
 
     def to_pandas(self) -> "pd.DataFrame":
         """Return the table as a pandas DataFrame.
@@ -545,11 +537,11 @@ class LanceTable(Table):
         Returns
         -------
         pa.Table"""
-        return self._dataset.to_table()
+        return self.to_lance().to_table()
 
     @property
     def _dataset_uri(self) -> str:
-        return os.path.join(self._conn.uri, f"{self.name}.lance")
+        return join_uri(self._conn.uri, f"{self.name}.lance")
 
     def create_index(
         self,
@@ -572,10 +564,11 @@ class LanceTable(Table):
             accelerator=accelerator,
             index_cache_size=index_cache_size,
         )
-        self._reset_dataset()
         register_event("create_index")
 
-    def create_fts_index(self, field_names: Union[str, List[str]]):
+    def create_fts_index(
+        self, field_names: Union[str, List[str]], *, replace: bool = False
+    ):
         """Create a full-text search index on the table.
 
         Warning - this API is highly experimental and is highly likely to change
@@ -585,17 +578,35 @@ class LanceTable(Table):
         ----------
         field_names: str or list of str
             The name(s) of the field to index.
+        replace: bool, default False
+            If True, replace the existing index if it exists. Note that this is
+            not yet an atomic operation; the index will be temporarily
+            unavailable while the new index is being created.
         """
         from .fts import create_index, populate_index
 
         if isinstance(field_names, str):
             field_names = [field_names]
+
+        fs, path = fs_from_uri(self._get_fts_index_path())
+        index_exists = fs.get_file_info(path).type != pa_fs.FileType.NotFound
+        if index_exists:
+            if not replace:
+                raise ValueError(
+                    f"Index already exists. Use replace=True to overwrite."
+                )
+            try:
+                fs.delete_dir(path)
+            except FileNotFoundError as e:
+                if "Cannot get information for path" in str(e):
+                    pass
+
         index = create_index(self._get_fts_index_path(), field_names)
         populate_index(index, self, field_names)
         register_event("create_fts_index")
 
     def _get_fts_index_path(self):
-        return os.path.join(self._dataset_uri, "_indices", "tantivy")
+        return join_uri(self._dataset_uri, "_indices", "tantivy")
 
     @cached_property
     def _dataset(self) -> LanceDataset:
@@ -643,8 +654,7 @@ class LanceTable(Table):
             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()
+        self.to_lance().write(data, mode=mode)
         register_event("add")
 
     def merge(
@@ -705,10 +715,9 @@ class LanceTable(Table):
             other_table = other_table.to_lance()
         if isinstance(other_table, LanceDataset):
             other_table = other_table.to_table()
-        self._dataset.merge(
+        self.to_lance().merge(
             other_table, left_on=left_on, right_on=right_on, schema=schema
         )
-        self._reset_dataset()
         register_event("merge")
 
     @cached_property
@@ -785,7 +794,7 @@ class LanceTable(Table):
             and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
-        register_event("search")
+        register_event("search_table")
         return LanceQueryBuilder.create(
             self, query, query_type, vector_column_name=vector_column_name
         )
@@ -906,35 +915,42 @@ class LanceTable(Table):
                 f"Table {name} does not exist."
                 f"Please first call db.create_table({name}, data)"
             )
+        register_event("open_table")
+
         return tbl
 
     def delete(self, where: str):
-        self._dataset.delete(where)
+        self.to_lance().delete(where)
 
-    def update(self, where: str, values: dict):
+    def update(
+        self,
+        where: Optional[str] = None,
+        values: Optional[dict] = None,
+        *,
+        values_sql: Optional[Dict[str, str]] = None,
+    ):
         """
-        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
+        where: str, optional
             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
+        values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.
+        values_sql: dict, optional
+            The values to update, expressed as SQL expression strings. These can
+            reference existing columns. For example, {"x": "x + 1"} will increment
+            the x column by 1.
 
         Examples
         --------
         >>> import lancedb
-        >>> data = [
-        ...    {"x": 1, "vector": [1, 2]},
-        ...    {"x": 2, "vector": [3, 4]},
-        ...    {"x": 3, "vector": [5, 6]}
-        ... ]
+        >>> 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()
@@ -950,19 +966,15 @@ class LanceTable(Table):
         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()
+        if values is not None and values_sql is not None:
+            raise ValueError("Only one of values or values_sql can be provided")
+        if values is None and values_sql is None:
+            raise ValueError("Either values or values_sql must be provided")
+
+        if values is not None:
+            values_sql = {k: value_to_sql(v) for k, v in values.items()}
+
+        self.to_lance().update(values_sql, where)
         register_event("update")
 
     def _execute_query(self, query: Query) -> pa.Table:

python/lancedb/util.py

@@ -12,9 +12,13 @@
 #  limitations under the License.
 
 import os
-from typing import Tuple
+from datetime import date, datetime
+from functools import singledispatch
+import pathlib
+from typing import Tuple, Union
 from urllib.parse import urlparse
 
+import numpy as np
 import pyarrow.fs as pa_fs
 
 
@@ -59,6 +63,12 @@ def get_uri_location(uri: str) -> str:
     str: Location part of the URL, without scheme
     """
     parsed = urlparse(uri)
+    if len(parsed.scheme) == 1:
+        # Windows drive names are parsed as the scheme
+        # e.g. "c:\path" -> ParseResult(scheme="c", netloc="", path="/path", ...)
+        # So we add special handling here for schemes that are a single character
+        return uri
+
     if not parsed.netloc:
         return parsed.path
     else:
@@ -81,6 +91,29 @@ def fs_from_uri(uri: str) -> Tuple[pa_fs.FileSystem, str]:
     return pa_fs.FileSystem.from_uri(uri)
 
 
+def join_uri(base: Union[str, pathlib.Path], *parts: str) -> str:
+    """
+    Join a URI with multiple parts, handles both local and remote paths
+
+    Parameters
+    ----------
+    base : str
+        The base URI
+    parts : str
+        The parts to join to the base URI, each separated by the
+        appropriate path separator for the URI scheme and OS
+    """
+    if isinstance(base, pathlib.Path):
+        return base.joinpath(*parts)
+    base = str(base)
+    if get_uri_scheme(base) == "file":
+        # using pathlib for local paths make this windows compatible
+        # `get_uri_scheme` returns `file` for windows drive names (e.g. `c:\path`)
+        return str(pathlib.Path(base, *parts))
+    # for remote paths, just use os.path.join
+    return "/".join([p.rstrip("/") for p in [base, *parts]])
+
+
 def safe_import_pandas():
     try:
         import pandas as pd
@@ -88,3 +121,53 @@ def safe_import_pandas():
         return pd
     except ImportError:
         return None
+
+
+@singledispatch
+def value_to_sql(value):
+    raise NotImplementedError("SQL conversion is not implemented for this type")
+
+
+@value_to_sql.register(str)
+def _(value: str):
+    return f"'{value}'"
+
+
+@value_to_sql.register(int)
+def _(value: int):
+    return str(value)
+
+
+@value_to_sql.register(float)
+def _(value: float):
+    return str(value)
+
+
+@value_to_sql.register(bool)
+def _(value: bool):
+    return str(value).upper()
+
+
+@value_to_sql.register(type(None))
+def _(value: type(None)):
+    return "NULL"
+
+
+@value_to_sql.register(datetime)
+def _(value: datetime):
+    return f"'{value.isoformat()}'"
+
+
+@value_to_sql.register(date)
+def _(value: date):
+    return f"'{value.isoformat()}'"
+
+
+@value_to_sql.register(list)
+def _(value: list):
+    return "[" + ", ".join(map(value_to_sql, value)) + "]"
+
+
+@value_to_sql.register(np.ndarray)
+def _(value: np.ndarray):
+    return value_to_sql(value.tolist())

python/lancedb/utils/events.py

@@ -64,8 +64,10 @@ class _Events:
         Initializes the Events object with default values for events, rate_limit, and metadata.
         """
         self.events = []  # events list
-        self.max_events = 25  # max events to store in memory
-        self.rate_limit = 60.0  # rate limit (seconds)
+        self.throttled_event_names = ["search_table"]
+        self.throttled_events = set()
+        self.max_events = 5  # max events to store in memory
+        self.rate_limit = 60.0 * 5  # rate limit (seconds)
         self.time = 0.0
 
         if is_git_dir():
@@ -112,18 +114,21 @@ class _Events:
             return
         if (
             len(self.events) < self.max_events
-        ):  # Events list limited to 25 events (drop any events past this)
+        ):  # Events list limited to self.max_events (drop any events past this)
             params.update(self.metadata)
-            self.events.append(
-                {
-                    "event": event_name,
-                    "properties": params,
-                    "timestamp": datetime.datetime.now(
-                        tz=datetime.timezone.utc
-                    ).isoformat(),
-                    "distinct_id": CONFIG["uuid"],
-                }
-            )
+            event = {
+                "event": event_name,
+                "properties": params,
+                "timestamp": datetime.datetime.now(
+                    tz=datetime.timezone.utc
+                ).isoformat(),
+                "distinct_id": CONFIG["uuid"],
+            }
+            if event_name not in self.throttled_event_names:
+                self.events.append(event)
+            elif event_name not in self.throttled_events:
+                self.throttled_events.add(event_name)
+                self.events.append(event)
 
         # Check rate limit
         t = time.time()
@@ -135,7 +140,6 @@ class _Events:
             "distinct_id": CONFIG["uuid"],  # posthog needs this to accepts the event
             "batch": self.events,
         }
-
         # POST equivalent to requests.post(self.url, json=data).
         # threaded request is used to avoid blocking, retries are disabled, and verbose is disabled
         # to avoid any possible disruption in the console.
@@ -150,6 +154,7 @@ class _Events:
 
         # Flush & Reset
         self.events = []
+        self.throttled_events = set()
         self.time = t
 
 

python/pyproject.toml

@@ -1,12 +1,12 @@
 [project]
 name = "lancedb"
-version = "0.3.4"
+version = "0.4.0"
 dependencies = [
     "deprecation",
-    "pylance==0.8.17",
+    "pylance==0.9.1",
     "ratelimiter~=1.0",
     "retry>=0.9.2",
-    "tqdm>=4.1.0",
+    "tqdm>=4.27.0",
     "aiohttp",
     "pydantic>=1.10",
     "attrs>=21.3.0",

python/tests/test_fts.py

@@ -43,7 +43,15 @@ def table(tmp_path) -> ldb.table.LanceTable:
         for _ in range(100)
     ]
     table = db.create_table(
-        "test", data=pd.DataFrame({"vector": vectors, "text": text, "text2": text})
+        "test",
+        data=pd.DataFrame(
+            {
+                "vector": vectors,
+                "text": text,
+                "text2": text,
+                "nested": [{"text": t} for t in text],
+            }
+        ),
     )
     return table
 
@@ -75,6 +83,24 @@ def test_create_index_from_table(tmp_path, table):
     assert len(df) == 10
     assert "text" in df.columns
 
+    # Check whether it can be updated
+    table.add(
+        [
+            {
+                "vector": np.random.randn(128),
+                "text": "gorilla",
+                "text2": "gorilla",
+                "nested": {"text": "gorilla"},
+            }
+        ]
+    )
+
+    table.create_fts_index("text", replace=True)
+    assert len(table.search("gorilla").limit(1).to_pandas()) == 1
+
+    with pytest.raises(ValueError, match="already exists"):
+        table.create_fts_index("text")
+
 
 def test_create_index_multiple_columns(tmp_path, table):
     table.create_fts_index(["text", "text2"])
@@ -89,3 +115,9 @@ def test_empty_rs(tmp_path, table, mocker):
     mocker.patch("lancedb.fts.search_index", return_value=([], []))
     df = table.search("puppy").limit(10).to_pandas()
     assert len(df) == 0
+
+
+def test_nested_schema(tmp_path, table):
+    table.create_fts_index("nested.text")
+    rs = table.search("puppy").limit(10).to_list()
+    assert len(rs) == 10

python/tests/test_remote_db.py

@@ -26,6 +26,9 @@ class FakeLanceDBClient:
         t = pa.schema([]).empty_table()
         return VectorQueryResult(t)
 
+    async def post(self, path: str):
+        pass
+
 
 def test_remote_db():
     conn = lancedb.connect("db://client-will-be-injected", api_key="fake")

python/tests/test_table.py

@@ -12,7 +12,7 @@
 #  limitations under the License.
 
 import functools
-from datetime import timedelta
+from datetime import date, datetime, timedelta
 from pathlib import Path
 from typing import List
 from unittest.mock import PropertyMock, patch
@@ -22,6 +22,7 @@ import numpy as np
 import pandas as pd
 import pyarrow as pa
 import pytest
+from pydantic import BaseModel
 
 from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.db import LanceDBConnection
@@ -141,14 +142,44 @@ def test_add(db):
 
 
 def test_add_pydantic_model(db):
-    class TestModel(LanceModel):
-        vector: Vector(16)
-        li: List[int]
+    # https://github.com/lancedb/lancedb/issues/562
 
-    data = TestModel(vector=list(range(16)), li=[1, 2, 3])
-    table = LanceTable.create(db, "test", data=[data])
-    assert len(table) == 1
-    assert table.schema == TestModel.to_arrow_schema()
+    class Metadata(BaseModel):
+        source: str
+        timestamp: datetime
+
+    class Document(BaseModel):
+        content: str
+        meta: Metadata
+
+    class LanceSchema(LanceModel):
+        id: str
+        vector: Vector(2)
+        li: List[int]
+        payload: Document
+
+    tbl = LanceTable.create(db, "mytable", schema=LanceSchema, mode="overwrite")
+    assert tbl.schema == LanceSchema.to_arrow_schema()
+
+    # add works
+    expected = LanceSchema(
+        id="id",
+        vector=[0.0, 0.0],
+        li=[1, 2, 3],
+        payload=Document(
+            content="foo", meta=Metadata(source="bar", timestamp=datetime.now())
+        ),
+    )
+    tbl.add([expected])
+
+    result = tbl.search([0.0, 0.0]).limit(1).to_pydantic(LanceSchema)[0]
+    assert result == expected
+
+    flattened = tbl.search([0.0, 0.0]).limit(1).to_pandas(flatten=1)
+    assert len(flattened.columns) == 6  # _distance is automatically added
+
+    really_flattened = tbl.search([0.0, 0.0]).limit(1).to_pandas(flatten=True)
+    assert len(really_flattened.columns) == 7
 
 
 def _add(table, schema):
@@ -195,39 +226,38 @@ def test_versioning(db):
 
 
 def test_create_index_method():
-    with patch.object(LanceTable, "_reset_dataset", return_value=None):
-        with patch.object(
-            LanceTable, "_dataset", new_callable=PropertyMock
-        ) as mock_dataset:
-            # Setup mock responses
-            mock_dataset.return_value.create_index.return_value = None
+    with patch.object(
+        LanceTable, "_dataset", new_callable=PropertyMock
+    ) as mock_dataset:
+        # Setup mock responses
+        mock_dataset.return_value.create_index.return_value = None
 
-            # Create a LanceTable object
-            connection = LanceDBConnection(uri="mock.uri")
-            table = LanceTable(connection, "test_table")
+        # Create a LanceTable object
+        connection = LanceDBConnection(uri="mock.uri")
+        table = LanceTable(connection, "test_table")
 
-            # Call the create_index method
-            table.create_index(
-                metric="L2",
-                num_partitions=256,
-                num_sub_vectors=96,
-                vector_column_name="vector",
-                replace=True,
-                index_cache_size=256,
-            )
+        # Call the create_index method
+        table.create_index(
+            metric="L2",
+            num_partitions=256,
+            num_sub_vectors=96,
+            vector_column_name="vector",
+            replace=True,
+            index_cache_size=256,
+        )
 
-            # Check that the _dataset.create_index method was called
-            # with the right parameters
-            mock_dataset.return_value.create_index.assert_called_once_with(
-                column="vector",
-                index_type="IVF_PQ",
-                metric="L2",
-                num_partitions=256,
-                num_sub_vectors=96,
-                replace=True,
-                accelerator=None,
-                index_cache_size=256,
-            )
+        # Check that the _dataset.create_index method was called
+        # with the right parameters
+        mock_dataset.return_value.create_index.assert_called_once_with(
+            column="vector",
+            index_type="IVF_PQ",
+            metric="L2",
+            num_partitions=256,
+            num_sub_vectors=96,
+            replace=True,
+            accelerator=None,
+            index_cache_size=256,
+        )
 
 
 def test_add_with_nans(db):
@@ -348,14 +378,79 @@ def test_update(db):
     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.list_versions()) == 3
+    assert table.version == 3
     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_update_types(db):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[
+            {
+                "id": 0,
+                "str": "foo",
+                "float": 1.1,
+                "timestamp": datetime(2021, 1, 1),
+                "date": date(2021, 1, 1),
+                "vector1": [1.0, 0.0],
+                "vector2": [1.0, 1.0],
+            }
+        ],
+    )
+    # Update with SQL
+    table.update(
+        values_sql=dict(
+            id="1",
+            str="'bar'",
+            float="2.2",
+            timestamp="TIMESTAMP '2021-01-02 00:00:00'",
+            date="DATE '2021-01-02'",
+            vector1="[2.0, 2.0]",
+            vector2="[3.0, 3.0]",
+        )
+    )
+    actual = table.to_arrow().to_pylist()[0]
+    expected = dict(
+        id=1,
+        str="bar",
+        float=2.2,
+        timestamp=datetime(2021, 1, 2),
+        date=date(2021, 1, 2),
+        vector1=[2.0, 2.0],
+        vector2=[3.0, 3.0],
+    )
+    assert actual == expected
+
+    # Update with values
+    table.update(
+        values=dict(
+            id=2,
+            str="baz",
+            float=3.3,
+            timestamp=datetime(2021, 1, 3),
+            date=date(2021, 1, 3),
+            vector1=[3.0, 3.0],
+            vector2=np.array([4.0, 4.0]),
+        )
+    )
+    actual = table.to_arrow().to_pylist()[0]
+    expected = dict(
+        id=2,
+        str="baz",
+        float=3.3,
+        timestamp=datetime(2021, 1, 3),
+        date=date(2021, 1, 3),
+        vector1=[3.0, 3.0],
+        vector2=[4.0, 4.0],
+    )
+    assert actual == expected
+
+
 def test_create_with_embedding_function(db):
     class MyTable(LanceModel):
         text: str

python/tests/test_util.py

@@ -11,7 +11,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from lancedb.util import get_uri_scheme
+import os
+import pathlib
+
+import pytest
+
+from lancedb.util import get_uri_scheme, join_uri
 
 
 def test_normalize_uri():
@@ -28,3 +33,55 @@ def test_normalize_uri():
     for uri, expected_scheme in zip(uris, schemes):
         parsed_scheme = get_uri_scheme(uri)
         assert parsed_scheme == expected_scheme
+
+
+def test_join_uri_remote():
+    schemes = ["s3", "az", "gs"]
+    for scheme in schemes:
+        expected = f"{scheme}://bucket/path/to/table.lance"
+        base_uri = f"{scheme}://bucket/path/to/"
+        parts = ["table.lance"]
+        assert join_uri(base_uri, *parts) == expected
+
+        base_uri = f"{scheme}://bucket"
+        parts = ["path", "to", "table.lance"]
+        assert join_uri(base_uri, *parts) == expected
+
+
+# skip this test if on windows
+@pytest.mark.skipif(os.name == "nt", reason="Windows paths are not POSIX")
+def test_join_uri_posix():
+    for base in [
+        # relative path
+        "relative/path",
+        "relative/path/",
+        # an absolute path
+        "/absolute/path",
+        "/absolute/path/",
+        # a file URI
+        "file:///absolute/path",
+        "file:///absolute/path/",
+    ]:
+        joined = join_uri(base, "table.lance")
+        assert joined == str(pathlib.Path(base) / "table.lance")
+        joined = join_uri(pathlib.Path(base), "table.lance")
+        assert joined == pathlib.Path(base) / "table.lance"
+
+
+# skip this test if not on windows
+@pytest.mark.skipif(os.name != "nt", reason="Windows paths are not POSIX")
+def test_local_join_uri_windows():
+    # https://learn.microsoft.com/en-us/dotnet/standard/io/file-path-formats
+    for base in [
+        # windows relative path
+        "relative\\path",
+        "relative\\path\\",
+        # windows absolute path from current drive
+        "c:\\absolute\\path",
+        # relative path from root of current drive
+        "\\relative\\path",
+    ]:
+        joined = join_uri(base, "table.lance")
+        assert joined == str(pathlib.Path(base) / "table.lance")
+        joined = join_uri(pathlib.Path(base), "table.lance")
+        assert joined == pathlib.Path(base) / "table.lance"

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.3.8"
+version = "0.4.0"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"

rust/ffi/node/src/error.rs

@@ -23,7 +23,7 @@ pub enum Error {
     #[snafu(display("column '{name}' is missing"))]
     MissingColumn { name: String },
     #[snafu(display("{name}: {message}"))]
-    RangeError { name: String, message: String },
+    OutOfRange { name: String, message: String },
     #[snafu(display("{index_type} is not a valid index type"))]
     InvalidIndexType { index_type: String },
 

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

@@ -65,12 +65,10 @@ fn get_index_params_builder(
             obj.get_opt::<JsString, _, _>(cx, "index_name")?
                 .map(|s| index_builder.index_name(s.value(cx)));
 
-            obj.get_opt::<JsString, _, _>(cx, "metric_type")?
-                .map(|s| MetricType::try_from(s.value(cx).as_str()))
-                .map(|mt| {
-                    let metric_type = mt.unwrap();
-                    index_builder.metric_type(metric_type);
-                });
+            if let Some(metric_type) = obj.get_opt::<JsString, _, _>(cx, "metric_type")? {
+                let metric_type = MetricType::try_from(metric_type.value(cx).as_str()).unwrap();
+                index_builder.metric_type(metric_type);
+            }
 
             let num_partitions = obj.get_opt_usize(cx, "num_partitions")?;
             let max_iters = obj.get_opt_usize(cx, "max_iters")?;
@@ -85,23 +83,29 @@ fn get_index_params_builder(
                 index_builder.ivf_params(ivf_params)
             });
 
-            obj.get_opt::<JsBoolean, _, _>(cx, "use_opq")?
-                .map(|s| pq_params.use_opq = s.value(cx));
+            if let Some(use_opq) = obj.get_opt::<JsBoolean, _, _>(cx, "use_opq")? {
+                pq_params.use_opq = use_opq.value(cx);
+            }
 
-            obj.get_opt_usize(cx, "num_sub_vectors")?
-                .map(|s| pq_params.num_sub_vectors = s);
+            if let Some(num_sub_vectors) = obj.get_opt_usize(cx, "num_sub_vectors")? {
+                pq_params.num_sub_vectors = num_sub_vectors;
+            }
 
-            obj.get_opt_usize(cx, "num_bits")?
-                .map(|s| pq_params.num_bits = s);
+            if let Some(num_bits) = obj.get_opt_usize(cx, "num_bits")? {
+                pq_params.num_bits = num_bits;
+            }
 
-            obj.get_opt_usize(cx, "max_iters")?
-                .map(|s| pq_params.max_iters = s);
+            if let Some(max_iters) = obj.get_opt_usize(cx, "max_iters")? {
+                pq_params.max_iters = max_iters;
+            }
 
-            obj.get_opt_usize(cx, "max_opq_iters")?
-                .map(|s| pq_params.max_opq_iters = s);
+            if let Some(max_opq_iters) = obj.get_opt_usize(cx, "max_opq_iters")? {
+                pq_params.max_opq_iters = max_opq_iters;
+            }
 
-            obj.get_opt::<JsBoolean, _, _>(cx, "replace")?
-                .map(|s| index_builder.replace(s.value(cx)));
+            if let Some(replace) = obj.get_opt::<JsBoolean, _, _>(cx, "replace")? {
+                index_builder.replace(replace.value(cx));
+            }
 
             Ok(index_builder)
         }

rust/ffi/node/src/lib.rs

@@ -237,6 +237,7 @@ fn main(mut cx: ModuleContext) -> NeonResult<()> {
     cx.export_function("tableAdd", JsTable::js_add)?;
     cx.export_function("tableCountRows", JsTable::js_count_rows)?;
     cx.export_function("tableDelete", JsTable::js_delete)?;
+    cx.export_function("tableUpdate", JsTable::js_update)?;
     cx.export_function("tableCleanupOldVersions", JsTable::js_cleanup)?;
     cx.export_function("tableCompactFiles", JsTable::js_compact)?;
     cx.export_function("tableListIndices", JsTable::js_list_indices)?;

rust/ffi/node/src/neon_ext/js_object_ext.rs

@@ -47,15 +47,15 @@ fn f64_to_u32_safe(n: f64, key: &str) -> Result<u32> {
     use conv::*;
 
     n.approx_as::<u32>().map_err(|e| match e {
-        FloatError::NegOverflow(_) => Error::RangeError {
+        FloatError::NegOverflow(_) => Error::OutOfRange {
             name: key.into(),
             message: "must be > 0".to_string(),
         },
-        FloatError::PosOverflow(_) => Error::RangeError {
+        FloatError::PosOverflow(_) => Error::OutOfRange {
             name: key.into(),
             message: format!("must be < {}", u32::MAX),
         },
-        FloatError::NotANumber(_) => Error::RangeError {
+        FloatError::NotANumber(_) => Error::OutOfRange {
             name: key.into(),
             message: "not a valid number".to_string(),
         },
@@ -66,15 +66,15 @@ fn f64_to_usize_safe(n: f64, key: &str) -> Result<usize> {
     use conv::*;
 
     n.approx_as::<usize>().map_err(|e| match e {
-        FloatError::NegOverflow(_) => Error::RangeError {
+        FloatError::NegOverflow(_) => Error::OutOfRange {
             name: key.into(),
             message: "must be > 0".to_string(),
         },
-        FloatError::PosOverflow(_) => Error::RangeError {
+        FloatError::PosOverflow(_) => Error::OutOfRange {
             name: key.into(),
             message: format!("must be < {}", usize::MAX),
         },
-        FloatError::NotANumber(_) => Error::RangeError {
+        FloatError::NotANumber(_) => Error::OutOfRange {
             name: key.into(),
             message: "not a valid number".to_string(),
         },

rust/ffi/node/src/query.rs

@@ -23,8 +23,14 @@ impl JsQuery {
         let query_obj = cx.argument::<JsObject>(0)?;
 
         let limit = query_obj
-            .get::<JsNumber, _, _>(&mut cx, "_limit")?
-            .value(&mut cx);
+            .get_opt::<JsNumber, _, _>(&mut cx, "_limit")?
+            .map(|value| {
+                let limit = value.value(&mut cx);
+                if limit <= 0.0 {
+                    panic!("Limit must be a positive integer");
+                }
+                limit as u64
+            });
         let select = query_obj
             .get_opt::<JsArray, _, _>(&mut cx, "_select")?
             .map(|arr| {
@@ -48,6 +54,10 @@ impl JsQuery {
             .map(|s| s.value(&mut cx))
             .map(|s| MetricType::try_from(s.as_str()).unwrap());
 
+        let prefilter = query_obj
+            .get::<JsBoolean, _, _>(&mut cx, "_prefilter")?
+            .value(&mut cx);
+
         let is_electron = cx
             .argument::<JsBoolean>(1)
             .or_throw(&mut cx)?
@@ -57,19 +67,23 @@ impl JsQuery {
 
         let (deferred, promise) = cx.promise();
         let channel = cx.channel();
-        let query_vector = query_obj.get::<JsArray, _, _>(&mut cx, "_queryVector")?;
-        let query = convert::js_array_to_vec(query_vector.deref(), &mut cx);
+        let query_vector = query_obj.get_opt::<JsArray, _, _>(&mut cx, "_queryVector")?;
         let table = js_table.table.clone();
+        let query = query_vector.map(|q| convert::js_array_to_vec(q.deref(), &mut cx));
 
         rt.spawn(async move {
-            let builder = table
-                .search(Float32Array::from(query))
-                .limit(limit as usize)
+            let mut builder = table
+                .search(query.map(Float32Array::from))
                 .refine_factor(refine_factor)
                 .nprobes(nprobes)
                 .filter(filter)
                 .metric_type(metric_type)
-                .select(select);
+                .select(select)
+                .prefilter(prefilter);
+            if let Some(limit) = limit {
+                builder = builder.limit(limit as usize);
+            };
+
             let record_batch_stream = builder.execute();
             let results = record_batch_stream
                 .and_then(|stream| {

rust/ffi/node/src/table.rs

@@ -45,7 +45,7 @@ impl JsTable {
         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)?;
+            arrow_buffer_to_record_batch(buffer.as_slice(&cx)).or_throw(&mut cx)?;
 
         // Write mode
         let mode = match cx.argument::<JsString>(2)?.value(&mut cx).as_str() {
@@ -93,7 +93,7 @@ impl JsTable {
         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)?;
+            arrow_buffer_to_record_batch(buffer.as_slice(&cx)).or_throw(&mut cx)?;
         let rt = runtime(&mut cx)?;
         let channel = cx.channel();
         let mut table = js_table.table.clone();
@@ -165,6 +165,69 @@ impl JsTable {
         Ok(promise)
     }
 
+    pub(crate) fn js_update(mut cx: FunctionContext) -> JsResult<JsPromise> {
+        let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+        let mut table = js_table.table.clone();
+
+        let rt = runtime(&mut cx)?;
+        let (deferred, promise) = cx.promise();
+        let channel = cx.channel();
+
+        // create a vector of updates from the passed map
+        let updates_arg = cx.argument::<JsObject>(1)?;
+        let properties = updates_arg.get_own_property_names(&mut cx)?;
+        let mut updates: Vec<(String, String)> =
+            Vec::with_capacity(properties.len(&mut cx) as usize);
+
+        let len_properties = properties.len(&mut cx);
+        for i in 0..len_properties {
+            let property = properties
+                .get_value(&mut cx, i)?
+                .downcast_or_throw::<JsString, _>(&mut cx)?;
+
+            let value = updates_arg
+                .get_value(&mut cx, property)?
+                .downcast_or_throw::<JsString, _>(&mut cx)?;
+
+            let property = property.value(&mut cx);
+            let value = value.value(&mut cx);
+            updates.push((property, value));
+        }
+
+        // get the filter/predicate if the user passed one
+        let predicate = cx.argument_opt(0);
+        let predicate = predicate.unwrap().downcast::<JsString, _>(&mut cx);
+        let predicate = match predicate {
+            Ok(_) => {
+                let val = predicate.map(|s| s.value(&mut cx)).unwrap();
+                Some(val)
+            }
+            Err(_) => {
+                // if the predicate is not string, check it's null otherwise an invalid
+                // type was passed
+                cx.argument::<JsNull>(0)?;
+                None
+            }
+        };
+
+        rt.spawn(async move {
+            let updates_arg = updates
+                .iter()
+                .map(|(k, v)| (k.as_str(), v.as_str()))
+                .collect::<Vec<_>>();
+
+            let predicate = predicate.as_deref();
+
+            let update_result = table.update(predicate, updates_arg).await;
+            deferred.settle_with(&channel, move |mut cx| {
+                update_result.or_throw(&mut cx)?;
+                Ok(cx.boxed(JsTable::from(table)))
+            })
+        });
+
+        Ok(promise)
+    }
+
     pub(crate) fn js_cleanup(mut cx: FunctionContext) -> JsResult<JsPromise> {
         let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
         let rt = runtime(&mut cx)?;

rust/vectordb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb"
-version = "0.3.8"
+version = "0.4.0"
 edition = "2021"
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"

rust/vectordb/src/io/object_store.rs

@@ -26,7 +26,7 @@ use futures::{stream::BoxStream, FutureExt, StreamExt};
 use lance::io::object_store::WrappingObjectStore;
 use object_store::{
     path::Path, Error, GetOptions, GetResult, ListResult, MultipartId, ObjectMeta, ObjectStore,
-    Result,
+    PutOptions, PutResult, Result,
 };
 
 use async_trait::async_trait;
@@ -72,13 +72,28 @@ impl PrimaryOnly for Path {
 /// Note: this object store does not mirror writes to *.manifest files
 #[async_trait]
 impl ObjectStore for MirroringObjectStore {
-    async fn put(&self, location: &Path, bytes: Bytes) -> Result<()> {
+    async fn put(&self, location: &Path, bytes: Bytes) -> Result<PutResult> {
         if location.primary_only() {
             self.primary.put(location, bytes).await
         } else {
             self.secondary.put(location, bytes.clone()).await?;
-            self.primary.put(location, bytes).await?;
-            Ok(())
+            self.primary.put(location, bytes).await
+        }
+    }
+
+    async fn put_opts(
+        &self,
+        location: &Path,
+        bytes: Bytes,
+        options: PutOptions,
+    ) -> Result<PutResult> {
+        if location.primary_only() {
+            self.primary.put_opts(location, bytes, options).await
+        } else {
+            self.secondary
+                .put_opts(location, bytes.clone(), options.clone())
+                .await?;
+            self.primary.put_opts(location, bytes, options).await
         }
     }
 
@@ -129,8 +144,8 @@ impl ObjectStore for MirroringObjectStore {
         self.primary.delete(location).await
     }
 
-    async fn list(&self, prefix: Option<&Path>) -> Result<BoxStream<'_, Result<ObjectMeta>>> {
-        self.primary.list(prefix).await
+    fn list(&self, prefix: Option<&Path>) -> BoxStream<'_, Result<ObjectMeta>> {
+        self.primary.list(prefix)
     }
 
     async fn list_with_delimiter(&self, prefix: Option<&Path>) -> Result<ListResult> {
@@ -359,7 +374,9 @@ mod test {
         assert_eq!(t.count_rows().await.unwrap(), 100);
 
         let q = t
-            .search(PrimitiveArray::from_iter_values(vec![0.1, 0.1, 0.1, 0.1]))
+            .search(Some(PrimitiveArray::from_iter_values(vec![
+                0.1, 0.1, 0.1, 0.1,
+            ])))
             .limit(10)
             .execute()
             .await

rust/vectordb/src/query.rs

@@ -24,14 +24,16 @@ use crate::error::Result;
 /// A builder for nearest neighbor queries for LanceDB.
 pub struct Query {
     pub dataset: Arc<Dataset>,
-    pub query_vector: Float32Array,
-    pub limit: usize,
+    pub query_vector: Option<Float32Array>,
+    pub column: String,
+    pub limit: Option<usize>,
     pub filter: Option<String>,
     pub select: Option<Vec<String>>,
     pub nprobes: usize,
     pub refine_factor: Option<u32>,
     pub metric_type: Option<MetricType>,
     pub use_index: bool,
+    pub prefilter: bool,
 }
 
 impl Query {
@@ -45,17 +47,19 @@ impl Query {
     /// # Returns
     ///
     /// * A [Query] object.
-    pub(crate) fn new(dataset: Arc<Dataset>, vector: Float32Array) -> Self {
+    pub(crate) fn new(dataset: Arc<Dataset>, vector: Option<Float32Array>) -> Self {
         Query {
             dataset,
             query_vector: vector,
-            limit: 10,
+            column: crate::table::VECTOR_COLUMN_NAME.to_string(),
+            limit: None,
             nprobes: 20,
             refine_factor: None,
             metric_type: None,
             use_index: true,
             filter: None,
             select: None,
+            prefilter: false,
         }
     }
 
@@ -67,13 +71,17 @@ impl Query {
     pub async fn execute(&self) -> Result<DatasetRecordBatchStream> {
         let mut scanner: Scanner = self.dataset.scan();
 
-        scanner.nearest(
-            crate::table::VECTOR_COLUMN_NAME,
-            &self.query_vector,
-            self.limit,
-        )?;
+        if let Some(query) = self.query_vector.as_ref() {
+            // If there is a vector query, default to limit=10 if unspecified
+            scanner.nearest(&self.column, query, self.limit.unwrap_or(10))?;
+        } else {
+            // If there is no vector query, it's ok to not have a limit
+            scanner.limit(self.limit.map(|limit| limit as i64), None)?;
+        }
         scanner.nprobs(self.nprobes);
         scanner.use_index(self.use_index);
+        scanner.prefilter(self.prefilter);
+
         self.select.as_ref().map(|p| scanner.project(p.as_slice()));
         self.filter.as_ref().map(|f| scanner.filter(f));
         self.refine_factor.map(|rf| scanner.refine(rf));
@@ -81,13 +89,23 @@ impl Query {
         Ok(scanner.try_into_stream().await?)
     }
 
+    /// Set the column to query
+    ///
+    /// # Arguments
+    ///
+    /// * `column` - The column name
+    pub fn column(mut self, column: &str) -> Query {
+        self.column = column.into();
+        self
+    }
+
     /// Set the maximum number of results to return.
     ///
     /// # Arguments
     ///
     /// * `limit` - The maximum number of results to return.
     pub fn limit(mut self, limit: usize) -> Query {
-        self.limit = limit;
+        self.limit = Some(limit);
         self
     }
 
@@ -97,7 +115,7 @@ impl Query {
     ///
     /// * `vector` - The vector that will be used for search.
     pub fn query_vector(mut self, query_vector: Float32Array) -> Query {
-        self.query_vector = query_vector;
+        self.query_vector = Some(query_vector);
         self
     }
 
@@ -158,6 +176,11 @@ impl Query {
         self.select = columns;
         self
     }
+
+    pub fn prefilter(mut self, prefilter: bool) -> Query {
+        self.prefilter = prefilter;
+        self
+    }
 }
 
 #[cfg(test)]
@@ -165,9 +188,14 @@ mod tests {
     use std::sync::Arc;
 
     use super::*;
-    use arrow_array::{Float32Array, RecordBatch, RecordBatchIterator, RecordBatchReader};
+    use arrow_array::{
+        cast::AsArray, Float32Array, Int32Array, RecordBatch, RecordBatchIterator,
+        RecordBatchReader,
+    };
     use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
+    use futures::StreamExt;
     use lance::dataset::Dataset;
+    use lance_testing::datagen::{BatchGenerator, IncrementingInt32, RandomVector};
 
     use crate::query::Query;
 
@@ -176,7 +204,7 @@ mod tests {
         let batches = make_test_batches();
         let ds = Dataset::write(batches, "memory://foo", None).await.unwrap();
 
-        let vector = Float32Array::from_iter_values([0.1, 0.2]);
+        let vector = Some(Float32Array::from_iter_values([0.1, 0.2]));
         let query = Query::new(Arc::new(ds), vector.clone());
         assert_eq!(query.query_vector, vector);
 
@@ -190,8 +218,8 @@ mod tests {
             .metric_type(Some(MetricType::Cosine))
             .refine_factor(Some(999));
 
-        assert_eq!(query.query_vector, new_vector);
-        assert_eq!(query.limit, 100);
+        assert_eq!(query.query_vector.unwrap(), new_vector);
+        assert_eq!(query.limit.unwrap(), 100);
         assert_eq!(query.nprobes, 1000);
         assert_eq!(query.use_index, true);
         assert_eq!(query.metric_type, Some(MetricType::Cosine));
@@ -200,13 +228,64 @@ mod tests {
 
     #[tokio::test]
     async fn test_execute() {
-        let batches = make_test_batches();
-        let ds = Dataset::write(batches, "memory://foo", None).await.unwrap();
+        let batches = make_non_empty_batches();
+        let ds = Arc::new(Dataset::write(batches, "memory://foo", None).await.unwrap());
 
-        let vector = Float32Array::from_iter_values([0.1; 128]);
-        let query = Query::new(Arc::new(ds), vector.clone());
-        let result = query.execute().await;
-        assert_eq!(result.is_ok(), true);
+        let vector = Some(Float32Array::from_iter_values([0.1; 4]));
+
+        let query = Query::new(ds.clone(), vector.clone());
+        let result = query
+            .limit(10)
+            .filter(Some("id % 2 == 0".to_string()))
+            .execute()
+            .await;
+        let mut stream = result.expect("should have result");
+        // should only have one batch
+        while let Some(batch) = stream.next().await {
+            // post filter should have removed some rows
+            assert!(batch.expect("should be Ok").num_rows() < 10);
+        }
+
+        let query = Query::new(ds, vector.clone());
+        let result = query
+            .limit(10)
+            .filter(Some("id % 2 == 0".to_string()))
+            .prefilter(true)
+            .execute()
+            .await;
+        let mut stream = result.expect("should have result");
+        // should only have one batch
+        while let Some(batch) = stream.next().await {
+            // pre filter should return 10 rows
+            assert!(batch.expect("should be Ok").num_rows() == 10);
+        }
+    }
+
+    #[tokio::test]
+    async fn test_execute_no_vector() {
+        // test that it's ok to not specify a query vector (just filter / limit)
+        let batches = make_non_empty_batches();
+        let ds = Arc::new(Dataset::write(batches, "memory://foo", None).await.unwrap());
+
+        let query = Query::new(ds.clone(), None);
+        let result = query
+            .filter(Some("id % 2 == 0".to_string()))
+            .execute()
+            .await;
+        let mut stream = result.expect("should have result");
+        // should only have one batch
+        while let Some(batch) = stream.next().await {
+            let b = batch.expect("should be Ok");
+            // cast arr into Int32Array
+            let arr: &Int32Array = b["id"].as_primitive();
+            assert!(arr.iter().all(|x| x.unwrap() % 2 == 0));
+        }
+    }
+
+    fn make_non_empty_batches() -> impl RecordBatchReader + Send + 'static {
+        let vec = Box::new(RandomVector::new().named("vector".to_string()));
+        let id = Box::new(IncrementingInt32::new().named("id".to_string()));
+        BatchGenerator::new().col(vec).col(id).batch(512)
     }
 
     fn make_test_batches() -> impl RecordBatchReader + Send + 'static {

rust/vectordb/src/table.rs

@@ -23,7 +23,7 @@ use lance::dataset::cleanup::RemovalStats;
 use lance::dataset::optimize::{
     compact_files, CompactionMetrics, CompactionOptions, IndexRemapperOptions,
 };
-use lance::dataset::{Dataset, WriteParams};
+use lance::dataset::{Dataset, UpdateBuilder, WriteParams};
 use lance::index::DatasetIndexExt;
 use lance::io::object_store::WrappingObjectStore;
 use std::path::Path;
@@ -308,10 +308,14 @@ impl Table {
     /// # Returns
     ///
     /// * A [Query] object.
-    pub fn search(&self, query_vector: Float32Array) -> Query {
+    pub fn search(&self, query_vector: Option<Float32Array>) -> Query {
         Query::new(self.dataset.clone(), query_vector)
     }
 
+    pub fn filter(&self, expr: String) -> Query {
+        Query::new(self.dataset.clone(), None).filter(Some(expr))
+    }
+
     /// Returns the number of rows in this Table
     pub async fn count_rows(&self) -> Result<usize> {
         Ok(self.dataset.count_rows().await?)
@@ -338,6 +342,27 @@ impl Table {
         Ok(())
     }
 
+    pub async fn update(
+        &mut self,
+        predicate: Option<&str>,
+        updates: Vec<(&str, &str)>,
+    ) -> Result<()> {
+        let mut builder = UpdateBuilder::new(self.dataset.clone());
+        if let Some(predicate) = predicate {
+            builder = builder.update_where(predicate)?;
+        }
+
+        for (column, value) in updates {
+            builder = builder.set(column, value)?;
+        }
+
+        let operation = builder.build()?;
+        let new_ds = operation.execute().await?;
+        self.dataset = new_ds;
+
+        Ok(())
+    }
+
     /// Remove old versions of the dataset from disk.
     ///
     /// # Arguments
@@ -413,11 +438,14 @@ mod tests {
     use std::sync::Arc;
 
     use arrow_array::{
-        Array, FixedSizeListArray, Float32Array, Int32Array, RecordBatch, RecordBatchIterator,
-        RecordBatchReader,
+        Array, BooleanArray, Date32Array, FixedSizeListArray, Float32Array, Float64Array,
+        Int32Array, Int64Array, LargeStringArray, RecordBatch, RecordBatchIterator,
+        RecordBatchReader, StringArray, TimestampMillisecondArray, TimestampNanosecondArray,
+        UInt32Array,
     };
     use arrow_data::ArrayDataBuilder;
-    use arrow_schema::{DataType, Field, Schema};
+    use arrow_schema::{DataType, Field, Schema, TimeUnit};
+    use futures::TryStreamExt;
     use lance::dataset::{Dataset, WriteMode};
     use lance::index::vector::pq::PQBuildParams;
     use lance::io::object_store::{ObjectStoreParams, WrappingObjectStore};
@@ -540,6 +568,272 @@ mod tests {
         assert_eq!(table.name, "test");
     }
 
+    #[tokio::test]
+    async fn test_update_with_predicate() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("id", DataType::Int32, false),
+            Field::new("name", DataType::Utf8, false),
+        ]));
+
+        let record_batch_iter = RecordBatchIterator::new(
+            vec![RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(Int32Array::from_iter_values(0..10)),
+                    Arc::new(StringArray::from_iter_values(vec![
+                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
+                    ])),
+                ],
+            )
+            .unwrap()]
+            .into_iter()
+            .map(Ok),
+            schema.clone(),
+        );
+
+        Dataset::write(record_batch_iter, uri, None).await.unwrap();
+        let mut table = Table::open(uri).await.unwrap();
+
+        table
+            .update(Some("id > 5"), vec![("name", "'foo'")])
+            .await
+            .unwrap();
+
+        let ds_after = Dataset::open(uri).await.unwrap();
+        let mut batches = ds_after
+            .scan()
+            .project(&["id", "name"])
+            .unwrap()
+            .try_into_stream()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        while let Some(batch) = batches.pop() {
+            let ids = batch
+                .column(0)
+                .as_any()
+                .downcast_ref::<Int32Array>()
+                .unwrap()
+                .iter()
+                .collect::<Vec<_>>();
+            let names = batch
+                .column(1)
+                .as_any()
+                .downcast_ref::<StringArray>()
+                .unwrap()
+                .iter()
+                .collect::<Vec<_>>();
+            for (i, name) in names.iter().enumerate() {
+                let id = ids[i].unwrap();
+                let name = name.unwrap();
+                if id > 5 {
+                    assert_eq!(name, "foo");
+                } else {
+                    assert_eq!(name, &format!("{}", (b'a' + id as u8) as char));
+                }
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn test_update_all_types() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("int32", DataType::Int32, false),
+            Field::new("int64", DataType::Int64, false),
+            Field::new("uint32", DataType::UInt32, false),
+            Field::new("string", DataType::Utf8, false),
+            Field::new("large_string", DataType::LargeUtf8, false),
+            Field::new("float32", DataType::Float32, false),
+            Field::new("float64", DataType::Float64, false),
+            Field::new("bool", DataType::Boolean, false),
+            Field::new("date32", DataType::Date32, false),
+            Field::new(
+                "timestamp_ns",
+                DataType::Timestamp(TimeUnit::Nanosecond, None),
+                false,
+            ),
+            Field::new(
+                "timestamp_ms",
+                DataType::Timestamp(TimeUnit::Millisecond, None),
+                false,
+            ),
+            Field::new(
+                "vec_f32",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+                false,
+            ),
+            Field::new(
+                "vec_f64",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float64, true)), 2),
+                false,
+            ),
+        ]));
+
+        let record_batch_iter = RecordBatchIterator::new(
+            vec![RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(Int32Array::from_iter_values(0..10)),
+                    Arc::new(Int64Array::from_iter_values(0..10)),
+                    Arc::new(UInt32Array::from_iter_values(0..10)),
+                    Arc::new(StringArray::from_iter_values(vec![
+                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
+                    ])),
+                    Arc::new(LargeStringArray::from_iter_values(vec![
+                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
+                    ])),
+                    Arc::new(Float32Array::from_iter_values(
+                        (0..10).into_iter().map(|i| i as f32),
+                    )),
+                    Arc::new(Float64Array::from_iter_values(
+                        (0..10).into_iter().map(|i| i as f64),
+                    )),
+                    Arc::new(Into::<BooleanArray>::into(vec![
+                        true, false, true, false, true, false, true, false, true, false,
+                    ])),
+                    Arc::new(Date32Array::from_iter_values(0..10)),
+                    Arc::new(TimestampNanosecondArray::from_iter_values(0..10)),
+                    Arc::new(TimestampMillisecondArray::from_iter_values(0..10)),
+                    Arc::new(
+                        create_fixed_size_list(
+                            Float32Array::from_iter_values((0..20).into_iter().map(|i| i as f32)),
+                            2,
+                        )
+                        .unwrap(),
+                    ),
+                    Arc::new(
+                        create_fixed_size_list(
+                            Float64Array::from_iter_values((0..20).into_iter().map(|i| i as f64)),
+                            2,
+                        )
+                        .unwrap(),
+                    ),
+                ],
+            )
+            .unwrap()]
+            .into_iter()
+            .map(Ok),
+            schema.clone(),
+        );
+
+        Dataset::write(record_batch_iter, uri, None).await.unwrap();
+        let mut table = Table::open(uri).await.unwrap();
+
+        // check it can do update for each type
+        let updates: Vec<(&str, &str)> = vec![
+            ("string", "'foo'"),
+            ("large_string", "'large_foo'"),
+            ("int32", "1"),
+            ("int64", "1"),
+            ("uint32", "1"),
+            ("float32", "1.0"),
+            ("float64", "1.0"),
+            ("bool", "true"),
+            ("date32", "1"),
+            ("timestamp_ns", "1"),
+            ("timestamp_ms", "1"),
+            ("vec_f32", "[1.0, 1.0]"),
+            ("vec_f64", "[1.0, 1.0]"),
+        ];
+
+        // for (column, value) in test_cases {
+        table.update(None, updates).await.unwrap();
+
+        let ds_after = Dataset::open(uri).await.unwrap();
+        let mut batches = ds_after
+            .scan()
+            .project(&[
+                "string",
+                "large_string",
+                "int32",
+                "int64",
+                "uint32",
+                "float32",
+                "float64",
+                "bool",
+                "date32",
+                "timestamp_ns",
+                "timestamp_ms",
+                "vec_f32",
+                "vec_f64",
+            ])
+            .unwrap()
+            .try_into_stream()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let batch = batches.pop().unwrap();
+
+        macro_rules! assert_column {
+            ($column:expr, $array_type:ty, $expected:expr) => {
+                let array = $column
+                    .as_any()
+                    .downcast_ref::<$array_type>()
+                    .unwrap()
+                    .iter()
+                    .collect::<Vec<_>>();
+                for v in array {
+                    assert_eq!(v, Some($expected));
+                }
+            };
+        }
+
+        assert_column!(batch.column(0), StringArray, "foo");
+        assert_column!(batch.column(1), LargeStringArray, "large_foo");
+        assert_column!(batch.column(2), Int32Array, 1);
+        assert_column!(batch.column(3), Int64Array, 1);
+        assert_column!(batch.column(4), UInt32Array, 1);
+        assert_column!(batch.column(5), Float32Array, 1.0);
+        assert_column!(batch.column(6), Float64Array, 1.0);
+        assert_column!(batch.column(7), BooleanArray, true);
+        assert_column!(batch.column(8), Date32Array, 1);
+        assert_column!(batch.column(9), TimestampNanosecondArray, 1);
+        assert_column!(batch.column(10), TimestampMillisecondArray, 1);
+
+        let array = batch
+            .column(11)
+            .as_any()
+            .downcast_ref::<FixedSizeListArray>()
+            .unwrap()
+            .iter()
+            .collect::<Vec<_>>();
+        for v in array {
+            let v = v.unwrap();
+            let f32array = v.as_any().downcast_ref::<Float32Array>().unwrap();
+            for v in f32array {
+                assert_eq!(v, Some(1.0));
+            }
+        }
+
+        let array = batch
+            .column(12)
+            .as_any()
+            .downcast_ref::<FixedSizeListArray>()
+            .unwrap()
+            .iter()
+            .collect::<Vec<_>>();
+        for v in array {
+            let v = v.unwrap();
+            let f64array = v.as_any().downcast_ref::<Float64Array>().unwrap();
+            for v in f64array {
+                assert_eq!(v, Some(1.0));
+            }
+        }
+    }
+
     #[tokio::test]
     async fn test_search() {
         let tmp_dir = tempdir().unwrap();
@@ -554,8 +848,8 @@ mod tests {
         let table = Table::open(uri).await.unwrap();
 
         let vector = Float32Array::from_iter_values([0.1, 0.2]);
-        let query = table.search(vector.clone());
-        assert_eq!(vector, query.query_vector);
+        let query = table.search(Some(vector.clone()));
+        assert_eq!(vector, query.query_vector.unwrap());
     }
 
     #[derive(Default, Debug)]