bump version for v0.1.5-python

Use fsspec to implement table_names with cloud storage support (#117 )
Co-authored-by: Will Jones <willjones127@gmail.com>
2025-12-23 05:19:58 +00:00 · 2023-06-02 09:18:26 -07:00 · 2023-06-01 16:56:26 -07:00 · 2023-06-01 16:36:11 -07:00 · 2023-06-01 15:58:45 -07:00 · 2023-06-01 13:53:07 -07:00
45 changed files with 2545 additions and 237 deletions
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -31,6 +31,7 @@ jobs:
    - name: Install lancedb
      run: |
        pip install -e .
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
        pip install pytest
    - name: Run tests
      run: pytest -x -v --durations=30 tests
@@ -49,10 +50,11 @@ jobs:
    - name: Set up Python
      uses: actions/setup-python@v4
      with:
-        python-version: "3.10"
+        python-version: "3.11"
    - name: Install lancedb
      run: |
        pip install -e .
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
        pip install pytest
    - name: Run tests
      run: pytest -x -v --durations=30 tests
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -1052,6 +1052,7 @@ dependencies = [
 "paste",
 "petgraph",
 "rand",
+ "regex",
 "uuid",
 ]

@@ -1645,9 +1646,9 @@ dependencies = [

 [[package]]
 name = "lance"
-version = "0.4.12"
+version = "0.4.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fc96cf89139af6f439a0e28ccd04ddf81be795b79fda3105b7a8952fadeb778e"
+checksum = "86dda8185bd1ffae7b910c1f68035af23be9b717c52e9cc4de176cd30b47f772"
 dependencies = [
 "accelerate-src",
 "arrow",
@@ -1684,6 +1685,7 @@ dependencies = [
 "rand",
 "reqwest",
 "shellexpand",
+ "snafu",
 "sqlparser-lance",
 "tokio",
 "url",
@@ -3359,8 +3361,12 @@ name = "vectordb"
 version = "0.0.1"
 dependencies = [
 "arrow-array",
+ "arrow-data",
 "arrow-schema",
 "lance",
+ "object_store",
+ "rand",
+ "snafu",
 "tempfile",
 "tokio",
 ]
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -19,6 +19,7 @@ nav:
 - Basics: basic.md
 - Embeddings: embedding.md
 - Indexing: ann_indexes.md
+- Full-text search: fts.md
 - Integrations: integrations.md
 - Python API: python.md

--- a/docs/src/fts.md
+++ b/docs/src/fts.md
@@ -0,0 +1,51 @@
+# [EXPERIMENTAL] Full text search
+
+LanceDB now provides experimental support for full text search.
+This is currently Python only. We plan to push the integration down to Rust in the future
+to make this available for JS as well.
+
+## Installation
+
+To use full text search, you must install optional dependency tantivy-py:
+
+# tantivy 0.19.2
+pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+
+
+## Quickstart
+
+Assume:
+1. `table` is a LanceDB Table
+2. `text` is the name of the Table column that we want to index
+
+To create the index:
+
+```python
+table.create_fts_index("text")
+```
+
+To search:
+
+```python
+df = table.search("puppy").limit(10).select(["text"]).to_df()
+```
+
+LanceDB automatically looks for an FTS index if the input is str.
+
+## Multiple text columns
+
+If you have multiple columns to index, pass them all as a list to `create_fts_index`:
+
+```python
+table.create_fts_index(["text1", "text2"])
+```
+
+Note that the search API call does not change - you can search over all indexed columns at once.
+
+## Current limitations
+
+1. Currently we do not yet support incremental writes.
+If you add data after fts index creation, it won't be reflected
+in search results until you do a full reindex.
+
+2. We currently only support local filesystem paths for the fts index.
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -38,12 +38,13 @@ result = table.search([100, 100]).limit(2).to_df()
 ## Complete Demos

 We will be adding completed demo apps built using LanceDB.
- [YouTube Transcript Search](../notebooks/youtube_transcript_search.ipynb)
+- [YouTube Transcript Search](../../notebooks/youtube_transcript_search.ipynb)


 ## Documentation Quick Links
 * [`Basic Operations`](basic.md) - basic functionality of LanceDB.
 * [`Embedding Functions`](embedding.md) - functions for working with embeddings.
 * [`Indexing`](ann_indexes.md) - create vector indexes to speed up queries.
+* [`Full text search`](fts.md) - [EXPERIMENTAL] full-text search API
 * [`Ecosystem Integrations`](integrations.md) - integrating LanceDB with python data tooling ecosystem.
 * [`API Reference`](python.md) - detailed documentation for the LanceDB Python SDK.
--- a/node/examples/js-openai/index.js
+++ b/node/examples/js-openai/index.js
@@ -0,0 +1,41 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+'use strict'
+
+async function example () {
+  const lancedb = require('vectordb')
+  // You need to provide an OpenAI API key, here we read it from the OPENAI_API_KEY environment variable
+  const apiKey = process.env.OPENAI_API_KEY
+  // The embedding function will create embeddings for the 'text' column(text in this case)
+  const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
+
+  const db = await lancedb.connect('data/sample-lancedb')
+
+  const data = [
+    { id: 1, text: 'Black T-Shirt', price: 10 },
+    { id: 2, text: 'Leather Jacket', price: 50 }
+  ]
+
+  const table = await db.createTable('vectors', data, embedding)
+  console.log(await db.tableNames())
+
+  const results = await table
+    .search('keeps me warm')
+    .limit(1)
+    .execute()
+  console.log(results[0].text)
+}
+
+example().then(_ => { console.log('All done!') })
--- a/node/examples/js-openai/package.json
+++ b/node/examples/js-openai/package.json
@@ -0,0 +1,15 @@
+{
+  "name": "vectordb-example-js-openai",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "Lance Devs",
+  "license": "Apache-2.0",
+  "dependencies": {
+    "vectordb": "file:../..",
+    "openai": "^3.2.1"
+  }
+}
--- a/node/examples/js/package.json
+++ b/node/examples/js/package.json
@@ -9,6 +9,6 @@
  "author": "Lance Devs",
  "license": "Apache-2.0",
  "dependencies": {
-    "vectordb": "^0.1.0"
+    "vectordb": "file:../.."
  }
 }
--- a/node/examples/ts/package.json
+++ b/node/examples/ts/package.json
@@ -17,6 +17,6 @@
    "typescript": "*"
  },
  "dependencies": {
-    "vectordb": "^0.1.0"
+    "vectordb": "file:../.."
  }
 }
--- a/node/package-lock.json
+++ b/node/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "vectordb",
-  "version": "0.1.1",
+  "version": "0.1.3",
  "lockfileVersion": 2,
  "requires": true,
  "packages": {
    "": {
      "name": "vectordb",
-      "version": "0.1.1",
+      "version": "0.1.3",
      "license": "Apache-2.0",
      "dependencies": {
        "@apache-arrow/ts": "^12.0.0",
@@ -16,6 +16,7 @@
        "@types/chai": "^4.3.4",
        "@types/mocha": "^10.0.1",
        "@types/node": "^18.16.2",
+        "@types/sinon": "^10.0.15",
        "@types/temp": "^0.9.1",
        "@typescript-eslint/eslint-plugin": "^5.59.1",
        "cargo-cp-artifact": "^0.1",
@@ -26,6 +27,8 @@
        "eslint-plugin-n": "^15.7.0",
        "eslint-plugin-promise": "^6.1.1",
        "mocha": "^10.2.0",
+        "openai": "^3.2.1",
+        "sinon": "^15.1.0",
        "temp": "^0.9.4",
        "ts-node": "^10.9.1",
        "ts-node-dev": "^2.0.0",
@@ -232,6 +235,50 @@
        "node": ">= 8"
      }
    },
+    "node_modules/@sinonjs/commons": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/@sinonjs/commons/-/commons-3.0.0.tgz",
+      "integrity": "sha512-jXBtWAF4vmdNmZgD5FoKsVLv3rPgDnLgPbU84LIJ3otV44vJlDRokVng5v8NFJdCf/da9legHcKaRuZs4L7faA==",
+      "dev": true,
+      "dependencies": {
+        "type-detect": "4.0.8"
+      }
+    },
+    "node_modules/@sinonjs/fake-timers": {
+      "version": "10.2.0",
+      "resolved": "https://registry.npmjs.org/@sinonjs/fake-timers/-/fake-timers-10.2.0.tgz",
+      "integrity": "sha512-OPwQlEdg40HAj5KNF8WW6q2KG4Z+cBCZb3m4ninfTZKaBmbIJodviQsDBoYMPHkOyJJMHnOJo5j2+LKDOhOACg==",
+      "dev": true,
+      "dependencies": {
+        "@sinonjs/commons": "^3.0.0"
+      }
+    },
+    "node_modules/@sinonjs/samsam": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmjs.org/@sinonjs/samsam/-/samsam-8.0.0.tgz",
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+      "dev": true,
+      "dependencies": {
+        "@sinonjs/commons": "^2.0.0",
+        "lodash.get": "^4.4.2",
+        "type-detect": "^4.0.8"
+      }
+    },
+    "node_modules/@sinonjs/samsam/node_modules/@sinonjs/commons": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/@sinonjs/commons/-/commons-2.0.0.tgz",
+      "integrity": "sha512-uLa0j859mMrg2slwQYdO/AkrOfmH+X6LTVmNTS9CqexuE2IvVORIkSpJLqePAbEnKJ77aMmCwr1NUZ57120Xcg==",
+      "dev": true,
+      "dependencies": {
+        "type-detect": "4.0.8"
+      }
+    },
+    "node_modules/@sinonjs/text-encoding": {
+      "version": "0.7.2",
+      "resolved": "https://registry.npmjs.org/@sinonjs/text-encoding/-/text-encoding-0.7.2.tgz",
+      "integrity": "sha512-sXXKG+uL9IrKqViTtao2Ws6dy0znu9sOaP1di/jKGW1M6VssO8vlpXCQcpZ+jisQ1tTFAC5Jo/EOzFbggBagFQ==",
+      "dev": true
+    },
    "node_modules/@tsconfig/node10": {
      "version": "1.0.9",
      "resolved": "https://registry.npmjs.org/@tsconfig/node10/-/node10-1.0.9.tgz",
@@ -307,6 +354,21 @@
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      "dev": true
    },
+    "node_modules/@types/sinon": {
+      "version": "10.0.15",
+      "resolved": "https://registry.npmjs.org/@types/sinon/-/sinon-10.0.15.tgz",
+      "integrity": "sha512-3lrFNQG0Kr2LDzvjyjB6AMJk4ge+8iYhQfdnSwIwlG88FUOV43kPcQqDZkDa/h3WSZy6i8Fr0BSjfQtB1B3xuQ==",
+      "dev": true,
+      "dependencies": {
+        "@types/sinonjs__fake-timers": "*"
+      }
+    },
+    "node_modules/@types/sinonjs__fake-timers": {
+      "version": "8.1.2",
+      "resolved": "https://registry.npmjs.org/@types/sinonjs__fake-timers/-/sinonjs__fake-timers-8.1.2.tgz",
+      "integrity": "sha512-9GcLXF0/v3t80caGs5p2rRfkB+a8VBGLJZVih6CNFkx8IZ994wiKKLSRs9nuFwk1HevWs/1mnUmkApGrSGsShA==",
+      "dev": true
+    },
    "node_modules/@types/strip-bom": {
      "version": "3.0.0",
      "resolved": "https://registry.npmjs.org/@types/strip-bom/-/strip-bom-3.0.0.tgz",
@@ -744,6 +806,12 @@
        "node": "*"
      }
    },
+    "node_modules/asynckit": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz",
+      "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==",
+      "dev": true
+    },
    "node_modules/available-typed-arrays": {
      "version": "1.0.5",
      "resolved": "https://registry.npmjs.org/available-typed-arrays/-/available-typed-arrays-1.0.5.tgz",
@@ -756,6 +824,15 @@
        "url": "https://github.com/sponsors/ljharb"
      }
    },
+    "node_modules/axios": {
+      "version": "0.26.1",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-0.26.1.tgz",
+      "integrity": "sha512-fPwcX4EvnSHuInCMItEhAGnaSEXRBjtzh9fOtsE6E1G6p7vl7edEeZe11QHf18+6+9gR5PbKV/sGKNaD8YaMeA==",
+      "dev": true,
+      "dependencies": {
+        "follow-redirects": "^1.14.8"
+      }
+    },
    "node_modules/balanced-match": {
      "version": "1.0.2",
      "resolved": "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.2.tgz",
@@ -968,6 +1045,18 @@
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      "dev": true
    },
+    "node_modules/combined-stream": {
+      "version": "1.0.8",
+      "resolved": "https://registry.npmjs.org/combined-stream/-/combined-stream-1.0.8.tgz",
+      "integrity": "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==",
+      "dev": true,
+      "dependencies": {
+        "delayed-stream": "~1.0.0"
+      },
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
    "node_modules/command-line-args": {
      "version": "5.2.1",
      "resolved": "https://registry.npmjs.org/command-line-args/-/command-line-args-5.2.1.tgz",
@@ -1179,6 +1268,15 @@
        "url": "https://github.com/sponsors/ljharb"
      }
    },
+    "node_modules/delayed-stream": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz",
+      "integrity": "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==",
+      "dev": true,
+      "engines": {
+        "node": ">=0.4.0"
+      }
+    },
    "node_modules/diff": {
      "version": "4.0.2",
      "resolved": "https://registry.npmjs.org/diff/-/diff-4.0.2.tgz",
@@ -1942,6 +2040,26 @@
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      "dev": true
    },
+    "node_modules/follow-redirects": {
+      "version": "1.15.2",
+      "resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.2.tgz",
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+      "dev": true,
+      "funding": [
+        {
+          "type": "individual",
+          "url": "https://github.com/sponsors/RubenVerborgh"
+        }
+      ],
+      "engines": {
+        "node": ">=4.0"
+      },
+      "peerDependenciesMeta": {
+        "debug": {
+          "optional": true
+        }
+      }
+    },
    "node_modules/for-each": {
      "version": "0.3.3",
      "resolved": "https://registry.npmjs.org/for-each/-/for-each-0.3.3.tgz",
@@ -1951,6 +2069,20 @@
        "is-callable": "^1.1.3"
      }
    },
+    "node_modules/form-data": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.0.tgz",
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+      "dev": true,
+      "dependencies": {
+        "asynckit": "^0.4.0",
+        "combined-stream": "^1.0.8",
+        "mime-types": "^2.1.12"
+      },
+      "engines": {
+        "node": ">= 6"
+      }
+    },
    "node_modules/fs.realpath": {
      "version": "1.0.0",
      "resolved": "https://registry.npmjs.org/fs.realpath/-/fs.realpath-1.0.0.tgz",
@@ -2584,6 +2716,12 @@
        "url": "https://github.com/sponsors/ljharb"
      }
    },
+    "node_modules/isarray": {
+      "version": "0.0.1",
+      "resolved": "https://registry.npmjs.org/isarray/-/isarray-0.0.1.tgz",
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+      "dev": true
+    },
    "node_modules/isexe": {
      "version": "2.0.0",
      "resolved": "https://registry.npmjs.org/isexe/-/isexe-2.0.0.tgz",
@@ -2644,6 +2782,12 @@
        "json5": "lib/cli.js"
      }
    },
+    "node_modules/just-extend": {
+      "version": "4.2.1",
+      "resolved": "https://registry.npmjs.org/just-extend/-/just-extend-4.2.1.tgz",
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+      "dev": true
+    },
    "node_modules/levn": {
      "version": "0.4.1",
      "resolved": "https://registry.npmjs.org/levn/-/levn-0.4.1.tgz",
@@ -2677,6 +2821,12 @@
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    },
+    "node_modules/lodash.get": {
+      "version": "4.4.2",
+      "resolved": "https://registry.npmjs.org/lodash.get/-/lodash.get-4.4.2.tgz",
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+      "dev": true
+    },
    "node_modules/lodash.merge": {
      "version": "4.6.2",
      "resolved": "https://registry.npmjs.org/lodash.merge/-/lodash.merge-4.6.2.tgz",
@@ -2748,6 +2898,27 @@
        "node": ">=8.6"
      }
    },
+    "node_modules/mime-db": {
+      "version": "1.52.0",
+      "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
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+      "dev": true,
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/mime-types": {
+      "version": "2.1.35",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
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+      "dev": true,
+      "dependencies": {
+        "mime-db": "1.52.0"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
    "node_modules/minimatch": {
      "version": "3.1.2",
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@@ -2925,6 +3096,28 @@
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+        "nise": "^5.1.4",
+        "supports-color": "^7.2.0"
+      },
+      "dependencies": {
+        "diff": {
+          "version": "5.1.0",
+          "resolved": "https://registry.npmjs.org/diff/-/diff-5.1.0.tgz",
+          "integrity": "sha512-D+mk+qE8VC/PAUrlAU34N+VfXev0ghe5ywmpqrawphmVZc1bEfn56uo9qpyGp1p4xpzOHkSW4ztBd6L7Xx4ACw==",
+          "dev": true
+        },
+        "supports-color": {
+          "version": "7.2.0",
+          "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
+          "integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==",
+          "dev": true,
+          "requires": {
+            "has-flag": "^4.0.0"
+          }
+        }
+      }
+    },
    "slash": {
      "version": "3.0.0",
      "resolved": "https://registry.npmjs.org/slash/-/slash-3.0.0.tgz",
--- a/node/package.json
+++ b/node/package.json
@@ -1,6 +1,6 @@
 {
  "name": "vectordb",
-  "version": "0.1.1",
+  "version": "0.1.3",
  "description": " Serverless, low-latency vector database for AI applications",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
@@ -27,6 +27,7 @@
    "@types/chai": "^4.3.4",
    "@types/mocha": "^10.0.1",
    "@types/node": "^18.16.2",
+    "@types/sinon": "^10.0.15",
    "@types/temp": "^0.9.1",
    "@typescript-eslint/eslint-plugin": "^5.59.1",
    "cargo-cp-artifact": "^0.1",
@@ -37,6 +38,8 @@
    "eslint-plugin-n": "^15.7.0",
    "eslint-plugin-promise": "^6.1.1",
    "mocha": "^10.2.0",
+    "sinon": "^15.1.0",
+    "openai": "^3.2.1",
    "temp": "^0.9.4",
    "ts-node": "^10.9.1",
    "ts-node-dev": "^2.0.0",
--- a/node/src/arrow.ts
+++ b/node/src/arrow.ts
@@ -15,15 +15,16 @@
 import {
  Field,
  Float32,
-  List,
+  List, type ListBuilder,
  makeBuilder,
  RecordBatchFileWriter,
-  Table,
+  Table, Utf8,
  type Vector,
  vectorFromArray
 } from 'apache-arrow'
+import { type EmbeddingFunction } from './index'

-export function convertToTable (data: Array<Record<string, unknown>>): Table {
+export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table> {
  if (data.length === 0) {
    throw new Error('At least one record needs to be provided')
  }
@@ -33,11 +34,7 @@ export function convertToTable (data: Array<Record<string, unknown>>): Table {

  for (const columnsKey of columns) {
    if (columnsKey === 'vector') {
-      const children = new Field<Float32>('item', new Float32())
-      const list = new List(children)
-      const listBuilder = makeBuilder({
-        type: list
-      })
+      const listBuilder = newVectorListBuilder()
      const vectorSize = (data[0].vector as any[]).length
      for (const datum of data) {
        if ((datum[columnsKey] as any[]).length !== vectorSize) {
@@ -52,15 +49,37 @@ export function convertToTable (data: Array<Record<string, unknown>>): Table {
      for (const datum of data) {
        values.push(datum[columnsKey])
      }
-      records[columnsKey] = vectorFromArray(values)
+
+      if (columnsKey === embeddings?.sourceColumn) {
+        const vectors = await embeddings.embed(values as T[])
+        const listBuilder = newVectorListBuilder()
+        vectors.map(v => listBuilder.append(v))
+        records.vector = listBuilder.finish().toVector()
+      }
+
+      if (typeof values[0] === 'string') {
+        // `vectorFromArray` converts strings into dictionary vectors, forcing it back to a string column
+        records[columnsKey] = vectorFromArray(values, new Utf8())
+      } else {
+        records[columnsKey] = vectorFromArray(values)
+      }
    }
  }

  return new Table(records)
 }

-export async function fromRecordsToBuffer (data: Array<Record<string, unknown>>): Promise<Buffer> {
-  const table = convertToTable(data)
+// Creates a new Arrow ListBuilder that stores a Vector column
+function newVectorListBuilder (): ListBuilder<Float32, any> {
+  const children = new Field<Float32>('item', new Float32())
+  const list = new List(children)
+  return makeBuilder({
+    type: list
+  })
+}
+
+export async function fromRecordsToBuffer<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
+  const table = await convertToTable(data, embeddings)
  const writer = RecordBatchFileWriter.writeAll(table)
  return Buffer.from(await writer.toUint8Array())
 }
--- a/node/src/embedding/embedding_function.ts
+++ b/node/src/embedding/embedding_function.ts
@@ -0,0 +1,28 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/**
+ * An embedding function that automatically creates vector representation for a given column.
+ */
+export interface EmbeddingFunction<T> {
+  /**
+     * The name of the column that will be used as input for the Embedding Function.
+     */
+  sourceColumn: string
+
+  /**
+     * Creates a vector representation for the given values.
+     */
+  embed: (data: T[]) => Promise<number[][]>
+}
--- a/node/src/embedding/openai.ts
+++ b/node/src/embedding/openai.ts
@@ -0,0 +1,51 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { type EmbeddingFunction } from '../index'
+
+export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
+  private readonly _openai: any
+  private readonly _modelName: string
+
+  constructor (sourceColumn: string, openAIKey: string, modelName: string = 'text-embedding-ada-002') {
+    let openai
+    try {
+      // eslint-disable-next-line @typescript-eslint/no-var-requires
+      openai = require('openai')
+    } catch {
+      throw new Error('please install openai using npm install openai')
+    }
+
+    this.sourceColumn = sourceColumn
+    const configuration = new openai.Configuration({
+      apiKey: openAIKey
+    })
+    this._openai = new openai.OpenAIApi(configuration)
+    this._modelName = modelName
+  }
+
+  async embed (data: string[]): Promise<number[][]> {
+    const response = await this._openai.createEmbedding({
+      model: this._modelName,
+      input: data
+    })
+    const embeddings: number[][] = []
+    for (let i = 0; i < response.data.data.length; i++) {
+      embeddings.push(response.data.data[i].embedding as number[])
+    }
+    return embeddings
+  }
+
+  sourceColumn: string
+}
--- a/node/src/index.ts
+++ b/node/src/index.ts
@@ -19,16 +19,21 @@ import {
  Vector
 } from 'apache-arrow'
 import { fromRecordsToBuffer } from './arrow'
+import type { EmbeddingFunction } from './embedding/embedding_function'

 // eslint-disable-next-line @typescript-eslint/no-var-requires
-const { databaseNew, databaseTableNames, databaseOpenTable, tableCreate, tableSearch, tableAdd } = require('../native.js')
+const { databaseNew, databaseTableNames, databaseOpenTable, tableCreate, tableSearch, tableAdd, tableCreateVectorIndex } = require('../native.js')
+
+export type { EmbeddingFunction }
+export { OpenAIEmbeddingFunction } from './embedding/openai'

 /**
 * Connect to a LanceDB instance at the given URI
 * @param uri The uri of the database.
 */
 export async function connect (uri: string): Promise<Connection> {
-  return new Connection(uri)
+  const db = await databaseNew(uri)
+  return new Connection(db, uri)
 }

 /**
@@ -38,9 +43,9 @@ export class Connection {
  private readonly _uri: string
  private readonly _db: any

-  constructor (uri: string) {
+  constructor (db: any, uri: string) {
    this._uri = uri
-    this._db = databaseNew(uri)
+    this._db = db
  }

  get uri (): string {
@@ -55,17 +60,50 @@ export class Connection {
  }

  /**
-     * Open a table in the database.
-     * @param name The name of the table.
-     */
-  async openTable (name: string): Promise<Table> {
+   * Open a table in the database.
+   *
+   * @param name The name of the table.
+   */
+  async openTable (name: string): Promise<Table>
+  /**
+   * Open a table in the database.
+   *
+   * @param name The name of the table.
+   * @param embeddings An embedding function to use on this Table
+   */
+  async openTable<T> (name: string, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
    const tbl = await databaseOpenTable.call(this._db, name)
-    return new Table(tbl, name)
+    if (embeddings !== undefined) {
+      return new Table(tbl, name, embeddings)
+    } else {
+      return new Table(tbl, name)
+    }
  }

-  async createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table> {
-    await tableCreate.call(this._db, name, await fromRecordsToBuffer(data))
-    return await this.openTable(name)
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param name The name of the table.
+   * @param data Non-empty Array of Records to be inserted into the Table
+   */
+
+  async createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param name The name of the table.
+   * @param data Non-empty Array of Records to be inserted into the Table
+   * @param embeddings An embedding function to use on this Table
+   */
+  async createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  async createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
+    const tbl = await tableCreate.call(this._db, name, await fromRecordsToBuffer(data, embeddings))
+    if (embeddings !== undefined) {
+      return new Table(tbl, name, embeddings)
+    } else {
+      return new Table(tbl, name)
+    }
  }

  async createTableArrow (name: string, table: ArrowTable): Promise<Table> {
@@ -75,16 +113,22 @@ export class Connection {
  }
 }

-/**
- * A table in a LanceDB database.
- */
-export class Table {
+export class Table<T = number[]> {
  private readonly _tbl: any
  private readonly _name: string
+  private readonly _embeddings?: EmbeddingFunction<T>

-  constructor (tbl: any, name: string) {
+  constructor (tbl: any, name: string)
+  /**
+   * @param tbl
+   * @param name
+   * @param embeddings An embedding function to use when interacting with this table
+   */
+  constructor (tbl: any, name: string, embeddings: EmbeddingFunction<T>)
+  constructor (tbl: any, name: string, embeddings?: EmbeddingFunction<T>) {
    this._tbl = tbl
    this._name = name
+    this._embeddings = embeddings
  }

  get name (): string {
@@ -92,72 +136,176 @@ export class Table {
  }

  /**
-     * Create a search query to find the nearest neighbors of the given query vector.
-     * @param queryVector The query vector.
-     */
-  search (queryVector: number[]): Query {
-    return new Query(this._tbl, queryVector)
+   * Creates a search query to find the nearest neighbors of the given search term
+   * @param query The query search term
+   */
+  search (query: T): Query<T> {
+    return new Query(this._tbl, query, this._embeddings)
  }

  /**
-   * Insert records into this Table
-   * @param data Records to be inserted into the Table
+   * Insert records into this Table.
   *
-   * @param mode Append / Overwrite existing records. Default: Append
+   * @param data Records to be inserted into the Table
   * @return The number of rows added to the table
   */
  async add (data: Array<Record<string, unknown>>): Promise<number> {
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data), WriteMode.Append.toString())
+    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Append.toString())
  }

+  /**
+   * Insert records into this Table, replacing its contents.
+   *
+   * @param data Records to be inserted into the Table
+   * @return The number of rows added to the table
+   */
  async overwrite (data: Array<Record<string, unknown>>): Promise<number> {
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data), WriteMode.Overwrite.toString())
+    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString())
+  }
+
+  /**
+   * Create an ANN index on this Table vector index.
+   *
+   * @param indexParams The parameters of this Index, @see VectorIndexParams.
+   */
+  async create_index (indexParams: VectorIndexParams): Promise<any> {
+    return tableCreateVectorIndex.call(this._tbl, indexParams)
  }
 }

+interface IvfPQIndexConfig {
+  /**
+   * The column to be indexed
+   */
+  column?: string
+
+  /**
+   * A unique name for the index
+   */
+  index_name?: string
+
+  /**
+   * Metric type, L2 or Cosine
+   */
+  metric_type?: MetricType
+
+  /**
+   * The number of partitions this index
+   */
+  num_partitions?: number
+
+  /**
+   * The max number of iterations for kmeans training.
+   */
+  max_iters?: number
+
+  /**
+   * Train as optimized product quantization.
+   */
+  use_opq?: boolean
+
+  /**
+   * Number of subvectors to build PQ code
+   */
+  num_sub_vectors?: number
+  /**
+   * The number of bits to present one PQ centroid.
+   */
+  num_bits?: number
+
+  /**
+   * Max number of iterations to train OPQ, if `use_opq` is true.
+   */
+  max_opq_iters?: number
+
+  type: 'ivf_pq'
+}
+
+export type VectorIndexParams = IvfPQIndexConfig
+
 /**
 * A builder for nearest neighbor queries for LanceDB.
 */
-export class Query {
+export class Query<T = number[]> {
  private readonly _tbl: any
-  private readonly _query_vector: number[]
+  private readonly _query: T
+  private _queryVector?: number[]
  private _limit: number
-  private readonly _refine_factor?: number
-  private readonly _nprobes: number
+  private _refineFactor?: number
+  private _nprobes: number
  private readonly _columns?: string[]
  private _filter?: string
-  private readonly _metric = 'L2'
+  private _metricType?: MetricType
+  private readonly _embeddings?: EmbeddingFunction<T>

-  constructor (tbl: any, queryVector: number[]) {
+  constructor (tbl: any, query: T, embeddings?: EmbeddingFunction<T>) {
    this._tbl = tbl
-    this._query_vector = queryVector
+    this._query = query
    this._limit = 10
    this._nprobes = 20
-    this._refine_factor = undefined
+    this._refineFactor = undefined
    this._columns = undefined
    this._filter = undefined
+    this._metricType = undefined
+    this._embeddings = embeddings
  }

-  limit (value: number): Query {
+  /***
+   * Sets the number of results that will be returned
+   * @param value number of results
+   */
+  limit (value: number): Query<T> {
    this._limit = value
    return this
  }

-  filter (value: string): Query {
+  /**
+   * Refine the results by reading extra elements and re-ranking them in memory.
+   * @param value refine factor to use in this query.
+   */
+  refineFactor (value: number): Query<T> {
+    this._refineFactor = value
+    return this
+  }
+
+  /**
+   * The number of probes used. A higher number makes search more accurate but also slower.
+   * @param value The number of probes used.
+   */
+  nprobes (value: number): Query<T> {
+    this._nprobes = value
+    return this
+  }
+
+  /**
+   * A filter statement to be applied to this query.
+   * @param value A filter in the same format used by a sql WHERE clause.
+   */
+  filter (value: string): Query<T> {
    this._filter = value
    return this
  }

  /**
-     * Execute the query and return the results as an Array of Objects
-     */
+   * The MetricType used for this Query.
+   * @param value The metric to the. @see MetricType for the different options
+   */
+  metricType (value: MetricType): Query<T> {
+    this._metricType = value
+    return this
+  }
+
+  /**
+   * Execute the query and return the results as an Array of Objects
+   */
  async execute<T = Record<string, unknown>> (): Promise<T[]> {
-    let buffer
-    if (this._filter != null) {
-      buffer = await tableSearch.call(this._tbl, this._query_vector, this._limit, this._filter)
+    if (this._embeddings !== undefined) {
+      this._queryVector = (await this._embeddings.embed([this._query]))[0]
    } else {
-      buffer = await tableSearch.call(this._tbl, this._query_vector, this._limit)
+      this._queryVector = this._query as number[]
    }
+
+    const buffer = await tableSearch.call(this._tbl, this)
    const data = tableFromIPC(buffer)
    return data.toArray().map((entry: Record<string, unknown>) => {
      const newObject: Record<string, unknown> = {}
@@ -177,3 +325,18 @@ export enum WriteMode {
  Overwrite = 'overwrite',
  Append = 'append'
 }
+
+/**
+ * Distance metrics type.
+ */
+export enum MetricType {
+  /**
+   * Euclidean distance
+   */
+  L2 = 'l2',
+
+  /**
+   * Cosine distance
+   */
+  Cosine = 'cosine'
+}
--- a/node/src/test/embedding/openai.ts
+++ b/node/src/test/embedding/openai.ts
@@ -0,0 +1,50 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { describe } from 'mocha'
+import { assert } from 'chai'
+
+import { OpenAIEmbeddingFunction } from '../../embedding/openai'
+
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { OpenAIApi } = require('openai')
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { stub } = require('sinon')
+
+describe('OpenAPIEmbeddings', function () {
+  const stubValue = {
+    data: {
+      data: [
+        {
+          embedding: Array(1536).fill(1.0)
+        },
+        {
+          embedding: Array(1536).fill(2.0)
+        }
+      ]
+    }
+  }
+
+  describe('#embed', function () {
+    it('should create vector embeddings', async function () {
+      const openAIStub = stub(OpenAIApi.prototype, 'createEmbedding').returns(stubValue)
+      const f = new OpenAIEmbeddingFunction('text', 'sk-key')
+      const vectors = await f.embed(['abc', 'def'])
+      assert.isTrue(openAIStub.calledOnce)
+      assert.equal(vectors.length, 2)
+      assert.deepEqual(vectors[0], stubValue.data.data[0].embedding)
+      assert.deepEqual(vectors[1], stubValue.data.data[1].embedding)
+    })
+  })
+})
--- a/node/src/test/io.ts
+++ b/node/src/test/io.ts
@@ -0,0 +1,52 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// IO tests
+
+import { describe } from 'mocha'
+import { assert } from 'chai'
+
+import * as lancedb from '../index'
+
+describe('LanceDB S3 client', function () {
+  if (process.env.TEST_S3_BASE_URL != null) {
+    const baseUri = process.env.TEST_S3_BASE_URL
+    it('should have a valid url', async function () {
+      const uri = `${baseUri}/valid_url`
+      const table = await createTestDB(uri, 2, 20)
+      const con = await lancedb.connect(uri)
+      assert.equal(con.uri, uri)
+
+      const results = await table.search([0.1, 0.3]).limit(5).execute()
+      assert.equal(results.length, 5)
+    })
+  } else {
+    describe.skip('Skip S3 test', function () {})
+  }
+})
+
+async function createTestDB (uri: string, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
+  const con = await lancedb.connect(uri)
+
+  const data = []
+  for (let i = 0; i < numRows; i++) {
+    const vector = []
+    for (let j = 0; j < numDimensions; j++) {
+      vector.push(i + (j * 0.1))
+    }
+    data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
+  }
+
+  return await con.createTable('vectors', data)
+}
--- a/node/src/test/test.ts
+++ b/node/src/test/test.ts
@@ -17,6 +17,7 @@ import { assert } from 'chai'
 import { track } from 'temp'

 import * as lancedb from '../index'
+import { type EmbeddingFunction, MetricType, Query } from '../index'

 describe('LanceDB client', function () {
  describe('when creating a connection to lancedb', function () {
@@ -67,7 +68,7 @@ describe('LanceDB client', function () {
      const uri = await createTestDB()
      const con = await lancedb.connect(uri)
      const table = await con.openTable('vectors')
-      const results = await table.search([0.1, 0.3]).filter('id == 2').execute()
+      const results = await table.search([0.1, 0.1]).filter('id == 2').execute()
      assert.equal(results.length, 1)
      assert.equal(results[0].id, 2)
    })
@@ -96,8 +97,8 @@ describe('LanceDB client', function () {
      const con = await lancedb.connect(dir)

      const data = [
-        { id: 1, vector: [0.1, 0.2], price: 10 },
-        { id: 2, vector: [1.1, 1.2], price: 50 }
+        { id: 1, vector: [0.1, 0.2], price: 10, name: 'a' },
+        { id: 2, vector: [1.1, 1.2], price: 50, name: 'b' }
      ]

      const table = await con.createTable('vectors', data)
@@ -105,8 +106,8 @@ describe('LanceDB client', function () {
      assert.equal(results.length, 2)

      const dataAdd = [
-        { id: 3, vector: [2.1, 2.2], price: 10 },
-        { id: 4, vector: [3.1, 3.2], price: 50 }
+        { id: 3, vector: [2.1, 2.2], price: 10, name: 'c' },
+        { id: 4, vector: [3.1, 3.2], price: 50, name: 'd' }
      ]
      await table.add(dataAdd)
      const resultsAdd = await table.search([0.1, 0.3]).execute()
@@ -130,16 +131,76 @@ describe('LanceDB client', function () {
      assert.equal(resultsAdd.length, 2)
    })
  })
+
+  describe('when creating a vector index', function () {
+    it('overwrite all records in a table', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      await table.create_index({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2 })
+    }).timeout(10_000) // Timeout is high partially because GH macos runner is pretty slow
+  })
+
+  describe('when using a custom embedding function', function () {
+    class TextEmbedding implements EmbeddingFunction<string> {
+      sourceColumn: string
+
+      constructor (targetColumn: string) {
+        this.sourceColumn = targetColumn
+      }
+
+      _embedding_map = new Map<string, number[]>([
+        ['foo', [2.1, 2.2]],
+        ['bar', [3.1, 3.2]]
+      ])
+
+      async embed (data: string[]): Promise<number[][]> {
+        return data.map(datum => this._embedding_map.get(datum) ?? [0.0, 0.0])
+      }
+    }
+
+    it('should encode the original data into embeddings', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+      const embeddings = new TextEmbedding('name')
+
+      const data = [
+        { price: 10, name: 'foo' },
+        { price: 50, name: 'bar' }
+      ]
+      const table = await con.createTable('vectors', data, embeddings)
+      const results = await table.search('foo').execute()
+      assert.equal(results.length, 2)
+    })
+  })
 })

-async function createTestDB (): Promise<string> {
+describe('Query object', function () {
+  it('sets custom parameters', async function () {
+    const query = new Query(undefined, [0.1, 0.3])
+      .limit(1)
+      .metricType(MetricType.Cosine)
+      .refineFactor(100)
+      .nprobes(20) as Record<string, any>
+    assert.equal(query._limit, 1)
+    assert.equal(query._metricType, MetricType.Cosine)
+    assert.equal(query._refineFactor, 100)
+    assert.equal(query._nprobes, 20)
+  })
+})
+
+async function createTestDB (numDimensions: number = 2, numRows: number = 2): Promise<string> {
  const dir = await track().mkdir('lancejs')
  const con = await lancedb.connect(dir)

-  const data = [
-    { id: 1, vector: [0.1, 0.2], name: 'foo', price: 10, is_active: true },
-    { id: 2, vector: [1.1, 1.2], name: 'bar', price: 50, is_active: false }
-  ]
+  const data = []
+  for (let i = 0; i < numRows; i++) {
+    const vector = []
+    for (let j = 0; j < numDimensions; j++) {
+      vector.push(i + (j * 0.1))
+    }
+    data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
+  }

  await con.createTable('vectors', data)
  return dir
--- a/notebooks/diffusiondb/datagen.py
+++ b/notebooks/diffusiondb/datagen.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+#
+#  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.
+
+"""Dataset hf://poloclub/diffusiondb
+"""
+
+import io
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+import lance
+import lancedb
+import pyarrow as pa
+from datasets import load_dataset
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
+
+MODEL_ID = "openai/clip-vit-base-patch32"
+
+device = "cuda"
+
+tokenizer = CLIPTokenizerFast.from_pretrained(MODEL_ID)
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+schema = pa.schema(
+    [
+        pa.field("prompt", pa.string()),
+        pa.field("seed", pa.uint32()),
+        pa.field("step", pa.uint16()),
+        pa.field("cfg", pa.float32()),
+        pa.field("sampler", pa.string()),
+        pa.field("width", pa.uint16()),
+        pa.field("height", pa.uint16()),
+        pa.field("timestamp", pa.timestamp("s")),
+        pa.field("image_nsfw", pa.float32()),
+        pa.field("prompt_nsfw", pa.float32()),
+        pa.field("vector", pa.list_(pa.float32(), 512)),
+        pa.field("image", pa.binary()),
+    ]
+)
+
+
+def pil_to_bytes(img) -> list[bytes]:
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def generate_clip_embeddings(batch) -> pa.RecordBatch:
+    image = processor(text=None, images=batch["image"], return_tensors="pt")[
+        "pixel_values"
+    ].to(device)
+    img_emb = model.get_image_features(image)
+    batch["vector"] = img_emb.cpu().tolist()
+
+    with Pool() as p:
+        batch["image_bytes"] = p.map(pil_to_bytes, batch["image"])
+    return batch
+
+
+def datagen(args):
+    """Generate DiffusionDB dataset, and use CLIP model to generate image embeddings."""
+    dataset = load_dataset("poloclub/diffusiondb", args.subset)
+    data = []
+    for b in dataset.map(
+        generate_clip_embeddings, batched=True, batch_size=256, remove_columns=["image"]
+    )["train"]:
+        b["image"] = b["image_bytes"]
+        del b["image_bytes"]
+        data.append(b)
+    tbl = pa.Table.from_pylist(data, schema=schema)
+    return tbl
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "-o", "--output", metavar="DIR", help="Output lance directory", required=True
+    )
+    parser.add_argument(
+        "-s",
+        "--subset",
+        choices=["2m_all", "2m_first_10k", "2m_first_100k"],
+        default="2m_first_10k",
+        help="subset of the hg dataset",
+    )
+
+    args = parser.parse_args()
+
+    batches = datagen(args)
+    lance.write_dataset(batches, args.output)
+
+
+if __name__ == "__main__":
+    main()
--- a/notebooks/diffusiondb/requirements.txt
+++ b/notebooks/diffusiondb/requirements.txt
@@ -0,0 +1,9 @@
+datasets
+Pillow
+lancedb
+isort
+black
+transformers
+--index-url https://download.pytorch.org/whl/cu118
+torch
+torchvision
--- a/notebooks/multimodal_search.ipynb
+++ b/notebooks/multimodal_search.ipynb
@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --quiet -U lancedb\n",
+    "!pip install --quiet gradio transformers torch torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import PIL\n",
+    "import duckdb\n",
+    "import lancedb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## First run setup: Download data and pre-process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<lance.dataset.LanceDataset at 0x3045db590>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove null prompts\n",
+    "import lance\n",
+    "import pyarrow.compute as pc\n",
+    "\n",
+    "# download s3://eto-public/datasets/diffusiondb/small_10k.lance to this uri\n",
+    "data = lance.dataset(\"~/datasets/rawdata.lance\").to_table()\n",
+    "\n",
+    "# First data processing and full-text-search index\n",
+    "db = lancedb.connect(\"~/datasets/demo\")\n",
+    "tbl = db.create_table(\"diffusiondb\", data.filter(~pc.field(\"prompt\").is_null()))\n",
+    "tbl = tbl.create_fts_index([\"prompt\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create / Open LanceDB Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = lancedb.connect(\"~/datasets/demo\")\n",
+    "tbl = db.open_table(\"diffusiondb\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create CLIP embedding function for the text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast\n",
+    "\n",
+    "MODEL_ID = \"openai/clip-vit-base-patch32\"\n",
+    "\n",
+    "tokenizer = CLIPTokenizerFast.from_pretrained(MODEL_ID)\n",
+    "model = CLIPModel.from_pretrained(MODEL_ID)\n",
+    "processor = CLIPProcessor.from_pretrained(MODEL_ID)\n",
+    "\n",
+    "def embed_func(query):\n",
+    "    inputs = tokenizer([query], padding=True, return_tensors=\"pt\")\n",
+    "    text_features = model.get_text_features(**inputs)\n",
+    "    return text_features.detach().numpy()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search functions for Gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_image_vectors(query):\n",
+    "    emb = embed_func(query)\n",
+    "    return _extract(tbl.search(emb).limit(9).to_df())\n",
+    "\n",
+    "def find_image_keywords(query):\n",
+    "    return _extract(tbl.search(query).limit(9).to_df())\n",
+    "\n",
+    "def find_image_sql(query):\n",
+    "    diffusiondb = tbl.to_lance()\n",
+    "    return _extract(duckdb.query(query).to_df())\n",
+    "\n",
+    "def _extract(df):\n",
+    "    image_col = \"image\"\n",
+    "    return [(PIL.Image.open(io.BytesIO(row[image_col])), row[\"prompt\"]) for _, row in df.iterrows()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Gradio interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7867\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        with gr.Tab(\"Embeddings\"):\n",
+    "            vector_query = gr.Textbox(value=\"portraits of a person\", show_label=False)\n",
+    "            b1 = gr.Button(\"Submit\")\n",
+    "        with gr.Tab(\"Keywords\"):\n",
+    "            keyword_query = gr.Textbox(value=\"ninja turtle\", show_label=False)\n",
+    "            b2 = gr.Button(\"Submit\")\n",
+    "        with gr.Tab(\"SQL\"):\n",
+    "            sql_query = gr.Textbox(value=\"SELECT * from diffusiondb WHERE image_nsfw >= 2 LIMIT 9\", show_label=False)\n",
+    "            b3 = gr.Button(\"Submit\")\n",
+    "    with gr.Row():\n",
+    "        gallery = gr.Gallery(\n",
+    "                label=\"Found images\", show_label=False, elem_id=\"gallery\"\n",
+    "            ).style(columns=[3], rows=[3], object_fit=\"contain\", height=\"auto\")   \n",
+    "        \n",
+    "    b1.click(find_image_vectors, inputs=vector_query, outputs=gallery)\n",
+    "    b2.click(find_image_keywords, inputs=keyword_query, outputs=gallery)\n",
+    "    b3.click(find_image_sql, inputs=sql_query, outputs=gallery)\n",
+    "    \n",
+    "demo.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
--- a/python/lancedb/db.py
+++ b/python/lancedb/db.py
@@ -13,13 +13,16 @@

 from __future__ import annotations

+import os
 from pathlib import Path
+import os

 import pyarrow as pa
+from pyarrow import fs

 from .common import DATA, URI
 from .table import LanceTable
-from .util import get_uri_scheme
+from .util import get_uri_scheme, get_uri_location


 class LanceDBConnection:
@@ -47,11 +50,20 @@ class LanceDBConnection:
        -------
        A list of table names.
        """
-        if get_uri_scheme(self.uri) == "file":
-            return [p.stem for p in Path(self.uri).glob("*.lance")]
-        raise NotImplementedError(
-            "List table_names is only supported for local filesystem for now"
-        )
+        try:
+            filesystem, path = fs.FileSystem.from_uri(self.uri)
+        except pa.ArrowInvalid:
+            raise NotImplementedError(
+                "Unsupported scheme: " + self.uri
+            )
+
+        try:
+            paths = filesystem.get_file_info(fs.FileSelector(get_uri_location(self.uri)))
+        except FileNotFoundError:
+            # It is ok if the file does not exist since it will be created
+            paths = []
+        tables = [os.path.splitext(file_info.base_name)[0] for file_info in paths if file_info.extension == 'lance']
+        return tables

    def __len__(self) -> int:
        return len(self.table_names())
@@ -112,3 +124,15 @@ class LanceDBConnection:
        A LanceTable object representing the table.
        """
        return LanceTable(self, name)
+
+    def drop_table(self, name: str):
+        """Drop a table from the database.
+
+        Parameters
+        ----------
+        name: str
+            The name of the table.
+        """
+        filesystem, path = pa.fs.FileSystem.from_uri(self.uri)
+        table_path = os.path.join(path, name + ".lance")
+        filesystem.delete_dir(table_path)
--- a/python/lancedb/fts.py
+++ b/python/lancedb/fts.py
@@ -0,0 +1,128 @@
+#  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.
+
+"""Full text search index using tantivy-py"""
+import os
+from typing import List, Tuple
+
+import pyarrow as pa
+
+try:
+    import tantivy
+except ImportError:
+    raise ImportError(
+        "Please install tantivy-py `pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985` to use the full text search feature."
+    )
+
+from .table import LanceTable
+
+
+def create_index(index_path: str, text_fields: List[str]) -> tantivy.Index:
+    """
+    Create a new Index (not populated)
+
+    Parameters
+    ----------
+    index_path : str
+        Path to the index directory
+    text_fields : List[str]
+        List of text fields to index
+
+    Returns
+    -------
+    index : tantivy.Index
+        The index object (not yet populated)
+    """
+    # Declaring our schema.
+    schema_builder = tantivy.SchemaBuilder()
+    # special field that we'll populate with row_id
+    schema_builder.add_integer_field("doc_id", stored=True)
+    # data fields
+    for name in text_fields:
+        schema_builder.add_text_field(name, stored=True)
+    schema = schema_builder.build()
+    os.makedirs(index_path, exist_ok=True)
+    index = tantivy.Index(schema, path=index_path)
+    return index
+
+
+def populate_index(index: tantivy.Index, table: LanceTable, fields: List[str]) -> int:
+    """
+    Populate an index with data from a LanceTable
+
+    Parameters
+    ----------
+    index : tantivy.Index
+        The index object
+    table : LanceTable
+        The table to index
+    fields : List[str]
+        List of fields to index
+    """
+    # first check the fields exist and are string or large string type
+    for name in fields:
+        f = table.schema.field(name)  # raises KeyError if not found
+        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")
+
+    # create a tantivy writer
+    writer = index.writer()
+    # write data into index
+    dataset = table.to_lance()
+    row_id = 0
+    for b in dataset.to_batches(columns=fields):
+        for i in range(b.num_rows):
+            doc = tantivy.Document()
+            doc.add_integer("doc_id", row_id)
+            for name in fields:
+                doc.add_text(name, b[name][i].as_py())
+            writer.add_document(doc)
+            row_id += 1
+    # commit changes
+    writer.commit()
+    return row_id
+
+
+def search_index(
+    index: tantivy.Index, query: str, limit: int = 10
+) -> Tuple[Tuple[int], Tuple[float]]:
+    """
+    Search an index for a query
+
+    Parameters
+    ----------
+    index : tantivy.Index
+        The index object
+    query : str
+        The query string
+    limit : int
+        The maximum number of results to return
+
+    Returns
+    -------
+    ids_and_score: list[tuple[int], tuple[float]]
+        A tuple of two tuples, the first containing the document ids
+        and the second containing the scores
+    """
+    searcher = index.searcher()
+    query = index.parse_query(query)
+    # get top results
+    results = searcher.search(query, limit)
+    return tuple(
+        zip(
+            *[
+                (searcher.doc(doc_address)["doc_id"][0], score)
+                for score, doc_address in results.hits
+            ]
+        )
+    )
--- a/python/lancedb/query.py
+++ b/python/lancedb/query.py
@@ -14,6 +14,7 @@ from __future__ import annotations

 import numpy as np
 import pandas as pd
+import pyarrow as pa

 from .common import VECTOR_COLUMN_NAME

@@ -131,7 +132,6 @@ class LanceQueryBuilder:
        vector and the returned vector.
        """
        ds = self._table.to_lance()
-        # TODO indexed search
        tbl = ds.to_table(
            columns=self._columns,
            filter=self._where,
@@ -145,3 +145,26 @@ class LanceQueryBuilder:
            },
        )
        return tbl.to_pandas()
+
+
+class LanceFtsQueryBuilder(LanceQueryBuilder):
+    def to_df(self) -> pd.DataFrame:
+        try:
+            import tantivy
+        except ImportError:
+            raise ImportError(
+                "Please install tantivy-py `pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985` to use the full text search feature."
+            )
+
+        from .fts import search_index
+
+        # get the index path
+        index_path = self._table._get_fts_index_path()
+        # open the index
+        index = tantivy.Index.open(index_path)
+        # get the scores and doc ids
+        row_ids, scores = search_index(index, self._query, self._limit)
+        scores = pa.array(scores)
+        output_tbl = self._table.to_lance().take(row_ids, columns=self._columns)
+        output_tbl = output_tbl.append_column("score", scores)
+        return output_tbl.to_pandas()
--- a/python/lancedb/table.py
+++ b/python/lancedb/table.py
@@ -14,7 +14,9 @@
 from __future__ import annotations

 import os
+import shutil
 from functools import cached_property
+from typing import List, Union

 import lance
 import numpy as np
@@ -24,7 +26,8 @@ from lance import LanceDataset
 from lance.vector import vec_to_table

 from .common import DATA, VEC, VECTOR_COLUMN_NAME
-from .query import LanceQueryBuilder
+from .query import LanceFtsQueryBuilder, LanceQueryBuilder
+from .util import get_uri_scheme


 def _sanitize_data(data, schema):
@@ -130,6 +133,27 @@ class LanceTable:
        )
        self._reset_dataset()

+    def create_fts_index(self, field_names: Union[str, List[str]]):
+        """Create a full-text search index on the table.
+
+        Warning - this API is highly experimental and is highly likely to change
+        in the future.
+
+        Parameters
+        ----------
+        field_names: str or list of str
+            The name(s) of the field to index.
+        """
+        from .fts import create_index, populate_index
+
+        if isinstance(field_names, str):
+            field_names = [field_names]
+        index = create_index(self._get_fts_index_path(), field_names)
+        populate_index(index, self, field_names)
+
+    def _get_fts_index_path(self):
+        return os.path.join(self._dataset_uri, "_indices", "tantivy")
+
    @cached_property
    def _dataset(self) -> LanceDataset:
        return lance.dataset(self._dataset_uri, version=self._version)
@@ -158,7 +182,7 @@ class LanceTable:
        self._reset_dataset()
        return len(self)

-    def search(self, query: VEC) -> LanceQueryBuilder:
+    def search(self, query: Union[VEC, str]) -> LanceQueryBuilder:
        """Create a search query to find the nearest neighbors
        of the given query vector.

@@ -174,6 +198,10 @@ class LanceTable:
        and also the "score" column which is the distance between the query
        vector and the returned vector.
        """
+        if isinstance(query, str):
+            # fts
+            return LanceFtsQueryBuilder(self, query)
+
        if isinstance(query, list):
            query = np.array(query)
        if isinstance(query, np.ndarray):
@@ -225,8 +253,7 @@ def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table
    vector_column_name: str
        The name of the vector column.
    """
-    i = data.column_names.index(vector_column_name)
-    if i < 0:
+    if vector_column_name not in data.column_names:
        raise ValueError(f"Missing vector column: {vector_column_name}")
    vec_arr = data[vector_column_name].combine_chunks()
    if pa.types.is_fixed_size_list(vec_arr.type):
@@ -238,4 +265,4 @@ def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table
        values = values.cast(pa.float32())
    list_size = len(values) / len(data)
    vec_arr = pa.FixedSizeListArray.from_arrays(values, list_size)
-    return data.set_column(i, vector_column_name, vec_arr)
+    return data.set_column(data.column_names.index(vector_column_name), vector_column_name, vec_arr)
--- a/python/lancedb/util.py
+++ b/python/lancedb/util.py
@@ -41,3 +41,23 @@ def get_uri_scheme(uri: str) -> str:
        # So we add special handling here for schemes that are a single character
        scheme = "file"
    return scheme
+
+
+def get_uri_location(uri: str) -> str:
+    """
+    Get the location of a URI. If the parameter is not a url, assumes it is just a path
+
+    Parameters
+    ----------
+    uri : str
+        The URI to parse.
+
+    Returns
+    -------
+    str: Location part of the URL, without scheme
+    """
+    parsed = urlparse(uri)
+    if not parsed.netloc:
+        return parsed.path
+    else:
+        return parsed.netloc + parsed.path
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "lancedb"
-version = "0.1.2"
-dependencies = ["pylance>=0.4.6", "ratelimiter", "retry", "tqdm"]
+version = "0.1.5"
+dependencies = ["pylance>=0.4.17", "ratelimiter", "retry", "tqdm"]
 description = "lancedb"
 authors = [
    { name = "LanceDB Devs", email = "dev@lancedb.com" },
--- a/python/tests/test_db.py
+++ b/python/tests/test_db.py
@@ -97,3 +97,26 @@ def test_create_mode(tmp_path):
    )
    tbl = db.create_table("test", data=new_data, mode="overwrite")
    assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"]
+
+
+def test_delete_table(tmp_path):
+    db = lancedb.connect(tmp_path)
+    data = pd.DataFrame(
+        {
+            "vector": [[3.1, 4.1], [5.9, 26.5]],
+            "item": ["foo", "bar"],
+            "price": [10.0, 20.0],
+        }
+    )
+    db.create_table("test", data=data)
+
+    with pytest.raises(Exception):
+        db.create_table("test", data=data)
+
+    assert db.table_names() == ["test"]
+
+    db.drop_table("test")
+    assert db.table_names() == []
+
+    db.create_table("test", data=data)
+    assert db.table_names() == ["test"]
--- a/python/tests/test_embeddings.py
+++ b/python/tests/test_embeddings.py
@@ -14,7 +14,6 @@ import sys

 import numpy as np
 import pyarrow as pa
-
 from lancedb.embeddings import with_embeddings


--- a/python/tests/test_fts.py
+++ b/python/tests/test_fts.py
@@ -0,0 +1,84 @@
+# 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 os
+import random
+
+import lancedb.fts
+import numpy as np
+import pandas as pd
+import pytest
+import tantivy
+
+import lancedb as ldb
+
+
+@pytest.fixture
+def table(tmp_path) -> ldb.table.LanceTable:
+    db = ldb.connect(tmp_path)
+    vectors = [np.random.randn(128) for _ in range(100)]
+
+    nouns = ("puppy", "car", "rabbit", "girl", "monkey")
+    verbs = ("runs", "hits", "jumps", "drives", "barfs")
+    adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.")
+    adj = ("adorable", "clueless", "dirty", "odd", "stupid")
+    text = [
+        " ".join(
+            [
+                nouns[random.randrange(0, 5)],
+                verbs[random.randrange(0, 5)],
+                adv[random.randrange(0, 5)],
+                adj[random.randrange(0, 5)],
+            ]
+        )
+        for _ in range(100)
+    ]
+    table = db.create_table(
+        "test", data=pd.DataFrame({"vector": vectors, "text": text, "text2": text})
+    )
+    return table
+
+
+def test_create_index(tmp_path):
+    index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
+    assert isinstance(index, tantivy.Index)
+    assert os.path.exists(str(tmp_path / "index"))
+
+
+def test_populate_index(tmp_path, table):
+    index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
+    assert ldb.fts.populate_index(index, table, ["text"]) == len(table)
+
+
+def test_search_index(tmp_path, table):
+    index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
+    ldb.fts.populate_index(index, table, ["text"])
+    index.reload()
+    results = ldb.fts.search_index(index, query="puppy", limit=10)
+    assert len(results) == 2
+    assert len(results[0]) == 10  # row_ids
+    assert len(results[1]) == 10  # scores
+
+
+def test_create_index_from_table(tmp_path, table):
+    table.create_fts_index("text")
+    df = table.search("puppy").limit(10).select(["text"]).to_df()
+    assert len(df) == 10
+    assert "text" in df.columns
+
+
+def test_create_index_multiple_columns(tmp_path, table):
+    table.create_fts_index(["text", "text2"])
+    df = table.search("puppy").limit(10).to_df()
+    assert len(df) == 10
+    assert "text" in df.columns
+    assert "text2" in df.columns
--- a/python/tests/test_io.py
+++ b/python/tests/test_io.py
@@ -0,0 +1,49 @@
+#  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 os
+import pytest
+
+import lancedb
+
+# You need to setup AWS credentials an a base path to run this test. Example
+#    AWS_PROFILE=default TEST_S3_BASE_URL=s3://my_bucket/dataset pytest tests/test_io.py
+
+@pytest.mark.skipif(
+    (os.environ.get("TEST_S3_BASE_URL") is None),
+    reason="please setup s3 base url",
+)
+def test_s3_io():
+    db = lancedb.connect(os.environ.get("TEST_S3_BASE_URL"))
+    assert db.table_names() == []
+
+    table = db.create_table(
+        "test",
+        data=[
+            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+            {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+        ],
+    )
+    rs = table.search([100, 100]).limit(1).to_df()
+    assert len(rs) == 1
+    assert rs["item"].iloc[0] == "bar"
+
+    rs = table.search([100, 100]).where("price < 15").limit(2).to_df()
+    assert len(rs) == 1
+    assert rs["item"].iloc[0] == "foo"
+
+    assert db.table_names() == ["test"]
+    assert "test" in db
+    assert len(db) == 1
+
+    assert db.open_table("test").name == db["test"].name
--- a/python/tests/test_query.py
+++ b/python/tests/test_query.py
@@ -17,7 +17,6 @@ import pandas as pd
 import pandas.testing as tm
 import pyarrow as pa
 import pytest
-
 from lancedb.query import LanceQueryBuilder


--- a/python/tests/test_table.py
+++ b/python/tests/test_table.py
@@ -16,7 +16,6 @@ from pathlib import Path
 import pandas as pd
 import pyarrow as pa
 import pytest
-
 from lancedb.table import LanceTable


--- a/rust/ffi/node/Cargo.toml
+++ b/rust/ffi/node/Cargo.toml
@@ -15,7 +15,7 @@ arrow-ipc = "37.0"
 arrow-schema = "37.0"
 once_cell = "1"
 futures = "0.3"
-lance = "0.4.3"
+lance = "0.4.17"
 vectordb = { path = "../../vectordb" }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 neon = {version = "0.10.1", default-features = false, features = ["channel-api", "napi-6", "promise-api", "task-api"] }
--- a/rust/ffi/node/src/index.rs
+++ b/rust/ffi/node/src/index.rs
@@ -0,0 +1,15 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+pub mod vector;
--- a/rust/ffi/node/src/index/vector.rs
+++ b/rust/ffi/node/src/index/vector.rs
@@ -0,0 +1,128 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+use std::convert::TryFrom;
+
+use lance::index::vector::ivf::IvfBuildParams;
+use lance::index::vector::pq::PQBuildParams;
+use lance::index::vector::MetricType;
+use neon::context::FunctionContext;
+use neon::prelude::*;
+
+use vectordb::index::vector::{IvfPQIndexBuilder, VectorIndexBuilder};
+
+use crate::{runtime, JsTable};
+
+pub(crate) fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsPromise> {
+    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+    let index_params = cx.argument::<JsObject>(0)?;
+    let index_params_builder = get_index_params_builder(&mut cx, index_params).unwrap();
+
+    let rt = runtime(&mut cx)?;
+    let channel = cx.channel();
+
+    let (deferred, promise) = cx.promise();
+    let table = js_table.table.clone();
+
+    rt.block_on(async move {
+        let add_result = table
+            .lock()
+            .unwrap()
+            .create_index(&index_params_builder)
+            .await;
+
+        deferred.settle_with(&channel, move |mut cx| {
+            add_result
+                .map(|_| cx.undefined())
+                .or_else(|err| cx.throw_error(err.to_string()))
+        });
+    });
+    Ok(promise)
+}
+
+fn get_index_params_builder(
+    cx: &mut FunctionContext,
+    obj: Handle<JsObject>,
+) -> Result<impl VectorIndexBuilder, String> {
+    let idx_type = obj
+        .get::<JsString, _, _>(cx, "type")
+        .map_err(|t| t.to_string())?
+        .value(cx);
+
+    match idx_type.as_str() {
+        "ivf_pq" => {
+            let mut index_builder: IvfPQIndexBuilder = IvfPQIndexBuilder::new();
+            let mut pq_params = PQBuildParams::default();
+
+            obj.get_opt::<JsString, _, _>(cx, "column")
+                .map_err(|t| t.to_string())?
+                .map(|s| index_builder.column(s.value(cx)));
+
+            obj.get_opt::<JsString, _, _>(cx, "index_name")
+                .map_err(|t| t.to_string())?
+                .map(|s| index_builder.index_name(s.value(cx)));
+
+            obj.get_opt::<JsString, _, _>(cx, "metric_type")
+                .map_err(|t| t.to_string())?
+                .map(|s| MetricType::try_from(s.value(cx).as_str()))
+                .map(|mt| {
+                    let metric_type = mt.unwrap();
+                    index_builder.metric_type(metric_type);
+                    pq_params.metric_type = metric_type;
+                });
+
+            let num_partitions = obj
+                .get_opt::<JsNumber, _, _>(cx, "num_partitions")
+                .map_err(|t| t.to_string())?
+                .map(|s| s.value(cx) as usize);
+
+            let max_iters = obj
+                .get_opt::<JsNumber, _, _>(cx, "max_iters")
+                .map_err(|t| t.to_string())?
+                .map(|s| s.value(cx) as usize);
+
+            num_partitions.map(|np| {
+                let max_iters = max_iters.unwrap_or(50);
+                let ivf_params = IvfBuildParams {
+                    num_partitions: np,
+                    max_iters,
+                };
+                index_builder.ivf_params(ivf_params)
+            });
+
+            obj.get_opt::<JsBoolean, _, _>(cx, "use_opq")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.use_opq = s.value(cx));
+
+            obj.get_opt::<JsNumber, _, _>(cx, "num_sub_vectors")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.num_sub_vectors = s.value(cx) as usize);
+
+            obj.get_opt::<JsNumber, _, _>(cx, "num_bits")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.num_bits = s.value(cx) as usize);
+
+            obj.get_opt::<JsNumber, _, _>(cx, "max_iters")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.max_iters = s.value(cx) as usize);
+
+            obj.get_opt::<JsNumber, _, _>(cx, "max_opq_iters")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.max_opq_iters = s.value(cx) as usize);
+
+            Ok(index_builder)
+        }
+        t => Err(format!("{} is not a valid index type", t).to_string()),
+    }
+}
--- a/rust/ffi/node/src/lib.rs
+++ b/rust/ffi/node/src/lib.rs
@@ -13,6 +13,7 @@
 // limitations under the License.

 use std::collections::HashMap;
+use std::convert::TryFrom;
 use std::ops::Deref;
 use std::sync::{Arc, Mutex};

@@ -21,6 +22,7 @@ use arrow_ipc::writer::FileWriter;
 use futures::{TryFutureExt, TryStreamExt};
 use lance::arrow::RecordBatchBuffer;
 use lance::dataset::WriteMode;
+use lance::index::vector::MetricType;
 use neon::prelude::*;
 use neon::types::buffer::TypedArray;
 use once_cell::sync::OnceCell;
@@ -34,17 +36,18 @@ use crate::arrow::arrow_buffer_to_record_batch;

 mod arrow;
 mod convert;
+mod index;

 struct JsDatabase {
    database: Arc<Database>,
 }

+impl Finalize for JsDatabase {}
+
 struct JsTable {
    table: Arc<Mutex<Table>>,
 }

-impl Finalize for JsDatabase {}
-
 impl Finalize for JsTable {}

 fn runtime<'a, C: Context<'a>>(cx: &mut C) -> NeonResult<&'static Runtime> {
@@ -53,23 +56,46 @@ fn runtime<'a, C: Context<'a>>(cx: &mut C) -> NeonResult<&'static Runtime> {
    RUNTIME.get_or_try_init(|| Runtime::new().or_else(|err| cx.throw_error(err.to_string())))
 }

-fn database_new(mut cx: FunctionContext) -> JsResult<JsBox<JsDatabase>> {
+fn database_new(mut cx: FunctionContext) -> JsResult<JsPromise> {
    let path = cx.argument::<JsString>(0)?.value(&mut cx);
-    let db = JsDatabase {
-        database: Arc::new(Database::connect(path).or_else(|err| cx.throw_error(err.to_string()))?),
-    };
-    Ok(cx.boxed(db))
+
+    let rt = runtime(&mut cx)?;
+    let channel = cx.channel();
+    let (deferred, promise) = cx.promise();
+
+    rt.spawn(async move {
+        let database = Database::connect(&path).await;
+
+        deferred.settle_with(&channel, move |mut cx| {
+            let db = JsDatabase {
+                database: Arc::new(database.or_else(|err| cx.throw_error(err.to_string()))?),
+            };
+            Ok(cx.boxed(db))
+        });
+    });
+    Ok(promise)
 }

-fn database_table_names(mut cx: FunctionContext) -> JsResult<JsArray> {
+fn database_table_names(mut cx: FunctionContext) -> JsResult<JsPromise> {
    let db = cx
        .this()
        .downcast_or_throw::<JsBox<JsDatabase>, _>(&mut cx)?;
-    let tables = db
-        .database
-        .table_names()
-        .or_else(|err| cx.throw_error(err.to_string()))?;
-    convert::vec_str_to_array(&tables, &mut cx)
+
+    let rt = runtime(&mut cx)?;
+    let (deferred, promise) = cx.promise();
+    let channel = cx.channel();
+    let database = db.database.clone();
+
+    rt.spawn(async move {
+        let tables_rst = database.table_names().await;
+
+        deferred.settle_with(&channel, move |mut cx| {
+            let tables = tables_rst.or_else(|err| cx.throw_error(err.to_string()))?;
+            let table_names = convert::vec_str_to_array(&tables, &mut cx);
+            table_names
+        });
+    });
+    Ok(promise)
 }

 fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
@@ -84,10 +110,12 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {

    let (deferred, promise) = cx.promise();
    rt.spawn(async move {
-        let table_rst = database.open_table(table_name).await;
+        let table_rst = database.open_table(&table_name).await;

        deferred.settle_with(&channel, move |mut cx| {
-            let table = Arc::new(Mutex::new(table_rst.or_else(|err| cx.throw_error(err.to_string()))?));
+            let table = Arc::new(Mutex::new(
+                table_rst.or_else(|err| cx.throw_error(err.to_string()))?,
+            ));
            Ok(cx.boxed(JsTable { table }))
        });
    });
@@ -96,15 +124,32 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {

 fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-    let query_vector = cx.argument::<JsArray>(0)?; //. .as_value(&mut cx);
-    let limit = cx.argument::<JsNumber>(1)?.value(&mut cx);
-    let filter = cx.argument_opt(2).map(|f| f.downcast_or_throw::<JsString, _>(&mut cx).unwrap().value(&mut cx));
+    let query_obj = cx.argument::<JsObject>(0)?;
+
+    let limit = query_obj
+        .get::<JsNumber, _, _>(&mut cx, "_limit")?
+        .value(&mut cx);
+    let filter = query_obj
+        .get_opt::<JsString, _, _>(&mut cx, "_filter")?
+        .map(|s| s.value(&mut cx));
+    let refine_factor = query_obj
+        .get_opt::<JsNumber, _, _>(&mut cx, "_refineFactor")?
+        .map(|s| s.value(&mut cx))
+        .map(|i| i as u32);
+    let nprobes = query_obj
+        .get::<JsNumber, _, _>(&mut cx, "_nprobes")?
+        .value(&mut cx) as usize;
+    let metric_type = query_obj
+        .get_opt::<JsString, _, _>(&mut cx, "_metricType")?
+        .map(|s| s.value(&mut cx))
+        .map(|s| MetricType::try_from(s.as_str()).unwrap());

    let rt = runtime(&mut cx)?;
    let channel = cx.channel();

    let (deferred, promise) = cx.promise();
    let table = js_table.table.clone();
+    let query_vector = query_obj.get::<JsArray, _, _>(&mut cx, "_queryVector")?;
    let query = convert::js_array_to_vec(query_vector.deref(), &mut cx);

    rt.spawn(async move {
@@ -113,7 +158,10 @@ fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
            .unwrap()
            .search(Float32Array::from(query))
            .limit(limit as usize)
-            .filter(filter);
+            .refine_factor(refine_factor)
+            .nprobes(nprobes)
+            .filter(filter)
+            .metric_type(metric_type);
        let record_batch_stream = builder.execute();
        let results = record_batch_stream
            .and_then(|stream| stream.try_collect::<Vec<_>>().map_err(Error::from))
@@ -161,10 +209,12 @@ fn table_create(mut cx: FunctionContext) -> JsResult<JsPromise> {

    rt.block_on(async move {
        let batch_reader: Box<dyn RecordBatchReader> = Box::new(RecordBatchBuffer::new(batches));
-        let table_rst = database.create_table(table_name, batch_reader).await;
+        let table_rst = database.create_table(&table_name, batch_reader).await;

        deferred.settle_with(&channel, move |mut cx| {
-            let table = Arc::new(Mutex::new(table_rst.or_else(|err| cx.throw_error(err.to_string()))?));
+            let table = Arc::new(Mutex::new(
+                table_rst.or_else(|err| cx.throw_error(err.to_string()))?,
+            ));
            Ok(cx.boxed(JsTable { table }))
        });
    });
@@ -178,9 +228,7 @@ fn table_add(mut cx: FunctionContext) -> JsResult<JsPromise> {
        ("overwrite", WriteMode::Overwrite),
    ]);

-    let js_table = cx
-        .this()
-        .downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
    let buffer = cx.argument::<JsBuffer>(0)?;
    let write_mode = cx.argument::<JsString>(1)?.value(&mut cx);
    let batches = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx));
@@ -204,7 +252,6 @@ fn table_add(mut cx: FunctionContext) -> JsResult<JsPromise> {
    Ok(promise)
 }

-
 #[neon::main]
 fn main(mut cx: ModuleContext) -> NeonResult<()> {
    cx.export_function("databaseNew", database_new)?;
@@ -213,5 +260,9 @@ fn main(mut cx: ModuleContext) -> NeonResult<()> {
    cx.export_function("tableSearch", table_search)?;
    cx.export_function("tableCreate", table_create)?;
    cx.export_function("tableAdd", table_add)?;
+    cx.export_function(
+        "tableCreateVectorIndex",
+        index::vector::table_create_vector_index,
+    )?;
    Ok(())
 }
--- a/rust/vectordb/Cargo.toml
+++ b/rust/vectordb/Cargo.toml
@@ -10,9 +10,13 @@ repository = "https://github.com/lancedb/lancedb"

 [dependencies]
 arrow-array = "37.0"
+arrow-data = "37.0"
 arrow-schema = "37.0"
-lance = "0.4.3"
+object_store = "0.5.6"
+snafu = "0.7.4"
+lance = "0.4.17"
 tokio = { version = "1.23", features = ["rt-multi-thread"] }

 [dev-dependencies]
 tempfile = "3.5.0"
+rand = { version = "0.8.3", features = ["small_rng"] }
--- a/rust/vectordb/src/database.rs
+++ b/rust/vectordb/src/database.rs
@@ -12,16 +12,20 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-use arrow_array::RecordBatchReader;
 use std::fs::create_dir_all;
-use std::path::{Path, PathBuf};
-use std::sync::Arc;
+use std::path::Path;

-use crate::error::Result;
+use arrow_array::RecordBatchReader;
+use lance::io::object_store::ObjectStore;
+use snafu::prelude::*;
+
+use crate::error::{CreateDirSnafu, Result};
 use crate::table::Table;

 pub struct Database {
-    pub(crate) path: Arc<PathBuf>,
+    object_store: ObjectStore,
+
+    pub(crate) uri: String,
 }

 const LANCE_EXTENSION: &str = "lance";
@@ -37,26 +41,38 @@ impl Database {
    /// # Returns
    ///
    /// * A [Database] object.
-    pub fn connect<P: AsRef<Path>>(path: P) -> Result<Database> {
-        if !path.as_ref().try_exists()? {
-            create_dir_all(&path)?;
+    pub async fn connect(uri: &str) -> Result<Database> {
+        let object_store = ObjectStore::new(uri).await?;
+        if object_store.is_local() {
+            Self::try_create_dir(uri).context(CreateDirSnafu { path: uri })?;
        }
        Ok(Database {
-            path: Arc::new(path.as_ref().to_path_buf()),
+            uri: uri.to_string(),
+            object_store,
        })
    }

+    /// Try to create a local directory to store the lancedb dataset
+    fn try_create_dir(path: &str) -> core::result::Result<(), std::io::Error> {
+        let path = Path::new(path);
+        if !path.try_exists()? {
+            create_dir_all(&path)?;
+        }
+        Ok(())
+    }
+
    /// Get the names of all tables in the database.
    ///
    /// # Returns
    ///
    /// * A [Vec<String>] with all table names.
-    pub fn table_names(&self) -> Result<Vec<String>> {
+    pub async fn table_names(&self) -> Result<Vec<String>> {
        let f = self
-            .path
-            .read_dir()?
-            .flatten()
-            .map(|dir_entry| dir_entry.path())
+            .object_store
+            .read_dir("/")
+            .await?
+            .iter()
+            .map(|fname| Path::new(fname))
            .filter(|path| {
                let is_lance = path
                    .extension()
@@ -76,10 +92,10 @@ impl Database {

    pub async fn create_table(
        &self,
-        name: String,
+        name: &str,
        batches: Box<dyn RecordBatchReader>,
    ) -> Result<Table> {
-        Table::create(self.path.clone(), name, batches).await
+        Table::create(&self.uri, name, batches).await
    }

    /// Open a table in the database.
@@ -90,8 +106,8 @@ impl Database {
    /// # Returns
    ///
    /// * A [Table] object.
-    pub async fn open_table(&self, name: String) -> Result<Table> {
-        Table::open(self.path.clone(), name).await
+    pub async fn open_table(&self, name: &str) -> Result<Table> {
+        Table::open(&self.uri, name).await
    }
 }

@@ -105,10 +121,10 @@ mod tests {
    #[tokio::test]
    async fn test_connect() {
        let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
-        let db = Database::connect(&path_buf);
+        let uri = tmp_dir.path().to_str().unwrap();
+        let db = Database::connect(uri).await.unwrap();

-        assert_eq!(db.unwrap().path.as_path(), path_buf.as_path())
+        assert_eq!(db.uri, uri);
    }

    #[tokio::test]
@@ -118,10 +134,16 @@ mod tests {
        create_dir_all(tmp_dir.path().join("table2.lance")).unwrap();
        create_dir_all(tmp_dir.path().join("invalidlance")).unwrap();

-        let db = Database::connect(&tmp_dir.into_path()).unwrap();
-        let tables = db.table_names().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+        let db = Database::connect(uri).await.unwrap();
+        let tables = db.table_names().await.unwrap();
        assert_eq!(tables.len(), 2);
        assert!(tables.contains(&String::from("table1")));
        assert!(tables.contains(&String::from("table2")));
    }
+
+    #[tokio::test]
+    async fn test_connect_s3() {
+        // let db = Database::connect("s3://bucket/path/to/database").await.unwrap();
+    }
 }
--- a/rust/vectordb/src/error.rs
+++ b/rust/vectordb/src/error.rs
@@ -12,32 +12,50 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-#[derive(Debug)]
-pub enum Error {
-    IO(String),
-    Lance(String),
-}
+use snafu::Snafu;

-impl std::fmt::Display for Error {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        let (catalog, message) = match self {
-            Self::IO(s) => ("I/O", s.as_str()),
-            Self::Lance(s) => ("Lance", s.as_str()),
-        };
-        write!(f, "LanceDBError({catalog}): {message}")
-    }
+#[derive(Debug, Snafu)]
+#[snafu(visibility(pub(crate)))]
+pub enum Error {
+    #[snafu(display("LanceDBError: Invalid table name: {name}"))]
+    InvalidTableName { name: String },
+    #[snafu(display("LanceDBError: Table '{name}' was not found"))]
+    TableNotFound { name: String },
+    #[snafu(display("LanceDBError: Table '{name}' already exists"))]
+    TableAlreadyExists { name: String },
+    #[snafu(display("LanceDBError: Unable to created lance dataset at {path}: {source}"))]
+    CreateDir {
+        path: String,
+        source: std::io::Error,
+    },
+    #[snafu(display("LanceDBError: {message}"))]
+    Store { message: String },
+    #[snafu(display("LanceDBError: {message}"))]
+    Lance { message: String },
 }

 pub type Result<T> = std::result::Result<T, Error>;

-impl From<std::io::Error> for Error {
-    fn from(e: std::io::Error) -> Self {
-        Self::IO(e.to_string())
+impl From<lance::Error> for Error {
+    fn from(e: lance::Error) -> Self {
+        Self::Lance {
+            message: e.to_string(),
+        }
    }
 }

-impl From<lance::Error> for Error {
-    fn from(e: lance::Error) -> Self {
-        Self::Lance(e.to_string())
+impl From<object_store::Error> for Error {
+    fn from(e: object_store::Error) -> Self {
+        Self::Store {
+            message: e.to_string(),
+        }
+    }
+}
+
+impl From<object_store::path::Error> for Error {
+    fn from(e: object_store::path::Error) -> Self {
+        Self::Store {
+            message: e.to_string(),
+        }
    }
 }
--- a/rust/vectordb/src/index.rs
+++ b/rust/vectordb/src/index.rs
@@ -0,0 +1,15 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+pub mod vector;
--- a/rust/vectordb/src/index/vector.rs
+++ b/rust/vectordb/src/index/vector.rs
@@ -0,0 +1,163 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+use lance::index::vector::ivf::IvfBuildParams;
+use lance::index::vector::pq::PQBuildParams;
+use lance::index::vector::{MetricType, VectorIndexParams};
+
+pub trait VectorIndexBuilder {
+    fn get_column(&self) -> Option<String>;
+    fn get_index_name(&self) -> Option<String>;
+    fn build(&self) -> VectorIndexParams;
+}
+
+pub struct IvfPQIndexBuilder {
+    column: Option<String>,
+    index_name: Option<String>,
+    metric_type: Option<MetricType>,
+    ivf_params: Option<IvfBuildParams>,
+    pq_params: Option<PQBuildParams>,
+}
+
+impl IvfPQIndexBuilder {
+    pub fn new() -> IvfPQIndexBuilder {
+        IvfPQIndexBuilder {
+            column: None,
+            index_name: None,
+            metric_type: None,
+            ivf_params: None,
+            pq_params: None,
+        }
+    }
+}
+
+impl IvfPQIndexBuilder {
+    pub fn column(&mut self, column: String) -> &mut IvfPQIndexBuilder {
+        self.column = Some(column);
+        self
+    }
+
+    pub fn index_name(&mut self, index_name: String) -> &mut IvfPQIndexBuilder {
+        self.index_name = Some(index_name);
+        self
+    }
+
+    pub fn metric_type(&mut self, metric_type: MetricType) -> &mut IvfPQIndexBuilder {
+        self.metric_type = Some(metric_type);
+        self
+    }
+
+    pub fn ivf_params(&mut self, ivf_params: IvfBuildParams) -> &mut IvfPQIndexBuilder {
+        self.ivf_params = Some(ivf_params);
+        self
+    }
+
+    pub fn pq_params(&mut self, pq_params: PQBuildParams) -> &mut IvfPQIndexBuilder {
+        self.pq_params = Some(pq_params);
+        self
+    }
+}
+
+impl VectorIndexBuilder for IvfPQIndexBuilder {
+    fn get_column(&self) -> Option<String> {
+        self.column.clone()
+    }
+
+    fn get_index_name(&self) -> Option<String> {
+        self.index_name.clone()
+    }
+
+    fn build(&self) -> VectorIndexParams {
+        let ivf_params = self.ivf_params.clone().unwrap_or(IvfBuildParams::default());
+        let pq_params = self.pq_params.clone().unwrap_or(PQBuildParams::default());
+
+        VectorIndexParams::with_ivf_pq_params(pq_params.metric_type, ivf_params, pq_params)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use lance::index::vector::ivf::IvfBuildParams;
+    use lance::index::vector::pq::PQBuildParams;
+    use lance::index::vector::{MetricType, StageParams};
+
+    use crate::index::vector::{IvfPQIndexBuilder, VectorIndexBuilder};
+
+    #[test]
+    fn test_builder_no_params() {
+        let index_builder = IvfPQIndexBuilder::new();
+        assert!(index_builder.get_column().is_none());
+        assert!(index_builder.get_index_name().is_none());
+
+        let index_params = index_builder.build();
+        assert_eq!(index_params.stages.len(), 2);
+        if let StageParams::Ivf(ivf_params) = index_params.stages.get(0).unwrap() {
+            let default = IvfBuildParams::default();
+            assert_eq!(ivf_params.num_partitions, default.num_partitions);
+            assert_eq!(ivf_params.max_iters, default.max_iters);
+        } else {
+            panic!("Expected first stage to be ivf")
+        }
+
+        if let StageParams::PQ(pq_params) = index_params.stages.get(1).unwrap() {
+            assert_eq!(pq_params.use_opq, false);
+        } else {
+            panic!("Expected second stage to be pq")
+        }
+    }
+
+    #[test]
+    fn test_builder_all_params() {
+        let mut index_builder = IvfPQIndexBuilder::new();
+
+        index_builder
+            .column("c".to_owned())
+            .metric_type(MetricType::Cosine)
+            .index_name("index".to_owned());
+
+        assert_eq!(index_builder.column.clone().unwrap(), "c");
+        assert_eq!(index_builder.metric_type.unwrap(), MetricType::Cosine);
+        assert_eq!(index_builder.index_name.clone().unwrap(), "index");
+
+        let ivf_params = IvfBuildParams::new(500);
+        let mut pq_params = PQBuildParams::default();
+        pq_params.use_opq = true;
+        pq_params.max_iters = 1;
+        pq_params.num_bits = 8;
+        pq_params.num_sub_vectors = 50;
+        pq_params.metric_type = MetricType::Cosine;
+        pq_params.max_opq_iters = 2;
+        index_builder.ivf_params(ivf_params);
+        index_builder.pq_params(pq_params);
+
+        let index_params = index_builder.build();
+        assert_eq!(index_params.stages.len(), 2);
+        if let StageParams::Ivf(ivf_params) = index_params.stages.get(0).unwrap() {
+            assert_eq!(ivf_params.num_partitions, 500);
+        } else {
+            assert!(false, "Expected first stage to be ivf")
+        }
+
+        if let StageParams::PQ(pq_params) = index_params.stages.get(1).unwrap() {
+            assert_eq!(pq_params.use_opq, true);
+            assert_eq!(pq_params.max_iters, 1);
+            assert_eq!(pq_params.num_bits, 8);
+            assert_eq!(pq_params.num_sub_vectors, 50);
+            assert_eq!(pq_params.metric_type, MetricType::Cosine);
+            assert_eq!(pq_params.max_opq_iters, 2);
+        } else {
+            assert!(false, "Expected second stage to be pq")
+        }
+    }
+}
--- a/rust/vectordb/src/lib.rs
+++ b/rust/vectordb/src/lib.rs
@@ -14,5 +14,6 @@

 pub mod database;
 pub mod error;
+pub mod index;
 pub mod query;
 pub mod table;
--- a/rust/vectordb/src/query.rs
+++ b/rust/vectordb/src/query.rs
@@ -29,7 +29,7 @@ pub struct Query {
    pub filter: Option<String>,
    pub nprobes: usize,
    pub refine_factor: Option<u32>,
-    pub metric_type: MetricType,
+    pub metric_type: Option<MetricType>,
    pub use_index: bool,
 }

@@ -51,9 +51,9 @@ impl Query {
            limit: 10,
            nprobes: 20,
            refine_factor: None,
-            metric_type: MetricType::L2,
+            metric_type: None,
            use_index: false,
-            filter: None
+            filter: None,
        }
    }

@@ -71,10 +71,10 @@ impl Query {
            self.limit,
        )?;
        scanner.nprobs(self.nprobes);
-        scanner.distance_metric(self.metric_type);
        scanner.use_index(self.use_index);
        self.filter.as_ref().map(|f| scanner.filter(f));
        self.refine_factor.map(|rf| scanner.refine(rf));
+        self.metric_type.map(|mt| scanner.distance_metric(mt));
        Ok(scanner.try_into_stream().await?)
    }

@@ -123,7 +123,7 @@ impl Query {
    /// # Arguments
    ///
    /// * `metric_type` - The distance metric to use. By default [MetricType::L2] is used.
-    pub fn metric_type(mut self, metric_type: MetricType) -> Query {
+    pub fn metric_type(mut self, metric_type: Option<MetricType>) -> Query {
        self.metric_type = metric_type;
        self
    }
@@ -174,14 +174,14 @@ mod tests {
            .limit(100)
            .nprobes(1000)
            .use_index(true)
-            .metric_type(MetricType::Cosine)
+            .metric_type(Some(MetricType::Cosine))
            .refine_factor(Some(999));

        assert_eq!(query.query_vector, new_vector);
        assert_eq!(query.limit, 100);
        assert_eq!(query.nprobes, 1000);
        assert_eq!(query.use_index, true);
-        assert_eq!(query.metric_type, MetricType::Cosine);
+        assert_eq!(query.metric_type, Some(MetricType::Cosine));
        assert_eq!(query.refine_factor, Some(999));
    }

--- a/rust/vectordb/src/table.rs
+++ b/rust/vectordb/src/table.rs
@@ -12,26 +12,35 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.

-use std::path::PathBuf;
+use std::path::Path;
 use std::sync::Arc;

 use arrow_array::{Float32Array, RecordBatchReader};
 use lance::dataset::{Dataset, WriteMode, WriteParams};
+use lance::index::IndexType;
+use snafu::prelude::*;

-use crate::error::{Error, Result};
+use crate::error::{Error, InvalidTableNameSnafu, Result};
+use crate::index::vector::VectorIndexBuilder;
 use crate::query::Query;

 pub const VECTOR_COLUMN_NAME: &str = "vector";
-
 pub const LANCE_FILE_EXTENSION: &str = "lance";

 /// A table in a LanceDB database.
+#[derive(Debug)]
 pub struct Table {
    name: String,
-    path: String,
+    uri: String,
    dataset: Arc<Dataset>,
 }

+impl std::fmt::Display for Table {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "Table({})", self.name)
+    }
+}
+
 impl Table {
    /// Opens an existing Table
    ///
@@ -43,18 +52,28 @@ impl Table {
    /// # Returns
    ///
    /// * A [Table] object.
-    pub async fn open(base_path: Arc<PathBuf>, name: String) -> Result<Self> {
-        let ds_path = base_path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
-        let ds_uri = ds_path
+    pub async fn open(base_uri: &str, name: &str) -> Result<Self> {
+        let path = Path::new(base_uri);
+
+        let table_uri = path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+        let uri = table_uri
+            .as_path()
            .to_str()
-            .ok_or(Error::IO(format!("Unable to find table {}", name)))?;
-        let dataset = Dataset::open(ds_uri).await?;
-        let table = Table {
-            name,
-            path: ds_uri.to_string(),
+            .context(InvalidTableNameSnafu { name })?;
+
+        let dataset = Dataset::open(&uri).await.map_err(|e| match e {
+            lance::Error::DatasetNotFound { .. } => Error::TableNotFound {
+                name: name.to_string(),
+            },
+            e => Error::Lance {
+                message: e.to_string(),
+            },
+        })?;
+        Ok(Table {
+            name: name.to_string(),
+            uri: uri.to_string(),
            dataset: Arc::new(dataset),
-        };
-        Ok(table)
+        })
    }

    /// Creates a new Table
@@ -69,18 +88,52 @@ impl Table {
    ///
    /// * A [Table] object.
    pub async fn create(
-        base_path: Arc<PathBuf>,
-        name: String,
+        base_uri: &str,
+        name: &str,
        mut batches: Box<dyn RecordBatchReader>,
    ) -> Result<Self> {
-        let ds_path = base_path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
-        let path = ds_path
+        let base_path = Path::new(base_uri);
+        let table_uri = base_path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+        let uri = table_uri
+            .as_path()
            .to_str()
-            .ok_or(Error::IO(format!("Unable to find table {}", name)))?;
+            .context(InvalidTableNameSnafu { name })?
+            .to_string();
+        let dataset = Dataset::write(&mut batches, &uri, Some(WriteParams::default()))
+            .await
+            .map_err(|e| match e {
+                lance::Error::DatasetAlreadyExists { .. } => Error::TableAlreadyExists {
+                    name: name.to_string(),
+                },
+                e => Error::Lance {
+                    message: e.to_string(),
+                },
+            })?;
+        Ok(Table {
+            name: name.to_string(),
+            uri,
+            dataset: Arc::new(dataset),
+        })
+    }

-        let dataset =
-            Arc::new(Dataset::write(&mut batches, path, Some(WriteParams::default())).await?);
-        Ok(Table { name, path: path.to_string(), dataset })
+    /// Create index on the table.
+    pub async fn create_index(&mut self, index_builder: &impl VectorIndexBuilder) -> Result<()> {
+        use lance::index::DatasetIndexExt;
+
+        let dataset = self
+            .dataset
+            .create_index(
+                &[index_builder
+                    .get_column()
+                    .unwrap_or(VECTOR_COLUMN_NAME.to_string())
+                    .as_str()],
+                IndexType::Vector,
+                index_builder.get_index_name(),
+                &index_builder.build(),
+            )
+            .await?;
+        self.dataset = Arc::new(dataset);
+        Ok(())
    }

    /// Insert records into this Table
@@ -95,12 +148,12 @@ impl Table {
    pub async fn add(
        &mut self,
        mut batches: Box<dyn RecordBatchReader>,
-        write_mode: Option<WriteMode>
+        write_mode: Option<WriteMode>,
    ) -> Result<usize> {
        let mut params = WriteParams::default();
        params.mode = write_mode.unwrap_or(WriteMode::Append);

-        self.dataset = Arc::new(Dataset::write(&mut batches, self.path.as_str(), Some(params)).await?);
+        self.dataset = Arc::new(Dataset::write(&mut batches, &self.uri, Some(params)).await?);
        Ok(batches.count())
    }

@@ -125,60 +178,88 @@ impl Table {

 #[cfg(test)]
 mod tests {
-    use arrow_array::{Float32Array, Int32Array, RecordBatch, RecordBatchReader};
+    use std::sync::Arc;
+
+    use arrow_array::{
+        Array, FixedSizeListArray, Float32Array, Int32Array, RecordBatch, RecordBatchReader,
+    };
+    use arrow_data::ArrayDataBuilder;
    use arrow_schema::{DataType, Field, Schema};
    use lance::arrow::RecordBatchBuffer;
    use lance::dataset::{Dataset, WriteMode};
-    use std::sync::Arc;
+    use lance::index::vector::ivf::IvfBuildParams;
+    use lance::index::vector::pq::PQBuildParams;
+    use rand::Rng;
    use tempfile::tempdir;

-    use crate::table::Table;
-
-    #[tokio::test]
-    async fn test_new_table_not_exists() {
-        let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
-
-        let table = Table::open(Arc::new(path_buf), "test".to_string()).await;
-        assert!(table.is_err());
-    }
+    use super::*;
+    use crate::index::vector::IvfPQIndexBuilder;

    #[tokio::test]
    async fn test_open() {
        let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = tmp_dir.path().to_str().unwrap();

        let mut batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
-        Dataset::write(
-            &mut batches,
-            path_buf.join("test.lance").to_str().unwrap(),
-            None,
-        )
-        .await
-        .unwrap();
-
-        let table = Table::open(Arc::new(path_buf), "test".to_string())
+        Dataset::write(&mut batches, dataset_path.to_str().unwrap(), None)
            .await
            .unwrap();

+        let table = Table::open(uri, "test").await.unwrap();
+
        assert_eq!(table.name, "test")
    }

    #[tokio::test]
-    async fn test_add() {
+    async fn test_open_not_found() {
        let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let uri = tmp_dir.path().to_str().unwrap();
+        let table = Table::open(uri, "test").await;
+        assert!(matches!(table.unwrap_err(), Error::TableNotFound { .. }));
+    }
+
+    #[test]
+    fn test_object_store_path() {
+        use std::path::Path as StdPath;
+        let p = StdPath::new("s3://bucket/path/to/file");
+        let c = p.join("subfile");
+        assert_eq!(c.to_str().unwrap(), "s3://bucket/path/to/file/subfile");
+    }
+
+    #[tokio::test]
+    async fn test_create_already_exists() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();

        let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
        let schema = batches.schema().clone();
-        let mut table = Table::create(Arc::new(path_buf), "test".to_string(), batches).await.unwrap();
+        Table::create(&uri, "test", batches).await.unwrap();
+
+        let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
+        let result = Table::create(&uri, "test", batches).await;
+        assert!(matches!(
+            result.unwrap_err(),
+            Error::TableAlreadyExists { .. }
+        ));
+    }
+
+    #[tokio::test]
+    async fn test_add() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+
+        let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
+        let schema = batches.schema().clone();
+        let mut table = Table::create(&uri, "test", batches).await.unwrap();
        assert_eq!(table.count_rows().await.unwrap(), 10);

-        let new_batches: Box<dyn RecordBatchReader> = Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
-            schema,
-            vec![Arc::new(Int32Array::from_iter_values(100..110))],
-        )
-       .unwrap()]));
+        let new_batches: Box<dyn RecordBatchReader> =
+            Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
+                schema,
+                vec![Arc::new(Int32Array::from_iter_values(100..110))],
+            )
+            .unwrap()]));

        table.add(new_batches, None).await.unwrap();
        assert_eq!(table.count_rows().await.unwrap(), 20);
@@ -188,19 +269,24 @@ mod tests {
    #[tokio::test]
    async fn test_add_overwrite() {
        let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let uri = tmp_dir.path().to_str().unwrap();

        let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
        let schema = batches.schema().clone();
-        let mut table = Table::create(Arc::new(path_buf), "test".to_string(), batches).await.unwrap();
+        let mut table = Table::create(uri, "test", batches).await.unwrap();
        assert_eq!(table.count_rows().await.unwrap(), 10);

-        let new_batches: Box<dyn RecordBatchReader> = Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
-            schema,
-            vec![Arc::new(Int32Array::from_iter_values(100..110))],
-        ).unwrap()]));
+        let new_batches: Box<dyn RecordBatchReader> =
+            Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
+                schema,
+                vec![Arc::new(Int32Array::from_iter_values(100..110))],
+            )
+            .unwrap()]));

-        table.add(new_batches, Some(WriteMode::Overwrite)).await.unwrap();
+        table
+            .add(new_batches, Some(WriteMode::Overwrite))
+            .await
+            .unwrap();
        assert_eq!(table.count_rows().await.unwrap(), 10);
        assert_eq!(table.name, "test");
    }
@@ -208,21 +294,16 @@ mod tests {
    #[tokio::test]
    async fn test_search() {
        let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = tmp_dir.path().to_str().unwrap();

        let mut batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
-        Dataset::write(
-            &mut batches,
-            path_buf.join("test.lance").to_str().unwrap(),
-            None,
-        )
-        .await
-        .unwrap();
-
-        let table = Table::open(Arc::new(path_buf), "test".to_string())
+        Dataset::write(&mut batches, dataset_path.to_str().unwrap(), None)
            .await
            .unwrap();

+        let table = Table::open(uri, "test").await.unwrap();
+
        let vector = Float32Array::from_iter_values([0.1, 0.2]);
        let query = table.search(vector.clone());
        assert_eq!(vector, query.query_vector);
@@ -236,4 +317,72 @@ mod tests {
        )
        .unwrap()])
    }
+
+    #[tokio::test]
+    async fn test_create_index() {
+        use arrow_array::RecordBatch;
+        use arrow_schema::{DataType, Field, Schema as ArrowSchema};
+        use rand;
+        use std::iter::repeat_with;
+
+        use arrow_array::Float32Array;
+
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+
+        let dimension = 16;
+        let schema = Arc::new(ArrowSchema::new(vec![Field::new(
+            "embeddings",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                dimension,
+            ),
+            false,
+        )]));
+
+        let mut rng = rand::thread_rng();
+        let float_arr = Float32Array::from(
+            repeat_with(|| rng.gen::<f32>())
+                .take(512 * dimension as usize)
+                .collect::<Vec<f32>>(),
+        );
+
+        let vectors = Arc::new(create_fixed_size_list(float_arr, dimension).unwrap());
+        let batches = RecordBatchBuffer::new(vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![vectors.clone()],
+        )
+        .unwrap()]);
+
+        let reader: Box<dyn RecordBatchReader + Send> = Box::new(batches);
+        let mut table = Table::create(uri, "test", reader).await.unwrap();
+
+        let mut i = IvfPQIndexBuilder::new();
+
+        let index_builder = i
+            .column("embeddings".to_string())
+            .index_name("my_index".to_string())
+            .ivf_params(IvfBuildParams::new(256))
+            .pq_params(PQBuildParams::default());
+
+        table.create_index(index_builder).await.unwrap();
+
+        assert_eq!(table.dataset.load_indices().await.unwrap().len(), 1);
+        assert_eq!(table.count_rows().await.unwrap(), 512);
+        assert_eq!(table.name, "test");
+    }
+
+    fn create_fixed_size_list<T: Array>(values: T, list_size: i32) -> Result<FixedSizeListArray> {
+        let list_type = DataType::FixedSizeList(
+            Arc::new(Field::new("item", values.data_type().clone(), true)),
+            list_size,
+        );
+        let data = ArrayDataBuilder::new(list_type)
+            .len(values.len() / list_size as usize)
+            .add_child_data(values.into_data())
+            .build()
+            .unwrap();
+
+        Ok(FixedSizeListArray::from(data))
+    }
 }
Author	SHA1	Message	Date
Chang She	fbd0bc7740	bump version for v0.1.5-python	2023-06-02 09:18:26 -07:00
gsilvestrin	f765a453cf	Use fsspec to implement table_names with cloud storage support (#117 ) Co-authored-by: Will Jones <willjones127@gmail.com>	2023-06-01 16:56:26 -07:00
gsilvestrin	45b3a14f26	Bumping vectordb to v0.1.3 (#124 )	2023-06-01 16:36:11 -07:00
Lei Xu	9965b4564d	[Python] Support drop table (#123 ) Closes #86	2023-06-01 15:58:45 -07:00
gsilvestrin	0719e4b3fb	Revert "refactor: pull node binaries into separate packages (#88 )" (#122 ) This reverts commit `e50b642d80`.	2023-06-01 13:53:07 -07:00
Jai	091fb9b665	add existence check (#112 )	2023-06-01 11:45:26 -07:00
Chang She	03013a4434	Multimodal search demo (#118 ) Slow roasted over 12 hours, Pairs well with #111 --------- Co-authored-by: Chang She <chang@lancedb.com>	2023-06-01 10:34:08 -07:00
gsilvestrin	3e14b357e7	add openai embedding function to nodejs client (#107 ) - openai is an optional dependency for lancedb - added an example to show how to use it	2023-06-01 10:25:00 -07:00
Lei Xu	99cbda8b07	Generate diffusiondb embeddings (#111 )	2023-06-01 10:23:29 -07:00
Will Jones	e50b642d80	refactor: pull node binaries into separate packages (#88 ) Changes: * Refactors the Node module to load the shared library from a separate package. When a user does `npm install vectordb`, the correct optional dependency is automatically downloaded by npm. * Brings Rust and Node versions in alignment at 0.1.2. * Add scripts and instructions to build Linux and MacOS node artifacts locally. * Add instructions for publishing the npm module and crates.	2023-06-01 09:17:19 -07:00
gsilvestrin	6d8cf52e01	Better error granularity for table operations (#113 )	2023-06-01 09:04:42 -07:00
Akash	53f3882d6e	Fixed documentation link for the Youtube Transcripts Jupyter Notebook (#105 ) Changed the link to the Youtube Transcripts jupyter notebook path on the documentation. Previously it went inside docs/notebooks (which does not exist). I've modified it to go inside the notebooks folder instead.	2023-06-01 09:00:40 -07:00
Chang She	2b26775ed1	python v0.1.4	2023-05-31 20:11:25 -07:00
Lei Xu	306ada5cb8	Support S3 and GCS from typescript SDK (#106 )	2023-05-30 21:32:17 -07:00
gsilvestrin	d3aa8bfbc5	add embedding functions to the nodejs client (#95 )	2023-05-26 18:09:20 -07:00
Chang She	04d97347d7	move tantivy-py installation to be separate from wheel (#97 ) pypi does not allow packages to be uploaded that has a direct reference for now we'll just ask the user to install tantivy separately --------- Co-authored-by: Chang She <chang@lancedb.com>	2023-05-25 17:57:26 -06:00
Chang She	22aa8a93c2	bump version for v0.1.3	2023-05-25 17:01:52 -06:00
Chang She	f485378ea4	Basic full text search capabilities (#62 ) This is v1 of integrating full text search index into LanceDB. # API The query API is roughly the same as before, except if the input is text instead of a vector we assume that its fts search. ## Example If `table` is a LanceDB LanceTable, then: Build index: `table.create_fts_index("text")` Query: `df = table.search("puppy").limit(10).select(["text"]).to_df()` # Implementation Here we use the tantivy-py package to build the index. We then use the row id's as the full-text-search index's doc id then we just do a Take operation to fetch the rows. # Limitations 1. don't support incremental row appends yet. New data won't show up in search 2. local filesystem only 3. requires building tantivy explicitly --------- Co-authored-by: Chang She <chang@lancedb.com>	2023-05-24 22:25:31 -06:00
gsilvestrin	f923cfe47f	add create index to nodejs client (#89 )	2023-05-24 16:45:58 -06:00
gsilvestrin	06cb7b6458	add query params to to nodejs client (#87 )	2023-05-24 15:48:31 -06:00
gsilvestrin	bdef634954	bugfix: string columns should be converted to Utf8Array (#94 )	2023-05-23 14:58:49 -07:00