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e1ae2bcbd8
Add `to_list` to return query results as list of python dict (so we're not too pandas-centric). Closes #555 Add `to_pandas` API and add deprecation warning on `to_df`. Closes #545 Co-authored-by: Chang She <chang@lancedb.com>
80 lines
2.9 KiB
Markdown
80 lines
2.9 KiB
Markdown
<div align="center">
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<p align="center">
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<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">
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**Developer-friendly, serverless vector database for AI applications**
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<a href="https://lancedb.github.io/lancedb/">Documentation</a> •
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<a href="https://blog.lancedb.com/">Blog</a> •
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<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
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<a href="https://twitter.com/lancedb">Twitter</a>
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</p>
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<img max-width="750px" alt="LanceDB Multimodal Search" src="https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec">
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</p>
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</div>
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<hr />
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LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
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The key features of LanceDB include:
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* Production-scale vector search with no servers to manage.
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* Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
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* Support for vector similarity search, full-text search and SQL.
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* Native Python and Javascript/Typescript support.
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* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
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* GPU support in building vector index(*).
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* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
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LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
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## Quick Start
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**Javascript**
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```shell
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npm install vectordb
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```
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```javascript
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const lancedb = require('vectordb');
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const db = await lancedb.connect('data/sample-lancedb');
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const table = await db.createTable('vectors',
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[{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
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{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }])
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const query = table.search([0.1, 0.3]).limit(2);
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const results = await query.execute();
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```
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**Python**
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```shell
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pip install lancedb
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```
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```python
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import lancedb
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uri = "data/sample-lancedb"
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db = lancedb.connect(uri)
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table = db.create_table("my_table",
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data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
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{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
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result = table.search([100, 100]).limit(2).to_pandas()
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```
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## Blogs, Tutorials & Videos
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* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
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* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>
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