base on Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming.. <div align="center"> <p align="center"> <img width="257" alt="Lance Logo" src="https://user-images.githubusercontent.com/917119/199353423-d3e202f7-0269-411d-8ff2-e747e419e492.png"> **The Open Lakehouse Format for Multimodal AI**<br/> **High-performance vector search, full-text search, random access, and feature engineering capabilities for the lakehouse.**<br/> **Compatible with Pandas, DuckDB, Polars, PyArrow, Ray, Spark, and more integrations on the way.** <a href="https://lance.org">Documentation</a> • <a href="https://lance.org/community">Community</a> • <a href="https://discord.gg/lance">Discord</a> [CI]: https://github.com/lance-format/lance/actions/workflows/rust.yml [CI Badge]: https://github.com/lance-format/lance/actions/workflows/rust.yml/badge.svg [Docs]: https://lance.org [Docs Badge]: https://img.shields.io/badge/docs-passing-brightgreen [crates.io]: https://crates.io/crates/lance [crates.io badge]: https://img.shields.io/crates/v/lance.svg [Python versions]: https://pypi.org/project/pylance/ [Python versions badge]: https://img.shields.io/pypi/pyversions/pylance [![CI Badge]][CI] [![Docs Badge]][Docs] [![crates.io badge]][crates.io] [![Python versions badge]][Python versions] </p> </div> <hr /> Lance is an open lakehouse format for multimodal AI. It contains a file format, table format, and catalog spec that allows you to build a complete lakehouse on top of object storage to power your AI workflows. Lance is perfect for: 1. Building search engines and feature stores with hybrid search capabilities. 2. Large-scale ML training requiring high performance IO and random access. 3. Storing, querying, and managing multimodal data including images, videos, audio, text, and embeddings. The key features of Lance include: * **Expressive hybrid search:** Combine vector similarity search, full-text search (BM25), and SQL analytics on the same dataset with accelerated secondary indices. * **Lightning-fast random access:** 100x faster than Parquet or Iceberg for random access without sacrificing scan performance. * **Native multimodal data support:** Store images, videos, audio, text, and embeddings in a single unified format with efficient blob encoding and lazy loading. * **Data evolution:** Efficiently add columns with backfilled values without full table rewrites, perfect for ML feature engineering. * **Zero-copy versioning:** ACID transactions, time travel, and automatic versioning without needing extra infrastructure. * **Rich ecosystem integrations:** Apache Arrow, Pandas, Polars, DuckDB, Apache Spark, Ray, Trino, Apache Flink, and open catalogs (Apache Polaris, Unity Catalog, Apache Gravitino). For more details, see the full [Lance format specification](https://lance.org/format). > [!TIP] > Lance is in active development and we welcome contributions. Please see our [contributing guide](https://lance.org/community/contributing/) for more information. ## Quick Start **Installation** ```shell pip install pylance ``` To install a preview release: ```shell pip install --pre --extra-index-url https://pypi.fury.io/lance-format/pylance ``` > [!NOTE] > For versions prior to 1.0.0-beta.4, you can find them at https://pypi.fury.io/lancedb/pylance > [!TIP] > Preview releases are released more often than full releases and contain the > latest features and bug fixes. They receive the same level of testing as full releases. > We guarantee they will remain published and available for download for at > least 6 months. When you want to pin to a specific version, prefer a stable release. **Converting to Lance** ```python import lance import pandas as pd import pyarrow as pa import pyarrow.dataset df = pd.DataFrame({"a": [5], "b": [10]}) uri = "/tmp/test.parquet" tbl = pa.Table.from_pandas(df) pa.dataset.write_dataset(tbl, uri, format='parquet') parquet = pa.dataset.dataset(uri, format='parquet') lance.write_dataset(parquet, "/tmp/test.lance") ``` **Reading Lance data** ```python dataset = lance.dataset("/tmp/test.lance") assert isinstance(dataset, pa.dataset.Dataset) ``` **Pandas** ```python df = dataset.to_table().to_pandas() df ``` **DuckDB** ```python import duckdb # If this segfaults, make sure you have duckdb v0.7+ installed duckdb.query("SELECT * FROM dataset LIMIT 10").to_df() ``` **Vector search** Download the sift1m subset ```shell wget ftp://ftp.irisa.fr/local/texmex/corpus/sift.tar.gz tar -xzf sift.tar.gz ``` Convert it to Lance ```python import lance from lance.vector import vec_to_table import numpy as np import struct nvecs = 1000000 ndims = 128 with open("sift/sift_base.fvecs", mode="rb") as fobj: buf = fobj.read() data = np.array(struct.unpack("<128000000f", buf[4 : 4 + 4 * nvecs * ndims])).reshape((nvecs, ndims)) dd = dict(zip(range(nvecs), data)) table = vec_to_table(dd) uri = "vec_data.lance" sift1m = lance.write_dataset(table, uri, max_rows_per_group=8192, max_rows_per_file=1024*1024) ``` Build the index ```python sift1m.create_index("vector", index_type="IVF_PQ", num_partitions=256, # IVF num_sub_vectors=16) # PQ ``` Search the dataset ```python # Get top 10 similar vectors import duckdb dataset = lance.dataset(uri) # Sample 100 query vectors. If this segfaults, make sure you have duckdb v0.7+ installed sample = duckdb.query("SELECT vector FROM dataset USING SAMPLE 100").to_df() query_vectors = np.array([np.array(x) for x in sample.vector]) # Get nearest neighbors for all of them rs = [dataset.to_table(nearest={"column": "vector", "k": 10, "q": q}) for q in query_vectors] ``` ## Directory structure | Directory | Description | |--------------------|--------------------------| | [rust](./rust) | Core Rust implementation | | [python](./python) | Python bindings (PyO3) | | [java](./java) | Java bindings (JNI) | | [docs](./docs) | Documentation source | ## Benchmarks ### Vector search We used the SIFT dataset to benchmark our results with 1M vectors of 128D 1. For 100 randomly sampled query vectors, we get <1ms average response time (on a 2023 m2 MacBook Air)  2. ANNs are always a trade-off between recall and performance  ### Vs. parquet We create a Lance dataset using the Oxford Pet dataset to do some preliminary performance testing of Lance as compared to Parquet and raw image/XMLs. For analytics queries, Lance is 50-100x better than reading the raw metadata. For batched random access, Lance is 100x better than both parquet and raw files.  ## Why Lance for AI/ML workflows? The machine learning development cycle involves multiple stages: ```mermaid graph LR A[Collection] --> B[Exploration]; B --> C[Analytics]; C --> D[Feature Engineer]; D --> E[Training]; E --> F[Evaluation]; F --> C; E --> G[Deployment]; G --> H[Monitoring]; H --> A; ``` Traditional lakehouse formats were designed for SQL analytics and struggle with AI/ML workloads that require: - **Vector search** for similarity and semantic retrieval - **Fast random access** for sampling and interactive exploration - **Multimodal data** storage (images, videos, audio alongside embeddings) - **Data evolution** for feature engineering without full table rewrites - **Hybrid search** combining vectors, full-text, and SQL predicates While existing formats (Parquet, Iceberg, Delta Lake) excel at SQL analytics, they require additional specialized systems for AI capabilities. Lance brings these AI-first features directly into the lakehouse format. A comparison of different formats across ML development stages: | | Lance | Parquet & ORC | JSON & XML | TFRecord | Database | Warehouse | |---------------------|-------|---------------|------------|----------|----------|-----------| | Analytics | Fast | Fast | Slow | Slow | Decent | Fast | | Feature Engineering | Fast | Fast | Decent | Slow | Decent | Good | | Training | Fast | Decent | Slow | Fast | N/A | N/A | | Exploration | Fast | Slow | Fast | Slow | Fast | Decent | | Infra Support | Rich | Rich | Decent | Limited | Rich | Rich | ", Assign "at most 3 tags" to the expected json: {"id":"904","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"