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Ayush Chaurasia
e921c90c1b
The basic idea of MRR is this - https://www.evidentlyai.com/ranking-metrics/mean-reciprocal-rank-mrr I've implemented a weighted version for allowing user to set weightage between vector and fts. The gist is something like this ### Scenario A: Document at rank 1 in one set, absent from another ``` # Assuming equal weights: weight_vector = 0.5, weight_fts = 0.5 vector_rr = 1.0 # rank 1 → 1/1 = 1.0 fts_rr = 0.0 # absent → 0.0 weighted_mrr = 0.5 × 1.0 + 0.5 × 0.0 = 0.5 ``` ### Scenario B: Document at rank 1 in one set, rank 2 in another ``` # Same weights: weight_vector = 0.5, weight_fts = 0.5 vector_rr = 1.0 # rank 1 → 1/1 = 1.0 fts_rr = 0.5 # rank 2 → 1/2 = 0.5 weighted_mrr = 0.5 × 1.0 + 0.5 × 0.5 = 0.5 + 0.25 = 0.75 ``` And so with `return_score="all"` the result looks something like this (this is from the reranker tests). Because this is a weighted rank based reranker, some results might have the same score ``` text vector _distance _rowid _score _relevance_score 0 I am your father [-0.010703234, 0.069315575, 0.030076642, 0.002... 8.149148e-13 8589934598 10.978719 1.000000 1 the ground beneath my feet [-0.09500901, 0.00092102867, 0.0755851, 0.0372... 1.376896e+00 8589934604 NaN 0.250000 2 I find your lack of faith disturbing [0.07525753, -0.0100010475, 0.09990541, 0.0209... NaN 8589934595 3.483394 0.250000 3 but I don't wanna die [0.033476487, -0.011235877, -0.057625435, -0.0... 1.538222e+00 8589934610 1.130355 0.238095 4 if you strike me down I shall become more powe... [0.00432201, 0.030120496, 5.3317923e-05, 0.033... 1.381086e+00 8589934594 0.715157 0.216667 5 I see a salty message written in the eves [-0.04213107, 0.0016004723, 0.061052393, -0.02... 1.638301e+00 8589934603 1.043785 0.133333 6 but his son was mortal [0.012462767, 0.049041674, -0.057339743, -0.04... 1.421566e+00 8589934620 NaN 0.125000 7 I've got a bad feeling about this [-0.06973199, -0.029960092, 0.02641632, -0.031... NaN 8589934596 1.043785 0.125000 8 now that's a name I haven't heard in a long time [-0.014374257, -0.013588792, -0.07487557, 0.03... 1.597573e+00 8589934593 0.848772 0.118056 9 he was a god [-0.0258895, 0.11925236, -0.029397793, 0.05888... 1.423147e+00 8589934618 NaN 0.100000 10 I wish they would make another one [-0.14737535, -0.015304729, 0.04318139, -0.061... NaN 8589934622 1.043785 0.100000 11 Kratos had a son [-0.057455737, 0.13734367, -0.03537109, -0.000... 1.488075e+00 8589934617 NaN 0.083333 12 I don't wanna live like this [-0.0028891307, 0.015214227, 0.025183653, 0.08... NaN 8589934609 1.043785 0.071429 13 I see a mansard roof through the trees [0.052383978, 0.087759204, 0.014739997, 0.0239... NaN 8589934602 1.043785 0.062500 14 great kid don't get cocky [-0.047043696, 0.054648954, -0.008509666, -0.0... 1.618125e+00 8589934592 NaN 0.055556 ```
The Multimodal AI Lakehouse
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/introduction |
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Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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