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based on https://github.com/lancedb/lancedb/pull/713 - The Reranker api can be plugged into vector only or fts only search but this PR doesn't do that (see example - https://txt.cohere.com/rerank/) ### Default reranker -- `LinearCombinationReranker(weight=0.7, fill=1.0)` ``` table.search("hello", query_type="hybrid").rerank(normalize="score").to_pandas() ``` ### Available rerankers LinearCombinationReranker ``` from lancedb.rerankers import LinearCombinationReranker # Same as default table.search("hello", query_type="hybrid").rerank( normalize="score", reranker=LinearCombinationReranker() ).to_pandas() # with custom params reranker = LinearCombinationReranker(weight=0.3, fill=1.0) table.search("hello", query_type="hybrid").rerank( normalize="score", reranker=reranker ).to_pandas() ``` Cohere Reranker ``` from lancedb.rerankers import CohereReranker # default model.. English and multi-lingual supported. See docstring for available custom params table.search("hello", query_type="hybrid").rerank( normalize="rank", # score or rank reranker=CohereReranker() ).to_pandas() ``` CrossEncoderReranker ``` from lancedb.rerankers import CrossEncoderReranker table.search("hello", query_type="hybrid").rerank( normalize="rank", reranker=CrossEncoderReranker() ).to_pandas() ``` ## Using custom Reranker ``` from lancedb.reranker import Reranker class CustomReranker(Reranker): def rerank_hybrid(self, vector_result, fts_result): combined_res = self.merge_results(vector_results, fts_results) # or use custom combination logic # Custom rerank logic here return combined_res ``` - [x] Expand testing - [x] Make sure usage makes sense - [x] Run simple benchmarks for correctness (Seeing weird result from cohere reranker in the toy example) - Support diverse rerankers by default: - [x] Cross encoding - [x] Cohere - [x] Reciprocal Rank Fusion --------- Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com> Co-authored-by: Prashanth Rao <35005448+prrao87@users.noreply.github.com>
Developer-friendly, serverless vector database for AI applications
LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
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 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.
Quick Start
Javascript
npm install vectordb
const lancedb = require('vectordb');
const db = await lancedb.connect('data/sample-lancedb');
const table = await db.createTable('vectors',
[{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }])
const query = table.search([0.1, 0.3]).limit(2);
const results = await query.execute();
Python
pip install lancedb
import lancedb
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
result = table.search([100, 100]).limit(2).to_pandas()