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docs: add evaluation example (#1552)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Rithik Kumar
2024-08-21 20:37:04 +05:30
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parent 7d65dd97cf
commit 0cbc9cd551
5 changed files with 32 additions and 8 deletions
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docs/src/examples/python_examples/vector_search.md
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**Vector Search: Unlock Efficient Document Retrieval 🔓👀**
====================================================================
Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊. Input text queries to find the most relevant documents from your corpus, and discover a new world of possibilities with our inbuilt hybrid search features 🌐.
Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊.
**Unlock the Future of Search🔝**
**Vector Search Capabilities in LanceDB🔝**
Experience the transformative power of vector search with LanceDB. Discover, analyze, and retrieve documents with unprecedented efficiency and accuracy. 💡
LanceDB implements vector search algorithms for efficient document retrieval and analysis 📊. This enables fast and accurate discovery of relevant documents, leveraging dense vector representations 🤖. The platform supports scalable indexing and querying of high-dimensional vector spaces, facilitating precise document matching and retrieval 📈.
| **Vector Search** | **Description** | **Links** |
|:-----------------|:---------------|:---------|