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docs: improve overall language on all example pages (#1582)
Refine and improve the language clarity and quality across all example pages in the documentation to ensure better understanding and readability. --------- Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Rithik Kumar
GitHub
@@ -1,4 +1,4 @@
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**Vector Search: Unlock Efficient Document Retrieval 🔓👀**
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**Vector Search: Efficient Retrieval 🔓👀**
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====================================================================
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Vector search with LanceDB, is a solution for efficient and accurate similarity searches in large datasets 📊.
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@@ -9,19 +9,19 @@ LanceDB implements vector search algorithms for efficient document retrieval and
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| **Vector Search** | **Description** | **Links** |
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|:-----------------|:---------------|:---------|
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| **Inbuilt Hybrid Search 🔄** | Combine the power of traditional search algorithms with LanceDB's vector-based search for a robust and efficient search experience 📊 | [][inbuilt_hybrid_search_github] <br>[][inbuilt_hybrid_search_colab] |
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| **Hybrid Search with BM25 and LanceDB 💡** | Synergizes BM25's keyword-focused precision (term frequency, document length normalization, bias-free retrieval) with LanceDB's semantic understanding (contextual analysis, query intent alignment) for nuanced search results in complex datasets 📈 | [][BM25_github] <br>[][BM25_colab] <br>[][BM25_ghost] |
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| **NER-powered Semantic Search 🔎** | Unlock contextual understanding with Named Entity Recognition (NER) methods: Dictionary-Based, Rule-Based, and Deep Learning-Based, to accurately identify and extract entities, enabling precise semantic search results 🗂️ | [][NER_github] <br>[][NER_colab] <br>[][NER_ghost]|
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| **Audio Similarity Search using Vector Embeddings 🎵** | Create vector embeddings of audio files to find similar audio content, enabling efficient audio similarity search and retrieval in LanceDB's vector store 📻 |[][audio_search_github] <br>[][audio_search_colab] <br>[][audio_search_python]|
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| **LanceDB Embeddings API: Multi-lingual Semantic Search 🌎** | Build a universal semantic search table with LanceDB's Embeddings API, supporting multiple languages (e.g., English, French) using cohere's multi-lingual model, for accurate cross-lingual search results 📄 | [][mls_github] <br>[][mls_colab] <br>[][mls_python] |
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| **Facial Recognition: Face Embeddings 🤖** | Detect, crop, and embed faces using Facenet, then store and query face embeddings in LanceDB for efficient facial recognition and top-K matching results 👥 | [][fr_github] <br>[][fr_colab] |
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| **Sentiment Analysis: Hotel Reviews 🏨** | Analyze customer sentiments towards the hotel industry using BERT models, storing sentiment labels, scores, and embeddings in LanceDB, enabling queries on customer opinions and potential areas for improvement 💬 | [][sentiment_analysis_github] <br>[][sentiment_analysis_colab] <br>[][sentiment_analysis_ghost] |
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| **Vector Arithmetic with LanceDB ⚖️** | Unlock powerful semantic search capabilities by performing vector arithmetic on embeddings, enabling complex relationships and nuances in data to be captured, and simplifying the process of retrieving semantically similar results 📊 | [][arithmetic_github] <br>[][arithmetic_colab] <br>[][arithmetic_ghost] |
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| **Imagebind Demo 🖼️** | Explore the multi-modal capabilities of Imagebind through a Gradio app, leveraging LanceDB API for seamless image search and retrieval experiences 📸 | [][imagebind_github] <br> [][imagebind_huggingface] |
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| **Search Engine using SAM & CLIP 🔍** | Build a search engine within an image using SAM and CLIP models, enabling object-level search and retrieval, with LanceDB indexing and search capabilities to find the closest match between image embeddings and user queries 📸 | [][swi_github] <br>[][swi_colab] <br>[][swi_ghost] |
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| **Zero Shot Object Localization and Detection with CLIP 🔎** | Perform object detection on images using OpenAI's CLIP, enabling zero-shot localization and detection of objects, with capabilities to split images into patches, parse with CLIP, and plot bounding boxes 📊 | [][zsod_github] <br>[][zsod_colab] |
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| **Accelerate Vector Search with OpenVINO 🚀** | Boost vector search applications using OpenVINO, achieving significant speedups with CLIP for text-to-image and image-to-image searching, through PyTorch model optimization, FP16 and INT8 format conversion, and quantization with OpenVINO NNCF 📈 | [][openvino_github] <br>[][openvino_colab] <br>[][openvino_ghost] |
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| **Zero-Shot Image Classification with CLIP and LanceDB 📸** | Achieve zero-shot image classification using CLIP and LanceDB, enabling models to classify images without prior training on specific use cases, unlocking flexible and adaptable image classification capabilities 🔓 | [][zsic_github] <br>[][zsic_colab] <br>[][zsic_ghost] |
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| **Inbuilt Hybrid Search 🔄** | Perform hybrid search in **LanceDB** by combining the results of semantic and full-text search via a reranking algorithm of your choice 📊 | [][inbuilt_hybrid_search_github] <br>[][inbuilt_hybrid_search_colab] |
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| **Hybrid Search with BM25 and LanceDB 💡** | Use **Synergizes BM25's** keyword-focused precision (term frequency, document length normalization, bias-free retrieval) with **LanceDB's** semantic understanding (contextual analysis, query intent alignment) for nuanced search results in complex datasets 📈 | [][BM25_github] <br>[][BM25_colab] <br>[][BM25_ghost] |
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| **NER-powered Semantic Search 🔎** | Extract and identify essential information from text with Named Entity Recognition **(NER)** methods: Dictionary-Based, Rule-Based, and Deep Learning-Based, to accurately extract and categorize entities, enabling precise semantic search results 🗂️ | [][NER_github] <br>[][NER_colab] <br>[][NER_ghost]|
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| **Audio Similarity Search using Vector Embeddings 🎵** | Create vector **embeddings of audio files** to find similar audio content, enabling efficient audio similarity search and retrieval in **LanceDB's** vector store 📻 |[][audio_search_github] <br>[][audio_search_colab] <br>[][audio_search_python]|
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| **LanceDB Embeddings API: Multi-lingual Semantic Search 🌎** | Build a universal semantic search table with **LanceDB's Embeddings API**, supporting multiple languages (e.g., English, French) using **cohere's** multi-lingual model, for accurate cross-lingual search results 📄 | [][mls_github] <br>[][mls_colab] <br>[][mls_python] |
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| **Facial Recognition: Face Embeddings 🤖** | Detect, crop, and embed faces using Facenet, then store and query face embeddings in **LanceDB** for efficient facial recognition and top-K matching results 👥 | [][fr_github] <br>[][fr_colab] |
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| **Sentiment Analysis: Hotel Reviews 🏨** | Analyze customer sentiments towards the hotel industry using **BERT models**, storing sentiment labels, scores, and embeddings in **LanceDB**, enabling queries on customer opinions and potential areas for improvement 💬 | [][sentiment_analysis_github] <br>[][sentiment_analysis_colab] <br>[][sentiment_analysis_ghost] |
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| **Vector Arithmetic with LanceDB ⚖️** | Perform **vector arithmetic** on embeddings, enabling complex relationships and nuances in data to be captured, and simplifying the process of retrieving semantically similar results 📊 | [][arithmetic_github] <br>[][arithmetic_colab] <br>[][arithmetic_ghost] |
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| **Imagebind Demo 🖼️** | Explore the multi-modal capabilities of **Imagebind** through a Gradio app, use **LanceDB API** for seamless image search and retrieval experiences 📸 | [][imagebind_github] <br> [][imagebind_huggingface] |
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| **Search Engine using SAM & CLIP 🔍** | Build a search engine within an image using **SAM** and **CLIP** models, enabling object-level search and retrieval, with LanceDB indexing and search capabilities to find the closest match between image embeddings and user queries 📸 | [][swi_github] <br>[][swi_colab] <br>[][swi_ghost] |
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| **Zero Shot Object Localization and Detection with CLIP 🔎** | Perform object detection on images using **OpenAI's CLIP**, enabling zero-shot localization and detection of objects, with capabilities to split images into patches, parse with CLIP, and plot bounding boxes 📊 | [][zsod_github] <br>[][zsod_colab] |
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| **Accelerate Vector Search with OpenVINO 🚀** | Boost vector search applications using **OpenVINO**, achieving significant speedups with **CLIP** for text-to-image and image-to-image searching, through PyTorch model optimization, FP16 and INT8 format conversion, and quantization with **OpenVINO NNCF** 📈 | [][openvino_github] <br>[][openvino_colab] <br>[][openvino_ghost] |
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| **Zero-Shot Image Classification with CLIP and LanceDB 📸** | Achieve zero-shot image classification using **CLIP** and **LanceDB**, enabling models to classify images without prior training on specific use cases, unlocking flexible and adaptable image classification capabilities 🔓 | [][zsic_github] <br>[][zsic_colab] <br>[][zsic_ghost] |
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