Vector Search: Efficient Retrieval 🔓👀
Vector search with LanceDB, is a solution for efficient and accurate similarity searches in large datasets 📊.
Vector Search Capabilities in LanceDB🔝
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 |
|---|---|---|
| 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 📊 |   |
| 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 📈 |  |
| 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 🗂️ | |
| 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 📻 |  |
| 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 📄 | |
| 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 👥 | |
| 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 💬 | |
| 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 📊 | |
| Imagebind Demo 🖼️ | Explore the multi-modal capabilities of Imagebind through a Gradio app, use LanceDB API for seamless image search and retrieval experiences 📸 |  |
| 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 📸 | |
| 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 📊 | |
| 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 📈 | |
| 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 🔓 | |