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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>
This commit is contained in:
Rithik Kumar
GitHub
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# AI Agents: Intelligent Collaboration🤖
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Think of a platform💻 where AI Agents🤖 can seamlessly exchange information, coordinate over tasks, and achieve shared targets with great efficiency📈🚀.
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Think of a platform where AI Agents can seamlessly exchange information, coordinate over tasks, and achieve shared targets with great efficiency💻📈.
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## Vector-Based Coordination: The Technical Advantage
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Leveraging LanceDB's vector-based capabilities, our coordination application enables AI agents to communicate and collaborate through dense vector representations 🤖. AI agents can exchange information, coordinate on a task or work towards a common goal, just by giving queries📝.
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Leveraging LanceDB's vector-based capabilities, we can enable **AI agents 🤖** to communicate and collaborate through dense vector representations. AI agents can exchange information, coordinate on a task or work towards a common goal, just by giving queries📝.
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| **AI Agents** | **Description** | **Links** |
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|:--------------|:----------------|:----------|
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| **AI Agents: Reducing Hallucinationt📊** | 🤖💡 Reduce AI hallucinations using Critique-Based Contexting! Learn by Simplifying and Automating tedious workflows by going through fitness trainer agent example.💪 | [][hullucination_github] <br>[][hullucination_colab] <br>[][hullucination_python] <br>[][hullucination_ghost] |
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| **AI Trends Searcher: CrewAI🔍️** | 🔍️ Learn about CrewAI Agents ! Utilize the features of CrewAI - Role-based Agents, Task Management, and Inter-agent Delegation ! Make AI agents work together to do tricky stuff 😺| [][trend_github] <br>[][trend_colab] <br>[][trend_ghost] |
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| **SuperAgent Autogen🤖** | 💻 AI interactions with the Super Agent! Integrating Autogen, LanceDB, LangChain, LiteLLM, and Ollama to create AI agent that excels in understanding and processing complex queries.🤖 | [][superagent_github] <br>[][superagent_colab] |
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| **AI Agents: Reducing Hallucinationt📊** | 🤖💡 **Reduce AI hallucinations** using Critique-Based Contexting! Learn by Simplifying and Automating tedious workflows by going through fitness trainer agent example.💪 | [][hullucination_github] <br>[][hullucination_colab] <br>[][hullucination_python] <br>[][hullucination_ghost] |
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| **AI Trends Searcher: CrewAI🔍️** | 🔍️ Learn about **CrewAI Agents** ! Utilize the features of CrewAI - Role-based Agents, Task Management, and Inter-agent Delegation ! Make AI agents work together to do tricky stuff 😺| [][trend_github] <br>[][trend_colab] <br>[][trend_ghost] |
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| **SuperAgent Autogen🤖** | 💻 AI interactions with the Super Agent! Integrating **Autogen**, **LanceDB**, **LangChain**, **LiteLLM**, and **Ollama** to create AI agent that excels in understanding and processing complex queries.🤖 | [][superagent_github] <br>[][superagent_colab] |
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[hullucination_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents
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# **Build from Scratch with LanceDB 🛠️🚀**
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Start building your GenAI applications from the ground up using LanceDB's efficient vector-based document retrieval capabilities! 📑
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Start building your GenAI applications from the ground up using **LanceDB's** efficient vector-based document retrieval capabilities! 📑
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**Get Started in Minutes ⏱️**
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These examples provide a solid foundation for building your own GenAI applications using LanceDB. Jump from idea to proof of concept quickly with applied examples. Get started and see what you can create! 💻
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These examples provide a solid foundation for building your own GenAI applications using LanceDB. Jump from idea to **proof of concept** quickly with applied examples. Get started and see what you can create! 💻
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| **Build From Scratch** | **Description** | **Links** |
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|:-------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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**Chatbot Application with LanceDB 🤖**
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**Chatbot applications with LanceDB 🤖**
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====================================================================
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Create an innovative chatbot application that utilizes LanceDB for efficient vector-based response generation! 🌐✨
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Create innovative chatbot applications that utilizes LanceDB for efficient vector-based response generation! 🌐✨
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**Introduction 👋✨**
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| **Chatbot** | **Description** | **Links** |
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|:----------------|:-----------------|:-----------|
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| **Databricks DBRX Website Bot ⚡️** | Unlock magical conversations with the Hogwarts chatbot, powered by Open-source RAG, DBRX, LanceDB, LLama-index, and Hugging Face Embeddings, delivering enchanting user experiences and spellbinding interactions ✨ | [][databricks_github] <br>[][databricks_python] |
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| **CLI SDK Manual Chatbot Locally 💻** | CLI chatbot for SDK/hardware documents, powered by Local RAG, LLama3, Ollama, LanceDB, and Openhermes Embeddings, built with Phidata Assistant and Knowledge Base for instant technical support 🤖 | [][clisdk_github] <br>[][clisdk_python] |
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| **Youtube Transcript Search QA Bot 📹** | Unlock the power of YouTube transcripts with a Q&A bot, leveraging natural language search and LanceDB for effortless data management and instant answers 💬 | [][youtube_github] <br>[][youtube_colab] <br>[][youtube_python] |
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| **Code Documentation Q&A Bot with LangChain 🤖** | Revolutionize code documentation with a Q&A bot, powered by LangChain and LanceDB, allowing effortless querying of documentation using natural language, demonstrated with Numpy 1.26 docs 📚 | [][docs_github] <br>[][docs_colab] <br>[][docs_python] |
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| **Context-aware Chatbot using Llama 2 & LanceDB 🤖** | Experience the future of conversational AI with a context-aware chatbot, powered by Llama 2, LanceDB, and LangChain, enabling intuitive and meaningful conversations with your data 📚💬 | [][aware_github] <br>[][aware_colab] <br>[][aware_ghost] |
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| **Chat with csv using Hybrid Search 📊** | Revolutionize data interaction with a chat application that harnesses LanceDB's hybrid search capabilities to converse with CSV and Excel files, enabling efficient and scalable data exploration and analysis 🚀 | [][csv_github] <br>[][csv_colab] <br>[][csv_ghost] |
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| **Databricks DBRX Website Bot ⚡️** | Engage with the **Hogwarts chatbot**, that uses Open-source RAG with **DBRX**, **LanceDB** and **LLama-index with Hugging Face Embeddings**, to provide interactive and engaging user experiences. ✨ | [][databricks_github] <br>[][databricks_python] |
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| **CLI SDK Manual Chatbot Locally 💻** | CLI chatbot for SDK/hardware documents using **Local RAG** with **LLama3**, **Ollama**, **LanceDB**, and **Openhermes Embeddings**, built with **Phidata** Assistant and Knowledge Base 🤖 | [][clisdk_github] <br>[][clisdk_python] |
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| **Youtube Transcript Search QA Bot 📹** | Search through **youtube transcripts** using natural language with a Q&A bot, leveraging **LanceDB** for effortless data storage and management 💬 | [][youtube_github] <br>[][youtube_colab] <br>[][youtube_python] |
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| **Code Documentation Q&A Bot with LangChain 🤖** | Query your own documentation easily using questions in natural language with a Q&A bot, powered by **LangChain** and **LanceDB**, demonstrated with **Numpy 1.26 docs** 📚 | [][docs_github] <br>[][docs_colab] <br>[][docs_python] |
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| **Context-aware Chatbot using Llama 2 & LanceDB 🤖** | Build **conversational AI** with a **context-aware chatbot**, powered by **Llama 2**, **LanceDB**, and **LangChain**, that enables intuitive and meaningful conversations with your data 📚💬 | [][aware_github] <br>[][aware_colab] <br>[][aware_ghost] |
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| **Chat with csv using Hybrid Search 📊** | **Chat** application that interacts with **CSV** and **Excel files** using **LanceDB’s** hybrid search capabilities, performing direct operations on large-scale columnar data efficiently 🚀 | [][csv_github] <br>[][csv_colab] <br>[][csv_ghost] |
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[databricks_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot
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**Evaluation: Assessing Text Performance with Precision 📊💡**
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====================================================================
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**Evaluation Fundamentals 📊**
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Evaluation is a comprehensive tool designed to measure the performance of text-based inputs, enabling data-driven optimization and improvement 📈.
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**Text Evaluation 101 📚**
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By leveraging cutting-edge technologies, this provides a robust framework for evaluating reference and candidate texts across various metrics 📊, ensuring high-quality text outputs that meet specific requirements and standards 📝.
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Using robust framework for assessing reference and candidate texts across various metrics📊, ensure that the text outputs are high-quality and meet specific requirements and standards📝.
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| **Evaluation** | **Description** | **Links** |
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| -------------- | --------------- | --------- |
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| **Evaluating Prompts with Prompttools 🤖** | Compare, visualize & evaluate embedding functions (incl. OpenAI) across metrics like latency & custom evaluation 📈📊 | [][prompttools_github] <br>[][prompttools_colab] |
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| **Evaluating RAG with RAGAs and GPT-4o 📊** | Evaluate RAG pipelines with cutting-edge metrics and tools, integrate with CI/CD for continuous performance checks, and generate responses with GPT-4o 🤖📈 | [][RAGAs_github] <br>[][RAGAs_colab] |
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| **Evaluating Prompts with Prompttools 🤖** | Compare, visualize & evaluate **embedding functions** (incl. OpenAI) across metrics like latency & custom evaluation 📈📊 | [][prompttools_github] <br>[][prompttools_colab] |
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| **Evaluating RAG with RAGAs and GPT-4o 📊** | Evaluate **RAG pipelines** with cutting-edge metrics and tools, integrate with CI/CD for continuous performance checks, and generate responses with GPT-4o 🤖📈 | [][RAGAs_github] <br>[][RAGAs_colab] |
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# **Multimodal Search with LanceDB 🤹♂️🔍**
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Experience the future of search with LanceDB's multimodal capabilities. Combine text and image queries to find the most relevant results in your corpus ! 🔓💡
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Using LanceDB's multimodal capabilities, combine text and image queries to find the most relevant results in your corpus ! 🔓💡
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**Explore the Future of Search 🚀**
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| **Multimodal** | **Description** | **Links** |
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|:----------------|:-----------------|:-----------|
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| **Multimodal CLIP: DiffusionDB 🌐💥** | Revolutionize search with Multimodal CLIP and DiffusionDB, combining text and image understanding for a new dimension of discovery! 🔓 | [][Clip_diffusionDB_github] <br>[][Clip_diffusionDB_colab] <br>[][Clip_diffusionDB_python] <br>[][Clip_diffusionDB_ghost] |
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| **Multimodal CLIP: Youtube Videos 📹👀** | Search Youtube videos using Multimodal CLIP, finding relevant content with ease and accuracy! 🎯 | [][Clip_youtube_github] <br>[][Clip_youtube_colab] <br> [][Clip_youtube_python] <br>[][Clip_youtube_python] |
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| **Multimodal Image + Text Search 📸🔍** | Discover relevant documents and images with a single query, using LanceDB's multimodal search capabilities to bridge the gap between text and visuals! 🌉 | [](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search) <br>[](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb) <br> [](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
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| **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Dive into vision-centric exploration of images with Cambrian-1, powered by LanceDB's multimodal search to uncover new insights! 🔎 | [](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/)<br> [](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/) |
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| **Multimodal CLIP: DiffusionDB 🌐💥** | Multi-Modal Search with **CLIP** and **LanceDB** Using **DiffusionDB** Data for Combined Text and Image Understanding ! 🔓 | [][Clip_diffusionDB_github] <br>[][Clip_diffusionDB_colab] <br>[][Clip_diffusionDB_python] <br>[][Clip_diffusionDB_ghost] |
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| **Multimodal CLIP: Youtube Videos 📹👀** | Search **Youtube videos** using Multimodal CLIP, finding relevant content with ease and accuracy! 🎯 | [][Clip_youtube_github] <br>[][Clip_youtube_colab] <br> [][Clip_youtube_python] <br>[][Clip_youtube_python] |
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| **Multimodal Image + Text Search 📸🔍** | Find **relevant documents** and **images** with a single query using **LanceDB's** multimodal search capabilities, to seamlessly integrate text and visuals ! 🌉 | [](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search) <br>[](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb) <br> [](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
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| **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Learn how **Cambrian-1** works, using an example of **Vision-Centric** exploration on images found through vector search ! Work on **Flickr-8k** dataset 🔎 | [](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/)<br> [](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/) |
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[Clip_diffusionDB_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb
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**RAG: Revolutionize Information Retrieval with LanceDB 🔓🧐**
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**RAG (Retrieval-Augmented Generation) with LanceDB 🔓🧐**
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====================================================================
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Build RAG (Retrieval-Augmented Generation) with LanceDB, a powerful solution for efficient vector-based information retrieval 📊.
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| **Advanced RAG: Parent Document Retriever** 📑🔗 | Use **Parent Document & Bigger Chunk Retriever** to maintain context and relevance when generating related content. 🎵📄 | [][parent_doc_retriever_github] <br>[][parent_doc_retriever_colab] <br>[][parent_doc_retriever_ghost] |
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| **Corrective RAG with Langgraph** 🔧📊 | Enhance RAG reliability with **Corrective RAG (CRAG)** by self-reflecting and fact-checking for accurate and trustworthy results. ✅🔍 |[][corrective_rag_github] <br>[][corrective_rag_colab] <br>[][corrective_rag_ghost] |
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| **Contextual Compression with RAG** 🗜️🧠 | Apply **contextual compression techniques** to condense large documents while retaining essential information. 📄🗜️ | [][compression_rag_github] <br>[][compression_rag_colab] <br>[][compression_rag_ghost] |
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| **Improve RAG with FLARE** 🔥| Enable users to ask questions directly to academic papers, focusing on ArXiv papers, with Forward-Looking Active REtrieval augmented generation.🚀🌟 | [][flare_github] <br>[][flare_colab] <br>[][flare_ghost] |
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| **Query Expansion and Reranker** 🔍🔄 | Enhance RAG with query expansion using Large Language Models and advanced **reranking methods** like Cross Encoders, ColBERT v2, and FlashRank for improved document retrieval precision and recall 🔍📈 | [][query_github] <br>[][query_colab] |
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| **RAG Fusion** ⚡🌐 | Revolutionize search with RAG Fusion, utilizing the **RRF algorithm** to rerank documents based on user queries, and leveraging LanceDB and OPENAI Embeddings for efficient information retrieval ⚡🌐 | [][fusion_github] <br>[][fusion_colab] |
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| **Agentic RAG** 🤖📚 | Unlock autonomous information retrieval with **Agentic RAG**, a framework of **intelligent agents** that collaborate to synthesize, summarize, and compare data across sources, enabling proactive and informed decision-making 🤖📚 | [][agentic_github] <br>[][agentic_colab] |
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| **Improve RAG with FLARE** 🔥| Enable users to ask questions directly to **academic papers**, focusing on **ArXiv papers**, with **F**orward-**L**ooking **A**ctive **RE**trieval augmented generation.🚀🌟 | [][flare_github] <br>[][flare_colab] <br>[][flare_ghost] |
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| **Query Expansion and Reranker** 🔍🔄 | Enhance RAG with query expansion using Large Language Models and advanced **reranking methods** like **Cross Encoders**, **ColBERT v2**, and **FlashRank** for improved document retrieval precision and recall 🔍📈 | [][query_github] <br>[][query_colab] |
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| **RAG Fusion** ⚡🌐 | Build RAG Fusion, utilize the **RRF algorithm** to rerank documents based on user queries ! Use **LanceDB** as vector database to store and retrieve documents related to queries via **OPENAI Embeddings**⚡🌐 | [][fusion_github] <br>[][fusion_colab] |
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| **Agentic RAG** 🤖📚 | Build autonomous information retrieval with **Agentic RAG**, a framework of **intelligent agents** that collaborate to synthesize, summarize, and compare data across sources, that enables proactive and informed decision-making 🤖📚 | [][agentic_github] <br>[][agentic_colab] |
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@@ -9,10 +9,10 @@ Deliver personalized experiences with Recommender Systems. 🎁
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| **Recommender System** | **Description** | **Links** |
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| ---------------------- | --------------- | --------- |
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| **Movie Recommender System🎬** | 🤝 Use **collaborative filtering** to predict user preferences, assuming similar users will like similar movies, and leverage **Singular Value Decomposition** (SVD) from Numpy for precise matrix factorization and accurate recommendations📊 | [][movie_github] <br>[][movie_colab] <br>[][movie_python] |
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| **🎥 Movie Recommendation with Genres** | 🔍 Creates movie embeddings using Doc2Vec, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations through similarity searches🎥 | [][genre_github] <br>[][genre_colab] <br>[][genre_ghost] |
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| **🎥 Movie Recommendation with Genres** | 🔍 Creates movie embeddings using **Doc2Vec**, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations through **similarity searches**🎥 | [][genre_github] <br>[][genre_colab] <br>[][genre_ghost] |
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| **🛍️ Product Recommender using Collaborative Filtering and LanceDB** | 📈 Using **Collaborative Filtering** and **LanceDB** to analyze your past purchases, recommends products based on user's past purchases. Demonstrated with the Instacart dataset in our example🛒 | [][product_github] <br>[][product_colab] <br>[][product_python] |
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| **🔍 Arxiv Search with OpenCLIP and LanceDB** | 💡 Build a semantic search engine for Arxiv papers using LanceDB, and benchmarks its performance against traditional keyword-based search on Nomic's Atlas, to demonstrate the power of semantic search in finding relevant research papers📚 | [][arxiv_github] <br>[][arxiv_colab] <br>[][arxiv_python] |
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| **Food Recommendation System🍴** | 🍔 Build a food recommendation system with LanceDB, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌 | [][food_github] <br>[][food_colab] |
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| **🔍 Arxiv Search with OpenCLIP and LanceDB** | 💡 Build a semantic search engine for **Arxiv papers** using **LanceDB**, and benchmarks its performance against traditional keyword-based search on **Nomic's Atlas**, to demonstrate the power of semantic search in finding relevant research papers📚 | [][arxiv_github] <br>[][arxiv_colab] <br>[][arxiv_python] |
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| **Food Recommendation System🍴** | 🍔 Build a food recommendation system with **LanceDB**, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌 | [][food_github] <br>[][food_colab] |
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[movie_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender
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[movie_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb
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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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| **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] |
|
||||
| **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] |
|
||||
| **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] |
|
||||
| **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] |
|
||||
| **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] |
|
||||
| **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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|
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|
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|
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Reference in New Issue
Block a user