GenAI 2
1.0.0
該存儲庫致力於通過各種項目和代碼片段探索大語言模型（LLMS）的功能和潛力。在這裡，我探索了一系列基於LLM的項目，以應對不同的挑戰並展示不同的用例。無論是文字生成，翻譯，問題回答還是全新的東西，都激發了我的好奇心。
在計算機視覺中，對象識別和細分是至關重要的任務。但是，它們通常需要手動註釋邊界框或分割口罩，這可能是耗時且昂貴的。
所以？
忘記手動圖像編輯！ AI模型中的最新進步正在解鎖全新的方法，可以使用文本命令與圖像和視頻進行交互。
這種令人興奮的方法利用了三個關鍵模型的優勢：
1. Grounding DINO: Identifies objects within the image based on your text description.
2. Meta's SAM: Segments the identified objects, creating precise masks.
3. Stable Diffusion: Modifies the image based on your further text instructions, seamlessly integrating the new elements.
這個強大的三重奏打開了一個可能性的世界：
• Faster Design Prototyping: Visualize ideas instantly, enabling quicker feedback and iteration cycles.
• Inclusive Content Creation: Easily translate and localize images for broader audiences.
• Streamlined Editing: Edit images and videos efficiently with text prompts, ideal for both individual creators and large-scale content management.
• Seamless Object Manipulation: Effortlessly identify and replace objects within an image, offering endless creative possibilities.
- Creating an AI chatbot involves more than just using a model API and adding context with our data. We should also consider the various intents users might have and how to manage them.
你為什麼需要它？
let's say that we have defined 5 intent categories: "Order Status", "Product Information", "Payments", "Returns" and "Feedback”.
For each category, there should be a distinct step where the LLM-powered chatbot, figures out the user's intent. It does this by placing the user's question into the right category. After identifying the intent, the chatbot can then take the next appropriate actions for that particular category.
Having separate steps for the prompts and intent handlers is useful because each of your intents might need to do different actions. For example: “Returns” might need to be handled by an external service/API that a handler action should call, and the handler for “Product information” might just call an LLM and a context doc to answer with text response. Also, adding too many instructions in one prompt can also influence the performance.
Identifying these intents accurately allows the chatbot to respond better, call an external API or route the query to the correct personnel for further assistance.
這篇驚人的文章詳細討論了意圖檢測。
查詢是一種旨在成為我們個人知識中心的Web應用程序，由Google Gemini Pro，Langchain，Vector DB和Shertlit的尖端功能提供支持。
查詢可以做什麼？
Queryverse提供了三個不同的應用程序，每個應用程序都利用大型語言模型（LLMS）的流行音樂進行有意義的互動：
* Q&A: Unleash your curiosity! Ask QueryVerse anything, across diverse topics, and receive informative, comprehensive answers. Its knowledge base is vast and continuously expanding, ensuring you stay informed and empowered.
* Vision: See beyond the pixels! Upload an image and QueryVerse will analyze it, providing insights and details, answering your questions with remarkable accuracy. Imagine understanding complex visuals with just a click!
* Chatbot: Dive deep into your documents! Upload documents and ask QueryVerse questions directly related to their content. Its contextual understanding allows it to answer with precision, streamlining our information retrieval process.
強大互動的技術力量：
在查詢的核心是技術的複雜融合：
* Google Gemini Pro: This powerhouse LLM delivers exceptional natural language processing, enabling QueryVerse to understand our questions and respond coherently.
* Langchain: This framework seamlessly combines different AI models, allowing QueryVerse to leverage the strengths of various specialists for comprehensive problem-solving.
* Vector DB: This efficient database stores and retrieves information with impressive speed, ensuring QueryVerse delivers answers rapidly.
* Streamlit: This user-friendly platform creates a clean and intuitive web interface, making QueryVerse accessible and enjoyable to use.
商業價值：可能性的世界：
LLMS超越功能，在各個領域提供有形價值：
Customer Service: Enhance customer experiences by providing instant, 24/7 support through LLM's Q&A and Chatbot features.
Education: Foster deeper learning by empowering us with a personalized knowledge assistant.
Content Creation: Streamline research and content generation by leveraging LLM's ability to process information and answer questions creatively.
Data Analysis: Extract insights from unstructured data efficiently with LLM's Vision and Chatbot capabilities.
升級：VerseVoyage -2.0應用程序已升級，現在正在擁抱面部空間。現在，您可以從幾個提供商那裡選擇您選擇的模型，並享受詩意的時光。
HF空間：https：//lnkd.in/gzem273s
當我們在這個特殊的情人節慶祝愛與聯繫時，我很高興揭露我的最新項目：Versevoyage？這是一種由AI驅動的詩歌應用程序，旨在捕捉我們在迷人的經文中發自內心情感的本質。
Versevoyage不僅僅是另一個應用程序；這是一個詩意的旅程，等待探索。這是尖端技術的高潮，包括：AWS Bedrock：為應用程序的平穩操作提供岩石固定基礎。 Llama-2和Jurassic-2：這些強大的語言模型充當您的文字史密斯，製作詩歌，與您的愛的獨特旋律相呼應。 BOTO3：確保與AWS服務的無縫互動，以保持AI的愛情流動。 Python＆Sparlit：構建該應用程序的用戶友好界面，使詩歌表達與單擊按鈕一樣簡單。
？詩歌唱片說，這是一種額外的魔力：1️⃣個性化的詩：表達愛，慶祝友誼或珍惜生活，經文voyage手工藝是個性化的詩歌，這些詩是捕捉情感本質的個性化詩歌。 2️⃣AI-Craunt的作品：由Llama2和Jurrasic模型提供動力，Versevoyage的詩歌生成引擎證明了人工智能的奇觀。 3️⃣無縫的體驗：借助Sparlit的直觀界面，VerseVoyage提供了無縫的用戶體驗，使用戶可以輕鬆地探索和欣賞詩歌的世界。 4️⃣雲驅動的性能：憑藉其核心AWS-BETROCK，VerseVoyage確保了可靠性和性能，因此我們可以在沒有任何中斷的情況下集中精力。
