GenAI 2
1.0.0
该存储库致力于通过各种项目和代码片段探索大语言模型（LLMS）的功能和潜力。在这里，我探索了一系列基于LLM的项目，以应对不同的挑战并展示不同的用例。无论是文字生成，翻译，问题回答还是全新的东西，都激发了我的好奇心。
在计算机视觉中，对象识别和细分是至关重要的任务。但是，它们通常需要手动注释边界框或分割口罩，这可能是耗时且昂贵的。
所以？
忘记手动图像编辑！ AI模型中的最新进步正在解锁全新的方法，可以使用文本命令与图像和视频进行交互。
这种令人兴奋的方法利用了三个关键模型的优势：
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.
这个强大的三重奏打开了一个可能性的世界：
• 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.
你为什么需要它？
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.
这篇惊人的文章详细讨论了意图检测。
查询是一种旨在成为我们个人知识中心的Web应用程序，由Google Gemini Pro，Langchain，Vector DB和Shertlit的尖端功能提供支持。
查询可以做什么？
Queryverse提供了三个不同的应用程序，每个应用程序都利用大型语言模型（LLMS）的流行音乐进行有意义的互动：
* 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.
强大互动的技术力量：
在查询的核心是技术的复杂融合：
* 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.
商业价值：可能性的世界：
LLMS超越功能，在各个领域提供有形价值：
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.
升级：VerseVoyage -2.0应用程序已升级，现在正在拥抱面部空间。现在，您可以从几个提供商那里选择您选择的模型，并享受诗意的时光。
HF空间：https：//lnkd.in/gzem273s
当我们在这个特殊的情人节庆祝爱与联系时，我很高兴揭露我的最新项目：Versevoyage？这是一种由AI驱动的诗歌应用程序，旨在捕捉我们在迷人的经文中发自内心情感的本质。
Versevoyage不仅仅是另一个应用程序；这是一个诗意的旅程，等待探索。这是尖端技术的高潮，包括：AWS Bedrock：为应用程序的平稳操作提供岩石固定基础。 Llama-2和Jurassic-2：这些强大的语言模型充当您的文字史密斯，制作诗歌，与您的爱的独特旋律相呼应。 BOTO3：确保与AWS服务的无缝互动，以保持AI的爱情流动。 Python＆Sparlit：构建该应用程序的用户友好界面，使诗歌表达与单击按钮一样简单。
？诗歌唱片说，这是一种额外的魔力：1️⃣个性化的诗：表达爱，庆祝友谊或珍惜生活，经文voyage手工艺是个性化的诗歌，这些诗是捕捉情感本质的个性化诗歌。 2️⃣AI-Craunt的作品：由Llama2和Jurrasic模型提供动力，Versevoyage的诗歌生成引擎证明了人工智能的奇观。 3️⃣无缝的体验：借助Sparlit的直观界面，VerseVoyage提供了无缝的用户体验，使用户可以轻松地探索和欣赏诗歌的世界。 4️⃣云驱动的性能：凭借其核心AWS-BETROCK，VerseVoyage确保了可靠性和性能，因此我们可以在没有任何中断的情况下集中精力。
