Transform unstructured digital assets into semantically organized knowledge: https://lnkd.in/ekfZXG3B Fluree’s Content Auto-Tagging Manager (CAM), is a best-in-class content classification and tagging software, powered by NLP. Out-of-the-Box Connectors For: PDFs Word Documents Microsoft Exchange Drupal OpenText WordPress SharePoint Zendesk Amazon Reviews Facebook Images Videos Google Speech
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At Superteams.ai, we have tested and built Retrieval Augmented Generation (RAG) pipeline with Open Source models to generate enterprise content that will power future SaaS stacks. Here’s is a simple lowdown of what RAG is. In simple terms, RAG comprises two main components: 1. Retriever: This component retrieves relevant documents based on the query and document index. 2. Generator: The Generator generates sequences based on the large language model and combines the input with the retrieved content. A typical RAG stack involves decisions around: 1. Information Source, Dataset, or Corpus, essentially, the ‘knowledge’ 2. Embedding Generator, which will convert text chunks into ‘embeddings’. 3. Search engine: Vector Search, or Knowledge Graph, or other technologies 4. Ranking algorithm 5. LLM to be used — such as Llama2, Mistral etc. Finally, prompt engineering helps bring the pipeline together. Frameworks like LlamaIndex and LangChain help simplify the entire process. We have RAG pipeline with a Pharmaceutical company's articles data to generate enterprise content by leveraging Mixtral 8x7B model and Qdrant Vector Database. To know more, visit our medium blog: https://lnkd.in/g-WKVYkM To learn more about how RAG can be applied to your business, reach out to us. https://lnkd.in/ghpZ_Pnp
How to Build an Advanced AI-Powered Enterprise Content Pipeline Using Mixtral 8x7B and Qdrant
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OmniParse is a platform that ingests and parses any unstructured data into structured, actionable data optimized for #GenAI (#LLM) applications. Whether you are working with documents, tables, images, videos, audio files, or web pages https://buff.ly/4cQPm7v #ai
GitHub - adithya-s-k/omniparse: Ingest, parse, and optimize any data format ➡️ from documents to multimedia ➡️ for enhanced compatibility with GenAI frameworks
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Ex Summer Intern @HMSI || Pre-Final year student at TIET ||Data Science Enthusiast ||Front End Development
Movie Recommendation System Using TMDB Dataset Made a Machine Learning Project Project Overview This project involves building a movie recommendation system using a dataset of 5000 movies from The Movie Database (TMDB). The project is divided into several key stages: data processing, vectorization, model building, front-end development, and deployment. The final application is hosted on Streamlit Cloud. Key Stages 1. Data Processing Data Collection: Acquire the dataset containing details of 5000 movies from TMDB. Data Cleaning: Handle missing values, remove duplicates, and standardize data formats. Feature Engineering: Extract and create relevant features (e.g., genres, cast, director, keywords). 2. Vectorization Text Vectorization: Convert textual data (e.g., movie descriptions, genres) into numerical vectors using techniques like TF-IDF, Count Vectorizer, or Word2Vec. Similarity Calculation: Compute similarity scores between movies using cosine similarity or other relevant metrics. 3. Model Building Recommendation Algorithm: Implement a recommendation algorithm that suggests movies based on the similarity scores. Collaborative filtering and content-based filtering can be used. Evaluation: Test the model using appropriate metrics to ensure accuracy and relevance of recommendations. 4. Front-End Development Streamlit: Build an interactive web interface using Streamlit. User Input: Allow users to input a movie title to get recommendations. Display Results: Show recommended movies with details like title, genre, and poster. Additional Features: Include features such as filtering by genre, rating, or release year. 5. Deployment Streamlit Cloud: Deploy the Streamlit application on Streamlit Cloud for easy access and sharing. You can check this out at: https://lnkd.in/e33tzf_K The code for this project is available on my Github.
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#generativeai is the hype of the moment, and #Copilot integrates best with Microsoft’s extensive technology portfolio. This has put #coding, application use, #automation, #analytics and #cloudcomputing right at the fingertips of all users, with natural language prompts generating the desired computing output. The 10 best ways to use Copilot in Microsoft Technologies Analytics and BI Deep technical skills are no longer required for Analytics and Business Intelligence. Use natural language in Power BI to generate dashboards, perform predictive analytics and more, all aimed at novice users. Just describe the desired output and request the results – all done by Copilot. Automation Process automation is now child’s play with Power Automate. Just define the workflow requirements and see automation appear immediately. Use Copilot for a wide range of workflows in your organization. App Development Need a custom app? Copilot takes care of it for you. Say goodbye to complex coding and dependence on technical resources. It is now possible for anyone to create an app without coding skills. Chatbots Virtual assistants or chatbots are now the norm. With Copilot, you can create a chatbot based on your specifications. Your chatbot becomes essential to your business thanks to built-in data mining and natural language processing features. Website Development Forget complex web page coding. Describe your page and let Copilot create it for you. Add forms and database tables effortlessly. Modern Workplace Standard office tasks are a thing of the past. Copilot integrates into Microsoft 365 and provides advanced features such as document idea generation, email summarization and complex spreadsheet creation. Communications Teams has redefined the way we communicate. Copilot takes this experience to the next level with features such as call summaries and automatic generation of action items. Cloud Computing AI models, content moderation and more are now more accessible than ever. Work seamlessly with Azure and reap the benefits of cloud computing with Copilot. Security Cybersecurity is now easier thanks to Microsoft Security Copilot. Detect threats and identify the best strategies with the help of AI. AI Pair Programming with GitHub Copilot Coding without technical skills is now possible. Let GitHub Copilot generate your code or make suggestions using OpenAI Codex. For more information on Microsoft Copilot, contact us and our experts will be happy to help. F9 INFOTECH #analytics #automation #websitedevelopment
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Dir-Assistant: Simplifying File Management with Local and API Language Models https://lnkd.in/drZiTQhD Dir-Assistant: Simplifying File Management with Local and API Language Models Managing files and directories efficiently can be challenging, especially when dealing with large structures. Navigating numerous files to find relevant information often consumes much time and effort. This becomes more complicated when users need to understand or edit multiple files quickly. Traditional file management and search methods must be revised to handle these tasks effectively. Practical Solutions and Value Several solutions exist to help manage files, including essential search functions and file indexing tools. However, these solutions usually have limitations. Essential search functions often need the ability to understand context, making it hard to find exactly what one needs. File indexing tools can improve search speed but still need to improve in providing contextual understanding and relevant summarization. A new tool named ‘dir-assistant’ addresses these issues by utilizing local and API-based language models (LLMs) capabilities. Dir-Assistant leverages advanced LLMs to enable users to interact with their directories more intuitively. This tool can analyze the content of files, provide summaries, and help locate specific information efficiently. It supports various platforms and models, ensuring flexibility and customization for different user needs. The performance metrics of dir-assistant are truly impressive. Its integration with state-of-the-art LLMs allows the tool to process and comprehend extensive amounts of text efficiently. For instance, the API LLMs used in dir-assistant can handle up to 1 million tokens, enabling a significant context window for deep understanding and precise information retrieval from multiple files. Furthermore, the local models demonstrate robust performance across different hardware setups, ensuring accessibility and usability even without powerful servers. In conclusion, dir-assistant represents a significant step forward in file and directory management. Harnessing the power of local and API-based language models offers a more innovative and efficient way to navigate and understand file structures. This tool simplifies finding and summarizing information within files, making it helpful in dealing with large amounts of data. With its flexible setup and powerful capabilities, dir-assistant can improve productivity in extensively managing directories and files. AI Solutions for Business Evolution If you want to evolve your company with AI, stay competitive, use for your advantage Dir-Assistant: Simplifying File Management with Local and API Language Models. Discover how AI can redefine your way of work. Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. Define KPIs: Ensure your A...
Dir-Assistant: Simplifying File Management with Local and API Language Models https://itinai.com/dir-assistant-simplifying-file-management-with-local-and-api-language-models/ Dir-Assistant: Simplifying File Management with Local and API Language Models Managing files and directories efficiently can be challenging, especially when dealing with large structures. Navigating numerous files t...
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🔗 Chat with your Confluence - A Guide to Building AI Chatbots for Confluence Our latest blog post explores the process of creating an AI-powered chatbot that integrates with Confluence data, aiming to improve how teams access and interact with their knowledge base. We begin by discussing Airbyte, an open-source data integration platform that provides more than 300 connectors, allowing for amazing integration scenarios. The integration of PGVector, an extension for efficient vector operations, optimizes the system for executing large language model (LLM) queries. The guide also covers LangChain, a tool that connects different technologies, enabling smooth data workflows. This integration is essential for developing a chatbot powered by LLMs like GPT-4, which can understand complex queries and provide accurate, context-aware responses. We provide step-by-step instructions to help you get started, from setting up your environment to using PyAirbyte (an incredible python module for running Airbyte jobs in any python script) for data syncing and leveraging Langchain's capabilities for a dynamic AI interaction layer. This guide offers both theoretical knowledge and practical skills to help you implement your own AI chatbot, improving data retrieval and utilization within your organization. For those interested in combining AI with data management—including software developers and data enthusiasts—this guide serves as a starting point for developing advanced chatbot solutions that push the boundaries of AI in business processes. Check out the full guide to learn more! 👉 Read the full guide here: https://lnkd.in/dSWqMqzC #AI #DataManagement #Confluence #Chatbot #Airbyte #Langchain #PGVector #TechnologyIntegration #Optimization
Chat with your Confluence: A Step-by-Step Guide using Airbyte, Langchain and PGVector
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Founding Team Member/SDE at AI Planet(formerly DPhi) | Passionate Problem Solver and a Tech Enthusiast 😀
🚀 The GenAI Stack Journey: Realizing Open-Source Power for Developers 🚀 In a landscape dominated by Large Language Models (LLMs), the road to adopting AI has been anything but smooth. For developers and organizations alike, the hurdles have been numerous, from navigating the maze of data privacy concerns to deciphering the complexities of embedding raw domain data into usable applications. And let's not forget the quirkiness of LLMs, often caught "hallucinating" and generating less-than-accurate content, especially in business contexts. As a core contributor I'm really proud of the team work, the effort, the late nighters that we pulled off to make this happen. 🚀 Introducing GenAI Stack: For Developers, by Developers 🚀 Today, I'm thrilled to introduce GenAI Stack, a project that's been a rollercoaster ride for our development team. Our mission was simple yet daunting: make AI accessible to developers, with all the warts and wonders it entails. GenAI Stack isn't just another tool; it's a comprehensive framework designed to seamlessly integrate LLMs into your applications. And here's the kicker - you can deploy it on your infrastructure, keeping your data under lock and key. The best part? It's now open-source, so you can dive into the nuts and bolts. 🌟Features of GenAI Stack: 🔹 ETL Simplified: Navigate data processing complexities effortlessly. 🔹 Hallucination-Free Inference: Trustworthy AI-generated content for decision-making and research. 🔹 Seamless Integration: Easy adoption, whether you're a pro or just starting. 🔹 Customization and Control: Tailor processes to your project's needs. 🌟 Applications of GenAI Stack: 🔹Enhance search engines 🔹 Quick and dynamic knowledge base Q&A 🔹 Real-time sentiment analysis 🔹 Efficient customer support chatbots 🔹Streamlined information retrieval Contributions are welcomed: https://lnkd.in/gKWPpDWQ #ai #opensource #opensourcecommunity #machinelearning #largelanguagemodels #llms #datascience
GitHub - aiplanethub/genai-stack: An End to End GenAI Framework
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Are you ready for your OWN No-Code LOCAL AI for FREE with out-of-the-box ability to make Hive of Agents (will explain below, it's mega-cool, trust me)? I’ll tell you more… Listen → I’ve been using this masterpiece for last 2 month. It’ super easy: 1. Insert your OpenAI Key , 2. Create your own Agents (or use default, they are great!) , 3. Create your own Materials ( Text files or code with api… AI Console will help to create them… Just ask ;) ) And you UNLOCK a POWER TO CONTROL Your AI! You did a setup once. I added, as an example, a Product Manager Agent with Top Frameworks, a Management Consultant Agent + Guides how to structure any business problem, Project Manager Agent with information about the top Project Frameworks, Pre-Sales Engineer Agent that is power by OUR INTERNAL COMPANY’S DOCUMENTS (and I’m sleeping well, everything is stored on the local machine, no safety concerns at all!!!)… And what I need to do is just to DROP the TASK into the chat and BOOM… bunch of Agents work on my task iteratively (deciding on the right Agent, I don’t need to worry at all). Like I can tell the console, that I have a new customer, send the call log and voila 1️⃣ Product manager agent will evaluate customer requests and our latest experience as a worldwide company. And will create a Product Epics and Features Suggestions 2️⃣ Pre-Sales Engineer will come to have a look and evaluate the timelines and prices 3️⃣ Project Manager Agent will cover the gaps (like draft questions to clarify with customer) and create a Project Roadmap proposal. The sweet thing is that they (Agents) work in one chat together. I don’t need to switch from chat to chat. Just sit and look at how they are communicating. We have there Programmer, Automator, Planner … Lots of guys out-of-the-box. You could ask to create a Python program to combine all hour pdfs into one big file (to use it as a knowledge base, as an example) and AGENTS COLLABORATIVELY not only will write a code but also will be able to execute it right in the console → so your next message will be the PATH to your folder, where pdf to combine are. BOOM - it’s ready. SOOOOO I can tell you more and more. But better go right now to the https://aiconsole.ai/ download this UNIQE thing for FREE and try it yourself. And you could always ping me for help 😎 #businessautomation , #businessboost , #business , #ai #chatgpt #ml #gpt4 #businessinsights
Launching AIConsole – An Open-Source Desktop AI Editor to Personalize Your Workflow I was frustrated with feeling like an assistant to most AI systems and chats that I used. All I wanted was to have a fully controllable AI System that will potentially do everything for me, and that I can easily teach to do new things and get my context without constant copy and pasting. This is why, together with my team at 10Clouds AI Labs, I built AIConsole, an open-source desktop AI editor that allows you to create your own AI toolset. Giving you the power of AI personalization right on your local machine. Imagine a desktop app that can schedule your meetings, automate responses, craft unique content, code snippets, or even edit code – all while respecting your privacy and preferences. What does it do? • Runs code locally - it can fully operate within your local environment with the tasks your machine can execute – it can do everything that is accessible from your machine. • Gets better the more you put into it - teach it once to perform a task and AIConsole retains the skill indefinitely. • Use your notes to show AI how to complete and automate tasks. • Expert level prompt engineering - AIConsole leverages a multi-agent Retrieval-Augmented Generation (RAG) system not based on a vector database, akin to expert prompt engineering. • Fully open-sourced – This software maintains privacy and only uses dedicated LLM apis with well known privacy policies - and you can verify that yourself Share and collaborate on your tools with the community - you can create and share your domain-specific AI tool using Github, Google Drive etc. While we are at an early stage, we have a stable desktop application, and are working on sharing actual use-cases and further improving the tooling around it. We’ve got a lot planned on our roadmap, including a full IDE-like experience, generative interfaces, and open source LLMs support. I would love to hear your feedback.
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Just completed my first CI/CD for Machine Learning course on DataCamp and it was an enlightening journey! Excited to leverage this new skill in AI projects and explore more opportunities in the future. #MachineLearning #ContinuousIntegration #DataCamp
Yulius Adyan Mandataputra's Statement of Accomplishment | DataCamp
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🚀 ** NesisAI (Free and Open-Source AI solution) ** 🚀 I tried NesisAI and was amazed by its capabilities. 👍 👍 Thumbs up Ametnes team. While many are exploring ChatGPT, NesisAI (https://lnkd.in/deWDnq9N), an open-source enterprise knowledge discovery solution that connects to various data sources (like S3, Windows Shares, Google Drive, Dropbox and more) and uses generative AI to aggregate and collect information from documents in various formats (PDF, DOCX, XLSX, PNG, JPG, TIFF, MP3, MP4). This information is then made available in a conversational manner: 1. You can converse with your documents like chatGPT via a simple chat interface. 2. Conveniently view comparisons between documents. 3. Summarise large documents. 4. Get the text summary from the videos or images. And many more.. The latest version includes an API that allows developers to create or enhance applications to interact with private documents and data securely. With the NesisAI API, you can integrate generative AI into mobile/desktop apps, extend internal customer service applications, or add AI-powered conversational interactions to websites (web apps), providing users with tailored responses based on their personalized data. 😍 All interactions occur within your private network, ensuring top-notch security 😍. NesisAI (Open Source) ==> https://lnkd.in/deWDnq9N #ametnes #nesisai #nesisaiapi #opensource #generativeAI #openai #ai #chatgpt #s3 #api #googledrive #dropbox #windowshare #pdf #docx #xlsx #csv #png #jpg #tiff #mp3 #mp4 #software #softwaredeveloper #softwareengineer #softwaredevelopment #softwareengineering #mobileapp #website #embeddings #enterprisesoftware #aig #rag #genaiusecase #genaichatbot #python #javascript #docker #html #css #privateGPT #localGPT #llamacpp #Unstructured #llamaindex
GitHub - ametnes/nesis: Your AI Powered Enterprise Knowledge Partner. Discover knowledge from your private documents in your enterprise. Designed to be used at scale from ingesting large amounts of documents formats such as pdfs, docx, xlsx, png, jpgs, tiff, mp3, mp4, jpeg. Integrates with s3, Windows Shares, Google Drive and more.
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