Yogesh Jadhav’s Post

Predicting Text Sentiment with OpenAI GPT using MindsDB Docker Extension Let's create a predictive model to infer emotions behind a text, a task also known as sentiment analysis. The model, or an AI table, leverages OpenAI’s large language models to complete this task. https://lnkd.in/gN9SrH9v

Check out our new guide and open-source code library to integrate Gretel's synthetic data capabilities with major MLOps platforms. 🏗️ Blog: https://lnkd.in/dZrZPtcw #SyntheticData #AI #MLOps

This tutorial shows how to build an agent using a retriever to extract context from unstructured data, while invoking an API to get more data. https://lnkd.in/ghdKn5_k #AI #SoftwareDevelopment by Janakiram MSV

#Techstrong.ai discussing how the #database underpins #AI, how #AI helps in #application #modernisation and how #Couchbase underpins this

Discover the future of sustainable business with #PySpark and #HuggingFace Transformers! Learn how these innovative technologies unlock the potential of big data, driving unprecedented innovation and efficiency. Read more about this game-changing synergy in the article below 👇 #AI #SustainableBusiness https://lnkd.in/eaT79TeW

Predicting Text Sentiment with OpenAI GPT using MindsDB Docker Extension MindsDB Docker Extension integrates MindsDB's machine learning capabilities directly into Docker Desktop, simplifying the process of using AI within development workflows. It aims to make AI more accessible to developers by eliminating the need for in-depth Docker knowledge or complex setup procedures. Key Features: - Simplified Installation: Installs with a single click, removing manual setup and configuration challenges. - SQL-Based AI Interaction: Allows interaction with MindsDB's AI features using familiar SQL queries, making it accessible to those with SQL experience. - Integration with Databases and Applications: Connects with various data sources (databases, APIs, real-time streams) for AI training and inference. - Automated Tasks: Schedules queries and model training at specific times or intervals for efficient workflows. Benefits: - Streamlines AI Integration: Simplifies the process of incorporating AI into applications, saving time and effort. - Democratizes AI for Developers: Makes AI accessible to a wider range of developers, regardless of their Docker expertise. - Facilitates SQL-Based AI: Leverages the familiarity of SQL for interacting with AI, reducing the learning curve. - Enhances Productivity: Automates tasks and seamlessly integrates with existing workflows for greater efficiency. Use Cases: - Predictive Analytics: Build predictive models within applications using SQL queries for tasks like customer churn prediction, sales forecasting, and anomaly detection. - Recommendation Systems: Create personalized recommendations based on user behavior and preferences using AI-powered SQL queries. - Sentiment Analysis: Analyze customer feedback and social media sentiment using MindsDB's AI models within applications. - Image and Text Classification: Classify images and text content using AI within applications for tasks like product categorization and content moderation. MindsDB #ai #aiml #docker https://lnkd.in/g2Xv5Qid

A nice read - Ai with Rag on unstructured datasets. Function calling within the RAG framework enables more structured retrieval processes. For example, a function can be predefined to extract specific information based on user queries, which the RAG system will retrieve from a comprehensive knowledge base. #ai #genai #rag #data #unstructured https://lnkd.in/eSNq-n36

🚀 𝐋𝐢𝐭𝐃𝐚𝐭𝐚 𝐯0.2.24 𝐢𝐬 𝐇𝐞𝐫𝐞!🔥🔥🔥 🔗 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐃𝐞𝐭𝐚𝐢𝐥𝐬: - 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐲: https://lnkd.in/gpunqb48 - 𝐑𝐞𝐥𝐞𝐚𝐬𝐞 𝐍𝐨𝐭𝐞𝐬: https://lnkd.in/gaDqVujf If you find this library useful, don't forget to ⭐ the repo. Thank you! #DataScience #AI #LitData #LightningAI #ReleaseUpdate Lightning AI

Graph RAG — A conceptual introduction Graph RAG — A Conceptual Introduction Graph RAG answers the big questions where text embeddings won’t help you. Retrieval Augmented Generation (RAG) has dominated the discussion around making Gen AI applications useful since ChatGPT’s advent exploded the AI hype. The idea is simple. LLMs become especially useful once we connect them to our private data. A foundational model, that everyone has access to, combined with our domain-specific data as the secret sauce results in a potent, unique tool. Just like in the human world, AI systems seem to develop into an economy of experts. General knowledge is a useful base, but expert knowledge will work out your AI system’s unique selling proposition. Recap: RAG itself does not yet describe any specific architecture or method. It only depicts the augmentation of a given generation task with an arbitrary retrieval method. The original RAG paper (Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Lewis et. al.) compares a two-tower embedding approach with bag-of-words retrieval. Local and Global Questions Text Embedding-based retrieval has been described in many occurrences. It already allows our LLM application to answer questions based on the content of a given knowledge base extremely reliably. The core strength of Text2Vec retrieval remains: Extracting a given fact represented in the embedded knowledge base and formulating an answer to the user query that is grounded using that extracted fact. However, text embedding search also comes with major challenges. Usually, every text embedding represents one specific chunk from the unstructured dataset. The nearest neighbor search finds embeddings that represent chunks semantically similar to the incoming user query. That also means the search is semantic but still highly specific. Thus candidate quality is highly dependent on query quality. Furthermore, embeddings represent the content mentioned in your knowledge base. This does not represent cases in which you are looking to answer questions that require an abstraction across documents or concepts within a document in your knowledge base. For example, imagine a knowledge base containing the bios of all past Nobel Peace Prize winners. Asking the Text2Vec-RAG system “Who won the Nobel Peace Prize 2023?” would be an easy question to answer. This fact is well represented in the embedded document chunks. Thus the final answer can be grounded in the correct context. On the other hand, the RAG system might struggle by asking “Who were the most notable Nobel Peace Prize winners of the last decade?”. We might be successful after adding more context such as “Who were the most notable Nobel Peace Prize winners fighting against the Middle East conflict?”, but even that will be a difficult one to solve solely based on text embeddings (given the current quality of embedding models). Another example is whole dataset reasoning. For example, your user mi...

Happy to share my first article published at Snowflake's official Medium Blog as a contributing writer ❄ I've been working with Cortex for the past few months and wrote some words on how it empowers organizations to scale AI and MLOps from inside their database. https://lnkd.in/ddR9snvv #snowflake #snowflakecortex #enterpriseai #ai #datascience