Peter S.’s Post

Gretel Navigator's synthetic data generation outperformed OpenAI's GPT-4 by 25.6%, surpassed Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%. 🤩 Here's how to use Navigator to create high-quality synthetic data for fine-tuning LLMs. https://lnkd.in/eg2tSFes

🌟 Knowledge graphs with LangChain! 🌐📊 Building a knowledge graph from text data using LangChain and OpenAI's GPT-3.5 is super easy. This powerful combo lets you pull structured info out of unstructured text, giving you deep insights and making data navigation more efficient. In just a few steps, you can turn text into a visual graph, showing complex relationships and patterns. Perfect for enhancing AI applications and making smarter decisions. Check out this quick guide on building your own knowledge graph 🚀 #LangChain #KnowledgeGraph https://lnkd.in/gprW7npM

🚀Ekohe Principal Data Scientist Keira Liu shares the 3rd article in our LLM series! 📊 Key pinpoints to success include: ✅Choose the right model ✅Understand the components of the GPT app to build ✅Prompt Tuning ✅Building an external knowledge base and retrieving relevant information, etc 👉Read on to learn more: https://lnkd.in/grfsV2gk #ekohe #ai #gpt #llm #llmevaluation 

In his latest tutorial, Janakiram MSV demonstrates how to build an AI agent that uses the Semantic Router to dynamically retrieve data from OpenAI’s LLM and other tools. 🧠 Semantic Router Flexibility: The tutorial highlights how the Semantic Router dynamically routes queries based on intent, ensuring relevant data is fetched from the appropriate sources. 🚀 Real-time Data Integration: It showcases real-time flight tracking by integrating FlightAware’s AeroAPI with the LLM for up-to-date flight status responses. 🔧 Tool Setup: Key libraries like OpenAI, ChromaDB, and Semantic Router are installed and configured, simplifying the environment setup for users. 📊 Combining LLMs and Structured Data: The AI agent uses both vector databases and the LLM, blending conversational queries with specific data retrieval like baggage policies. 💡 Adaptive Assistance: The AI agent intelligently handles different types of queries—whether it’s real-time data, baggage policies, or even casual chats like writing poems. #AI #SemanticRouter #CloudComputing ✈️ Flight Tracking: Real-time flight data is fetched using API integrations, showing the potential of combining LLMs with external data sources. 🎒 Baggage Policies: The agent can query ChromaDB for baggage details, offering tailored responses to user queries. 🤖 Scalable AI Models: The setup uses OpenAI embeddings and GPT-4o-mini to ensure the system can handle diverse requests. ♻️ Repost if you enjoyed this post and follow me, César Beltrán Miralles, for more curated content about generative AI! How to Build an AI Agent With Semantic Router and LLM Tools https://lnkd.in/gfVmGFqM

Here's a step-by-step tutorial on transforming your data and customizing your LLM models using data integration. Discover how to: 📊 Derive actionable insights from LLMs 🧩 Fine-tune models with fresh, reliable data 🔄 Transform free-text data into vector embeddings 🤖 Integrate with OpenAI and Pinecone for superior performance Dive into the transformative world of LLM operations and stay ahead in the AI game! 👉 Read the blog: https://lnkd.in/gNY9ubWd #AI #DataScience #LLM #DataIntegration #GenAI

Here's another example of how a domain or task-oriented language model (as opposed to a monolithic Large Language Model) will drive significant value for enterprises. The attached blog post explains how Snowflake has created a Document AI capability (powered by a Snowflake-grown compact LLM called Arctic-TILT - Text Image Layout Transformer) that outperforms competitors in extracting content from their unstructured document data. The proprietary approach leads to reduced operational cost and increased scalability, making it a much more accessible capability for those organizations looking to extract the most value from their document data. https://lnkd.in/eRV6jgvU

A couple of thoughts on the new GPT-4o-mini regarding zero-shot classification and extraction tasks. Up until now, there was a relatively simple strategy. If you have a high volume of transactions, you try to fine-tune GPT-3.5 to keep the cost under control. Fine-tuning is a time-intensive process, so it does make sense to look into this only for large volumes, and a fine-tuned GPT-3.5 is still more than 50% cheaper than GPT-4o. Also, fine-tuned models usually work well with short prompts, reducing costs, especially in many transactions. For low volume, GPT-4o or even GPT-4 is better—you can start immediately, and the models will most likely be good enough for zero-shot classification. The definition of high/low volume depends on your budget, the duration of how long you intend to run the process and the cost of the team that would do the fine-tuning. So, we now have a new model significantly cheaper than any of the options above. Well, you cannot fine-tune it, but at this cost, there are a few options: 1. You can break down complex tasks running on the expensive or fine-tuned models and run them on the new model. 2. The new model allows you to use few-shot examples—even though the prompts become longer, the cost will still be exponentially lower. 3. You can try the cheaper model, and if your validation fails, always upscale to the more expensive models. If you use OpenAI, I would evaluate existing processes and test the new model using the options above. It is enough for your use case, and you will likely look at a 5-10x decrease in the current costs. 

I recently read an article about LLM and Vector Data Bases. At first it was a-lot to rap my head around but this is what you need to know! In the digital age, small businesses have a unique opportunity to level the playing field using Large Language Models (LLMs) and Vector Databases. Here's a quick guide to getting started: 🤖 LLMs can automate customer service and market analysis, saving time and resources. With tools like OpenAI, integrating AI into your operations is more accessible than ever. 🔍 Vector Databases (e.g., Pinecone, Weaviate) organize your data efficiently, allowing faster, smarter decision-making. They're scalable and user-friendly. So, when it comes to organizing the data for a quick and accurate AI generated responds to your customers, a vector data base is what you need. How to Implement: 1)Identify a Need: Start with a specific use case, like automating FAQs. 2)Choose Your Tools: Opt for platforms with straightforward integration and scalability. 3)Pilot and Learn: Test on a small scale, gather feedback, and iterate. 4)Educate Your Team: Ensure everyone understands the benefits and limitations. 5)Consult Experts: Don’t hesitate to seek advice to customize solutions to your needs. 💡 Takeaway: Leveraging LLMs and Vector Databases can significantly boost efficiency and innovation in small businesses, making now the perfect time to explore these technologies. Original Article: https://lnkd.in/gxpCSDyG #BusinessTech #Innovation #AIForBusiness

It's been 30 days since GPT-4o was announced. Have teams adopted it? We analysed usage across 183M requests in the past couple of months for openai and azure-openai and found some interesting stats I'm happy to share. 🤯 ADOPTION Since its launch, GPT-4o has garnered significant attention and adoption among companies utilizing OpenAI models in production. A noteworthy 35.2% of organizations have already experimented with GPT-4o! What’s even more impressive is the transition from trial to production. 43% of the companies that have tested GPT-4o are now using it in their production environments. 🤩 PERFORMANCE Performance is a critical factor in the adoption of AI models, and GPT-4o does not disappoint. Here’s a look at the median response times for various OpenAI models: - GPT-4o: 2.8 seconds - GPT-3.5-turbo: 1.5 seconds - GPT-4-turbo: 6.8 seconds While GPT-3.5-turbo remains the fastest, GPT-4o strikes a balance between speed and advanced capabilities, positioning itself as a robust choice for complex tasks requiring nuanced understanding and generation. 🚨 TRAFFIC SHIFT Another key indicator of GPT-4o’s success (or the lack of) is the traffic shift. Currently, 1% of the total traffic has moved to GPT-4o from other models. While in absolute terms, the shift has been fast but in my opinion -- teams might be hesitant to experiment with newer models once they find their footing. --- The swift adoption and integration of GPT-4o reflect a broader trend towards faster, better, cheaper. The data suggests that companies are not only willing to explore new technologies but are also quick to recognize and implement those that offer significant performance and capability enhancements. As we continue to monitor GPT-4o’s impact, it will be fascinating to see how it shapes various industries and use cases. What has your experience been. Have you started using GPT-4o in your projects?

Hey GEN AI peeps, Let's dive into the world of 𝗦𝘂𝗽𝗲𝗿 𝗙𝗔𝗦𝗧 𝗥𝗔𝗚'𝘀 🚀 𝘂𝘀𝗶𝗻𝗴 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗲𝗱 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 and Groq 😎 Have you faced this question -> 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗿𝗲𝗱𝘂𝗰𝗲 𝘁𝗵𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗳𝗮𝘀𝘁𝗲𝗿? The answer lies in today's post. RAGs are great, leveraging the power of LLM and allowing a more meaningful conversation on your data but we don't realize the moment we try and scale it. Storage and Retrieval speeds become a headache. Storage - When using RAGs for our project we use some embedding model and whether it's Mistral AI, Cohere or OpenAI these models by default store embeddings in n dimensions and each dimension is 32bits (4bytes) and supposedly our n is 1024 and we are computing embeddings for 20 million chunks. Suddenly we are dealing with ~76 GB of data but what if I can store my embeddings to say ~19 GB that's a lot of cost saved right? But how So comes the concept of 𝗦𝗰𝗮𝗹𝗮𝗿 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 where we map the continuous range of float32 values to the discrete set of int8 values, which can represent 256 distinct levels (from -128 to 127). We used to do this in Min-Max scaling using scikit-learn's Min-Max Scaler. Find a Minimum and Maximum value and then rescale it but the caveat is to do this we use the concept of a Calibrated Dataset ( a Chunk of data from where we want to find max and min ) for our embedding scale choosing this dataset makes much difference because if chosen wisely it can very beautifully represent the spread of your data and hence on rescaling there is very less loss of data. So till now, all is good, right? So cost is saved but how about faster Inference -> Groq joins the party with its Language Processing Unit (LPU) for super fast inference. Hence Problem Solved :). But that's not it - A practical solution using Groq, mixedbread.ai 's quantized embeddings over LangChain as a framework gets our work done. Link to the code - https://lnkd.in/g2nWJYv5 To learn more about 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗲𝗱 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 1. https://lnkd.in/gRhiKUYE 2. https://lnkd.in/gWSyVUrU Tagging friends - VASANTH P, Vetrivel PS Harbhajan Singh Ravi Tanwar Hetarth Chopra Jan Daniel Semrau (B.Sc, M.A., CAIO) Sean Benhur And special thanks to Tom Aarsen Nils Reimers