Ola Musè’s Post

If you are using LlamaIndex to build AI applications, We just added support for native open telemetry(o11y) tracing for LlamaIndex in the Langtrace SDK. This enables you to ship traces, debug and evaluate not just to Langtrace but to also to all the other popular observability tools like SigNoz, Grafana Labs, Datadog, honeycomb.io etc. that support the visualization of o11y traces and spans. What is LlamaIndex? In simple terms, LlamaIndex is a framework for building AI applications. LlamaIndex has clean interfaces and constructs for - Data connections and ingestion - Data indexing - Querying And LlamaIndex has support for the popular LLMs and VectorDBs so you can swap in and out of different models and vector stores with ease without worrying about a code migration. Why o11y tracing matters? Open telemetry is a CNCF project that aims to have a standard data model for telemetry data. The open telemetry spans and traces are designed for high cardinality that can be ingested and visualized in any number of o11y supported observability solutions. This provides optionality for engineering teams and prevents vendor lock in. Teams can visualize the same data in different tools to debug and gain insights better and fix issues faster. With more products starting to leverage the incredible power of LLMs and VectorDBs to build novel features, we are moving from a world of mostly deterministic, well defined systems that can be reasoned through logic to non deterministic LLM inference endpoints that are primarily black boxes. In order to effectively debug, reason and fix issues around these new systems, engineering teams need access to deep non-intrusive tracing and high cardinality metrics from the AI framework layers to vectorDBs down to the LLM layers. What is Langtrace? This is the problem we are solving with Langtrace, a fully open source o11y compatible SDK for tracing any LLM stack. With Langtrace, you can not only trace the calls made at the framework layer, but also tap into vectorDBs and LLMs. We have support for the most popular LLMs, VectorDBs and Frameworks. Today, we are happy to announce native o11y support for tracing LlamaIndex. More details on how to set it up and get started can be found in the link on the first comment of this post. If you are curious about o11y tracing for LLM applications or would like to contribute to the project, reach out to me.

This will set you up for successfully operationalizing GenAI apps. So, What Are Your Expectations From A Well-Operationalized GenAI Application? A successful GenAI application seamlessly integrates generative models into production workflows, driving performance, accuracy, and efficiency. This is a complex undertaking, demanding careful attention to three fundamental prerequisites: 1. Secure Data Pipeline Design: ⇰ Identify and map data sources for ingestion and preprocessing. ⇰ Leverage vector databases like Pinecone or Weaviate for efficient data retrieval. ⇰ Ensure data security and privacy compliance throughout the pipeline. 2. Strategic Model Selection & Architecture: ⇰Choose models based on specific application needs and performance metrics. ⇰Decide trade-offs between speed, accuracy, and computational cost. ⇰Utilize tools like MTEB and TruthfulQA for benchmarking models and understanding their limitations in addition to provider's benchmarks. ⇰ Choose architecture like prompt engineering, RAG, fine-tuning, or agentic systems. 3. Stable Monitoring & Governance Framework: ⇰Establish a robust monitoring system to track model performance and identify potential issues. ⇰Implement governance frameworks to promote ethical AI development and mitigate risks. ⇰Log and track model parameters, performance metrics, and data usage for transparency and accountability. Additional Considerations for Building Production-Ready GenAI Applications: ⇰Managing Model Ambiguity: - Understand the inherent stochastic nature of generative models and design workflows that can handle output variations. - Employ prompt engineering techniques to guide models toward desired outcomes and improve consistency. - Explore architecture choices that balance accuracy and consistency, such as Retrieval Augmented Generation (RAG), which combines LLM generation with information retrieval. ⇰Selecting the Right Model: -Choose models based on your specific needs for tasks like embedding generation, query completion, and content creation. -Consider computational cost, latency, and benchmark performance when making model choices. ⇰Optimizing Prompts: -Use Mlflow Prompt Engineering UI or Azure Prompt Flow to iterate and refine prompts ⇰LLMOps Orchestration: - Leverage tools like Langchain, LlamaIndex, haystack, and Semantic Kernel to seamlessly integrate GenAI models into production pipelines. ⇰Deployment and Inference: -Design scalable architectures to ensure smooth performance and user experience. -Consider deployment strategies like GPU clusters or hosted models for efficient inference Deal with ethical concerns: -Bias and Fairness: Models trained on biased data can perpetuate harmful biases. -Misinformation and Manipulation: GenAI models can create convincing but inaccurate content, raising concerns about spreading misinformation and manipulation. What's your experience in dealing these challenges? Comment below. #GenAI #Operationalization #DigitalTransformation #AIEthics

#Day37 #75HardGenAI #GenerativeAI 🤖 Pinecone: A Cloud-Native Vector Database for LLMs 🤖 Pinecone is a popular cloud-native vector database specifically designed for efficient storage, retrieval, and search of high-dimensional vector data. 📍 Key Features of Pinecone: 🧠 Fast and Scalable Search: Pinecone excels at performing similarity searches on massive datasets of vectors. This is crucial for LLMs, which rely on finding relevant information quickly within vast corpora of text or code. 🧠 Cloud-Native Architecture 🧠 Simplicity and Ease of Use: Pinecone offers a user-friendly API and intuitive tools. 🧠 Security and Reliability: Pinecone prioritizes data security with features like access control and encryption. 🧠 Integration with LLMs and RAG Systems: Pinecone integrates smoothly with LLMs and Retrieval-Augmented Generation (RAG) frameworks, providing them with the ability to efficiently access and utilize contextual information for enhanced performance. 📍 Benefits of Using Pinecone with LLMs: 🧠 Improved Accuracy of LLM Outputs: By enabling LLMs to find relevant information from large datasets quickly, Pinecone helps them generate more accurate, informative, and contextually relevant responses. 🧠 Faster Response Times: The high-speed search capabilities of Pinecone translate to faster response times for LLM-powered applications, leading to a smoother user experience. 🧠 Reduced Data Storage Requirements: Pinecone's efficient storage mechanisms can significantly reduce the amount of data needed to be stored by the LLM itself, freeing up resources. 📍 Beyond LLMs: Applications of Pinecone: 🧠 Image and Video Retrieval: Pinecone can be used for efficient image and video similarity search, making it valuable for applications like content moderation or product search. 🧠 Recommendation Systems: By identifying similar user profiles or items based on vector representations, Pinecone can power personalized recommendations in e-commerce or entertainment platforms. 🧠 Anomaly Detection: Pinecone can help detect anomalies in data streams by identifying points that deviate from the expected vector representations. 🗂️ In Next video - I will show how to use Pincone Vector DB with LLM and LangChain to Build a ChatBot with RAG. 🗂️ To get the Source Code, GitHub - https://lnkd.in/diehTu44 Watch previous day videos now to get started with Generative AI and Large Language Modelling 🦍 🤖 Day 1 to Day 36 - https://bit.ly/4bJwZla 🔖 Book your call with me at topmate.io and learn about Python, Data Science, ML/DL, Large Language Models (LLM) or Interview Preparation 🤖. 📲 Book your call at - https://lnkd.in/d3r6wAMz Simranjeet Singh ❤️ Follow Now 🐺

Supercharging Databases with SuperDuperDB's Versatile AI Integration ⚡️Data transforms from static archives 🗄️ to active assets in an AI-driven world. 💎 We're thrilled to highlight SuperDuperDB, created by co-founders Duncan and Timo, as our AI Startup of the Day. 🧠 Their versatile open-source framework eliminates friction connecting models and APIs to enhance databases with AI's capabilities. 🤖 The key insight driving SuperDuperDB is that while AI promises tremendous value, complex MLOps pipelines hamper adoption. ⛓️ Requiring companies to duplicate data into specialized stores creates overhead and risks losing connection to source systems of record. 📁 SuperDuperDB circumvents this by synergizing AI natively into existing infrastructure. 🔌 With a simple Python interface, 🐍 developers and data scientists can integrate vectors and models directly within familiar databases like MongoDB and Postgres. 🎛️ This powers classic use cases like search and recommendation and emerging applications in generative AI and conversational interfaces. 💬 Unified deployment avoids disjointed experiences. ✅ And SuperDuperDB's transparency ensures expert users can customize implementations to their unique needs. 🔧 Extend any workflow by combining the Python ecosystem's abundant libraries 📚 while training specialized models on subsets of data. 🧮 For enterprises, multi-tenancy support also enables managing security and scale. 🔒 We applaud startups pursuing this notion of data-centric AI. 👏 Offering turnkey augmentation of existing systems with intelligence saves time and cost while retaining meaning. 🚀 What other data-native approaches excite you in AI's evolution? 🤔 How could connecting intelligence deployment to proven data management platforms impact your roadmap? Let us know your perspectives as we collectively shape what's next. 🙌 🧙🏽 Follow The AI Engineer for more about SuperDuperDB and daily insights tailored to AI engineers. Subscribe to our newsletter. We are the AI community for hackers! ��️ Repost this to help SuperDuperDB become more popular. Support AI Startups! ⚠️ If you want us to highlight your favorite AI company, startup, or not, please share it in the comments section. 

How can a highly scalable and fault-tolerant system that supports high-throughput ingestion and interactive query latencies be designed? A Meta team designed Logarithm, a logging engine for AI training workflows and services. It's a hosted, serverless, multitenant service used only internally at Meta that consumes and indexes these logs and provides an interactive query interface to retrieve and view logs. It provides strong guarantees on availability, durability, freshness, completeness, and query latency. At a high level, Logarithm comprises the following components: - 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 emit logs using logging APIs—the APIs support emitting unstructured log lines along with typed metadata key-value pairs (per line). - 𝐀 𝐡𝐨𝐬𝐭-𝐬𝐢𝐝𝐞 𝐚𝐠𝐞𝐧𝐭 discovers the format of lines and parses lines for common fields, such as timestamp, severity, process ID, and callsite. - The resulting object is buffered and written to a 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐪𝐮𝐞𝐮𝐞 (for that log stream), providing durability guarantees with days of object lifetime. - 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬 read objects from queues and support additional parsing based on user-defined regex extraction rules – the extracted key-value pairs are written in the line's metadata. - 𝐐𝐮𝐞𝐫𝐲 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬 support interactive and bulk queries on one or more log streams with predicate filters on log text and metadata. The Logarithm design has centered around simplicity for scalability guarantees. The team continuously builds domain-specific and agnostic log analytics capabilities within or layered on Logarithm with appropriate pushdowns for performance optimizations. They also invest in storage and query-time improvements, such as lightweight disaggregated inverted indices for text search, storage layouts optimized for queries, and distributed debugging UI primitives for AI systems. Complete post: https://lnkd.in/dtnTN-jb

🌐 Planning and Orchestration Layer of AI Agents🌐 Persistence involves securely storing important data (like user interactions, task states, and execution logs) in databases or other storage media, ensuring long-term availability and reliable retrieval when needed. 🔹 Inngest @inngest - Features: Event-driven persistence workflow engine, supports running on any platform. Offers SDK for writing persistent functions and workflows within existing codebases, callable via HTTP endpoints, without extra infrastructure management. 🔹 Hatchet @hatchet_dev - Features: End-to-end task orchestration platform, supports distributed, fault-tolerant task queues. Solves scalability issues like concurrency, fairness, and rate limiting. Supports complex task orchestration and visual DAG (Directed Acyclic Graph) workflow design. - Funding: Backed by YC W24. Vision includes running slow OpenAI requests with asynchronous tasks in the background, chaining complex tasks into workflows, and setting retries and timeouts for failure recovery. 🔹 Temporal @temporalio - Features: Open-source workflow and orchestration system, supports persistent task storage, ensuring reliable execution and recovery. 🔹 Trigger.dev @triggerdotdev - Features: Event-driven approach for persistent management of tasks and workflows, helping developers manage complex tasks efficiently. Orchestration 🎛️ Orchestration refers to coordinating and managing different AI components and services to ensure efficient collaboration within a predefined workflow. 🔹 DSPy @lateinteraction - Features: Introduces a modular and declarative approach, providing a structured, programmatic method to replace traditional prompt engineering. Developed by Stanford researchers with 14k stars on GitHub. 🔹 AutoGen @pyautogen - Features: Open-source framework developed by Microsoft, automatically generates and manages AI models and workflows, simplifying model development and deployment. Provides a multi-agent conversation framework as a high-level abstraction. 🔹 Sema4.ai @sema4ai - **Features**: Intelligent orchestration solutions for optimizing and automating various steps in machine learning and AI projects. 🔹 LangGraph - Features: Extension of the LangChain framework, designed for creating multi-agent workflows graphically. Handles stateful, looping, and multi-role applications, suitable for building complex AI systems requiring multiple agents to collaborate. 🔹 Griptape @griptapeai - Features: Flexible orchestration framework, enabling developers to easily define, manage, and execute complex AI workflows. 🔹 CrewAI @crewAIInc @joaomdmoura - Features: Multi-agent system platform designed to automate complex workflows efficiently. 🔹 Fixpoint @gofixpoint - Features: Provides reliable orchestration tools, ensuring efficient operation and management of AI and data workflows, suitable for various AI and data-intensive applications. #AI #Persistence #Orchestration #TechStack #Innovation #Startup

All bets are in, and 2024 will likely be another eventful year for AI. Below are two areas of interest for AI Engineers in 2024 1️⃣ The evaluation of Large Language Models (LLMs) outputs 2️⃣ The Cost Optimisation of Retrieval-Augmented Generation (RAG) architecture in AI applications. Number 2 is a mouthful, so let's coin a term for it: RAGonomics. RAGonomics is the evaluation and optimisation of the cost associated with implementing RAG architectural design patterns within the AI application. The objective of RAGonomics is to optimise costs while maintaining the effectiveness of RAG systems. ⬆️ It is a strategic play that becomes relevant once you move from prototype and development to production. Areas RAGonomics touches include, but are not limited to: ↪️ Efficient Prompting: Lengthy and descriptive prompts might improve the performance of LLM output, but each token racks up that OpenAI bill ever so slightly. Prompt Compression is an exciting area to explore. ↪️ Monitoring Tools: Utilising real-time performance and cost tracking tools, enabling proactive optimisation. I started using PromptLayer a few months ago to get a visual overview of the prompts users enter and a more streamlined approach to how different prompts engineering techniques perform in production. ↪️ Infrastructure/tooling consolidation: The AI ecosystem has plentiful options for frameworks and tools to achieve the same results. Vector database providers are popping out every month or so. However, the cost of having vector and operational databases will become a factor for contention for enterprise production GenAI applications. Solutions like MongoDB that combine operational and vector database offerings into one are favourable options in the long run, especially when considering database cost, management and required training. 🔥 But here's the even more exciting part As AI solutions scale in 2024, from small deployments to enterprise-level applications, understanding RAGonomics will be essential. This puts AI Engineers as Strategic Partners. AI Engineers who understand and can implement LLM Evaluation methods and RAGonomics strategies will be vital in discussions around the scalability and cost of AI applications. These Engineers will sit in the same meeting rooms with the company's key decision-makers and stakeholders. Over the following months, I'll explore more tools, frameworks and methods to improve the performance and reduce the cost of RAG applications. Anything interesting I come across will be documented here. 📖 In the meantime, here are some interesting tools: https://llmlingua.com/ https://promptlayer.com/ PromptLayer https://bit.ly/41VjTgq (LlamaIndex Cost Analysis) #genai #rag #ragonomics #generatieveai

Exciting news for AI developers! 🚀 Matillion is leveraging Pinecone Connect to optimize AI workflows for their users. By integrating with their vector database, AI developers can effortlessly enhance their AI applications. 🌟 #AI #Innovation #PineconeConnect #matillion

I love highlighting unique open-source libraries every day as part of our AI Library of the Day series. However, one tool category has been tricky to keep up with: vector databases. Our goal is to showcase tools that help AI engineers build better, faster, and more efficient AI apps. However, dealing with the wide variety of VectorDBs can take a lot of work. Last year, one of the most significant trends for AI engineers was the surge in popularity of vector databases. What was once a specialized topic became a must-have tool for everyone developing AI apps. The shift prompted all sorts of database vendors, whether relational or document-based, closed or open-source, and even those associated with search or caching, to incorporate "AI functionality." It meant adding support for storing and retrieving vector embeddings. The field quickly became crowded and confusing. Every database claimed to do it all, making it unclear even for experts which vector database to choose for their latest project, let alone for beginners. That's why initiatives like the Vector DB Comparison at the VectorHub are so valuable. It offers an unbiased overview of each player's features and strengths in the market, helping AI engineers make sense of over 30 providers. Have you had a chance to check out the Vector DB Comparison? I've included a link in the comments. 👇🏽 I'm curious – which databases do you use to host your vector embedding data? And what do you think the Vector DB Comparison should cover? Share your thoughts!

Weaviate - AI library of the day: Weaviate is an open-source vector database designed for building customized semantic search applications powered by state-of-the-art AI models. It lets you vectorize text, images, or any data type and create blazingly fast vector indexes tailored to your use case. Key Highlights ⚡️ Optimized for speed with sub-100ms latency on million-scale vector datasets 🌀 Flexible data ingestion: bring your own vectors or use Weaviate's vectorization modules 🏭 Scale from prototyping to production while maintaining performance 🚚 Easy migration of ML models from research to production environments 🔎 Comprehensive tooling for applications like search, recommendations, classification, etc. 🔗 Integrations with LangChain, LlamaIndex, HuggingFace etc. Whether you want to build a semantic search engine, conversational app or recommendations system powered by vectors, Weaviate, equipped with the HNSW index, provides the foundation. Robust and optimized for scale in the cloud, its storage separates objects and vectors for versatility. The modular architecture makes customizing for innovative use cases smooth. 📊 Stats * 👷🏽♀️ Builders: Etienne Dilocker, Bob van Luijt, Dirk Kulawiak, Marcin Antas, Connor Shorten * 👩🏽💻 Contributors: 75 * 💫 GitHub Stars: 8.3k * 🍴 Forks: 534 * 👁️ Watch: 100 * 🪪 License: BSD-3-Clause * 🔗 Links: Below 👇🏽 🧙🏽 Follow The AI Engineer for daily insights tailored to AI engineers and subscribe to our newsletter. We are the AI community for hackers! ⚠️ If you want me to highlight your favorite AI library, open-source or not, please share it in the comments section!