Pondhouse Data OG’s Post

Chat with your Confluence - a guide. Confluence is used by many to store documentation. It's also one of these tools where people just add data, but never use and find them anymore. This is a great opportunity, to add AI (LLMs) to the game. Using AI embeddings and LLMs to search confluence and answer questions based on this potentially vast ecosystem of documentation provides some great opportunities to drastically reduce the time needed to find stuff in confluence. The linked article provides a step by step guide in how to Airbyte, LangChain and pgvector to implement a fully working AI chatbot - which answers questions based on your full confluence document repository. Topics covered: 1. Use the Airbyte conflunce connector to extract the data 2. Integrate PGVector into PostgreSQL for efficient vector operations, which are essential for running complex queries using large language models. 3. Employ Langchain to connect Airbyte and PGVector, facilitating smooth data transfer and optimizing queries for a more responsive chatbot. 4. Prepare Your Environment& install necessary tools like Docker and set up your PostgreSQL database to handle incoming data and operations. Implement Loaders 5. Use Langchain's AirbyteLoader with PyAirbyte to efficiently pull data from Confluence, streamlining the process. 6. Clean up the data from Confluence, remove unnecessary HTML, and store the processed data in your PostgreSQL database enhanced with PGVector. 7. Connect the prepared data to OpenAIs GPT-4 By following these steps, you can set up an AI chatbot that simplifies data retrieval and enhances your team's productivity by leveraging AI to transform data interaction within your organization. Read our full guide for more details on each step: https://lnkd.in/dKx8rf_V #AIIntegration #ConfluenceChatbot #ProductivityHack #DataManagement #TeamCollaboration

DataOps programs ensure the data pipelines that feed generative AI applications are modular, scalable, robust, flexible, and governed. My latest blog, sponsored by Matillion, explains how: https://lnkd.in/gcJ73uNp Here is an excerpt. DataOps and GenAI experts, how does this map to your experience so far? The most popular form of GenAI centers on language models that interpret and generate content such as text or imagery in response to natural language prompts. GenAI often consumes text that has been transformed and loaded into a vector database. A common scenario is retrieval-augmented generation (RAG), in which an application retrieves relevant vectors and injects that content into a user’s prompt to make the response more accurate. Language models also can query data within the vector database as part of the fine-tuning process, so it better understands domain-specific data. Both RAG and fine-tuning need timely, trustworthy data. And that’s where DataOps enters the scene. The discipline of DataOps adapts methodologies from DevOps, agile software development, and total quality management to improve the quality and timeliness of data delivery. It has four pillars: > Testing of data pipelines > Continuous integration and continuous delivery (CI/CD) > Pipeline orchestration > Data observability Many companies use DataOps to support rising business demands for analytics.  Modular CI/CD is a methodology for frequently iterating software by branching, updating, and merging versions of code. CI/CD techniques make GenAI data pipelines more modular by enabling data/AI teams to branch and merge different pipelines as they adjust individual elements. For example, they might change chunking techniques or add/remove data sources to optimize RAG or fine-tuning. Observability tools, meanwhile, make GenAI data pipelines modular by isolating the root cause of issues with data quality or pipeline performance. Armed with this information, data/AI teams can fix or replace, then test and deploy the problematic module—perhaps a transformation script, server cluster, or runaway application. They also can reuse vetted modules. Scalable Orchestration makes GenAI data pipelines automatically synchronizing events and tasks across elements—clusters, GenAI applications, LMs, and so on—as data/AI teams add them. Orchestrating these workflows reduces the effort of expansion. Data/AI teams can automate the addition of text files that help fine-tune LMs, documents that support RAG, or users that rely on a popular GenAI application. In addition, observability helps optimize workloads as they scale by measuring the utilization and performance of pipeline elements. Shawn Rogers Debra Peryea Timm Grosser Eckerson Group Wayne Eckerson Mark Balkenende 🎯 Kathleen O'Neil Cyril Sonnefraud Mark Johnston April Educalane, MBA #datapipelines #generativeai #dataops

📄 Unlocking Document Intelligence with ExtractThinker 🌐 Discover the future of document intelligence with ExtractThinker, an advanced library combining OCR and LLMs to streamline document extraction. Júlio Almeida's comprehensive article highlights the potential and efficiency of this tool in transforming unstructured data into actionable insights. 🔍 Key Features and Insights: 🔹 Modular Pipeline Structure: Integrates multiple document loaders like Tesseract OCR, Azure Form Recognizer, AWS Textract, and Google Document AI for versatile document handling. 🔹 ORM-Style Interaction: Maps document fields to class attributes, simplifying data extraction and integration into workflows. 🔹 Customizable Extraction: Utilizes contract definitions for tailored data extraction processes. 🔹 Asynchronous Processing: Enhances efficiency by handling documents asynchronously. 🔹 Broad Document Format Support: Ensures compatibility with various document types for comprehensive data extraction. 🔹 Anti-Hallucination and Reasoning Tools: Incorporates advanced tools to improve extraction quality and accuracy. 🔹 Open-Source Collaboration: Encourages contributions and continuous improvement from the community. 🌐 Real-World Applications: 🔹 Data Management: Streamline extraction processes for business documents, enhancing productivity. 🔹 Research: Automate data extraction from academic papers and reports, facilitating faster analysis. 🔹 Finance: Efficiently handle invoices and financial documents, improving accuracy and speed. 👉 https://lnkd.in/gDaeU4z7 Engage with Us: 🔹 How can ExtractThinker transform your document processing workflows? 🔹 What benefits do you see in adopting an ORM-style approach for document intelligence? #AI #MachineLearning #OCR #DocumentIntelligence #TechInnovation #DataExtraction #DigitalTransformation #LLM #Automation #OpenSource

When an iPaaS (aka integration Platform as a Service) meets Generative AI technologies, great solutions happen. In the article below, I’m detailing how any company with access to Integrator.io and OpenAI accounts, can build a no-code/low-code RAG application, enabling their business users to access all the enterprise knowledge stored in databases / data warehouses with no SQL skill needed. The application is using Slack (or MS Teams, Discord) , as the front of the chatbot. Celigo’s Integrator’s IO is orchestrating, in a flow, the different interaction between the apps we are using (db/dw , OpenAI, and Slack). Some of the key benefits (iPaaS + GenAI): - No-code solution - Built-in Error/Exception management in Integrator.IO ( Error management : https://lnkd.in/ehwFGz5c)  - Fast way to build your logic  - Secure easily the GenAI app using embedded Integrator.io tools like “filters” (Prompt injection) , “branching” (Insecure Output Handling) etc .. (for more about LLM applications sec , check this : https://lnkd.in/e-X68j2U )   For more details take a look at the article and the related video : https://lnkd.in/eNvfRNJ2 #ipaas #celigo #generativeai #llm #rag #nocode #texttosql

🚀 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

Meet Laminar AI: A Developer Platform that Combines Orchestration, Evaluations, Data, and Observability to Empower AI Developers to Ship Reliable LLM Applications 10x Faster Because LLMs are inherently random, building reliable software (like LLM agents) requires continuous monitoring, a systematic approach to testing modifications, and quick iteration on fundamental logic and prompts. Current solutions are vertical, and developers still have to worry about keeping the “glue” between them, which will slow them down. Laminar is an AI developer platform that streamlines the process of delivering dependable LLM apps ten times faster by integrating orchestration, assessments, data, and observability. Laminar’s graphical user interface (GUI) allows LLM applications to be built as dynamic graphs that seamlessly interface with local code. Developers can immediately import an open-source package that generates code without abstractions from these graphs. Moreover, Laminar offers a data infrastructure with integrated support for vector search across datasets and files and a state-of-the-art evaluation platform that enables developers to create unique evaluators quickly and easily without having to manage the evaluation infrastructure themselves.  A self-improving data flywheel can be created when data is easily absorbed into LLMs and LLMs write to datasets. Laminar provides a low-latency logging and observability architecture. An excellent LLM “IDE” has been developed by the Laminar AI team. With this IDE, you may construct LLM applications as dynamic graphs. Integrating graphs with local code is a breeze. A “function node” can access server-side functions using the user interface or software development kit. The testing of LLM agents, which invoke various tools and then loop back to LLMs with the response, is completely transformed by this. User have complete control over the code since it is created as pure functions within the repository. Developers who are sick of frameworks with many abstraction levels will find it invaluable. The proprietary async engine, built in Rust, executes pipelines. As scalable API endpoints, they are easily deployable. Customizable and adaptable evaluation pipelines that integrate with local code are easy to construct with a laminar pipeline builder. A simple task like precise matching can provide a foundation for a more complex, application-specific LLM-as-a-judge pipeline. User can simultaneously run evaluations on thousands of data points, upload massive datasets, and get all run statistics in real-time. Without the hassle of taking on evaluation infrastructure management on their own, and get all of this. Whether users host LLM pipelines on the platform or create code from graphs, they can analyze the traces in the easy UI. Laminar AI log all pipeline runs. User may view comprehensive traces of each pipeline run, and all endpoint requests are logged. To minimize latency overhead, logs are written asynchron...

What we are building is fundamentally different from Open-Source Workflow Orchestration tools 🌋 In our latest blog, CEO Hugo Lu dives into how the Orchestra Data Platform has many features of an Orchestration tool but is so much more. We are increasingly seeing that many parts of the data stack include workflow orchestration as a feature. However, there are few that combine this feature with complete visibility of exactly what's happening in your data stack and how you can optimise it. There are even fewer platforms built specifically with #data and #ai products in mind. If you want to understand how we're leading the charge towards Data and AI Products, the below is a must-read. 👇 ---> https://lnkd.in/er_YTE9i #dataengineering #visibility #dataorchestration

Supercharge LLMs by giving them the context of your unstructured data

Everyone is trying to figure out how to get the most out of generative AI. Here's a decision I made, and one I'm confident in passing along. We became customers of DataStax and LlamaIndex. Why? They're both leaders in their respective categories, and now offer an integrated solution. - LlamaIndex lets you import your documents (.pdf, .docx, and hundreds more) into a format that is optimized for Generative AI. DataStax provides a database that is optimized for Generative AI, and now they're offering an out-of-the-box solution that instantly connects to LlamaIndex. Together, they've created a convenient on-ramp to make your unstructured business data available to your workforce. - Now, Imprompt's Chat Assistant customers can add files to their workspace and use them in all your prompts. Together with our partners, we take care of the dirty work: parsing documents, creating embeddings, and batch loading them into vector databases. This is a key pillar to our solution; we welcome DataStax and LlamaIndex to our solution offering! See the announcement for the details. https://lnkd.in/gfAMXF3m

LLMs are everywhere and of many flavors. What is your feedback? #ai #artificalintelligence #leadership #innovation #ChatGPT #futureofwork #GenerativeAI #businessautomation #digitaltransformation #processautomation #ibm IBM #chatbot #startup #marketing #strategy #business #publicsector #technology #metaverse #airegulation #llm #data #ml #machinelearning #customerservice #aigovernance #aitools #aileadership #aiagents OpenAI #promp