Spade’s Post

I'm thrilled to share a recent project where I leveraged the power of AI to process documents(mainly bills and invoices). The challenge was to extract structured data from documents, a task that is both time-consuming and prone to errors when done manually. To address this, I built a robust pipeline using cutting-edge language models and document processing tools. Key Highlights: 1. Framework: Utilized LangChain to construct and manage the processing chain. 2. Model: Integrated the Ollama model for extracting key information from invoices. 3. Data Validation: Defined a schema with Pydantic to ensure accurate and structured output. 4. PDF Processing: Employed UnstructuredPDFLoader to read and parse the PDF documents seamlessly. The workflow involved: 1. Loading the PDF: Using UnstructuredPDFLoader. 2. Prompt Construction: Create a custom prompt with ChatPromptTemplate for context and formatting instructions. 3. Model Integration: Extracting relevant details with the Ollama model. 4. Output Parsing: Ensuring the data adheres to the schema with JsonOutputParser. The result was a significant reduction in manual effort and improved accuracy, as the invoice data was automatically converted into a clean, structured JSON format. This project showcases the potential of AI to transform everyday tasks and enhance efficiency. I'm excited about AI's possibilities for automating and optimizing business processes. Please reach out if you're interested in the technical details or potential applications! #AI #MachineLearning #Automation #LangChain #Ollama #Pydantic #DocumentProcessing

Like to build powerful #GenAI applications? Possible with #LangChain! Developed by Harrison Chase, and debuted in October 2022, LangChain serves as an open-source platform designed for constructing sturdy applications powered by Large Language Models, such as chatbots like ChatGPT and various tailor-made applications. Langchain seeks to equip data engineers with an all-encompassing toolkit for utilizing LLMs in diverse use-cases, such as chatbots, automated question-answering, text summarization, and beyond. The image below shows how LangChain handles and processes information to respond to user prompts. Initially, the system starts with a large document containing a vast array of data. This document is then broken down into smaller, more manageable chunks. These chunks are subsequently embedded into vectors — a process that transforms the data into a format that can be quickly and efficiently retrieved by the system. These vectors are stored in a vector store, essentially a database optimized for handling vectorized data. When a user inputs a prompt into the system, LangChain queries this vector store to find information that closely matches or is relevant to the user's request. The system employs large LLMs to understand the context and intent of the user's prompt, which guides the retrieval of pertinent information from the vector store. Once the relevant information is identified, the LLM uses it to generate or complete an answer that accurately addresses the query. This final step culminates in the user receiving a tailored response, which is the output of the system's data processing and language generation capabilities. Get started with LangChain in my tutorial: https://lnkd.in/d44ni9f2

Leveled up to Level 5 in AI Journey!💻📚 At this level, I learnt about Retrieval Augmented Generation (RAG)."The previous module by Pathway x GTech MuLearn covered discussions on RAG, power up the LLM with real-time accurate data."🖋️ •All of us know about chatGPT, But we must know about its most powerful techniques to improve pre-trained Large Language Models RAG, it could scour the web, understand PDFs, and learn from our own data? That's the power of RAG! 1.RAG supercharges Large Language Models (LLMs) like ChatGPT by giving them access to: Fresh web data: No more outdated responses! Custom PDFs and text files: Train on the data that truly matters to you! RAG mitigates these challenges by providing a cost-effective solution that ensures model accuracy without the need for frequent and expensive retraining cycles, making fine-tuning effective with significant resource investment in data preparation, retraining, and deployment. 2. Prompt Engineering vs RAG 📝 Although prompt engineering may seem like a lighter alternative, it comes with its own set of challenges, including data privacy concerns, inefficient data retrieval, and technical limitations due to token constraints.at last, RAG emerges as a more viable and efficient option for addressing the challenges presented by prompt engineering and fine-tuning. RAG's Advantages: Cost-Effective: RAG models with vector embeddings API are roughly 80 times less expensive than commonly used fine-tuning APIs. Data Freshness: RAG ensures the model delivers current and pertinent output without frequent retraining. Efficient Retrieval: Vector indexing in RAG enables quick and semantically accurate data retrieval. No Token Limit Constraints: RAG's approach of storing data in efficient vector indexes facilitates dealing with large and complex data sets. 3.Challenges with Fine-Tuning: Data Preparation Challenges: Addressing biases and ensuring balanced data distribution demand in-depth data analysis skills. Cost Efficiency: Retraining and deployment are time-consuming and financially taxing. Data Freshness: Model accuracy can decline if data isn't regularly updated, requiring frequent and costly retraining. at last, RAG emerges as a more viable and efficient option for addressing the challenges presented by prompt engineering and fine-tuning. #AI #RAG #LLM #CostEfficiency #Technology #GenAIBootcamp #GTechMuLearn #pathway

🤖Why not use artificial intelligence to create test data? In this blog, James Walker takes an in depth look at the effectiveness and drawbacks of using ChatGPT for data generation: https://hubs.li/Q02n81q-0 When it comes to devising an effective test data strategy, it's important to understand that using tools like ChatGPT for test data generation is not a strategy in itself. Model-Based Testing, offers a more systematic approach to test data generation, ensuring comprehensive coverage of business rules. ✅Read the full blog to learn more: https://hubs.li/Q02n81q-0 #devops #qa #testing #softwaretesting #qualityassurance #softwaredevelopment #tdm #testdata #testdatamanagement #data

There is a lot of talk about how Gen AI can create solopreneur unicorns. In the recent months, my team and I have been working hands on trying to embed Gen AI across select use cases for our business, using ChatGPT4 / ChatGPT4o / Devin / Gemini. Here is my view on hype vs reality: 1.      Impact on Software Build Efficiencies (Very Real) As someone with rusty programming experience, I was able to create simple “apps” with the help of my data scientist and numerous attempts at generating and debugging the code with Google colab and GPT4o. Bottomline, our own tech stack which took over 10 months with a few engineers / data scientists can be done now in Half the time or less ( not a scientific estimate). Huge… and I mean game changing gains in software development efficiencies is quite real.   2.      Impact on Data Engineering / Data Science functions (Hype as of now) The real Achilles heel for GenAI right now seems to be working with structured data (excel / flat file). It is quite flimsy at transforming and analysing structured data. This is understandable because LLM and its transformer architectures were not meant for this. So, Data engineers and Data scientists will have to continue to do most of the heavy lifting, until a different model emerges. 3.      Solopreneur unicorns (Hype): While the technology is not yet ready for someone without any programming experience to create complex software products using prompts, I wouldn’t be surprised if the subsequent versions of ChatGPT, Gemini, Devin, Llama etc.. get us there in a couple of years.   However, such solopreneur products / services created using Gen AI will become commodities,  given the low barrier to entry. The Product is not the differentiator anymore.  The secret sauce maybe your team, your data, your ability to hit bulls eye on the customer problem and your execution.  In summary, I think solopreneur Unicorns are a Hype.  On the flip side, small start ups become much easier to start given much lower capital requirements.  This is great of course.  

Finally I have an idea how it works! (Even if it's a simple one 😁) After so long since I started using AI Chatbot tools Such as ChatGPT, Bard, Perplexity, etc, Feels good to know how to create one Using the Langchain Framework. Thanks to the short yet power-packed project coursework, Offered by DeepLearning.AI. If you're someone Wanting to build a high-level understanding Of a Chatbot creation framework, And you're short on time THIS ONE IS FOR YOU! Learning Never Stops! #chatbot #langchain #deeplearning #ai #upskilling

AI can be of great help when you need to make quick and effective decisions based on vast amounts of data. In the past, our team used to manually request lots of information from our clients in order to generate reports and make data-driven decisions. So we developed AI Reports, a ChatGPT-esque ML tool trained on our clients’ raw data. Now it allows us to simply type in any data-related questions using normal human language (requiring no special knowledge of SQL or analytics), and then instantly receive a concise answer or effortlessly generate a clearly visualized report. As a result, we can make faster decisions to help our clients achieve their goals. AI-powered data analytics and reporting is essential in providing a comprehensive and transparent 360° view of the business, enabling more efficient decision-making. Discover more on Grit Daily News: https://lnkd.in/eksmWW5V

You can now build scalable enterprise applications with Multi-agentic RAG. This technique lets you overcome the limitations of Vanilla RAG, which acts like a single-agent system handling retrieval, ranking, and generation sequentially. Key Highlights: Multiple Agents for Document Retrieval: These agents communicate and compare documents to ensure relevant retrieval. A meta-agent checks for bias. Assistive Agents: Perform tasks like math calculations, API calls, and web searches to handle various operations. Planning Frameworks: Use ReAct or Reflexion frameworks to plan which agents perform specific tasks. All agents rely on the same LLM for generation but differ in prompting schemes and constraints. For design insights, check out the video "Designing ChatGPT like AI Assistant" in the comments. ↓ Are you technical? Check out https://AlphaSignal.ai to get a daily summary of breakthrough models, repos and papers in AI. Read by 200,000+ devs.

With Growth Of AI In Wealthtech, Need For Accurate Data Rises With #AI changing sectors such as #banking, #wealthmanagement and #financialservices, it is essential that #data is #accurate and #controlled. If "garbage" goes into the system, then the results will be a problem. Joe Stensland, chief executive of BridgeFT, a #technology #architecture firm, writes on how important it is to have #accuratedata at a time when #artificialintelligence is on the march. If AI is only as accurate and useful as available data, then mistaken and partial information is going to be a big problem. https://lnkd.in/gDea-v2g Stephen Harris Philip Harris Tom Burroughes Amanda Cheesley Rachel Fokes Andrew Deane Theodora Viney

This is a basic overview of the differences between a “traditional” RAG and an “Agentic” RAG. At Kimberly-Clark, we have successfully implemented “traditional” RAG in production and are currently exploring single and multi-agent architectures to understand their benefits and limitations. The challenging part of this process is the frequent changes to framework libraries and guidelines. It involves identifying when accuracy becomes an ethical issue, properly evaluating accuracy levels, and controlling costs associated with using LLMs at various stages. It’s a journey that requires confidence, patience, and respect for the unknown.