--- title: Best GoPenAI posts — June 2024 | daily.dev description: The most upvoted GoPenAI posts from June 2024, curated by the daily.dev community. canonical: https://daily.dev/sources/gopenai/best-of/2024/06 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/gopenai/best-of/2024/06 og:type: website og:site_name: daily.dev og:title: Best GoPenAI posts — June 2024 | daily.dev og:description: The most upvoted GoPenAI posts from June 2024, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of GoPenAI — June 2024 1. 1 [](https://daily.dev/posts/building-a-custom-chatbot-with-next-js-langchain-openai-and-supabase--oh5tz3vkt "Building a Custom Chatbot with Next.js, Langchain, OpenAI, and Supabase.") Article GoPenAI · 2y Building a Custom Chatbot with Next.js, Langchain, OpenAI, and Supabase. Build a custom chatbot with Next.js for the frontend, MaterialUI for UI components, Langchain and OpenAI for language model interactions, and Supabase to store data and embeddings. The chatbot can be trained with custom data from PDF files and answer questions based on the content. 79 3 2. 2 [](https://daily.dev/posts/mastering-rag-chunking-techniques-for-enhanced-document-processing-kcu8eh79h "Mastering RAG Chunking Techniques for Enhanced Document Processing") Article GoPenAI · 2y Mastering RAG Chunking Techniques for Enhanced Document Processing Dividing large documents into smaller segments, known as chunking, is crucial for optimizing Retrieval-Augmented Generation (RAG) systems. These systems combine retrieval-based and generative approaches to improve output quality. Various chunking methods, such as sentence, token, and regex splitters, are discussed with a focus on a novel technique using sentence embeddings to identify topic changes. This new method ensures that each chunk represents a coherent topic, enhancing the system's ability to generate accurate and relevant responses. 49 3. 3 [](https://daily.dev/posts/building-a-web-page-summarization-app-with-next-js-openai-langchain-and-supabase-n2rsy3jya "Building a Web Page Summarization App with Next.js, OpenAI, LangChain, and Supabase") Article GoPenAI · 2y Building a Web Page Summarization App with Next.js, OpenAI, LangChain, and Supabase Build a web page summarization app using Next.js, OpenAI, LangChain, and Supabase. This app allows users to input any website URL and quickly get a brief summary of the page. Key steps include setting up a Supabase project and API, configuring OpenAI, creating the web app front end, and running the app. This tool helps users save time by providing summaries of long articles, blog posts, or research papers. 44 1 4. 4 [](https://daily.dev/posts/introduction-to-retrieval-augmented-generation-rag-a-beginner-s-guide-oh8de7fas "Introduction to Retrieval-Augmented Generation (RAG): A Beginner’s Guide") Article GoPenAI · 2y Introduction to Retrieval-Augmented Generation (RAG): A Beginner’s Guide Introduction to Retrieval-Augmented Generation (RAG): A Beginner's Guide. RAG combines retrieval and generative AI techniques to ensure accurate and meaningful responses. The RAG process involves document ingestion, retrieval, and response generation. RAG systems provide precise and top-notch text responses, elevating the performance of AI applications. 25 5. 5 [](https://daily.dev/posts/building-a-multi-pdf-rag-chatbot-langchain-streamlit-with-code-doxqtirc5 "Building a Multi PDF RAG Chatbot: Langchain, Streamlit with code") Article GoPenAI · 2y Building a Multi PDF RAG Chatbot: Langchain, Streamlit with code This post provides a step-by-step breakdown of how to build a Multi PDF RAG Chatbot using Langchain and Streamlit. It covers the necessary tools, reading and processing PDF files, creating a searchable text database, setting up conversational AI using OpenAI, and user interaction through a user-friendly interface. 20 6. 6 [](https://daily.dev/posts/can-2-llm-calls-boost-your-rag-s-performance--knerrysh2 "Can 2 LLM calls boost your RAG’s performance?") Article GoPenAI · 2y Can 2 LLM calls boost your RAG’s performance? Building a real-world Retrieval Augmented Generation (RAG) system for handling company reports presents unique challenges and solutions. Initially struggling with generating accurate responses from unstructured data, the author experimented with different models and retrieval methods. Ultimately, using a smaller in-house LLM, Mistral 7B, for both generating metadata and crafting responses, outperformed even a powerful LLM like GPT-4\. The key takeaway is the effective use of metadata filters and strategic application of smaller LLMs for enhanced performance. 18 2 7. 7 [](https://daily.dev/posts/revolutionizing-document-interaction-an-ai-powered-pdf-text-2-voice-chatbot-using-llamaindex-l-bwwb4gcgj "🚀 Revolutionizing Document Interaction: An AI-Powered PDF Text-2-Voice Chatbot Using LlamaIndex 🐑, Langchain 🔗 and Azure AI Speech 🔊") Article GoPenAI · 2y 🚀 Revolutionizing Document Interaction: An AI-Powered PDF Text-2-Voice Chatbot Using LlamaIndex 🐑, Langchain 🔗 and Azure AI Speech 🔊 The post delves into creating an AI-powered PDF Text-2-Voice chatbot using LangChain, LlamaIndex, and Azure AI Speech. The chatbot is designed to read out answers from the content in PDF documents. The technologies used include LangChain for chaining language models, LlamaIndex for document indexing, Azure AI Speech for speech synthesis, and Streamlit for the user interface. Detailed steps for setting up the environment, uploading files, vectorization, fetching data, and voice synthesis are provided. The end product is a Streamlit app where users can upload PDFs, ask questions, and receive voice responses. 18 8. 8 [](https://daily.dev/posts/revolutionizing-document-interaction-an-ai-powered-pdf-voice-2-voice-chatbot-using-llamaindex--kzlgsmrjo "🚀 Revolutionizing Document Interaction: An AI-Powered PDF Voice-2-Voice Chatbot Using LlamaIndex 🐑, Langchain 🔗 Azure AI Speech 🎤and Google Audio 🔊") Article GoPenAI · 2y 🚀 Revolutionizing Document Interaction: An AI-Powered PDF Voice-2-Voice Chatbot Using LlamaIndex 🐑, Langchain 🔗 Azure AI Speech 🎤and Google Audio 🔊 Experience a breakthrough in document interaction with an AI-driven PDF voice-2-voice chatbot. Utilizing LlamaIndex, Langchain, Azure AI Speech, and Google Audio, this advanced system allows for seamless verbal interactions with PDF documents, enhancing accessibility and usability. Learn about its evolution from text-based dialogue to voice-enabled functionalities and explore the technical components, including dependencies, document handling, and user interaction features. 17 9. 9 [](https://daily.dev/posts/understanding-kolmogorov-arnold-networks-kans-and-their-application-in-variational-autoencoders-se2xk20tt "Understanding Kolmogorov-Arnold Networks (KANs) and Their Application in Variational Autoencoders") Article GoPenAI · 2y Understanding Kolmogorov-Arnold Networks (KANs) and Their Application in Variational Autoencoders Kolmogorov-Arnold Networks (KANs) are based on a mathematical theorem that allows any continuous function of multiple variables to be represented as a combination of one-dimensional functions. These networks could revolutionize neural network design, particularly for Variational Autoencoders (VAEs), by improving efficiency, interpretability, and flexibility. Key methods involve using splines and piecewise polynomials. Although the post features a standard VAE implementation, it discusses how KAN layers could be incorporated, highlighting potential future research directions in KAN-based models. 16 10. 10 [](https://daily.dev/posts/caching-in-llm-based-applications-sa0fi3khq "Caching in LLM-Based Applications") Article GoPenAI · 2y Caching in LLM-Based Applications Caching improves the performance and cost-efficiency of LLM-based applications by storing frequently accessed data. Standard caching saves prompts and their responses in a database but struggles with similar prompts being processed separately. Semantic caching addresses this by performing similarity searches between new and cached prompts, returning cached responses when appropriate. Implementing these caching techniques can significantly enhance the efficiency, responsiveness, and cost-effectiveness of applications. 16 11. 11 [](https://daily.dev/posts/introducing-shelldon-a-new-rust-cli-tool-with-gpt-features-qfdh7xdi1 "Introducing Shelldon: A New Rust CLI Tool with GPT Features") Article GoPenAI · 2y Introducing Shelldon: A New Rust CLI Tool with GPT Features Shelldon is a new command-line tool written in Rust that integrates GPT features for executing shell commands, managing prompts, and interacting with multiple LLMs. It supports various AI providers like OpenAI, Anthropic, and Gemini, allowing users to choose their preferred model. Users can easily install Shelldon via Homebrew or build it from source. The tool simplifies tasks like analyzing Docker logs, troubleshooting Kubernetes, and generating scripts or configuration files, with custom prompt management for enhanced flexibility. 14 3 12. 12 [](https://daily.dev/posts/building-a-document-summarization-web-app-with-openai-s-llm-vbgvehoq3 "Building a Document Summarization Web App with OpenAI’s LLM") Article GoPenAI · 2y Building a Document Summarization Web App with OpenAI’s LLM Learn to build a document summarization web app by leveraging OpenAI's Large Language Model (LLM) and Streamlit. This guide covers creating a front-end for file uploads, back-end processing with helper functions, generating embeddings, and summarizing PDFs. The approach can be scaled to multiple file types and integrated into existing applications, providing valuable use cases for LLMs in industry. 13 13. 13 [](https://daily.dev/posts/productionizing-llms-monitor-llm-applications-with-langsmith-7f3zk46ig "Productionizing LLMs: Monitor LLM Applications with LangSmith") Article GoPenAI · 2y Productionizing LLMs: Monitor LLM Applications with LangSmith LangSmith is a platform that allows for monitoring and evaluation of Large Language Model applications. It can work independently of LangChain framework and enables monitoring of LLM calls, tool usage, LLM model latency, token count, and cost. 13 14. 14 [](https://daily.dev/posts/empowering-llms-with-real-time-capabilities-using-agents-custom-tools-in-langchain-n4o8qfobz "Empowering LLMs with Real-Time Capabilities Using Agents & Custom Tools in LangChain") Article GoPenAI · 2y Empowering LLMs with Real-Time Capabilities Using Agents & Custom Tools in LangChain Learn how LangChain empowers LLMs with real-time capabilities using agents and custom tools. Create an LLM application called QuikNews to get the latest news headlines. Set up the LangServe application to host the QuikNews application locally. 13 15. 15 [](https://daily.dev/posts/leveraging-openai-and-pymupdf-for-comprehensive-text-error-checking-in-pdf-documents-u41b2ffp6 "Leveraging OpenAI and PyMuPDF for Comprehensive Text Error Checking in PDF Documents") Article GoPenAI · 2y Leveraging OpenAI and PyMuPDF for Comprehensive Text Error Checking in PDF Documents Learn how to use OpenAI and PyMuPDF to perform comprehensive text error checking in PDF documents. 12 [See all GoPenAI archives](/sources/gopenai/best-of) ```json 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