--- title: Best GoPenAI posts — July 2024 | daily.dev description: The most upvoted GoPenAI posts from July 2024, curated by the daily.dev community. canonical: https://daily.dev/sources/gopenai/best-of/2024/07 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/gopenai/best-of/2024/07 og:type: website og:site_name: daily.dev og:title: Best GoPenAI posts — July 2024 | daily.dev og:description: The most upvoted GoPenAI posts from July 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 — July 2024 1. 1 [](https://daily.dev/posts/openai-basics-in-python-l8t0aipau "OpenAI Basics in Python") Article GoPenAI · 2y OpenAI Basics in Python Learn the basics of using the OpenAI API with Python by setting up a virtual environment, creating an OpenAI account, and using Jupyter notebooks. The guide covers tasks such as grammar correction, translating text to emojis, code explanation, and image analysis using the GPT model. 95 3 2. 2 [](https://daily.dev/posts/building-an-effective-rag-pipeline-a-guide-to-integrating-self-rag-corrective-rag-and-adaptive-ra-dqnzcllwa "Building an Effective RAG Pipeline: A Guide to Integrating Self-RAG, Corrective RAG, and Adaptive RAG") Article GoPenAI · 2y Building an Effective RAG Pipeline: A Guide to Integrating Self-RAG, Corrective RAG, and Adaptive RAG A comprehensive guide to building an effective Retrieval-Augmented Generation (RAG) pipeline by integrating Self-RAG, Corrective RAG, and Adaptive RAG. This pipeline aims to intelligently handle questions of varying complexity, ensure information accuracy, and generate useful answers. It leverages LangGraph for stateful, multi-agent workflows, and includes methods for routing questions, retrieving documents, evaluating relevance, and grading output quality. 78 2 3. 3 [](https://daily.dev/posts/fine-tuning-llms-efficiently-ueei8j99q "Fine-tuning LLMs efficiently") Article GoPenAI · 2y Fine-tuning LLMs efficiently Fine-tuning large language models (LLMs) tailors pre-trained models to specific tasks, improving their performance and efficiency. Techniques like Simple Fine-tuning, Adapter Layers, and Low-Rank Adaptation (LoRA) offer distinct advantages. Simple Fine-tuning retrains final layers for task-specific adaptation. Adapter Layers conserve general language knowledge while adding task-specific modules, and LoRA reduces trainable parameters using rank decomposition. These methods enhance task performance, mitigate overfitting, and reduce training times. Experimentation indicates Adapter Layers as the most efficient, with LoRA closely following. 33 4. 4 [](https://daily.dev/posts/localrag-deploy-a-production-grade-rag-chatbot-on-your-local-machine-or-server-tphvkpqsh "LocalRAG: Deploy a production-grade RAG chatbot on your Local Machine or Server") Article GoPenAI · 2y LocalRAG: Deploy a production-grade RAG chatbot on your Local Machine or Server LocalRAG is a self-hosting application designed to deploy a production-level RAG chatbot on a local machine or server, ensuring data privacy and security. The application utilizes LangChain for backend processing, Streamlit for the frontend, Qdrant as the vector store, and Redis for storing chat messages. Prerequisites include setting up a Python virtual environment and Docker. Users also need to install Ollama for running large language models locally. The guide provides steps to clone the necessary repository and run the application using Streamlit. 24 5. 5 [](https://daily.dev/posts/build-your-own-crag-system-with-cohere-a-step-by-step-guide-to-improving-language-model-accuracy-wi-hmkmkyry8 "Build Your Own CRAG System with Cohere: A Step-by-Step Guide to Improving Language Model Accuracy with Corrective Retrieval-Augmented Generation") Article GoPenAI · 2y Build Your Own CRAG System with Cohere: A Step-by-Step Guide to Improving Language Model Accuracy with Corrective Retrieval-Augmented Generation CRAG (Corrective Retrieval-Augmented Generation) aims to enhance the accuracy of language models. By using a lightweight retrieval evaluator, CRAG assesses the quality of retrieved documents, refining or discarding them based on confidence scores. It integrates with existing RAG systems, significantly improving their accuracy and generalizability across various tasks. CRAG remains stable even when retrieval quality declines, making it a promising addition to retrieval-augmented generation. The post provides a detailed guide on setting up a CRAG system using the LangGraph library and Cohere models. 22 6. 6 [](https://daily.dev/posts/lab-3-implementing-rag-to-build-a-chat-with-multiple-pdfs-app-brhlvy4pw "Lab #3: Implementing RAG to build a “Chat with Multiple PDFs” app") Article GoPenAI · 2y Lab #3: Implementing RAG to build a “Chat with Multiple PDFs” app This post explains how to build a 'Chat with Multiple PDFs' app using Retrieval-Augmented Generation (RAG), and covers its benefits, such as reducing model hallucination and enhancing reliability. It details phases for pre-processing and inference, including loading, chunking, and embedding data into a vector database, and setting up a retrieval chain using Langchain and OpenAI integration. 22 2 7. 7 [](https://daily.dev/posts/let-s-explore-scrapegraphai-9sgnunhiy "Let’s explore ScrapeGraphAI") Article GoPenAI · 2y Let’s explore ScrapeGraphAI ScrapeGraphAI is an open-source Python library that revolutionizes web scraping by integrating Large Language Models (LLMs) with modular graph-based pipelines. It offers various pre-built graphs to handle different types of data extraction tasks, adapting to website changes and supporting multiple document formats. The library aims to democratize data access by automating complex processes and reducing the need for extensive coding knowledge. 20 8. 8 [](https://daily.dev/posts/create-a-simple-voice-to-voice-translation-app-with-python-acefbs0vo "Create a Simple Voice-to-Voice Translation App with Python") Article GoPenAI · 2y Create a Simple Voice-to-Voice Translation App with Python Learn to create a simple voice-to-voice translator application using Python, integrating Gradio for the interface, AssemblyAI for voice recognition, the translate library for text translation, and ElevenLabs for text-to-speech conversion. The process involves setting up the environment, obtaining necessary API keys, transcribing audio to text, translating text into multiple languages, and converting translated text back into speech. The tutorial also covers building a user-friendly interface using Gradio. 20 9. 9 [](https://daily.dev/posts/human-like-ai-cognitive-llm-agents-d408d7niq "Human-like AI: Cognitive LLM Agents") Article GoPenAI · 2y Human-like AI: Cognitive LLM Agents Cognitive LLM Agents leverage large language models (LLMs) for complex and probabilistic symbol transformations. These agents emulate human cognitive functions through a modular architecture, integrating elements like decision-making, memory, and learning. The CoALA framework demonstrates how to structure cognitive language agents utilizing LLMs as a core component, offering insights into modular agent development and structured reasoning techniques. 18 1 10. 10 [](https://daily.dev/posts/openai-text-to-speech-bridging-language-barriers-with-versatile-voice-solutions-u68gmcxxx "OpenAI Text-to-Speech 📢: Bridging Language Barriers with Versatile Voice Solutions") Article GoPenAI · 2y OpenAI Text-to-Speech 📢: Bridging Language Barriers with Versatile Voice Solutions OpenAI's Text-to-Speech (TTS) model offers versatile voice solutions, supporting various languages and audio formats. The setup involves creating a Python project, using OpenAI's API, and configuring voice and speech speed options. The model supports multiple voices, including 'alloy' and 'echo', and can convert text into speech in numerous languages, such as Spanish. Supported audio formats include MP3, AAC, FLAC, WAV, and PCM. 16 11. 11 [](https://daily.dev/posts/part-5-of-building-my-first-chatbot-a-picture-is-worth-a-1000-words-kdec8hwma "Part 5 of Building My First Chatbot: A Picture Is Worth a 1000 Words") Article GoPenAI · 2y Part 5 of Building My First Chatbot: A Picture Is Worth a 1000 Words The post discusses how the author enhanced their home automation chatbots by integrating DALL-E’s API to generate images based on chatbot responses. It highlights the cost considerations between using DALL-E-2 and DALL-E-3, the technical design for handling asynchronous requests, and challenges related to generating effective prompts for image creation. The article also emphasizes the benefits of adding visual elements to chatbot interactions, making them more engaging and informative. 15 12. 12 [](https://daily.dev/posts/advanced-rag-for-database-without-exposing-db-data-text-to-sql-aer7wnk79 "Advanced RAG for Database without exposing DB Data: Text to SQL") Article GoPenAI · 2y Advanced RAG for Database without exposing DB Data: Text to SQL This post discusses a method to interact with databases without exposing sensitive data by using LangChain, OpenAI's language models, and SQLAlchemy. It explains how to extract the database schema and generate SQL queries in response to user questions. The proposed approach circumvents data exposure by using the schema as input instead of actual data, addressing privacy concerns. Example code snippets demonstrate key components of the system, including extracting the schema, creating prompt templates, and generating SQL queries. Drawbacks like context length limitations and potential query inaccuracies are also highlighted. 15 1 13. 13 [](https://daily.dev/posts/exploring-the-power-of-openai-api-with-python-a-step-by-step-guide-zicexekvz "Exploring the Power of OpenAI API with Python: A Step-by-Step Guide") Article GoPenAI · 2y Exploring the Power of OpenAI API with Python: A Step-by-Step Guide This guide offers a step-by-step approach to using the OpenAI API with Python, covering installation, setup, making API calls, and error handling. It includes practical examples to generate text using OpenAI's GPT-3 model, providing code snippets and expected outputs. 15 1 [See all GoPenAI archives](/sources/gopenai/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/sources/gopenai/best-of/2024/07#page","url":"https://daily.dev/sources/gopenai/best-of/2024/07","name":"Best GoPenAI Posts — July 2024","description":"The most upvoted GoPenAI posts from July 2024, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/sources/gopenai/best-of/2024/07#items","numberOfItems":13,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/openai-basics-in-python-l8t0aipau","name":"OpenAI Basics in Python"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/building-an-effective-rag-pipeline-a-guide-to-integrating-self-rag-corrective-rag-and-adaptive-ra-dqnzcllwa","name":"Building an Effective RAG Pipeline: A Guide to Integrating Self-RAG, Corrective RAG, and Adaptive RAG"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/fine-tuning-llms-efficiently-ueei8j99q","name":"Fine-tuning LLMs efficiently"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/localrag-deploy-a-production-grade-rag-chatbot-on-your-local-machine-or-server-tphvkpqsh","name":"LocalRAG: Deploy a production-grade RAG chatbot on your Local Machine or Server"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/build-your-own-crag-system-with-cohere-a-step-by-step-guide-to-improving-language-model-accuracy-wi-hmkmkyry8","name":"Build Your Own CRAG System with Cohere: A Step-by-Step Guide to Improving Language Model Accuracy with Corrective Retrieval-Augmented Generation"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/lab-3-implementing-rag-to-build-a-chat-with-multiple-pdfs-app-brhlvy4pw","name":"Lab #3: Implementing RAG to build a “Chat with Multiple PDFs” app"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/let-s-explore-scrapegraphai-9sgnunhiy","name":"Let’s explore ScrapeGraphAI"},{"@type":"ListItem","position":8,"url":"https://daily.dev/posts/create-a-simple-voice-to-voice-translation-app-with-python-acefbs0vo","name":"Create a Simple Voice-to-Voice Translation App with Python"},{"@type":"ListItem","position":9,"url":"https://daily.dev/posts/human-like-ai-cognitive-llm-agents-d408d7niq","name":"Human-like AI: Cognitive LLM Agents"},{"@type":"ListItem","position":10,"url":"https://daily.dev/posts/openai-text-to-speech-bridging-language-barriers-with-versatile-voice-solutions-u68gmcxxx","name":"OpenAI Text-to-Speech 📢: Bridging Language Barriers with Versatile Voice Solutions"},{"@type":"ListItem","position":11,"url":"https://daily.dev/posts/part-5-of-building-my-first-chatbot-a-picture-is-worth-a-1000-words-kdec8hwma","name":"Part 5 of Building My First Chatbot: A Picture Is Worth a 1000 Words"},{"@type":"ListItem","position":12,"url":"https://daily.dev/posts/advanced-rag-for-database-without-exposing-db-data-text-to-sql-aer7wnk79","name":"Advanced RAG for Database without exposing DB Data: Text to SQL"},{"@type":"ListItem","position":13,"url":"https://daily.dev/posts/exploring-the-power-of-openai-api-with-python-a-step-by-step-guide-zicexekvz","name":"Exploring the Power of OpenAI API with Python: A Step-by-Step Guide"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sources","item":"https://daily.dev/sources"},{"@type":"ListItem","position":3,"name":"GoPenAI","item":"https://daily.dev/sources/gopenai"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/sources/gopenai/best-of"},{"@type":"ListItem","position":5,"name":"July 2024"}]}]} ```