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Helpful AI GPT Agents for automation
Today, I will be exploring the different aspects of the Auto-GPT project, a Python and LangChain based software that leverages the capabilities of GPT for automation. This analysis covers a detailed code review, the creation of Gherkin Syntax Features and Scenario Outlines, and the visualization of the code flow through Sequence Diagrams… and all of these tasks were executed autonomously by an Auto-GPT agent. Section 1: Code Review Structure and Logic Analysis The first step in this journey was to watch Auto-GPT conducting a comprehensive code review of the fastapi_jwt_auth_refresh.py file. Auto-GPT found a well-structured code, adhering to the principles of the FastAPI framework. Potential Improvements AI generated by Auto-GPT…
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Auto-GPT Architecture
Today, I am happy to introduce you to the transformative potential of AI, specifically the capabilities of Auto-GPT, in revolutionizing the way we: conceive experiments and projects, and the way we develop, track and test our ideas. SYSTEM_PROMPT Your task is to devise up to 5 highly effective goals and an appropriate role-based name (_GPT) for an autonomous agent, ensuring that the goals are optimally aligned with the successful completion of its assigned task. The user will provide the task, you will provide only the output in the exact format specified below with no explanation or conversation. Example input Help me with marketing my business Example output Name: CMOGPT Description:…
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Mind Maps for LangChain Framework Fans
Alright, let’s simplify this. Imagine you’ve got a super-smart buddy named LangChain. This buddy isn’t just brainy from reading tons of books, but also knows how to connect different bits of information and interact with the surroundings. LangChain is like a toolkit for crafting smart projects using words and sentences. Think of it like LEGO blocks. Each block, or component, is a piece you can use to build something. And the best part? LangChain already provides a bunch of these blocks for you to play with! Now, sometimes starting from scratch can be a drag. Maybe you want a head start on building, say, a LEGO castle or a spaceship.…
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Supercharge Your Wisdom
Okay, folks, here’s the deal. This project is showing us how we can team up OpenAI with our knowledge base or other documents. And the cool part? We can do these fancy ‘semantic searches’ and even whip up prompts that we can tweak the generation of the LLM response just the way we like. This project contains a Streamlit Chat interface and a Luigi ETL Pipeline that processes and stores documents into a Weaviate Vectorstore instance. Github Repository The ETL pipeline performs several tasks: converting Jupyter notebooks and Python scripts to Markdown format, cleaning the code blocks in the Markdown files, removing unnecessary files and directories, and uploading the processed…
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Dialogue with your Documents for Data-Driven Decision Making
When I started developing this application, my goal was to build an interactive and intelligent document reader. I wanted users to upload a document, ask a question about it, and have an AI generate responses based on the document’s content. So, here’s how I put everything together: github.com/josoroma/data-driven-decision-making OpenAI and Pinecone First, I created a user-friendly sidebar for users to input their API keys and environment variables. This is the first interaction point between the user and the application. The application relies on OpenAI and Pinecone for retrieving information and generating responses, hence the necessity of API keys. If you do not have a .env file with the necessary environment…
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Evite el ruido y preserve el contexto
Esencialmente, se trata de desglosar contenido de texto grande en partes manejables para optimizar la relevancia del contenido que obtenemos de una base de datos vectorial utilizando LLM. Esto me recuerda a la búsqueda semántica. En este contexto, indexamos documentos llenos de información específica del tema. Si nuestra segmentación se hace correctamente, los resultados de la búsqueda se alinean bien con lo que el usuario está buscando. Pero si nuestros segmentos son demasiado pequeños o demasiado gigantes, podríamos pasar por alto contenido importante o devolver resultados menos precisos. Por lo tanto, es crucial encontrar ese punto dulce para el tamaño del segmento para asegurarnos de que los resultados de la…
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Avoid noise and preserve context
Essentially, it’s about breaking down large text content into manageable parts to optimize the relevance of the content we retrieve from a vector database using LLM. This reminds me of semantic search. In this context, we index documents filled with topic-specific information. If our chunking is done just right, the search results align nicely with what the user is looking for. But if our chunks are too tiny or too gigantic, we might overlook important content or return less precise results. Hence, it’s crucial to find that sweet spot for chunk size to make sure search results are spot-on. OpenAIEmbeddings The OpenAIEmbeddings class is a wrapper around OpenAI’s API for…
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Rockeando el Mundo de la IA con Modelos de Lenguaje Avanzados
En nuestro mundo siempre en evolución de la tecnología, es esencial apreciar el notable progreso del Procesamiento de Lenguaje Natural (NLP). Regresando un poco en el tiempo, cada tarea de NLP necesitaba un modelo distinto, un proceso tedioso y que consumía mucho tiempo. Esto cambió con la introducción de los Transformers y el concepto de aprendizaje de transferencia en NLP. LLMs Generalistas Grandes corporaciones como Google encabezaron esta transformación al invertir pesadamente en la formación de modelos transformadores. Estos modelos funcionan como “generalistas“, con una sólida comprensión del lenguaje, lo que les permite realizar diversas tareas. Hoy en día, este avance ha evolucionado hacia el uso de modelos de lenguaje…
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Rocking the AI World with Advanced Language Models
In our ever-evolving world of technology, it’s essential to appreciate the remarkable progress of Natural Language Processing (NLP). Rewinding back a little, each NLP task necessitated a distinct model, a tedious and time-consuming process. This changed with the introduction of Transformers and the concept of transfer learning in NLP. Generalist LLMs Large corporations like Google spearheaded this transformation by investing heavily in training transform models. These models serve as “generalists” with a robust understanding of language, allowing them to perform diverse tasks. Today, this advancement has morphed into the use of large language models (LLMs) capable of tasks like classification or question answering. It’s astounding to realize that the technology…
