Tiempo de lectura: < 1 minutoWe need to understand that not all models are created equal. The main difference between general models and instructional models. General models Ejemplo:Pregunta: “Explain how a neural network works”Response from a general model: can give an extensive explanation, include unnecessary concepts or jump topics. Instruct Models Ejemplo:Pregunta: “Explain how a neural network works step by … Read more
Tiempo de lectura: 2 minutosEnable GPU access is essential if we need to start a LLM model and use GPU with VRAM. Previous requirements Please ensure you have an NVIDIA GPU installed on your machine with the drivers installed and updated. Check using: nvidia-smi You should see your GPU and driver version. Additionally, Docker must be installed. NVIDIA Container … Read more
Tiempo de lectura: 2 minutosIt’s an inference server optimized (uses paged attention) that supports models like Llama 3, Mistral, Gemma, Phi, Qwen, etc. This offers an OpenAI-compatible API, perfect for easy integration. We will create the Docker Compose that allows us to deploy it: File: docker-compose.yml version: “3.9” services: vllm: image: vllm/vllm-openai:latest container_name: vllm restart: unless-stopped ports: – “8000:8000” … Read more
Tiempo de lectura: < 1 minutoThe artificial intelligence (AI) has stopped being a topic of science fiction to become a common tool in software development. The AI offers unique opportunities to increase productivity, improve software quality and accelerate development time. However, many developers feel that integrating it can be complex or overwhelming. In this article, we explore how to use … Read more
Tiempo de lectura: < 1 minutoToday we are going to see what types of AI agents are available with LangChain. ➡️ The most used. What it does:The LLM decides which tool to use at each step based on only the textual description of the tools.No needs examples or prior instances. For: Typical usage example: agent = initialize_agent( tools=mis_tools, llm=llm, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, … Read more
Tiempo de lectura: < 1 minutoWe will today make our AI agent have a tool that allows it to perform internet searches. First, we will install the necessary libraries: pip install langchain langchain_community pip install ddgs NOW INITIALIZING CONNECTION WITH OLLAMA SERVER (https://devcodelight.com/ollama-con-llama-3-2-in-docker/) from langchain_community.chat_models import ChatOllama from langchain_core.prompts import ChatPromptTemplate # … other imports … # ⚠️ YOUR REMOTE … Read more
Tiempo de lectura: 3 minutosToday we are going to learn how to use Tools in LongChain to create AI Agents. To execute this tutorial well, I recommend visiting the one on how to install LangChain. First: What are Tools in LangChain? In the context of a LangChain Agent, a Tool is any external function or resource that the Large … Read more
Tiempo de lectura: < 1 minutoWhat is a «Chain» (Link) in LangChain? It is a specific instruction: llm_chain = prompt | llm | output_parser An example perfect and modern of what means a Chain in the architecture of LangChain (specifically using the LangChain Expression Language or LCEL). A Chain (A Chain) in LangChain is the structured sequence of steps or … Read more
Tiempo de lectura: < 1 minutoWe are going to learn how to connect LangChain to our deployed Ollama server today with a small example, for instance, an IA Tools. We first need to have Ollama deployed: here is how. You will obtain the endpoint once Ollama is deployed, and then use LangChain with Python to connect to it remotely. You … Read more
Tiempo de lectura: 2 minutosIn 2025, LLaMA (Large Language Model Meta AI) has consolidated as one of the most versatile options for chatbots local or cloud-based, capable of processing text, images, and audio. In this tutorial, you will learn to create a multimodal chatbot using only LLaMA. LLaMA 3 has versions with 7B, 13B, and 70B parameters; for local … Read more
