Ollama Tutorial Collection

General Introduction Ollama Deep Researcher is a fully locally running web research and report generation assistant developed by the LangChain team. It uses an arbitrary Large Language Model (LLM) hosted by Ollama to allow users to enter a research topic and then automatically generate web search queries, collect...

Introduction This document details how to build a localized RAG (Retrieval Augmented Generation) application using DeepSeek R1 and Ollama. It also complements the use of LangChain to build localized RAG applications. We will demonstrate the complete implementation flow with examples, including document processing, vector storage...

Introduction ReAct (Reasoning and Acting) is a framework that combines reasoning and action to enhance the performance of intelligences in complex tasks. The framework enables intelligences to accomplish tasks more effectively in dynamic environments by tightly integrating logical reasoning with practical action. Source : ReAct: ...

Introduction This document will detail how to use the LlamaIndex framework to build a local RAG (Retrieval-Augmented Generation) application. By integrating LlamaIndex, it is possible to build a RAG system in a local environment that combines the capabilities of Retrieval and Generation to improve the efficiency of information retrieval...

Dify supports access to large-scale language model inference and embedding capabilities deployed by Ollama. Quick Access Download Ollama Access Ollama installation and configuration, view Ollama local deployment tutorials. Run Ollama and chat with Llama ollama run llama3.1 Launch into ...

Introduction This document describes how to build a local Copilot-like programming assistant to help you write more beautiful and efficient code. In this course you will learn how to use Ollama to integrate local programming assistants, including Continue Aider Note: We will focus on VScode...

I. Deploying with Node.js 1. Installing Node.js Download and install the Node.js tool: https://www.nodejs.com.cn/download.html Set up a mirror source, for example, using the following mirror source. npm config set registry http://mirrors.cloud.tencent.com/np...

I. Directory structure Under the C6 folder of the repository notebook: fastapi_chat_app/ │ ├── app.py ├── websocket_handler.py ├── static/ │ └── index.html └── requirements.txt app.py FastAPI The main settings and routing of the application. webso...

Introduction This document describes how to use Ollama in a JavaScript environment to integrate with LangChain to create powerful AI applications.Ollama is an open source deployment tool for large language models, while LangChain is a framework for building language model-based applications. By combining...

Introduction This document describes how to use Ollama in a Python environment to integrate with LangChain to create powerful AI applications.Ollama is an open source deployment tool for large language models, while LangChain is a framework for building language model-based applications. By combining these two...

This article describes how to use the Ollama API in Java.This document is designed to help developers get started quickly and take full advantage of Ollama's capabilities. You can call the Ollama API directly in your program, or you can call Ollama from a Spring AI component.By studying this document, you can easily set...

Introduction Ollama provides a powerful REST API that enables developers to easily interact with large language models. With the Ollama API, users can send requests and receive responses generated by the model, applied to tasks such as natural language processing, text generation, and so on. In this paper, we will introduce in detail the generation of complementary, dialog generation ...

Windows The following is an example of how to customize Ollama to run in the GPU on a Windows system.Ollama uses the CPU for inference by default. For faster inference, you can configure the GPU used by Ollama.This tutorial will guide you on how to set up the ring on a Windows...