Building Agentic RAG with Llamaindex


课程链接:https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/1/introduction 整理的代码: https://github.com/MSzgy/Building-Agentic

ChatGPT Prompt Engineering for Developers


https://learn.deeplearning.ai/courses/chatgpt-prompt-eng/lesson/1/introduction git https://github.com/MSzgy/ChatGPT-Prompt-Engineering-for-Developers/

Prompt Compression and Query Optimization

AILLM 

https://learn.deeplearning.ai/courses/prompt-compression-and-query-optimization/lesson/1/introduction Introduction 代码见 https://github.com/MSzgy/Prompt

Function-calling and data extraction with LLMs

AILLM 

Introduction https://learn.deeplearning.ai/courses/function-calling-and-data-extraction-with-llms/lesson/1/introduction 使用了NexusRavenV2-13B model by N

Large Multimodal Model Prompting with Gemini


Introduction https://learn.deeplearning.ai/courses/large-multimodal-model-prompting-with-gemini/lesson/1/introduction Gemini 是谷歌推出的LLM model. 介绍了LMM的含

Building AI Applications With Haystack

AILLM 

https://learn.deeplearning.ai/courses/building-ai-applications-with-haystack git https://github.com/MSzgy/Building-AI-Applications-With-Haystack Intro

Dify

AILLM 

今天(2024.08.24)去参加亚马逊云线下的关于GenAI的会议,在会上,有个称为Dify的项目方对他们项目做了介绍https://github.com/langgenius/dify, 我也在服务器上部署下。

RAG 向量数据库介绍


可以白嫖的云数据库: zilliz LanceDB https://lancedb.com/ LanceDB 是一个专门用于管理向量数据的数据库,广泛应用于机器学习和人工智能领域,尤其是在相似性搜索、最近邻搜索和聚类等任务中。以下是对 LanceDB 的简要介绍: ### 主要特点: 1. 向量数据

各大AI平台

AILLM 

在选anyLLM模型时,发现了很多AI平台,大部分都没见过,在这查些资料,记录下 LLM Ranking: https://openrouter.ai/rankings Summary: 感觉新颖的: Perplexity AI 界面令人眼前一亮 问答界面,图片与信息源同生成且很流畅 OpenRou