Functions, Tools and Agents with LangChain


课程链接:https://learn.deeplearning.ai/courses/functions-tools-agents-langchain/lesson/rtwb1/introduction github链接:https://github.com/MSzgy/Functions-Tool

Knowledge Graphs for RAG


课程连接:https://learn.deeplearning.ai/courses/knowledge-graphs-rag github连接:https://github.com/MSzgy/Knowledge-Graphs-for-RAG Introduction 在本章课程中,以SEC(美国

Retrieval Optimization: Tokenization to Vector Quantization


课程链接:https://learn.deeplearning.ai/courses/retrieval-optimization-from-tokenization-to-vector-quantization/lesson/1/introduction github: https://githu

LangChain for LLM Application Development


课程链接:https://learn.deeplearning.ai/courses/langchain/lesson/1/introduction 代码: https://github.com/MSzgy/LangChain-for-LLM-Application-Development Intr

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

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