- 2024/9/21:將之前的課程內容基礎部分的
application
資料夾整理進LangChain_Basic_course_integration
資料夾,以便更清晰地了解應用級別並保持乾淨。 - 202409_LangGraph:下增加了一些任務,持續更新中。詳情請點進去查看 :)
- RAG_Tech:這個倉庫展示了各種高級檢索增強生成(RAG)系統的技術。RAG 系統將信息檢索與生成模型結合,提供準確且具上下文豐富的回應。
- llava_Muti-Modal Model Training : 增加圖像理解模型 - llava系列的資料處理過程和一些實驗過程。
- llava_MutimodalModel_experience增加README.md 的說明文檔。主要用llava next的pretrained model訓練DPO dataset. llava index 官方README.md doc
- 新增Streamlit - 最新應用的folder去練習streamlit有哪些酷東西可以用或整合
- ** 新增Deepgram for text to audio or audio to text 任務 文字轉語音 <--> 語音轉文字 **
- 新增Agent Zero 一個強大的自動化代理工具
- 新增auto Agent 官方教學文檔和jupyter note book (ipynb 檔)
- 新增基於mathematica 強大資料庫和搜尋引擎的 api 開發apps
這個 Repository 專注於大語言模型的應用,特別是使用 Langchain、Langserve 和 Llamaindex 進行 RAG(Retrieval-Augmented Generation)應用及 Agentic 應用。我們將逐步建立 RAG 的知識體系,涵蓋從基本到進階的概念。
本 Repo 將持續更新課程內容,預計持續到 2025 年底。以下是我們的課程規劃:
- Naive RAG
- Advanced RAG
- Modular RAG
- Agentic RAG
- Multi-Agent App
隨著課程的推進,您將逐步建立層層的知識概念和知識圖譜,並學會如何應用 LangGraph 和 Langserve、GraphRAG 等技術。
- 2024 年底至 2025 年初:課程規劃和內容將不斷更新。
我們已經在 Repo 中新增 LangGraph 相關的最新知識和代碼,包括:
- LangGraph 最新消息的 README.md 更新
- LangGraph 的基本概念
- 實作示例
- 應用範例
This repository serves as a comprehensive guide to leveraging LangGraph for building stateful AI agents and conversational applications. It combines the capabilities of language models with robust data management using MongoDB, enabling developers to create intelligent, interactive solutions.
- Introduction: An overview of the project and its objectives.
- Features: A list of key functionalities provided by the application.
- Prerequisites: Requirements for setting up the project.
- Installation: Step-by-step instructions on how to get started.
- Database Seeding: Guidance on populating the database with initial data.
- Usage: Instructions for interacting with the API.
- Project Structure: A breakdown of the project's organization.
- How It Works: Insight into the inner workings of the application.
- Contributing: Information on how to contribute to the project.
- License: Licensing details for the project.
Feel free to explore each section to understand how to effectively utilize LangGraph in your applications!
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
LermoAI is an open-source project aimed at transforming your learning experience. By generating personalized content tailored to your preferences, LermoAI ensures that your journey is efficient and enjoyable. Whether you prefer articles, podcasts, or videos, LermoAI crafts custom materials just for you!
我們將在此 Repo 中新增資料夾來整理最新的知識和代碼,請隨時查看最新的進展!
歡迎大家提交建議和貢獻!如果您對任何內容有意見或建議,請提出問題或發送 Pull Request。
持續學習,掌握未來的技術!