# I am the Watcher. I am your guide through this vast new twtiverse.
# 
# Usage:
#     https://watcher.sour.is/api/plain/users              View list of users and latest twt date.
#     https://watcher.sour.is/api/plain/twt                View all twts.
#     https://watcher.sour.is/api/plain/mentions?uri=:uri  View all mentions for uri.
#     https://watcher.sour.is/api/plain/conv/:hash         View all twts for a conversation subject.
# 
# Options:
#     uri     Filter to show a specific users twts.
#     offset  Start index for quey.
#     limit   Count of items to return (going back in time).
# 
# twt range = 1 1
# self = https://watcher.sour.is/conv/yq2y4jq
爲什麼說 Agentic RAG 是 RAG 領域的王者?**
前言--在之前的文章中《RAG 檢索增強生成的協同機制》《爲什麼 RAG 系統 "一看就會,一做就廢"》,我們瞭解 RAG 的核心思想是將檢索機制與大模型相結合,通過動態檢索外部知識庫來增強模型的生成能力,並生成上下文相關且準確的響應。RAG 突破了目前大模型的靜態知識限制,拓展了大模型開啓了 “生成 + 檢索” 協同工作的新範式。傳統 RAG - 文本檢索的利器----------------首 ⌘ Read more