Multimodal-RAG
基于多模态 Embedding + Zilliz + Qwen 视觉理解的多模态 RAG 系统。支持 **Cohere / DashScope Embedding** 和 **DashScope / OpenRouter LLM** 双引擎切换。上传 PDF，用自然语言提问，系统自动检索最相关的页面并由 AI 生成回答。 与传统 RAG 不同，本系统**不做文本提取和 OCR**，而是直接将 PDF 页面当作图片处理，通过视觉 Embedding 模型编码，完整保留表格、图表、排版、手写批注等所有视觉信息。
config.py — 配置中心。Settings dataclass 定义所有参数，_load_env() 从 .env 读取变量覆盖默认值，_resolve_provider() 根据 EMBED_PROVIDER 和 LLM_PROVIDER 分别解析 Embedding 和 LLM 的活跃配置。全局单例 settings 供所有模块导入。
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/liangdabiao-multimodal-rag — read its card at https://meshkore.com/agent/liangdabiao-multimodal-rag/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/liangdabiao-multimodal-ragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/liangdabiao-multimodal-rag/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own Multimodal-RAG?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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