bkyungee
60 lines
2.2 KiB
Plaintext
60 lines
2.2 KiB
Plaintext
# =============================================================================
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# 로컬 Mac/Docker + exdev(chatbot.exdev.co.kr) 호스트 AI (SSH 터널)
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# 사용법:
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# 1) ./local-dev/start-exdev-ai-tunnel.sh (별도 터미널 유지)
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# 2) cp local-dev/env.exdev-ai.template exAiChatBot-chatbot2.0-agent/.env
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# 3) exAiChatBot / chatbotApi / kakaoChatbotSkill / chatbotApp 기동
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# =============================================================================
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# ── exdev AI (Mac localhost:16000~16002 → SSH 터널) ──
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# Docker Desktop(Mac): host.docker.internal
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# Linux host network / 로컬 python: 127.0.0.1
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LLM_BASE_URL=http://host.docker.internal:16000
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TEI_EMBED_URL=http://host.docker.internal:16001
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TEI_RERANK_URL=http://host.docker.internal:16002
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# exdev Mac Studio 실제 모델명 (curl http://127.0.0.1:16000/v1/models 참고)
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LLM_MODEL_NAME=mlx-community/Qwen2.5-7B-Instruct-4bit
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EMBED_MODEL_NAME=Qwen/Qwen3-Embedding-8B
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TEI_RERANK_MODEL=qwen3-reranker-4b
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# exdev 호스트 AI API 스타일 (게이트웨이와 다름)
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EMBED_API_STYLE=tei
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RERANK_API_STYLE=legacy
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MODEL_VERIFY_SSL=false
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API_TIMEOUT=120
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# 게이트웨이 키 불필요 (exdev 호스트 AI는 Bearer 없음)
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MODEL_API_KEY=
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# ── exdev MongoDB / Qdrant (stock.exdev.co.kr, 로컬 미기동) ──
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EXDEV_DATA_HOST=stock.exdev.co.kr
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MONGO_HOST=stock.exdev.co.kr
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MONGO_PORT=27017
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MONGO_USER=
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MONGO_PASSWORD=
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MONGO_DATABASE=chat_history
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MONGO_COLLECTION=rag_conversations
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MONGO_FEEDBACK_DATABASE=chatbot
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MONGO_FEEDBACK_COLLECTION=answer_feedback
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MONGO_IMPROVEMENT_COLLECTION=improvement_suggestions
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VECTOR_STORE=qdrant
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QDRANT_HOST=stock.exdev.co.kr
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QDRANT_PORT=6333
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# stock.exdev.co.kr 운영 컬렉션 (8488 vectors, 2026-07)
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QDRANT_COLLECTION=qa_vectors_v2
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# chatbotApi (WAS) — stock MongoDB chatbot DB
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MONGODB_URI=mongodb://exDev:ehfhrhdtk!!@stock.exdev.co.kr:27017/chatbot?authSource=chatbot&heartbeatFrequencyMS=30000&minPoolSize=3&maxIdleTimeMS=1800000
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# ── 로컬 WAS / Skill 연동 ──
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CHATBOT_API_BASE_URL=http://127.0.0.1:8086/api
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INTERNAL_TOOL_API_KEY=local-dev-key
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AGENT_MAX_ROUNDS=6
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CHATBOT_TOOL_TIMEOUT=60
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ADMIN_PORT=28013
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UVICORN_WORKERS=2
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