1 line
25 KiB
JSON
1 line
25 KiB
JSON
{"repo": "alexmold/pi-agent", "files": {"docker-compose.yml": {"f09afc9358add85adeff482895d0bbcb32b48817": {"t": 1785745237.5995705, "n": 1679, "h": "b175b9ad6a5ad266", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n build:\n context: .\n dockerfile: docker/llama.Dockerfile\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n environment:\n - HF_TOKEN=${HF_TOKEN}\n healthcheck:\n test: [\"CMD\", \"curl\", \"-f\", \"http://localhost:8081/health\"]\n interval: 10s\n timeout: 5s\n retries: 30\n start_period: 30s\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "faa8f12733f7641f62ba191a01a1f73e998f434d": {"t": 1785749092.0980716, "n": 1679, "h": "b175b9ad6a5ad266", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n build:\n context: .\n dockerfile: docker/llama.Dockerfile\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n environment:\n - HF_TOKEN=${HF_TOKEN}\n healthcheck:\n test: [\"CMD\", \"curl\", \"-f\", \"http://localhost:8081/health\"]\n interval: 10s\n timeout: 5s\n retries: 30\n start_period: 30s\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "e9eb69b857c103dfbbdfeff64a06add5a3762c5d": {"t": 1785749093.1977859, "n": 1516, "h": "089428b97bdb4819", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n build:\n context: .\n dockerfile: docker/llama.Dockerfile\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n environment:\n - HF_TOKEN=${HF_TOKEN}\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "e43a6420d0c180a55cafe632ca027b114b1454b3": {"t": 1785749094.2757697, "n": 1470, "h": "e4c3ca9a0724ed59", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n build:\n context: .\n dockerfile: docker/llama.Dockerfile\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "c24e68f99f6f1ec401d1541e19e8a47e38fc2cc6": {"t": 1785749095.3688583, "n": 1978, "h": "b8acd772719c2037", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n image: ghcr.io/ggml-org/llama.cpp:server\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n entrypoint: [\"sh\", \"-c\"]\n command:\n - |\n MODEL=/models/qwen3.5-1b-q4_k_m.gguf\n if [ ! -f \"$$MODEL\" ]; then\n echo \"⬇️ Downloading Qwen 3.5-1B Q4_K_M GGUF (~800 MB)...\"\n curl -L -o \"$$MODEL\" \\\n https://huggingface.co/Qwen/Qwen3.5-1B-GGUF/resolve/main/qwen3.5-1b-q4_k_m.gguf\n echo \"✅ Download complete\"\n else\n echo \"✅ Model already downloaded\"\n fi\n exec /llama-server --host 0.0.0.0 --port 8081 --model \"$$MODEL\" --ctx-size 2048 --threads 4\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "4607d2b8fda57505393375ea15c7c28102de52bc": {"t": 1785749096.4207158, "n": 1993, "h": "d908649eca64f931", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n image: ghcr.io/ggml-org/llama.cpp:server\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n entrypoint: [\"sh\", \"-c\"]\n command:\n - |\n MODEL=/models/qwen3.5-1b-q4_k_m.gguf\n if [ ! -f \"$$MODEL\" ]; then\n echo \"⬇️ Downloading Qwen 3.5-1B Q4_K_M GGUF (~800 MB)...\"\n wget -q --show-progress -O \"$$MODEL\" \\\n https://huggingface.co/Qwen/Qwen3.5-1B-GGUF/resolve/main/qwen3.5-1b-q4_k_m.gguf\n echo \"✅ Download complete\"\n else\n echo \"✅ Model already downloaded\"\n fi\n exec llama-server --host 0.0.0.0 --port 8081 --model \"$$MODEL\" --ctx-size 2048 --threads 4\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "a4854d1d460fc14dd9d0e6f62abb3c8d8d882f49": {"t": 1785749097.6509626, "n": 1990, "h": "89c20c7428f8cfc1", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n image: ghcr.io/ggml-org/llama.cpp:server\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n entrypoint: [\"sh\", \"-c\"]\n command:\n - |\n MODEL=/models/qwen3.5-1b-q4_k_m.gguf\n if [ ! -f \"$MODEL\" ]; then\n echo \"⬇️ Downloading Qwen 3.5-1B Q4_K_M GGUF (~800 MB)...\"\n wget -q --show-progress -O \"$MODEL\" \\\n https://huggingface.co/Qwen/Qwen3.5-1B-GGUF/resolve/main/qwen3.5-1b-q4_k_m.gguf\n echo \"✅ Download complete\"\n else\n echo \"✅ Model already downloaded\"\n fi\n exec llama-server --host 0.0.0.0 --port 8081 --model \"$MODEL\" --ctx-size 2048 --threads 4\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "13783177788dca153eceb39556a9223799473d59": {"t": 1785749098.726872, "n": 1652, "h": "98c1fb4450976c09", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=${WHISPER_HOST:-host.docker.internal:8080}\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n image: ghcr.io/ggml-org/llama.cpp:server\n container_name: llama-service\n restart: always\n volumes:\n - ./models:/models\n command:\n - \"--host\"\n - \"0.0.0.0\"\n - \"--port\"\n - \"8081\"\n - \"--model\"\n - \"/models/qwen3.5-1b-q4_k_m.gguf\"\n - \"--ctx-size\"\n - \"2048\"\n - \"--threads\"\n - \"4\"\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "d0689d4f183af13fb0e0251207167ed68f7764b4": {"t": 1785749099.823149, "n": 1666, "h": "50a6c5e8e7bf1010", "c": "services:\n # ── Bot + tools ──────────────────────────────────────────────────\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n depends_on:\n - llama-service\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - LLAMA_HOST=llama-service:8081\n - WHISPER_HOST=host.docker.internal:8080\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n\n # ── Tiny local model for routing (Qwen 3.5-1B via llama.cpp) ─────\n llama-service:\n image: ghcr.io/ggml-org/llama.cpp:server\n container_name: llama-service\n restart: always\n ports:\n - \"8081:8081\"\n volumes:\n - ./models:/models\n command:\n - \"--host\"\n - \"0.0.0.0\"\n - \"--port\"\n - \"8081\"\n - \"--model\"\n - \"/models/qwen3.5-1b-q4_k_m.gguf\"\n - \"--ctx-size\"\n - \"2048\"\n - \"--threads\"\n - \"4\"\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "f9fccff40e84ec47195cf71346c848740e2d048b": {"t": 1785749100.9041255, "n": 1164, "h": "027d92faba53178f", "c": "services:\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - OLLAMA_HOST=host.docker.internal:11434\n - WHISPER_HOST=host.docker.internal:8080\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n extra_hosts:\n - \"host.docker.internal:host-gateway\"\n\n # ── Lightweight cron worker for reminders ──────────────────────────\n cron-worker:\n build:\n context: .\n dockerfile: docker/cron.Dockerfile\n container_name: ai-cron-worker\n restart: always\n volumes:\n - ./workspace:/app/workspace # shared: reads reminders.json\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - REMINDER_DB=/app/workspace/reminders.json\n"}, "c6a496df1457174c397126d3383dfe7a02b382c0": {"t": 1785749101.994065, "n": 755, "h": "0f76fb2e071c07aa", "c": "services:\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n - ./credentials:/app/credentials\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - OLLAMA_HOST=host.docker.internal:11434\n - WHISPER_HOST=host.docker.internal:8080\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n extra_hosts:\n - \"host.docker.internal:host-gateway\""}, "af9ee9c75f9e43ed952b69185fa8049e047d05d7": {"t": 1785749103.058946, "n": 716, "h": "0fdb570d160d35d5", "c": "services:\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - SERPER_API_KEY=${SERPER_API_KEY}\n - OLLAMA_HOST=host.docker.internal:11434\n - WHISPER_HOST=host.docker.internal:8080\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n extra_hosts:\n - \"host.docker.internal:host-gateway\""}, "869c0671bb6e53ec1cb516e2c16afcc6abdb57a9": {"t": 1785749104.1417484, "n": 675, "h": "edbfc2d6b2912be0", "c": "services:\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - OLLAMA_HOST=host.docker.internal:11434\n - WHISPER_HOST=host.docker.internal:8080\n - WHISPER_LANG=${WHISPER_LANG:-ru}\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n extra_hosts:\n - \"host.docker.internal:host-gateway\""}, "7baf2e20f1b5ddc9d4fb279b1693c3fd7f07a5b5": {"t": 1785749105.2387733, "n": 650, "h": "7454756da7bebf5a", "c": "version: \"3.8\"\n\nservices:\n agent-brain:\n build:\n context: .\n dockerfile: docker/Dockerfile\n container_name: ai-assistant-pro\n restart: always\n volumes:\n - ./workspace:/app/workspace\n - ./memory_db:/app/memory_db\n - ./.env:/app/.env:ro\n - ./.pi/agent:/root/.pi/agent\n environment:\n - TELEGRAM_TOKEN=${TELEGRAM_TOKEN}\n - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}\n - TAVILY_API_KEY=${TAVILY_API_KEY}\n - OLLAMA_HOST=host.docker.internal:11434\n - WHISPER_HOST=host.docker.internal:8080\n - ALLOWED_USER_ID=${ALLOWED_USER_ID}\n extra_hosts:\n - \"host.docker.internal:host-gateway\""}, "2a678b36eb895654c0fa66e6569244f6289b9113": {"t": 1785749106.4764724, "n": 674, "h": "5d88522f19cc2806", "c": "version: \"3.8\"\n\nservices:\n telegram-bot:\n build: .\n container_name: agent-exp-accounting-bot\n restart: unless-stopped\n volumes:\n - ./uploads:/usr/src/app/uploads\n - ./output:/usr/src/app/output\n - ./wiki:/usr/src/app/wiki\n - ./.env:/usr/src/app/.env:ro\n environment:\n # OLLAMA_HOST is used by standard tools and pi-coding-agent to find Ollama\n # host.docker.internal works out of the box on Docker Desktop (Mac/Win)\n # and on Linux thanks to the extra_hosts mapping below.\n - OLLAMA_HOST=http://host.docker.internal:11434\n # PI_MODEL=ollama/gemma4:31b\n extra_hosts:\n - \"host.docker.internal:host-gateway\"\n"}, "7b90a5ec0d56fd0f36b9f9a772295c21cc481748": {"t": 1785749107.5375772, "n": 642, "h": "6a8af391c536320b", "c": "version: '3.8'\n\nservices:\n telegram-bot:\n build: .\n container_name: agent-exp-accounting-bot\n restart: unless-stopped\n volumes:\n - ./uploads:/usr/src/app/uploads\n - ./output:/usr/src/app/output\n - ./.env:/usr/src/app/.env:ro\n environment:\n # OLLAMA_HOST is used by standard tools and pi-coding-agent to find Ollama\n # host.docker.internal works out of the box on Docker Desktop (Mac/Win) \n # and on Linux thanks to the extra_hosts mapping below.\n - OLLAMA_HOST=http://host.docker.internal:11434\n # PI_MODEL=ollama/gemma4:31b\n extra_hosts:\n - \"host.docker.internal:host-gateway\"\n"}}}} |