javadandCursor
b93ecf1691
Keep source, config, and deploy files. Leave runtime data, uploads, and logs out of git. Co-authored-by: Cursor <cursoragent@cursor.com>
60 lines
2.0 KiB
TOML
60 lines
2.0 KiB
TOML
[system]
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# Load language from environment variable(It is set by the hook)
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language = "${env:DBGPT_LANG:-en}"
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api_keys = []
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encrypt_key = "your_secret_key"
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# Server Configurations
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[service.web]
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host = "0.0.0.0"
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port = "${env:PORT:-5670}"
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# CORS allowed origins: '*' allows all; set comma-separated origins to restrict.
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cors_allowed_origins = "${env:DBGPT_CORS_ALLOWED_ORIGINS:-*}"
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# Product history is in Mongo. Engine SQLite keeps GPTs message tables.
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disable_alembic_upgrade = true
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[service.web.agent_context]
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# Agent context-window budget. Set max_context_tokens to 0 to auto-detect from
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# the selected model's metadata. The effective budget shown in the UI is
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# max_context_tokens - reserved_tokens.
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max_context_tokens = 256000
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reserved_tokens = 65536
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warning_threshold = 0.70
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error_threshold = 0.90
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critical_threshold = 0.95
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min_keep_recent_rounds = 3
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max_observation_age_rounds = 5
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truncated_observation_max_chars = 200
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min_keep_tokens = 10000
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max_compact_failures = 3
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# Per dispatch_parallel_tasks call. DBGPT_MAX_PARALLEL_SUBAGENTS overrides it.
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max_parallel_subagents = 3
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[service.web.database]
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type = "sqlite"
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path = "data/meta/kamidso-engine.db"
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[rag.storage]
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[rag.storage.vector]
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type = "chroma"
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persist_path = "${env:KAMIDSO_VECTORS_PATH:-data/vectors}"
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# Model Configurations
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[models]
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[[models.llms]]
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name = "${env:LLM_MODEL_NAME:-gpt-4o}"
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provider = "${env:LLM_MODEL_PROVIDER:-proxy/openai}"
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api_base = "${env:OPENAI_API_BASE:-https://api.openai.com/v1}"
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api_key = "${env:OPENAI_API_KEY}"
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# Concurrent in-flight LLM streams. 40 covers ~10 chats with subagent fan-out.
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concurrency = 40
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# Hybrid thinking models (e.g. Qwen3 on OpenRouter): set LLM_REASONING_ENABLED=false
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# in .env, or uncomment the next line to force-disable reasoning tokens.
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# reasoning_enabled = false
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[[models.embeddings]]
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name = "${env:EMBEDDING_MODEL_NAME:-text-embedding-3-small}"
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provider = "${env:EMBEDDING_MODEL_PROVIDER:-proxy/openai}"
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api_url = "${env:EMBEDDING_MODEL_API_URL:-https://api.openai.com/v1/embeddings}"
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api_key = "${env:OPENAI_API_KEY}"
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