wehub-resource-sync
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49 lines
1.6 KiB
TOML
49 lines
1.6 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 = 5670
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[service.web.database]
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type = "sqlite"
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path = "pilot/meta_data/dbgpt.db"
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[service.model.worker]
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host = "127.0.0.1"
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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 = "pilot/data"
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# Model Configurations
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#
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# LiteLLM is used here as an embedded AI gateway (the Python SDK), NOT as a
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# separate proxy server — DB-GPT imports litellm directly and routes every
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# completion through litellm.acompletion(). Specify the model with a provider
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# prefix (e.g. "anthropic/...", "vertex_ai/...", "bedrock/...", "azure/...",
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# "groq/...") and set the matching provider environment variable
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# (ANTHROPIC_API_KEY, OPENAI_API_KEY, AWS_ACCESS_KEY_ID, AZURE_API_KEY, ...).
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# See https://docs.litellm.ai/docs/providers for the full list.
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[models]
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[[models.llms]]
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name = "anthropic/claude-3-5-sonnet-20241022"
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provider = "proxy/litellm"
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# api_key and api_base are usually unnecessary — LiteLLM resolves them per
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# provider from environment variables. Override only when using a custom or
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# OpenAI-compatible endpoint.
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# api_key = "your_anthropic_api_key"
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# api_base = "https://api.anthropic.com"
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[[models.embeddings]]
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name = "BAAI/bge-large-zh-v1.5"
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provider = "hf"
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# If not provided, the model will be downloaded from the Hugging Face model hub
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# uncomment the following line to specify the model path in the local file system
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# path = "the-model-path-in-the-local-file-system"
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path = "models/bge-large-zh-v1.5"
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