[system]
# Load language from environment variable(It is set by the hook)
language = "${env:DBGPT_LANG:-en}"
api_keys = []
encrypt_key = "your_secret_key"

# Server Configurations
[service.web]
host = "0.0.0.0"
port = 5670

[service.web.database]
type = "sqlite"
path = "pilot/meta_data/dbgpt.db"
[service.model.worker]
host = "127.0.0.1"

[rag.storage]
[rag.storage.vector]
type = "chroma"
persist_path = "pilot/data"

# Model Configurations
#
# LiteLLM is used here as an embedded AI gateway (the Python SDK), NOT as a
# separate proxy server — DB-GPT imports litellm directly and routes every
# completion through litellm.acompletion(). Specify the model with a provider
# prefix (e.g. "anthropic/...", "vertex_ai/...", "bedrock/...", "azure/...",
# "groq/...") and set the matching provider environment variable
# (ANTHROPIC_API_KEY, OPENAI_API_KEY, AWS_ACCESS_KEY_ID, AZURE_API_KEY, ...).
# See https://docs.litellm.ai/docs/providers for the full list.
[models]
[[models.llms]]
name = "anthropic/claude-3-5-sonnet-20241022"
provider = "proxy/litellm"
# api_key and api_base are usually unnecessary — LiteLLM resolves them per
# provider from environment variables. Override only when using a custom or
# OpenAI-compatible endpoint.
# api_key = "your_anthropic_api_key"
# api_base = "https://api.anthropic.com"

[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/bge-large-zh-v1.5"