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@ -40,10 +40,14 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
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# Copy the configuration directory first.
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# If only code changes, this layer remains cached.
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COPY config ./config
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COPY llm ./llm
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COPY app ./app
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# Copy your application source code.
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COPY main.py .
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COPY config.py .
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COPY shared_store.py .
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# Expose the port your application listens on.
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EXPOSE 8000
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65
config.py
65
config.py
@ -69,29 +69,16 @@ class ProcessorConfig(BaseSettings):
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class Settings:
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def __init__(self):
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try:
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# Load configuration from YAML file
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# Load settings from YAML file as a fallback
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yaml_config = self._load_yaml_config()
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# Initialize configurations
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llm_config_data = yaml_config.get('llm', {})
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# Extract and flatten nested LLM configurations
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mode = llm_config_data.get('mode', 'ollama')
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openai_settings = llm_config_data.get('openai') or {}
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ollama_settings = llm_config_data.get('ollama') or {}
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gemini_settings = llm_config_data.get('gemini') or {} # New: Get Gemini settings
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# Load settings from environment and .env file first
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self.llm = LLMConfig()
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self.processor = ProcessorConfig()
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self.langfuse = LangfuseConfig()
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# Combine all LLM settings, prioritizing top-level 'mode'
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combined_llm_settings = {
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'mode': mode,
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**{f'openai_{k}': v for k, v in openai_settings.items()},
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**{f'ollama_{k}': v for k, v in ollama_settings.items()},
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**{f'gemini_{k}': v for k, v in gemini_settings.items()} # New: Add Gemini settings
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}
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self.llm = LLMConfig(**combined_llm_settings)
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self.processor = ProcessorConfig(**yaml_config.get('processor', {}))
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self.langfuse = LangfuseConfig(**yaml_config.get('langfuse', {}))
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# Apply YAML configuration for any values not set by the environment
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self._apply_yaml_fallback(yaml_config)
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# Initialize Langfuse client if enabled
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self.langfuse_client: Optional[Langfuse] = None
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@ -111,6 +98,44 @@ class Settings:
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print(f"Configuration initialization failed: {e}")
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sys.exit(1)
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def _apply_yaml_fallback(self, yaml_config: dict):
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"""Applies YAML config values as a fallback to settings not set by environment."""
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# --- LLM Configuration ---
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llm_yaml_config = yaml_config.get('llm', {})
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if llm_yaml_config:
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# Flatten nested YAML structure to match LLMConfig fields
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flat_llm_yaml = {
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'mode': llm_yaml_config.get('mode'),
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**{f'openai_{k}': v for k, v in (llm_yaml_config.get('openai') or {}).items()},
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**{f'ollama_{k}': v for k, v in (llm_yaml_config.get('ollama') or {}).items()},
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**{f'gemini_{k}': v for k, v in (llm_yaml_config.get('gemini') or {}).items()}
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}
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for field_name in self.llm.model_fields:
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if field_name not in self.llm.model_fields_set:
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yaml_value = flat_llm_yaml.get(field_name)
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if yaml_value is not None:
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setattr(self.llm, field_name, yaml_value)
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# --- Processor Configuration ---
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processor_yaml_config = yaml_config.get('processor', {})
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if processor_yaml_config:
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for field_name in self.processor.model_fields:
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if field_name not in self.processor.model_fields_set:
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yaml_value = processor_yaml_config.get(field_name)
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if yaml_value is not None:
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setattr(self.processor, field_name, yaml_value)
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# --- Langfuse Configuration ---
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langfuse_yaml_config = yaml_config.get('langfuse', {})
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if langfuse_yaml_config:
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for field_name in self.langfuse.model_fields:
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if field_name not in self.langfuse.model_fields_set:
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yaml_value = langfuse_yaml_config.get(field_name)
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if yaml_value is not None:
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setattr(self.langfuse, field_name, yaml_value)
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def _load_yaml_config(self):
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config_path = Path('config/application.yml')
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if not config_path.exists():
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