Spaces:
Sleeping
Sleeping
| import logging | |
| from logging.handlers import TimedRotatingFileHandler | |
| import os | |
| from dotenv import load_dotenv | |
| from src.adapters.outbound.embeddings.huggingface_embedding_adapter import HuggingFaceEmbeddingAdapter | |
| from src.adapters.outbound.evaluation.noop_evaluation_adapter import NoOpEvaluationAdapter | |
| from src.adapters.outbound.evaluation.ragas_evaluation_adapter import RagasEvaluationAdapter | |
| from src.adapters.outbound.llm.gemini_llm_adapter import GeminiLlmAdapter | |
| from src.adapters.outbound.llm.rag_prompt_builder_adapter import RagPromptBuilderAdapter | |
| from src.adapters.outbound.observability.langfuse_observability_adapter import LangfuseObservabilityAdapter | |
| from src.adapters.outbound.observability.logging_observability_adapter import LoggingObservabilityAdapter | |
| from src.adapters.outbound.vectorstore.chroma_retrieval_adapter import NativeRetrievalAdapter | |
| from src.application.use_cases.ask_question import AskQuestionService | |
| from src.application.use_cases.submit_feedback import SubmitFeedbackService | |
| from src.config.default_answer import DEFAULT_ANSWER | |
| class Container: | |
| def __init__(self) -> None: | |
| load_dotenv() | |
| # 1. Création du handler quotidien | |
| handler = TimedRotatingFileHandler( | |
| filename=os.getenv("LOG_FILE", "./logs/app.log"), | |
| when="midnight", | |
| interval=1, | |
| backupCount=15, | |
| encoding="utf-8" | |
| ) | |
| # 2. Configuration globale via basicConfig | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format="%(asctime)s - %(name)s - [%(levelname)s] - %(message)s", | |
| handlers=[handler] | |
| ) | |
| embeddings = HuggingFaceEmbeddingAdapter() | |
| retrieval = NativeRetrievalAdapter(embedding_port=embeddings) | |
| prompt_builder = RagPromptBuilderAdapter() | |
| llm = GeminiLlmAdapter(prompt_builder=prompt_builder) | |
| observability = self._build_observability_adapter() | |
| evaluation = self._build_evaluation_adapter(observability, embeddings) | |
| self.ask_question = AskQuestionService( | |
| retrieval_port=retrieval, | |
| llm_port=llm, | |
| observability_port=observability, | |
| default_answer=os.getenv("DEFAULT_ANSWER", DEFAULT_ANSWER), | |
| evaluation_port=evaluation, | |
| ) | |
| self.submit_feedback = SubmitFeedbackService(observability_port=observability) | |
| def _build_observability_adapter(self): | |
| if os.getenv("LANGFUSE_PUBLIC_KEY") and os.getenv("LANGFUSE_SECRET_KEY"): | |
| return LangfuseObservabilityAdapter() | |
| return LoggingObservabilityAdapter() | |
| def _build_evaluation_adapter(self, observability, embeddings): | |
| if os.getenv("ENABLE_RAGAS_EVAL", "true").lower() != "true": | |
| return NoOpEvaluationAdapter() | |
| try: | |
| ragas_llm_name = os.getenv("RAGAS_LLM_MODEL_NAME", "gpt-4o-mini") | |
| return RagasEvaluationAdapter( | |
| observability_port=observability, | |
| llm_name=ragas_llm_name, | |
| embedding_model_name=embeddings.get_model_name() | |
| ) | |
| except Exception as exc: | |
| logging.getLogger("chatbot.backend.container").warning( | |
| "ragas.adapter.init_failed: %s", | |
| exc, | |
| exc_info=True, | |
| ) | |
| return NoOpEvaluationAdapter() | |