"""
Embedding Service Configuration

Environment variables for embedding generation, re-ranking, and PDF extraction.
"""

import os

# =============================================================================
# Embedding Configuration
# =============================================================================

# Backend: "local" (sentence-transformers) or "openai"
EMBEDDING_BACKEND = os.getenv("EMBEDDING_BACKEND", "local")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
OPENAI_EMBEDDING_MODEL = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small")

# Local embedding model
# Recommended: BAAI/bge-m3 (MIT, 1024 dim, multilingual)
LOCAL_EMBEDDING_MODEL = os.getenv("LOCAL_EMBEDDING_MODEL", "BAAI/bge-m3")

# Chunking configuration
CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", "1000"))
CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", "200"))
CHUNKING_STRATEGY = os.getenv("CHUNKING_STRATEGY", "semantic")

# =============================================================================
# Re-Ranker Configuration
# =============================================================================

# Backend: "local" (sentence-transformers CrossEncoder) or "cohere"
RERANKER_BACKEND = os.getenv("RERANKER_BACKEND", "local")
COHERE_API_KEY = os.getenv("COHERE_API_KEY", "")

# Local re-ranker model
# Recommended: BAAI/bge-reranker-v2-m3 (Apache 2.0, multilingual)
LOCAL_RERANKER_MODEL = os.getenv("LOCAL_RERANKER_MODEL", "BAAI/bge-reranker-v2-m3")

# =============================================================================
# PDF Extraction Configuration
# =============================================================================

# Backend: "auto", "unstructured", "pypdf"
PDF_EXTRACTION_BACKEND = os.getenv("PDF_EXTRACTION_BACKEND", "auto")
UNSTRUCTURED_API_KEY = os.getenv("UNSTRUCTURED_API_KEY", "")
UNSTRUCTURED_API_URL = os.getenv("UNSTRUCTURED_API_URL", "")

# =============================================================================
# Service Configuration
# =============================================================================

SERVICE_PORT = int(os.getenv("EMBEDDING_SERVICE_PORT", "8087"))
LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")

# Model dimensions lookup
MODEL_DIMENSIONS = {
    # Multilingual / German-optimized
    "BAAI/bge-m3": 1024,
    "deepset/mxbai-embed-de-large-v1": 1024,
    "jinaai/jina-embeddings-v2-base-de": 768,
    "intfloat/multilingual-e5-large": 1024,
    # English-focused (smaller, faster)
    "all-MiniLM-L6-v2": 384,
    "all-mpnet-base-v2": 768,
    # OpenAI
    "text-embedding-3-small": 1536,
    "text-embedding-3-large": 3072,
}


def get_model_dimensions(model_name: str) -> int:
    """Get embedding dimensions for a model."""
    if model_name in MODEL_DIMENSIONS:
        return MODEL_DIMENSIONS[model_name]
    for key, dim in MODEL_DIMENSIONS.items():
        if key in model_name or model_name in key:
            return dim
    return 384  # Default fallback


def get_current_dimensions() -> int:
    """Get dimensions for the currently configured model."""
    if EMBEDDING_BACKEND == "local":
        return get_model_dimensions(LOCAL_EMBEDDING_MODEL)
    else:
        return get_model_dimensions(OPENAI_EMBEDDING_MODEL)