from pathlib import Path
from typing import Literal

from pydantic import BaseModel, Field

from haiku.rag.utils import get_default_data_dir


class ModelConfig(BaseModel):
    """Configuration for a language model.

    Attributes:
        provider: Model provider (ollama, openai, anthropic, etc.)
        name: Model name/identifier
        base_url: Optional base URL for OpenAI-compatible servers (vLLM, LM Studio, etc.)
        enable_thinking: Control reasoning behavior (true/false/None for default)
        temperature: Sampling temperature (0.0 to 1.0+)
        max_tokens: Maximum tokens to generate
    """

    provider: str = "ollama"
    name: str = "gpt-oss"
    base_url: str | None = None

    enable_thinking: bool | None = None
    temperature: float | None = None
    max_tokens: int | None = None


class EmbeddingModelConfig(BaseModel):
    """Configuration for an embedding model.

    Attributes:
        provider: Model provider (ollama, openai, voyageai, cohere, sentence-transformers)
        name: Model name/identifier
        vector_dim: Vector dimensions produced by the model
        base_url: Optional base URL for OpenAI-compatible servers (vLLM, LM Studio, etc.)
    """

    provider: str = "ollama"
    name: str = "qwen3-embedding:4b"
    vector_dim: int = 2560
    base_url: str | None = None


class StorageConfig(BaseModel):
    data_dir: Path = Field(default_factory=get_default_data_dir)
    auto_vacuum: bool = True
    vacuum_retention_seconds: int = 86400


class MonitorConfig(BaseModel):
    directories: list[Path] = []
    ignore_patterns: list[str] = []
    include_patterns: list[str] = []
    delete_orphans: bool = False


class LanceDBConfig(BaseModel):
    uri: str = ""
    api_key: str = ""
    region: str = ""


class EmbeddingsConfig(BaseModel):
    model: EmbeddingModelConfig = Field(default_factory=EmbeddingModelConfig)


class RerankingConfig(BaseModel):
    model: ModelConfig | None = None


class QAConfig(BaseModel):
    model: ModelConfig = Field(
        default_factory=lambda: ModelConfig(
            provider="ollama",
            name="gpt-oss",
            enable_thinking=False,
        )
    )
    max_sub_questions: int = 3
    max_iterations: int = 2
    max_concurrency: int = 1


class ResearchConfig(BaseModel):
    model: ModelConfig = Field(
        default_factory=lambda: ModelConfig(
            provider="ollama",
            name="gpt-oss",
            enable_thinking=False,
        )
    )
    max_iterations: int = 3
    confidence_threshold: float = 0.8
    max_concurrency: int = 1


class PictureDescriptionConfig(BaseModel):
    """Configuration for VLM-based picture description."""

    enabled: bool = False
    model: ModelConfig = Field(
        default_factory=lambda: ModelConfig(
            provider="ollama",
            name="ministral-3",
        )
    )
    timeout: int = 90
    max_tokens: int = 200


class ConversionOptions(BaseModel):
    """Options for document conversion."""

    # OCR options
    do_ocr: bool = True
    force_ocr: bool = False
    ocr_engine: Literal[
        "auto", "easyocr", "ocrmac", "rapidocr", "tesserocr", "tesseract"
    ] = "auto"
    ocr_lang: list[str] = []

    # Table options
    do_table_structure: bool = True
    table_mode: Literal["fast", "accurate"] = "accurate"
    table_cell_matching: bool = True

    # Image options
    images_scale: float = 2.0
    generate_page_images: bool = True
    generate_picture_images: bool = False

    # VLM picture description
    picture_description: PictureDescriptionConfig = Field(
        default_factory=PictureDescriptionConfig
    )


class ProcessingConfig(BaseModel):
    chunk_size: int = 256
    converter: str = "docling-local"
    chunker: str = "docling-local"
    chunker_type: str = "hybrid"
    chunking_tokenizer: str = "Qwen/Qwen3-Embedding-0.6B"
    chunking_merge_peers: bool = True
    chunking_use_markdown_tables: bool = False
    conversion_options: ConversionOptions = Field(default_factory=ConversionOptions)


class SearchConfig(BaseModel):
    limit: int = 5
    context_radius: int = 0
    max_context_items: int = 10
    max_context_chars: int = 10000
    vector_index_metric: Literal["cosine", "l2", "dot"] = "cosine"
    vector_refine_factor: int = 30


class OllamaConfig(BaseModel):
    base_url: str = Field(
        default_factory=lambda: __import__("os").environ.get(
            "OLLAMA_BASE_URL", "http://localhost:11434"
        )
    )


class DoclingServeConfig(BaseModel):
    base_url: str = "http://localhost:5001"
    api_key: str = ""


class ProvidersConfig(BaseModel):
    ollama: OllamaConfig = Field(default_factory=OllamaConfig)
    docling_serve: DoclingServeConfig = Field(default_factory=DoclingServeConfig)


class PromptsConfig(BaseModel):
    domain_preamble: str = ""
    qa: str | None = None
    synthesis: str | None = None
    picture_description: str = (
        "Describe this image for a blind user. "
        "State the image type (screenshot, chart, photo, etc.), "
        "what it depicts, any visible text, and key visual details. "
        "Be concise and accurate."
    )


class AppConfig(BaseModel):
    environment: str = "production"
    storage: StorageConfig = Field(default_factory=StorageConfig)
    monitor: MonitorConfig = Field(default_factory=MonitorConfig)
    lancedb: LanceDBConfig = Field(default_factory=LanceDBConfig)
    embeddings: EmbeddingsConfig = Field(default_factory=EmbeddingsConfig)
    reranking: RerankingConfig = Field(default_factory=RerankingConfig)
    qa: QAConfig = Field(default_factory=QAConfig)
    research: ResearchConfig = Field(default_factory=ResearchConfig)
    processing: ProcessingConfig = Field(default_factory=ProcessingConfig)
    search: SearchConfig = Field(default_factory=SearchConfig)
    providers: ProvidersConfig = Field(default_factory=ProvidersConfig)
    prompts: PromptsConfig = Field(default_factory=PromptsConfig)