from dataclasses import dataclass

from techdocs_graphrag.answering import GraphRAGAssistant
from techdocs_graphrag.config import Settings, get_settings
from techdocs_graphrag.extraction import GraphExtractor
from techdocs_graphrag.graph_store import Neo4jGraphStore
from techdocs_graphrag.indexing import IndexingPipeline
from techdocs_graphrag.ingestion import ParagraphSplitter
from techdocs_graphrag.llm import OpenAIGateway
from techdocs_graphrag.retrieval import HybridRetriever


@dataclass(frozen=True, slots=True)
class Services:
    settings: Settings
    store: Neo4jGraphStore
    assistant: GraphRAGAssistant
    indexer: IndexingPipeline

    def close(self) -> None:
        self.store.close()


def build_services(settings: Settings | None = None) -> Services:
    settings = settings or get_settings()
    llm = OpenAIGateway(settings)
    store = Neo4jGraphStore(settings)
    retriever = HybridRetriever(
        llm=llm,
        store=store,
        top_k=settings.vector_top_k,
        max_hops=settings.graph_max_hops,
        max_facts=settings.graph_max_facts,
    )
    indexer = IndexingPipeline(
        splitter=ParagraphSplitter(settings.chunk_size, settings.chunk_overlap),
        llm=llm,
        extractor=GraphExtractor(llm),
        store=store,
        workers=settings.extraction_workers,
    )
    return Services(
        settings=settings,
        store=store,
        assistant=GraphRAGAssistant(retriever=retriever, llm=llm),
        indexer=indexer,
    )