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| 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 | |
| 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, | |
| ) | |