Rebuild as single-process Gradio app: hybrid BM25 + dense retrieval, reranking, cited streaming answers
9156337 verified | """Index construction for the app. | |
| The index lives for the life of one user session and is held in Gradio state. | |
| There is deliberately no persist_directory: the old on-disk Chroma was the | |
| source of the "embed succeeded, retrieval empty" bug when it ran across | |
| multiple serverless containers. | |
| """ | |
| from typing import List | |
| from langchain_core.documents import Document | |
| from src.config import settings | |
| from src.retrieval import HybridIndex | |
| def build_vectorstore(chunks: List[Document], with_reranker: bool = True) -> HybridIndex: | |
| """Embed chunks, build the BM25 side, attach the reranker; return the index.""" | |
| reranker = None | |
| if with_reranker and settings.retrieval_mode == "hybrid_rerank": | |
| from src.models import get_reranker | |
| reranker = get_reranker() | |
| return HybridIndex.build(chunks, reranker=reranker, candidates=settings.rerank_candidates) | |