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v2: cited answers with hybrid retrieval + cross-encoder rerank, Qwen3-1.7B on CPU, retrieval trace
a072368 verified | """Sentence-transformers wrappers for the embedder and the cross-encoder reranker (loaded lazily).""" | |
| from functools import cached_property | |
| import numpy as np | |
| class STEmbedder: | |
| def __init__(self, model_name: str, query_instruction: str = "", batch_size: int = 32) -> None: | |
| self.model_name, self.query_instruction, self.batch_size = model_name, query_instruction, batch_size | |
| def model(self): | |
| from sentence_transformers import SentenceTransformer | |
| return SentenceTransformer(self.model_name, device="cpu") | |
| def embed_passages(self, texts: list[str]) -> np.ndarray: | |
| return self.model.encode(texts, batch_size=self.batch_size, normalize_embeddings=True, convert_to_numpy=True) | |
| def embed_query(self, text: str) -> np.ndarray: | |
| return self.model.encode(self.query_instruction + text, normalize_embeddings=True, convert_to_numpy=True) | |
| def count_tokens(self, text: str) -> int: | |
| return len(self.model.tokenizer.tokenize(text)) | |
| class CrossEncoderReranker: | |
| def __init__(self, model_name: str) -> None: | |
| self.model_name = model_name | |
| def model(self): | |
| from sentence_transformers import CrossEncoder | |
| return CrossEncoder(self.model_name, device="cpu") | |
| def score(self, query: str, passages: list[str]) -> list[float]: | |
| return self.model.predict([(query, p) for p in passages], batch_size=16).tolist() | |