| def compute_confidence( |
| retrieval_scores: list[float], |
| verified: bool, |
| ) -> float: |
| """ |
| Computes a single confidence score [0.0 – 1.0] from two signals: |
| |
| - retrieval_quality (60%): average cosine similarity of retrieved chunks. |
| High similarity means the question closely matched NCERT content. |
| |
| - verifier_bonus (40%): whether the LLM verifier confirmed the answer |
| is supported by the retrieved context. Binary boost. |
| |
| Note: The BERT classifier confidence will be incorporated once the |
| classification layer (Phase 3) is built. The formula will then become: |
| 0.4 * bert_conf + 0.35 * avg_retrieval + 0.25 * verifier |
| |
| Args: |
| retrieval_scores: list of cosine similarity floats from pgvector (0–1) |
| verified: bool from verifier.py (did the answer pass the faithfulness check) |
| |
| Returns: |
| confidence: float in [0.0, 1.0] |
| """ |
| if not retrieval_scores: |
| avg_retrieval = 0.0 |
| else: |
| |
| avg_retrieval = max(retrieval_scores) |
|
|
| verifier_score = 1.0 if verified else 0.0 |
|
|
| confidence = (0.6 * avg_retrieval) + (0.4 * verifier_score) |
|
|
| |
| return round(min(max(confidence, 0.0), 1.0), 4) |
