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| import json | |
| import os | |
| from sentence_transformers import SentenceTransformer, util | |
| import torch | |
| FAQ_PATHS = ["data/faq_data.json", "data/clinical_faq.json"] | |
| _FAQ_CACHE = None | |
| _FAQ_EMBEDDINGS = None | |
| _MODEL = None | |
| def _get_model(): | |
| """Load and cache the embedding model (shared with main app if possible).""" | |
| global _MODEL | |
| if _MODEL is None: | |
| print("📦 [faq] Loading embedding model: all-MiniLM-L6-v2 ...") | |
| _MODEL = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") | |
| return _MODEL | |
| def load_faqs(): | |
| """Load FAQ data from JSON files and cache them.""" | |
| global _FAQ_CACHE | |
| if _FAQ_CACHE is not None: | |
| return _FAQ_CACHE | |
| all_faqs = [] | |
| for path in FAQ_PATHS: | |
| if os.path.exists(path): | |
| try: | |
| with open(path, "r", encoding="utf-8") as f: | |
| data = json.load(f) | |
| if isinstance(data, list): | |
| all_faqs.extend(data) | |
| elif isinstance(data, dict): | |
| for k, v in data.items(): | |
| all_faqs.append({"question": k, "answer": v}) | |
| except Exception as e: | |
| print(f"⚠️ Failed to load FAQ file {path}: {e}") | |
| _FAQ_CACHE = all_faqs | |
| print(f"✅ [faq] Loaded {len(_FAQ_CACHE)} FAQ entries.") | |
| return _FAQ_CACHE | |
| def _build_embeddings(): | |
| """Precompute embeddings for all FAQ questions.""" | |
| global _FAQ_EMBEDDINGS | |
| faqs = load_faqs() | |
| if not faqs: | |
| _FAQ_EMBEDDINGS = torch.empty(0) | |
| return _FAQ_EMBEDDINGS | |
| model = _get_model() | |
| questions = [f["question"] for f in faqs if f.get("question")] | |
| _FAQ_EMBEDDINGS = model.encode(questions, convert_to_tensor=True, show_progress_bar=False) | |
| print(f"✅ [faq] Encoded {len(_FAQ_EMBEDDINGS)} FAQ embeddings.") | |
| return _FAQ_EMBEDDINGS | |
| def get_faq_answer(query: str, top_k: int = 1) -> str: | |
| """ | |
| Return the most semantically similar FAQ answer to the query. | |
| Uses MiniLM embeddings and cosine similarity. | |
| """ | |
| faqs = load_faqs() | |
| if not faqs: | |
| return "" | |
| if _FAQ_EMBEDDINGS is None: | |
| _build_embeddings() | |
| model = _get_model() | |
| query_emb = model.encode(query, convert_to_tensor=True) | |
| sims = util.cos_sim(query_emb, _FAQ_EMBEDDINGS)[0] | |
| top_idx = int(torch.argmax(sims)) | |
| best_score = float(sims[top_idx]) | |
| best_item = faqs[top_idx] | |
| if best_score < 0.45: # threshold to avoid weak matches | |
| return "" | |
| answer = best_item.get("answer", "") | |
| print(f"💡 [faq] Best match: \"{best_item.get('question')}\" (score={best_score:.2f})") | |
| return answer | |
| def lookup_faq(query: str, top_k: int = 3) -> str: | |
| """ | |
| Return HTML-formatted list of top-k semantically similar FAQ matches. | |
| Useful for admin or verbose display. | |
| """ | |
| faqs = load_faqs() | |
| if not faqs: | |
| return "<i>No FAQ data loaded.</i>" | |
| if _FAQ_EMBEDDINGS is None: | |
| _build_embeddings() | |
| model = _get_model() | |
| query_emb = model.encode(query, convert_to_tensor=True) | |
| sims = util.cos_sim(query_emb, _FAQ_EMBEDDINGS)[0] | |
| top_indices = torch.topk(sims, k=min(top_k, len(faqs))).indices.tolist() | |
| html = [] | |
| for idx in top_indices: | |
| score = float(sims[idx]) | |
| item = faqs[idx] | |
| html.append(f"<b>{item['question']}</b><br>{item['answer']}<br><i>(score={score:.2f})</i>") | |
| return "<br><br>".join(html) | |