原始代码

user_prompt_optimizer.py

@@ -0,0 +1,52 @@
+import re
+import numpy as np
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics.pairwise import cosine_similarity
+
+from opro.ollama_client import call_qwen
+from opro.xinference_client import embed_texts
+from opro.prompt_utils import refine_instruction, refine_instruction_with_history
+
+def parse_candidates(raw: str) -> list:
+    lines = [l.strip() for l in re.split(r'\r?\n', raw) if l.strip()]
+    cleaned = []
+    for l in lines:
+        l = re.sub(r'^[\-\*\d\.\)\s]+', '', l).strip()
+        if len(l) >= 6:
+            cleaned.append(l)
+    return list(dict.fromkeys(cleaned))
+
+def cluster_and_select(candidates: list, top_k=5, distance_threshold=0.15):
+    if not candidates:
+        return []
+    vecs = embed_texts(candidates)
+    X = np.array(vecs)
+    if len(candidates) <= top_k:
+        return candidates
+
+    clustering = AgglomerativeClustering(n_clusters=None,
+                                         distance_threshold=distance_threshold,
+                                         metric="cosine",
+                                         linkage="average")
+    labels = clustering.fit_predict(X)
+
+    selected_idx = []
+    for label in sorted(set(labels)):
+        idxs = [i for i,l in enumerate(labels) if l == label]
+        sims = cosine_similarity(X[idxs]).mean(axis=1)
+        rep = idxs[int(np.argmax(sims))]
+        selected_idx.append(rep)
+
+    selected = [candidates[i] for i in sorted(selected_idx)]
+    return selected[:top_k]
+
+def generate_candidates(query: str, rejected=None, top_k=5):
+    rejected = rejected or []
+    if rejected:
+        prompt = refine_instruction_with_history(query, rejected)
+    else:
+        prompt = refine_instruction(query)
+
+    raw = call_qwen(prompt, temperature=0.9, max_tokens=512)
+    all_candidates = parse_candidates(raw)
+    return cluster_and_select(all_candidates, top_k=top_k)