API: Move OpenAI-compatible API from extensions/openai to modules/api

modules/api/moderations.py

@@ -0,0 +1,69 @@
+import time
+
+import numpy as np
+from numpy.linalg import norm
+
+from .embeddings import get_embeddings
+
+moderations_disabled = False  # return 0/false
+category_embeddings = None
+antonym_embeddings = None
+categories = ["sexual", "hate", "harassment", "self-harm", "sexual/minors", "hate/threatening", "violence/graphic", "self-harm/intent", "self-harm/instructions", "harassment/threatening", "violence"]
+flag_threshold = 0.5
+
+
+def get_category_embeddings() -> dict:
+    global category_embeddings, categories
+    if category_embeddings is None:
+        embeddings = get_embeddings(categories).tolist()
+        category_embeddings = dict(zip(categories, embeddings))
+
+    return category_embeddings
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    return np.dot(a, b) / (norm(a) * norm(b))
+
+
+# seems most openai like with all-mpnet-base-v2
+def mod_score(a: np.ndarray, b: np.ndarray) -> float:
+    return 2.0 * np.dot(a, b)
+
+
+def moderations(input):
+    global category_embeddings, categories, flag_threshold, moderations_disabled
+    results = {
+        "id": f"modr-{int(time.time()*1e9)}",
+        "model": "text-moderation-001",
+        "results": [],
+    }
+
+    if moderations_disabled:
+        results['results'] = [{
+            'categories': dict([(C, False) for C in categories]),
+            'category_scores': dict([(C, 0.0) for C in categories]),
+            'flagged': False,
+        }]
+        return results
+
+    category_embeddings = get_category_embeddings()
+
+    # input, string or array
+    if isinstance(input, str):
+        input = [input]
+
+    for in_str in input:
+        for ine in get_embeddings([in_str]):
+            category_scores = dict([(C, mod_score(category_embeddings[C], ine)) for C in categories])
+            category_flags = dict([(C, bool(category_scores[C] > flag_threshold)) for C in categories])
+            flagged = any(category_flags.values())
+
+            results['results'].extend([{
+                'flagged': flagged,
+                'categories': category_flags,
+                'category_scores': category_scores,
+            }])
+
+    print(results)
+
+    return results