extensions/openai: Major openai extension updates & fixes (#3049)

* many openai updates

* total reorg & cleanup.

* fixups

* missing import os for images

* +moderations, custom_stopping_strings, more fixes

* fix bugs in completion streaming

* moderation fix (flagged)

* updated moderation categories

---------

Co-authored-by: Matthew Ashton <mashton-gitlab@zhero.org>

extensions/openai/moderations.py

@@ -0,0 +1,70 @@
+import time
+import numpy as np
+from numpy.linalg import norm
+from extensions.openai.embeddings import get_embeddings_model
+
+
+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():
+    global category_embeddings, categories
+    if category_embeddings is None:
+        embeddings = get_embeddings_model().encode(categories).tolist()
+        category_embeddings = dict(zip(categories, embeddings))
+
+    return category_embeddings
+
+
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (norm(a) * norm(b))
+
+
+# seems most openai like with all-mpnet-base-v2
+def mod_score(a, b):
+    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": [],
+    }
+
+    embeddings_model = get_embeddings_model()
+    if not embeddings_model or 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 embeddings_model.encode([in_str]).tolist():
+            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