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/embeddings.py

@@ -0,0 +1,50 @@
+import os
+from sentence_transformers import SentenceTransformer
+from extensions.openai.utils import float_list_to_base64, debug_msg
+from extensions.openai.errors import *
+
+st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
+embeddings_model = None
+
+def load_embedding_model(model):
+    try:
+        emb_model = SentenceTransformer(model)
+        print(f"\nLoaded embedding model: {model}, max sequence length: {emb_model.max_seq_length}")
+    except Exception as e:
+        print(f"\nError: Failed to load embedding model: {model}")
+        raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message = repr(e))
+    
+    return emb_model
+
+def get_embeddings_model():
+    global embeddings_model, st_model
+    if st_model and not embeddings_model:
+        embeddings_model = load_embedding_model(st_model) # lazy load the model
+    return embeddings_model
+
+def get_embeddings_model_name():
+    global st_model
+    return st_model
+
+def embeddings(input: list, encoding_format: str):
+
+    embeddings = get_embeddings_model().encode(input).tolist()
+
+    if encoding_format == "base64":
+        data = [{"object": "embedding", "embedding": float_list_to_base64(emb), "index": n} for n, emb in enumerate(embeddings)]
+    else:
+        data = [{"object": "embedding", "embedding": emb, "index": n} for n, emb in enumerate(embeddings)]
+
+    response = {
+        "object": "list",
+        "data": data,
+        "model": st_model,  # return the real model
+        "usage": {
+            "prompt_tokens": 0,
+            "total_tokens": 0,
+        }
+    }
+
+    debug_msg(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
+
+    return response