openai ratelimit retry

examples/st_game/actions/gen_action_details.py

@@ -393,7 +393,10 @@ class GenActionDetails(STAction):
         act_sector = GenActionSector().run(role, access_tile, act_desp)
         act_arena = GenActionArena().run(role, act_desp, act_world, act_sector)
         act_address = f"{act_world}:{act_sector}:{act_arena}"
-        act_game_object = GenActionObject().run(role, act_desp, act_address)
+        if not role.s_mem.get_str_accessible_arena_game_objects(act_address):
+            act_game_object = "<random>"
+        else:
+            act_game_object = GenActionObject().run(role, act_desp, act_address)
         new_address = f"{act_world}:{act_sector}:{act_arena}:{act_game_object}"
         act_pron = GenPronunciatio().run(role, act_desp)
         act_event = GenEventTriple().run(role, act_desp)

examples/st_game/actions/st_action.py

@@ -73,18 +73,22 @@ class STAction(Action):
         """
         assert model_name in ["gpt-3.5-turbo-instruct", "text-davinci-002", "text-davinci-003"]
         for idx in range(retry):
-            tmp_model_name = self.llm.model
-            tmp_max_tokens_rsp = CONFIG.max_tokens_rsp
-            CONFIG.max_tokens_rsp = max_tokens
-            self.llm.model = model_name
+            try:
+                tmp_model_name = self.llm.model
+                tmp_max_tokens_rsp = CONFIG.max_tokens_rsp
+                CONFIG.max_tokens_rsp = max_tokens
+                self.llm.model = model_name
 
-            llm_resp = self._ask_nonchat(prompt)
+                llm_resp = self._ask_nonchat(prompt)
 
-            CONFIG.max_tokens_rsp = tmp_max_tokens_rsp
-            self.llm.model = tmp_model_name
-            logger.info(f"Action: {self.cls_name} llm _run_text_davinci raw resp: {llm_resp}")
-            if self._func_validate(llm_resp, prompt):
-                return self._func_cleanup(llm_resp, prompt)
+                CONFIG.max_tokens_rsp = tmp_max_tokens_rsp
+                self.llm.model = tmp_model_name
+                logger.info(f"Action: {self.cls_name} llm _run_text_davinci raw resp: {llm_resp}")
+                if self._func_validate(llm_resp, prompt):
+                    return self._func_cleanup(llm_resp, prompt)
+            except Exception as exp:
+                logger.warning(f"Action: {self.cls_name} _run_text_davinci exp: {exp}")
+                time.sleep(5)
         return self.fail_default_resp
 
     def _run_gpt35(self,

examples/st_game/utils/utils.py

@@ -7,6 +7,7 @@ import errno
 import json
 import os
 import shutil
+import time
 from pathlib import Path
 from typing import Any, Union
 
@@ -66,8 +67,14 @@ def get_embedding(text, model: str = "text-embedding-ada-002"):
     text = text.replace("\n", " ")
     if not text:
         text = "this is blank"
-    return openai.Embedding.create(
-        input=[text], model=model)['data'][0]['embedding']
+    for idx in range(3):
+        try:
+            embedding = openai.Embedding.create(input=[text], model=model)['data'][0]['embedding']
+        except Exception as exp:
+            time.sleep(5)
+    if not embedding:
+        raise ValueError("get_embedding failed")
+    return embedding
 
 
 def extract_first_json_dict(data_str: str) -> Union[None, dict]: