simplify code

examples/hello_world.py

@@ -13,7 +13,9 @@ from metagpt.logs import logger
 
 async def ask_and_print(question: str, llm: LLM, system_prompt) -> str:
     logger.info(f"Q: {question}")
-    rsp = await llm.aask(question, system_msgs=[system_prompt])
+    rsp = await llm.aask(question, system_msgs=[system_prompt], stream=True)
+    if llm.reasoning_content:
+        logger.info(f"A reasoning: {llm.reasoning_content}")
     logger.info(f"A: {rsp}")
     return rsp
 

metagpt/provider/openai_api.py

@@ -79,7 +79,7 @@ class OpenAILLM(BaseLLM):
     def _get_proxy_params(self) -> dict:
         params = {}
         if self.config.proxy:
-            params = {"proxies": self.config.proxy}
+            params = {"proxy": self.config.proxy}
             if self.config.base_url:
                 params["base_url"] = self.config.base_url
 
@@ -94,13 +94,16 @@ class OpenAILLM(BaseLLM):
         collected_reasoning_messages = []
         has_finished = False
         async for chunk in response:
-            if hasattr(chunk.choices[0].delta, "reasoning_content"):
-                collected_reasoning_messages.append(chunk.choices[0].delta.reasoning_content)  # for deepseek
+            if not chunk.choices:
                 continue
-            chunk_message = chunk.choices[0].delta.content or "" if chunk.choices else ""  # extract the message
-            finish_reason = (
-                chunk.choices[0].finish_reason if chunk.choices and hasattr(chunk.choices[0], "finish_reason") else None
-            )
+
+            choice0 = chunk.choices[0]
+            choice_delta = choice0.delta
+            if hasattr(choice_delta, "reasoning_content") and choice_delta.reasoning_content:
+                collected_reasoning_messages.append(choice_delta.reasoning_content)  # for deepseek
+                continue
+            chunk_message = choice_delta.content or ""  # extract the message
+            finish_reason = choice0.finish_reason if hasattr(choice0, "finish_reason") else None
             log_llm_stream(chunk_message)
             collected_messages.append(chunk_message)
             chunk_has_usage = hasattr(chunk, "usage") and chunk.usage
@@ -111,13 +114,10 @@ class OpenAILLM(BaseLLM):
             if finish_reason:
                 if chunk_has_usage:
                     # Some services have usage as an attribute of the chunk, such as Fireworks
-                    if isinstance(chunk.usage, CompletionUsage):
-                        usage = chunk.usage
-                    else:
-                        usage = CompletionUsage(**chunk.usage)
-                elif hasattr(chunk.choices[0], "usage"):
+                    usage = CompletionUsage(**chunk.usage) if isinstance(chunk.usage, dict) else chunk.usage
+                elif hasattr(choice0, "usage"):
                     # The usage of some services is an attribute of chunk.choices[0], such as Moonshot
-                    usage = CompletionUsage(**chunk.choices[0].usage)
+                    usage = CompletionUsage(**choice0.usage)
                 has_finished = True
 
         log_llm_stream("\n")

metagpt/utils/cost_manager.py

@@ -144,6 +144,6 @@ class FireworksCostManager(CostManager):
         cost = (prompt_tokens * token_costs["prompt"] + completion_tokens * token_costs["completion"]) / 1000000
         self.total_cost += cost
         logger.info(
-            f"Total running cost: ${self.total_cost:.4f}"
+            f"Total running cost: ${self.total_cost:.4f}, "
             f"Current cost: ${cost:.4f}, prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}"
         )

requirements.txt

@@ -13,7 +13,7 @@ lancedb==0.4.0
 loguru==0.6.0
 meilisearch==0.21.0
 numpy~=1.26.4
-openai~=1.39.0
+openai~=1.64.0
 openpyxl~=3.1.5
 beautifulsoup4==4.12.3
 pandas==2.1.1
@@ -59,7 +59,7 @@ nbformat==5.9.2
 ipython==8.17.2
 ipykernel==6.27.1
 scikit_learn==1.3.2
-typing-extensions==4.9.0
+typing-extensions==4.11.0
 socksio~=1.0.0
 gitignore-parser==0.1.9
 # connexion[uvicorn]~=3.0.5 # Used by metagpt/tools/openapi_v3_hello.py