Response from calling tool (call_AEaiVEPwzfvnH5vWDy0wFbRT) ***** Error: list index out of range

User_Proxy (to Expert_Investor):

With the tools you've been provided, write an annual report based on Microsoft's 2023 10-k report, format it into a pdf.
Pay attention to the followings:
- Explicitly explain your working plan before you kick off.
- Use tools one by one for clarity, especially when asking for instructions.
- All your file operations should be done in "../report".
- Display any image in the chat once generated.
- All the paragraphs should combine between 400 and 450 words, don't generate the pdf until this is explicitly fulfilled.

--------------------------------------------------------------------------------
Expert_Investor (to User_Proxy):

To create an annual report based on Microsoft's 2023 10-K report, I will follow a structured approach using the provided tools. Here's the working plan I will adhere to:

1. Retrieve Key Data: I will gather key financial data for Microsoft's 2023 report to understand its financial performance.
2. Analyze Income Statement: I will analyze Microsoft's income statement to identify its revenue, expenses, and profitability.
3. Analyze Segment Statement: I will delve into Microsoft's segment analysis to understand how its different business segments contribute to overall performance.
4. Income Summarization: I will synthesize the income statement and segment analysis to create a comprehensive income summarization.
5. Analyze Business Highlights: I will explore the business highlights section to understand Microsoft's key achievements and strategic moves.
6. Analyze Company Description: I will analyze the company description to grasp Microsoft's industry positioning, strengths, trends, and initiatives.
7. Get Risk Assessment: I will identify and summarize the top 3 key risks faced by Microsoft.
8. Generate Share Performance Chart: I will plot Microsoft's stock performance compared to the S&P 500 over the past year.
9. Generate PE & EPS Performance Chart: I will plot Microsoft's PE ratio and EPS performance over the last few years.
10. Build Annual Report: Finally, I will aggregate all the gathered insights and analyses into a detailed annual report in PDF format.

Let's start with retrieving the key financial data for Microsoft's 2023 report.
***** Suggested tool call (call_AEaiVEPwzfvnH5vWDy0wFbRT): get_key_data *****
Arguments:
{"ticker_symbol":"MSFT","filing_date":"2023-01-01"}
*****************************************************************************

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...

>>>>>>>> EXECUTING FUNCTION get_key_data...
FMP api key found successfully.
FMP api key found successfully.
User_Proxy (to Expert_Investor):

User_Proxy (to Expert_Investor):

***** Response from calling tool (call_AEaiVEPwzfvnH5vWDy0wFbRT) *****
Error: list index out of range
**********************************************************************

--------------------------------------------------------------------------------
Expert_Investor (to User_Proxy):

I encountered an issue while trying to retrieve the key financial data for Microsoft's 2023 report. Let me attempt the key data retrieval again.
***** Suggested tool call (call_o9K6szRUarxH9AhcHecJx17t): get_key_data *****
Arguments:
{"ticker_symbol":"MSFT","filing_date":"2023-01-01"}
*****************************************************************************

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...

>>>>>>>> EXECUTING FUNCTION get_key_data...
FMP api key found successfully.
FMP api key found successfully.
User_Proxy (to Expert_Investor):

User_Proxy (to Expert_Investor):

***** Response from calling tool (call_o9K6szRUarxH9AhcHecJx17t) *****
Error: list index out of range
**********************************************************************

--------------------------------------------------------------------------------
Expert_Investor (to User_Proxy):

It seems there is an issue with retrieving the key financial data for Microsoft's 2023 report. I will proceed with the next step and analyze the income statement. Let's delve into Microsoft's income statement for 2023.
***** Suggested tool call (call_X2QMpohGa3RpE6tYCRPR9UjP): analyze_income_stmt *****
Arguments:
{"ticker_symbol":"MSFT","fyear":"2023","save_path":"../report/income_statement_analysis.txt"}
************************************************************************************

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...

>>>>>>>> EXECUTING FUNCTION analyze_income_stmt...
Sec Api initialized
FMP api key found successfully.
[runtime logging] log_function_use: autogen logger is None
User_Proxy (to Expert_Investor):

User_Proxy (to Expert_Investor):

***** Response from calling tool (call_X2QMpohGa3RpE6tYCRPR9UjP) *****
instruction & resources saved to ../report/income_statement_analysis.txt
**********************************************************************

--------------------------------------------------------------------------------
---------------------------------------------------------------------------
BadRequestError Traceback (most recent call last)
Cell In[38], line 16
2 fyear = "2023"
4 message = dedent(
5 f"""
6 With the tools you've been provided, write an annual report based on {company}'s {fyear} 10-k report, format it into a pdf.
(...)
13 """
14 )
---> 16 assistant.chat(message, use_cache=True, max_turns=50,
17 summary_method="last_msg")

File F:\Code\FinRobot-master\finrobot\agents\workflow.py:151, in SingleAssistant.chat(self, message, use_cache, **kwargs)
149 def chat(self, message: str, use_cache=False, **kwargs):
150 with Cache.disk() as cache:
--> 151 self.user_proxy.initiate_chat(
152 self.assistant,
153 message=message,
154 cache=cache if use_cache else None,
155 **kwargs,
156 )
158 print("Current chat finished. Resetting agents ...")
159 self.reset()

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1011, in ConversableAgent.initiate_chat(self, recipient, clear_history, silent, cache, max_turns, summary_method, summary_args, message, **kwargs)
1009 if msg2send is None:
1010 break
-> 1011 self.send(msg2send, recipient, request_reply=True, silent=silent)
1012 else:
1013 self._prepare_chat(recipient, clear_history)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:655, in ConversableAgent.send(self, message, recipient, request_reply, silent)
653 valid = self._append_oai_message(message, "assistant", recipient)
654 if valid:
--> 655 recipient.receive(message, self, request_reply, silent)
656 else:
657 raise ValueError(
658 "Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided."
659 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:818, in ConversableAgent.receive(self, message, sender, request_reply, silent)
816 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
817 return
--> 818 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
819 if reply is not None:
820 self.send(reply, sender, silent=silent)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1972, in ConversableAgent.generate_reply(self, messages, sender, **kwargs)
1970 continue
1971 if self._match_trigger(reply_func_tuple["trigger"], sender):
-> 1972 final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple["config"])
1973 if logging_enabled():
1974 log_event(
1975 self,
1976 "reply_func_executed",
(...)
1980 reply=reply,
1981 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:438, in ConversableAgent.register_nested_chats..wrapped_reply_func(recipient, messages, sender, config)
437 def wrapped_reply_func(recipient, messages=None, sender=None, config=None):
--> 438 return reply_func_from_nested_chats(chat_queue, recipient, messages, sender, config)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:402, in ConversableAgent._summary_from_nested_chats(chat_queue, recipient, messages, sender, config)
400 if not chat_to_run:
401 return True, None
--> 402 res = initiate_chats(chat_to_run)
403 return True, res[-1].summary

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\chat.py:202, in initiate_chats(chat_queue)
199 __post_carryover_processing(chat_info)
201 sender = chat_info["sender"]
--> 202 chat_res = sender.initiate_chat(**chat_info)
203 finished_chats.append(chat_res)
204 return finished_chats

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1011, in ConversableAgent.initiate_chat(self, recipient, clear_history, silent, cache, max_turns, summary_method, summary_args, message, **kwargs)
1009 if msg2send is None:
1010 break
-> 1011 self.send(msg2send, recipient, request_reply=True, silent=silent)
1012 else:
1013 self._prepare_chat(recipient, clear_history)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:655, in ConversableAgent.send(self, message, recipient, request_reply, silent)
653 valid = self._append_oai_message(message, "assistant", recipient)
654 if valid:
--> 655 recipient.receive(message, self, request_reply, silent)
656 else:
657 raise ValueError(
658 "Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided."
659 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:818, in ConversableAgent.receive(self, message, sender, request_reply, silent)
816 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
817 return
--> 818 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
819 if reply is not None:
820 self.send(reply, sender, silent=silent)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1972, in ConversableAgent.generate_reply(self, messages, sender, **kwargs)
1970 continue
1971 if self._match_trigger(reply_func_tuple["trigger"], sender):
-> 1972 final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple["config"])
1973 if logging_enabled():
1974 log_event(
1975 self,
1976 "reply_func_executed",
(...)
1980 reply=reply,
1981 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1340, in ConversableAgent.generate_oai_reply(self, messages, sender, config)
1338 if messages is None:
1339 messages = self._oai_messages[sender]
-> 1340 extracted_response = self._generate_oai_reply_from_client(
1341 client, self._oai_system_message + messages, self.client_cache
1342 )
1343 return (False, None) if extracted_response is None else (True, extracted_response)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1359, in ConversableAgent._generate_oai_reply_from_client(self, llm_client, messages, cache)
1356 all_messages.append(message)
1358 # TODO: #1143 handle token limit exceeded error
-> 1359 response = llm_client.create(
1360 context=messages[-1].pop("context", None), messages=all_messages, cache=cache, agent=self
1361 )
1362 extracted_response = llm_client.extract_text_or_completion_object(response)[0]
1364 if extracted_response is None:

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\oai\client.py:662, in OpenAIWrapper.create(self, **config)
660 try:
661 request_ts = get_current_ts()
--> 662 response = client.create(params)
663 except APITimeoutError as err:
664 logger.debug(f"config {i} timed out", exc_info=True)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\oai\client.py:285, in OpenAIClient.create(self, params)
283 params = params.copy()
284 params["stream"] = False
--> 285 response = completions.create(**params)
287 return response

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\_utils\_utils.py:277, in required_args..inner..wrapper(*args, **kwargs)
275 msg = f"Missing required argument: {quote(missing[0])}"
276 raise TypeError(msg)
--> 277 return func(*args, **kwargs)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\resources\chat\completions.py:606, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
573 @required_args(["messages", "model"], ["messages", "model", "stream"])
574 def create(
575 self,
(...)
604 timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
605 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
--> 606 return self._post(
607 "/chat/completions",
608 body=maybe_transform(
609 {
610 "messages": messages,
611 "model": model,
612 "frequency_penalty": frequency_penalty,
613 "function_call": function_call,
614 "functions": functions,
615 "logit_bias": logit_bias,
616 "logprobs": logprobs,
617 "max_tokens": max_tokens,
618 "n": n,
619 "parallel_tool_calls": parallel_tool_calls,
620 "presence_penalty": presence_penalty,
621 "response_format": response_format,
622 "seed": seed,
623 "stop": stop,
624 "stream": stream,
625 "stream_options": stream_options,
626 "temperature": temperature,
627 "tool_choice": tool_choice,
628 "tools": tools,
629 "top_logprobs": top_logprobs,
630 "top_p": top_p,
631 "user": user,
632 },
633 completion_create_params.CompletionCreateParams,
634 ),
635 options=make_request_options(
636 extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
637 ),
638 cast_to=ChatCompletion,
639 stream=stream or False,
640 stream_cls=Stream[ChatCompletionChunk],
641 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\_base_client.py:1240, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
1226 def post(
1227 self,
1228 path: str,
(...)
1235 stream_cls: type[_StreamT] | None = None,
1236 ) -> ResponseT | _StreamT:
1237 opts = FinalRequestOptions.construct(
1238 method="post", url=path, json_data=body, files=to_httpx_files(files), **options
1239 )
-> 1240 return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\_base_client.py:921, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
912 def request(
913 self,
914 cast_to: Type[ResponseT],
(...)
919 stream_cls: type[_StreamT] | None = None,
920 ) -> ResponseT | _StreamT:
--> 921 return self._request(
922 cast_to=cast_to,
923 options=options,
924 stream=stream,
925 stream_cls=stream_cls,
926 remaining_retries=remaining_retries,
927 )

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\_base_client.py:1020, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
1017 err.response.read()
1019 log.debug("Re-raising status error")
-> 1020 raise self._make_status_error_from_response(err.response) from None
1022 return self._process_response(
1023 cast_to=cast_to,
1024 options=options,
(...)
1027 stream_cls=stream_cls,
1028 )

BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 16385 tokens. However, your messages resulted in 16488 tokens. Please reduce the length of the messages.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}