rponesoul
commented
2 months ago Moved to Databricks and will work through issues there.
Closed rponesoul closed 2 months ago
rponesoul
commented
2 months ago Moved to Databricks and will work through issues there.
I'm running Jupyter Lab on macOS 12.7.4 and trying to run the code in the deeplearning.ai course Multi AI Agent Systems with crewAI.
Editorial note: I was invited to and attended yesterday a thought-leaders' summit on AI & Business Agility that included a lot of influential people, including a signer of the Agile Manifesto. Very early on, the organizer, Peter Merel, in his keynote, mentioned Multi Agent AI and later when I brought it up in a breakout, I would have mentioned crewAI if I had it working. There is a lot of opportunity for me to showcase crewAI and I need help getting it to work.
I used the following command to install crewai, per the course instructions:
!pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29
Below is the code I ran to get to the error when running crew.kickoff
import warnings warnings.filterwarnings('ignore')
from crewai import Agent, Task, Crew
import os from utils import get_openai_api_key
openai_api_key = get_openai_api_key() os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'
planner = Agent( role="Content Planner", goal="Plan engaging and factually accurate content on {topic}", backstory="You're working on planning a blog article " "about the topic: {topic}." "You collect information that helps the " "audience learn something " "and make informed decisions. " "Your work is the basis for " "the Content Writer to write an article on this topic.", allow_delegation=False, verbose=True
writer = Agent( role="Content Writer", goal="Write insightful and factually accurate " "opinion piece about the topic: {topic}", backstory="You're working on a writing " "a new opinion piece about the topic: {topic}. " "You base your writing on the work of " "the Content Planner, who provides an outline " "and relevant context about the topic. " "You follow the main objectives and " "direction of the outline, " "as provide by the Content Planner. " "You also provide objective and impartial insights " "and back them up with information " "provide by the Content Planner. " "You acknowledge in your opinion piece " "when your statements are opinions " "as opposed to objective statements.", allow_delegation=False, verbose=True
editor = Agent( role="Editor", goal="Edit a given blog post to align with " "the writing style of the organization. ", backstory="You are an editor who receives a blog post " "from the Content Writer. " "Your goal is to review the blog post " "to ensure that it follows journalistic best practices," "provides balanced viewpoints " "when providing opinions or assertions, " "and also avoids major controversial topics " "or opinions when possible.", allow_delegation=False, verbose=True )
plan = Task( description=( "1. Prioritize the latest trends, key players, " "and noteworthy news on {topic}.\n" "2. Identify the target audience, considering " "their interests and pain points.\n" "3. Develop a detailed content outline including " "an introduction, key points, and a call to action.\n" "4. Include SEO keywords and relevant data or sources." ), expected_output="A comprehensive content plan document " "with an outline, audience analysis, " "SEO keywords, and resources.", agent=planner, )
write = Task( description=( "1. Use the content plan to craft a compelling " "blog post on {topic}.\n" "2. Incorporate SEO keywords naturally.\n" "3. Sections/Subtitles are properly named " "in an engaging manner.\n" "4. Ensure the post is structured with an " "engaging introduction, insightful body, " "and a summarizing conclusion.\n" "5. Proofread for grammatical errors and " "alignment with the brand's voice.\n" ), expected_output="A well-written blog post " "in markdown format, ready for publication, " "each section should have 2 or 3 paragraphs.", agent=writer, )
edit = Task( description=("Proofread the given blog post for " "grammatical errors and " "alignment with the brand's voice."), expected_output="A well-written blog post in markdown format, " "ready for publication, " "each section should have 2 or 3 paragraphs.", agent=editor )
crew = Crew( agents=[planner, writer, editor], tasks=[plan, write, edit], verbose=2 )
result = crew.kickoff(inputs={"topic": "Artificial Intelligence"})
Below is the output and Traceback from running crew.kickoff. Help on this error would be greatly appreciated.
[DEBUG]: == Working Agent: Content Planner [INFO]: == Starting Task: 1. Prioritize the latest trends, key players, and noteworthy news on Artificial Intelligence.
UnicodeEncodeError Traceback (most recent call last) Cell In[11], line 1 ----> 1 result = crew.kickoff(inputs={"topic": "Artificial Intelligence"})
File /opt/anaconda3/lib/python3.11/site-packages/crewai/crew.py:252, in Crew.kickoff(self, inputs) 249 metrics = [] 251 if self.process == Process.sequential: --> 252 result = self._run_sequential_process() 253 elif self.process == Process.hierarchical: 254 result, manager_metrics = self._run_hierarchical_process()
File /opt/anaconda3/lib/python3.11/site-packages/crewai/crew.py:293, in Crew._run_sequential_process(self) 288 if self.output_log_file: 289 self._file_handler.log( 290 agent=role, task=task.description, status="started" 291 ) --> 293 output = task.execute(context=task_output) 294 if not task.async_execution: 295 task_output = output
File /opt/anaconda3/lib/python3.11/site-packages/crewai/task.py:173, in Task.execute(self, agent, context, tools) 171 self.thread.start() 172 else: --> 173 result = self._execute( 174 task=self, 175 agent=agent, 176 context=context, 177 tools=tools, 178 ) 179 return result
File /opt/anaconda3/lib/python3.11/site-packages/crewai/task.py:182, in Task._execute(self, agent, task, context, tools) 181 def _execute(self, agent, task, context, tools): --> 182 result = agent.execute_task( 183 task=task, 184 context=context, 185 tools=tools, 186 ) 188 exported_output = self._export_output(result) 190 self.output = TaskOutput( 191 description=self.description, 192 exported_output=exported_output, 193 raw_output=result, 194 )
File /opt/anaconda3/lib/python3.11/site-packages/crewai/agent.py:221, in Agent.execute_task(self, task, context, tools) 218 self.agent_executor.tools_description = render_text_description(parsed_tools) 219 self.agent_executor.tools_names = self.__tools_names(parsed_tools) --> 221 result = self.agent_executor.invoke( 222 { 223 "input": task_prompt, 224 "tool_names": self.agent_executor.tools_names, 225 "tools": self.agent_executor.tools_description, 226 } 227 )["output"] 229 if self.max_rpm: 230 self._rpm_controller.stop_rpm_counter()
File /opt/anaconda3/lib/python3.11/site-packages/langchain/chains/base.py:163, in Chain.invoke(self, input, config, **kwargs) 161 except BaseException as e: 162 run_manager.on_chain_error(e) --> 163 raise e 164 run_manager.on_chain_end(outputs) 166 if include_run_info:
File /opt/anaconda3/lib/python3.11/site-packages/langchain/chains/base.py:153, in Chain.invoke(self, input, config, **kwargs) 150 try: 151 self._validate_inputs(inputs) 152 outputs = ( --> 153 self._call(inputs, run_manager=run_manager) 154 if new_arg_supported 155 else self._call(inputs) 156 ) 158 final_outputs: Dict[str, Any] = self.prep_outputs( 159 inputs, outputs, return_only_outputs 160 ) 161 except BaseException as e:
File /opt/anaconda3/lib/python3.11/site-packages/crewai/agents/executor.py:124, in CrewAgentExecutor._call(self, inputs, run_manager) 122 while self._should_continue(self.iterations, time_elapsed): 123 if not self.request_within_rpm_limit or self.request_within_rpm_limit(): --> 124 next_step_output = self._take_next_step( 125 name_to_tool_map, 126 color_mapping, 127 inputs, 128 intermediate_steps, 129 run_manager=run_manager, 130 ) 131 if self.step_callback: 132 self.step_callback(next_step_output)
File /opt/anaconda3/lib/python3.11/site-packages/langchain/agents/agent.py:1138, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 1129 def _take_next_step( 1130 self, 1131 name_to_tool_map: Dict[str, BaseTool], (...) 1135 run_manager: Optional[CallbackManagerForChainRun] = None, 1136 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: 1137 return self._consume_next_step( -> 1138 [ 1139 a 1140 for a in self._iter_next_step( 1141 name_to_tool_map, 1142 color_mapping, 1143 inputs, 1144 intermediate_steps, 1145 run_manager, 1146 ) 1147 ] 1148 )
File /opt/anaconda3/lib/python3.11/site-packages/langchain/agents/agent.py:1138, in(.0)
1129 def _take_next_step(
1130 self,
1131 name_to_tool_map: Dict[str, BaseTool],
(...)
1135 run_manager: Optional[CallbackManagerForChainRun] = None,
1136 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
1137 return self._consume_next_step(
-> 1138 [
1139 a
1140 for a in self._iter_next_step(
1141 name_to_tool_map,
1142 color_mapping,
1143 inputs,
1144 intermediate_steps,
1145 run_manager,
1146 )
1147 ]
1148 )
File /opt/anaconda3/lib/python3.11/site-packages/crewai/agents/executor.py:186, in CrewAgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager) 183 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps) 185 # Call the LLM to see what to do. --> 186 output = self.agent.plan( 187 intermediate_steps, 188 callbacks=run_manager.get_child() if run_manager else None, 189 **inputs, 190 ) 192 except OutputParserException as e: 193 if isinstance(self.handle_parsing_errors, bool):
File /opt/anaconda3/lib/python3.11/site-packages/langchain/agents/agent.py:397, in RunnableAgent.plan(self, intermediate_steps, callbacks, **kwargs) 389 final_output: Any = None 390 if self.stream_runnable: 391 # Use streaming to make sure that the underlying LLM is invoked in a 392 # streaming (...) 395 # Because the response from the plan is not a generator, we need to 396 # accumulate the output into final output and return that. --> 397 for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}): 398 if final_output is None: 399 final_output = chunk
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:2875, in RunnableSequence.stream(self, input, config, kwargs) 2869 def stream( 2870 self, 2871 input: Input, 2872 config: Optional[RunnableConfig] = None, 2873 kwargs: Optional[Any], 2874 ) -> Iterator[Output]: -> 2875 yield from self.transform(iter([input]), config, **kwargs)
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:2862, in RunnableSequence.transform(self, input, config, kwargs) 2856 def transform( 2857 self, 2858 input: Iterator[Input], 2859 config: Optional[RunnableConfig] = None, 2860 kwargs: Optional[Any], 2861 ) -> Iterator[Output]: -> 2862 yield from self._transform_stream_with_config( 2863 input, 2864 self._transform, 2865 patch_config(config, run_name=(config or {}).get("run_name") or self.name), 2866 **kwargs, 2867 )
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:1881, in Runnable._transform_stream_with_config(self, input, transformer, config, run_type, **kwargs) 1879 try: 1880 while True: -> 1881 chunk: Output = context.run(next, iterator) # type: ignore 1882 yield chunk 1883 if final_output_supported:
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:2826, in RunnableSequence._transform(self, input, run_manager, config) 2817 for step in steps: 2818 final_pipeline = step.transform( 2819 final_pipeline, 2820 patch_config( (...) 2823 ), 2824 ) -> 2826 for output in final_pipeline: 2827 yield output
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:1282, in Runnable.transform(self, input, config, **kwargs) 1279 final: Input 1280 got_first_val = False -> 1282 for ichunk in input: 1283 # The default implementation of transform is to buffer input and 1284 # then call stream. 1285 # It'll attempt to gather all input into a single chunk using 1286 # the
+operator. 1287 # If the input is not addable, then we'll assume that we can 1288 # only operate on the last chunk, 1289 # and we'll iterate until we get to the last chunk. 1290 if not got_first_val: 1291 final = ichunkFile /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:4736, in RunnableBindingBase.transform(self, input, config, kwargs) 4730 def transform( 4731 self, 4732 input: Iterator[Input], 4733 config: Optional[RunnableConfig] = None, 4734 kwargs: Any, 4735 ) -> Iterator[Output]: -> 4736 yield from self.bound.transform( 4737 input, 4738 self._merge_configs(config), 4739 {self.kwargs, **kwargs}, 4740 )
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/runnables/base.py:1300, in Runnable.transform(self, input, config, kwargs) 1297 final = ichunk 1299 if got_first_val: -> 1300 yield from self.stream(final, config, kwargs)
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:249, in BaseChatModel.stream(self, input, config, stop, **kwargs) 242 except BaseException as e: 243 run_manager.on_llm_error( 244 e, 245 response=LLMResult( 246 generations=[[generation]] if generation else [] 247 ), 248 ) --> 249 raise e 250 else: 251 run_manager.on_llm_end(LLMResult(generations=[[generation]]))
File /opt/anaconda3/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py:229, in BaseChatModel.stream(self, input, config, stop, kwargs) 227 generation: Optional[ChatGenerationChunk] = None 228 try: --> 229 for chunk in self._stream(messages, stop=stop, kwargs): 230 if chunk.message.id is None: 231 chunk.message.id = f"run-{run_manager.run_id}"
File /opt/anaconda3/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:408, in ChatOpenAI._stream(self, messages, stop, run_manager, kwargs) 405 params = {params, kwargs, "stream": True} 407 default_chunk_class = AIMessageChunk --> 408 for chunk in self.client.create(messages=message_dicts, params): 409 if not isinstance(chunk, dict): 410 chunk = chunk.dict()
File /opt/anaconda3/lib/python3.11/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 /opt/anaconda3/lib/python3.11/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 /opt/anaconda3/lib/python3.11/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 /opt/anaconda3/lib/python3.11/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 /opt/anaconda3/lib/python3.11/site-packages/openai/_base_client.py:942, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls) 939 self._prepare_options(options) 941 retries = self._remaining_retries(remaining_retries, options) --> 942 request = self._build_request(options) 943 self._prepare_request(request) 945 kwargs: HttpxSendArgs = {}
File /opt/anaconda3/lib/python3.11/site-packages/openai/_base_client.py:459, in BaseClient._build_request(self, options) 456 else: 457 raise RuntimeError(f"Unexpected JSON data type, {type(json_data)}, cannot merge with
extra_body") --> 459 headers = self._build_headers(options) 460 params = _merge_mappings(self._custom_query, options.params) 461 content_type = headers.get("Content-Type")File /opt/anaconda3/lib/python3.11/site-packages/openai/_base_client.py:417, in BaseClient._build_headers(self, options) 414 self._validate_headers(headers_dict, custom_headers) 416 # headers are case-insensitive while dictionaries are not. --> 417 headers = httpx.Headers(headers_dict) 419 idempotency_header = self._idempotency_header 420 if idempotency_header and options.method.lower() != "get" and idempotency_header not in headers:
File /opt/anaconda3/lib/python3.11/site-packages/httpx/_models.py:72, in Headers.init(self, headers, encoding) 70 self._list = list(headers._list) 71 elif isinstance(headers, Mapping): ---> 72 self._list = [ 73 ( 74 normalize_header_key(k, lower=False, encoding=encoding), 75 normalize_header_key(k, lower=True, encoding=encoding), 76 normalize_header_value(v, encoding), 77 ) 78 for k, v in headers.items() 79 ] 80 else: 81 self._list = [ 82 ( 83 normalize_header_key(k, lower=False, encoding=encoding), (...) 87 for k, v in headers 88 ]
File /opt/anaconda3/lib/python3.11/site-packages/httpx/_models.py:76, in(.0)
70 self._list = list(headers._list)
71 elif isinstance(headers, Mapping):
72 self._list = [
73 (
74 normalize_header_key(k, lower=False, encoding=encoding),
75 normalize_header_key(k, lower=True, encoding=encoding),
---> 76 normalize_header_value(v, encoding),
77 )
78 for k, v in headers.items()
79 ]
80 else:
81 self._list = [
82 (
83 normalize_header_key(k, lower=False, encoding=encoding),
(...)
87 for k, v in headers
88 ]
File /opt/anaconda3/lib/python3.11/site-packages/httpx/_utils.py:53, in normalize_header_value(value, encoding) 51 if isinstance(value, bytes): 52 return value ---> 53 return value.encode(encoding or "ascii")
UnicodeEncodeError: 'ascii' codec can't encode character '\u2018' in position 7: ordinal not in range(128)