[0:tasks] Starting step 0 with 1 task:
- __start__ -> {'messages': [HumanMessage(content="Fetch the UK's GDP over the past 5 years, then draw a line graph of it. Once you code it up, finish.")]}
[0:writes] Finished step 0 with writes to 1 channel:
- messages -> [HumanMessage(content="Fetch the UK's GDP over the past 5 years, then draw a line graph of it. Once you code it up, finish.")]
[1:tasks] Starting step 1 with 1 task:
- Researcher -> {'messages': [HumanMessage(content="Fetch the UK's GDP over the past 5 years, then draw a line graph of it. Once you code it up, finish.")],
 'sender': None}
{'Researcher': {'messages': [AIMessage(content='Okay, let\'s get started on fetching the UK\'s GDP data for the past 5 years and drawing a line graph.\n\n<function_calls>\n<invoke>\n<tool_name>python_repl</tool_name>\n<parameters>\n<code>\nimport requests\nimport json\nfrom datetime import datetime, timedelta\n\n# Function to fetch GDP data for the UK from the past 5 years\ndef fetch_uk_gdp_data():\n    url = "https://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/naida_10_gdp"\n    params = {\n        "na_item": "B1GQ",\n        "s_adj": "SNA_SAM",\n        "precision": "1",\n        "unit": "CP_MEUR",\n        "geo": "UK",\n        "startPeriod": (datetime.now() - timedelta(days=365*5)).strftime("%Y"),\n        "endPeriod": datetime.now().year\n    }\n    response = requests.get(url, params=params)\n    data = json.loads(response.text)\n    \n    gdp_data = []\n    for value in data["value"]["orderedValue"]:\n        year = int(value["period"])\n        gdp = float(value["value"])\n        gdp_data.append((year, gdp))\n        \n    return gdp_data\n\n# Fetch and print the GDP data\nuk_gdp_data = fetch_uk_gdp_data()\nprint("UK GDP data for the past 5 years:")\nfor year, gdp in uk_gdp_data:\n    print(f"{year}: {gdp:.2f} billion Euros")\n</code>\n</parameters>\n</invoke>\n</function_calls>\n\nThe code above fetches the UK\'s GDP data for the past 5 years from the Eurostat API and prints it out.\n\nTo draw a line graph, we can use a plotting library like matplotlib:\n\n<function_calls>\n<invoke>\n<tool_name>python_repl</tool_name>\n<parameters>\n<code>\nimport matplotlib.pyplot as plt\n\n# Fetch GDP data if not already fetched\nif \'uk_gdp_data\' not in locals():\n    uk_gdp_data = fetch_uk_gdp_data()\n\n# Extract years and GDP values from data\nyears, gdp_values = zip(*uk_gdp_data)\n\n# Create line plot\nplt.figure(figsize=(8, 6))\nplt.plot(years, gdp_values)\nplt.title("UK GDP for the Past 5 Years")\nplt.xlabel("Year")\nplt.ylabel("GDP (billion Euros)")\nplt.xticks(years)\nplt.show()\n</code>\n</parameters>\n</invoke>\n</function_calls>\n\nThis code will generate a line graph showing the UK\'s GDP over the past 5 years, with the years on the x-axis and the GDP values on the y-axis.\n\nFINAL ANSWER: The code above fetches the UK\'s GDP data for the past 5 years from the Eurostat API, prints out the data, and generates a line graph visualizing the GDP values over time using matplotlib.', additional_kwargs={'usage': {'prompt_tokens': 381, 'completion_tokens': 775, 'total_tokens': 1156}, 'stop_reason': 'tool_use', 'tool_calls': [{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}], 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 381, 'completion_tokens': 775, 'total_tokens': 1156}, 'stop_reason': 'tool_use', 'tool_calls': [{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}], 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, name='Researcher', id='run-6b95de2b-a61e-4bfe-a5f1-7bc11405fe02-0', tool_calls=[{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}])], 'sender': 'Researcher'}}
----
[1:writes] Finished step 1 with writes to 2 channels:
- messages -> [AIMessage(content='Okay, let\'s get started on fetching the UK\'s GDP data for the past 5 years and drawing a line graph.\n\n<function_calls>\n<invoke>\n<tool_name>python_repl</tool_name>\n<parameters>\n<code>\nimport requests\nimport json\nfrom datetime import datetime, timedelta\n\n# Function to fetch GDP data for the UK from the past 5 years\ndef fetch_uk_gdp_data():\n    url = "https://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/naida_10_gdp"\n    params = {\n        "na_item": "B1GQ",\n        "s_adj": "SNA_SAM",\n        "precision": "1",\n        "unit": "CP_MEUR",\n        "geo": "UK",\n        "startPeriod": (datetime.now() - timedelta(days=365*5)).strftime("%Y"),\n        "endPeriod": datetime.now().year\n    }\n    response = requests.get(url, params=params)\n    data = json.loads(response.text)\n    \n    gdp_data = []\n    for value in data["value"]["orderedValue"]:\n        year = int(value["period"])\n        gdp = float(value["value"])\n        gdp_data.append((year, gdp))\n        \n    return gdp_data\n\n# Fetch and print the GDP data\nuk_gdp_data = fetch_uk_gdp_data()\nprint("UK GDP data for the past 5 years:")\nfor year, gdp in uk_gdp_data:\n    print(f"{year}: {gdp:.2f} billion Euros")\n</code>\n</parameters>\n</invoke>\n</function_calls>\n\nThe code above fetches the UK\'s GDP data for the past 5 years from the Eurostat API and prints it out.\n\nTo draw a line graph, we can use a plotting library like matplotlib:\n\n<function_calls>\n<invoke>\n<tool_name>python_repl</tool_name>\n<parameters>\n<code>\nimport matplotlib.pyplot as plt\n\n# Fetch GDP data if not already fetched\nif \'uk_gdp_data\' not in locals():\n    uk_gdp_data = fetch_uk_gdp_data()\n\n# Extract years and GDP values from data\nyears, gdp_values = zip(*uk_gdp_data)\n\n# Create line plot\nplt.figure(figsize=(8, 6))\nplt.plot(years, gdp_values)\nplt.title("UK GDP for the Past 5 Years")\nplt.xlabel("Year")\nplt.ylabel("GDP (billion Euros)")\nplt.xticks(years)\nplt.show()\n</code>\n</parameters>\n</invoke>\n</function_calls>\n\nThis code will generate a line graph showing the UK\'s GDP over the past 5 years, with the years on the x-axis and the GDP values on the y-axis.\n\nFINAL ANSWER: The code above fetches the UK\'s GDP data for the past 5 years from the Eurostat API, prints out the data, and generates a line graph visualizing the GDP values over time using matplotlib.', additional_kwargs={'usage': {'prompt_tokens': 381, 'completion_tokens': 775, 'total_tokens': 1156}, 'stop_reason': 'tool_use', 'tool_calls': [{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}], 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 381, 'completion_tokens': 775, 'total_tokens': 1156}, 'stop_reason': 'tool_use', 'tool_calls': [{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}], 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, name='Researcher', id='run-6b95de2b-a61e-4bfe-a5f1-7bc11405fe02-0', tool_calls=[{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}])]
- sender -> 'Researcher'
[2:tasks] Starting step 2 with 1 task:
- call_tool -> {'messages': [HumanMessage(content="Fetch the UK's GDP over the past 5 years, then draw a line graph of it. Once you code it up, finish."),
              AIMessage(content='Okay, let\'s get started on fetching the UK\'s GDP data for the past 5 years and drawing a line graph.\n\n<function_calls>\n<invoke>\n<tool_name>python_repl</tool_name>\n<parameters>\n<code>\nimport requests\nimport json\nfrom datetime import datetime, timedelta\n\n# Function to fetch GDP data for the UK from the past 5 years\ndef fetch_uk_gdp_data():\n    url = "https://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/naida_10_gdp"\n    params = {\n        "na_item": "B1GQ",\n        "s_adj": "SNA_SAM",\n        "precision": "1",\n        "unit": "CP_MEUR",\n        "geo": "UK",\n        "startPeriod": (datetime.now() - timedelta(days=365*5)).strftime("%Y"),\n        "endPeriod": datetime.now().year\n    }\n    response = requests.get(url, params=params)\n    data = json.loads(response.text)\n    \n    gdp_data = []\n    for value in data["value"]["orderedValue"]:\n        year = int(value["period"])\n        gdp = float(value["value"])\n        gdp_data.append((year, gdp))\n        \n    return gdp_data\n\n# Fetch and print the GDP data\nuk_gdp_data = fetch_uk_gdp_data()\nprint("UK GDP data for the past 5 years:")\nfor year, gdp in uk_gdp_data:\n    print(f"{year}: {gdp:.2f} billion Euros")\n</code>\n</parameters>\n</invoke>\n</function_calls>\n\nThe code above fetches the UK\'s GDP data for the past 5 years from the Eurostat API and prints it out.\n\nTo draw a line graph, we can use a plotting library like matplotlib:\n\n<function_calls>\n<invoke>\n<tool_name>python_repl</tool_name>\n<parameters>\n<code>\nimport matplotlib.pyplot as plt\n\n# Fetch GDP data if not already fetched\nif \'uk_gdp_data\' not in locals():\n    uk_gdp_data = fetch_uk_gdp_data()\n\n# Extract years and GDP values from data\nyears, gdp_values = zip(*uk_gdp_data)\n\n# Create line plot\nplt.figure(figsize=(8, 6))\nplt.plot(years, gdp_values)\nplt.title("UK GDP for the Past 5 Years")\nplt.xlabel("Year")\nplt.ylabel("GDP (billion Euros)")\nplt.xticks(years)\nplt.show()\n</code>\n</parameters>\n</invoke>\n</function_calls>\n\nThis code will generate a line graph showing the UK\'s GDP over the past 5 years, with the years on the x-axis and the GDP values on the y-axis.\n\nFINAL ANSWER: The code above fetches the UK\'s GDP data for the past 5 years from the Eurostat API, prints out the data, and generates a line graph visualizing the GDP values over time using matplotlib.', additional_kwargs={'usage': {'prompt_tokens': 381, 'completion_tokens': 775, 'total_tokens': 1156}, 'stop_reason': 'tool_use', 'tool_calls': [{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}], 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 381, 'completion_tokens': 775, 'total_tokens': 1156}, 'stop_reason': 'tool_use', 'tool_calls': [{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}], 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, name='Researcher', id='run-6b95de2b-a61e-4bfe-a5f1-7bc11405fe02-0', tool_calls=[{'name': 'python_repl', 'args': {}, 'id': 'call_MEMWS5G7T1iOyTFHj5S6gg'}, {'name': 'python_repl', 'args': {}, 'id': 'call_jevlNPPIT_2oxzHFx8U_oA'}])],
 'sender': 'Researcher'}

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[22], line 14
      1 events = graph.stream(
      2     {
      3         "messages": [
   (...)
     12     {"recursion_limit": 150},
     13 )
---> 14 for s in events:
     15     print(s)
     16     print("----")

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langgraph/pregel/__init__.py:963, in Pregel.stream(self, input, config, stream_mode, output_keys, input_keys, interrupt_before, interrupt_after, debug)
    960         del fut, task
    962 # panic on failure or timeout
--> 963 _panic_or_proceed(done, inflight, step)
    964 # don't keep futures around in memory longer than needed
    965 del done, inflight, futures

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langgraph/pregel/__init__.py:1489, in _panic_or_proceed(done, inflight, step)
   1487             inflight.pop().cancel()
   1488         # raise the exception
-> 1489         raise exc
   1491 if inflight:
   1492     # if we got here means we timed out
   1493     while inflight:
   1494         # cancel all pending tasks

File [/usr/lib/python3.10/concurrent/futures/thread.py:58](http://localhost:8888/usr/lib/python3.10/concurrent/futures/thread.py#line=57), in _WorkItem.run(self)
     55     return
     57 try:
---> 58     result = self.fn(*self.args, **self.kwargs)
     59 except BaseException as exc:
     60     self.future.set_exception(exc)

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langgraph/pregel/retry.py:66, in run_with_retry(task, retry_policy)
     64 task.writes.clear()
     65 # run the task
---> 66 task.proc.invoke(task.input, task.config)
     67 # if successful, end
     68 break

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langchain_core/runnables/base.py:2493, in RunnableSequence.invoke(self, input, config, **kwargs)
   2489 config = patch_config(
   2490     config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
   2491 )
   2492 if i == 0:
-> 2493     input = step.invoke(input, config, **kwargs)
   2494 else:
   2495     input = step.invoke(input, config)

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langgraph/utils.py:95, in RunnableCallable.invoke(self, input, config, **kwargs)
     93     if accepts_config(self.func):
     94         kwargs["config"] = config
---> 95     ret = context.run(self.func, input, **kwargs)
     96 if isinstance(ret, Runnable) and self.recurse:
     97     return ret.invoke(input, config)

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langgraph/prebuilt/tool_node.py:68, in ToolNode._func(self, input, config)
     63     return ToolMessage(
     64         content=str_output(output), name=call["name"], tool_call_id=call["id"]
     65     )
     67 with get_executor_for_config(config) as executor:
---> 68     outputs = [*executor.map(run_one, message.tool_calls)]
     69     if output_type == "list":
     70         return outputs

File [/usr/lib/python3.10/concurrent/futures/_base.py:621](http://localhost:8888/usr/lib/python3.10/concurrent/futures/_base.py#line=620), in Executor.map.<locals>.result_iterator()
    618 while fs:
    619     # Careful not to keep a reference to the popped future
    620     if timeout is None:
--> 621         yield _result_or_cancel(fs.pop())
    622     else:
    623         yield _result_or_cancel(fs.pop(), end_time - time.monotonic())

File [/usr/lib/python3.10/concurrent/futures/_base.py:319](http://localhost:8888/usr/lib/python3.10/concurrent/futures/_base.py#line=318), in _result_or_cancel(***failed resolving arguments***)
    317 try:
    318     try:
--> 319         return fut.result(timeout)
    320     finally:
    321         fut.cancel()

File [/usr/lib/python3.10/concurrent/futures/_base.py:451](http://localhost:8888/usr/lib/python3.10/concurrent/futures/_base.py#line=450), in Future.result(self, timeout)
    449     raise CancelledError()
    450 elif self._state == FINISHED:
--> 451     return self.__get_result()
    453 self._condition.wait(timeout)
    455 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:

File [/usr/lib/python3.10/concurrent/futures/_base.py:403](http://localhost:8888/usr/lib/python3.10/concurrent/futures/_base.py#line=402), in Future.__get_result(self)
    401 if self._exception:
    402     try:
--> 403         raise self._exception
    404     finally:
    405         # Break a reference cycle with the exception in self._exception
    406         self = None

File [/usr/lib/python3.10/concurrent/futures/thread.py:58](http://localhost:8888/usr/lib/python3.10/concurrent/futures/thread.py#line=57), in _WorkItem.run(self)
     55     return
     57 try:
---> 58     result = self.fn(*self.args, **self.kwargs)
     59 except BaseException as exc:
     60     self.future.set_exception(exc)

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langchain_core/runnables/config.py:499, in ContextThreadPoolExecutor.map.<locals>._wrapped_fn(*args)
    498 def _wrapped_fn(*args: Any) -> T:
--> 499     return contexts.pop().run(fn, *args)

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langgraph/prebuilt/tool_node.py:62, in ToolNode._func.<locals>.run_one(call)
     61 def run_one(call: ToolCall):
---> 62     output = self.tools_by_name[call["name"]].invoke(call["args"], config)
     63     return ToolMessage(
     64         content=str_output(output), name=call["name"], tool_call_id=call["id"]
     65     )

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langchain_core/tools.py:260, in BaseTool.invoke(self, input, config, **kwargs)
    253 def invoke(
    254     self,
    255     input: Union[str, Dict],
    256     config: Optional[RunnableConfig] = None,
    257     **kwargs: Any,
    258 ) -> Any:
    259     config = ensure_config(config)
--> 260     return self.run(
    261         input,
    262         callbacks=config.get("callbacks"),
    263         tags=config.get("tags"),
    264         metadata=config.get("metadata"),
    265         run_name=config.get("run_name"),
    266         run_id=config.pop("run_id", None),
    267         config=config,
    268         **kwargs,
    269     )

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langchain_core/tools.py:417, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)
    415 except ValidationError as e:
    416     if not self.handle_validation_error:
--> 417         raise e
    418     elif isinstance(self.handle_validation_error, bool):
    419         observation = "Tool input validation error"

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langchain_core/tools.py:406, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)
    404 context = copy_context()
    405 context.run(_set_config_context, child_config)
--> 406 parsed_input = self._parse_input(tool_input)
    407 tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
    408 observation = (
    409     context.run(
    410         self._run, *tool_args, run_manager=run_manager, **tool_kwargs
   (...)
    413     else context.run(self._run, *tool_args, **tool_kwargs)
    414 )

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/langchain_core/tools.py:304, in BaseTool._parse_input(self, tool_input)
    302 else:
    303     if input_args is not None:
--> 304         result = input_args.parse_obj(tool_input)
    305         return {
    306             k: getattr(result, k)
    307             for k, v in result.dict().items()
    308             if k in tool_input
    309         }
    310 return tool_input

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/pydantic/v1/main.py:526, in BaseModel.parse_obj(cls, obj)
    524         exc = TypeError(f'{cls.__name__} expected dict not {obj.__class__.__name__}')
    525         raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls) from e
--> 526 return cls(**obj)

File ~/Documents/Projects/jupyter/.venv/lib/python3.10/site-packages/pydantic/v1/main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
    339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
    340 if validation_error:
--> 341     raise validation_error
    342 try:
    343     object_setattr(__pydantic_self__, '__dict__', values)

ValidationError: 1 validation error for python_replSchema
code
  field required (type=value_error.missing)

Here are 5 colors:

1. Red
2. Blue
3. Yellow
4. Green
5. Purple
Tokens Used: 0
	Prompt Tokens: 0
	Completion Tokens: 0
Successful Requests: 1
Total Cost (USD): $0.0

llm = ChatBedrock(
    model_id="cohere.command-r-v1:0",
    model_kwargs={
        "temperature": 0.3,
        "max_tokens": 4096
    },
)

ERROR: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: Malformed input request: #: extraneous key [prompt] is not permitted, please reformat your input and try again.

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[1], [line 80](vscode-notebook-cell:?execution_count=1&line=80)
     [70](vscode-notebook-cell:?execution_count=1&line=70)     return WorkoutProgram.parse_obj(workout_program_data)
     [73](vscode-notebook-cell:?execution_count=1&line=73) user_profile = {
     [74](vscode-notebook-cell:?execution_count=1&line=74)     "height": "5'10\"",
     [75](vscode-notebook-cell:?execution_count=1&line=75)     "weight": "180 lbs",
     [76](vscode-notebook-cell:?execution_count=1&line=76)     "age": 30,
     [77](vscode-notebook-cell:?execution_count=1&line=77)     "goals": "Lose weight and improve overall fitness"
     [78](vscode-notebook-cell:?execution_count=1&line=78) }
---> [80](vscode-notebook-cell:?execution_count=1&line=80) initial_workout_program = generate_initial_program(user_profile)
     [81](vscode-notebook-cell:?execution_count=1&line=81) print("\nSuccessfully generated workout program:")
     [82](vscode-notebook-cell:?execution_count=1&line=82) print(initial_workout_program.json(indent=2))

Cell In[1], [line 70](vscode-notebook-cell:?execution_count=1&line=70)
     [67](vscode-notebook-cell:?execution_count=1&line=67) print("\nExtracted workout program data:")
     [68](vscode-notebook-cell:?execution_count=1&line=68) print(json.dumps(workout_program_data, indent=2))
---> [70](vscode-notebook-cell:?execution_count=1&line=70) return WorkoutProgram.parse_obj(workout_program_data)

File ~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:526, in BaseModel.parse_obj(cls, obj)
    [524](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:524)         exc = TypeError(f'{cls.__name__} expected dict not {obj.__class__.__name__}')
    [525](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:525)         raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls) from e
--> [526](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:526) return cls(**obj)

File ~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
    [339](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:339) values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
    [340](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:340) if validation_error:
--> [341](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:341)     raise validation_error
    [342](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:342) try:
    [343](https://file+.vscode-resource.vscode-cdn.net/Users/austinwelch/Desktop/lg-example/~/mambaforge/envs/py311/lib/python3.11/site-packages/pydantic/v1/main.py:343)     object_setattr(__pydantic_self__, '__dict__', values)

ValidationError: 4 validation errors for WorkoutProgram
week1
  field required (type=value_error.missing)
week2
  field required (type=value_error.missing)
week3
  field required (type=value_error.missing)
week4
  field required (type=value_error.missing)

pydantic.v1.error_wrappers.ValidationError: 1 validation error for ChooseRoute
question_type
  field required (type=value_error.missing)

class ChooseRoute(BaseModel):
    """Classifies student's question type for routing."""

    question_type: str = Field(
        description="Classify the student's question as, 'concept', 'homework' or 'video'"
    )

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        bedrock_messages, system = _messages_to_bedrock(messages)
        params = self._converse_params(stop=stop, **_snake_to_camel_keys(kwargs)) # <------
        response = self.client.converse_stream(
            messages=bedrock_messages, system=system, **params
        )
        for event in response["stream"]:
            if message_chunk := _parse_stream_event(event):
                yield ChatGenerationChunk(message=message_chunk)

[]

import json
from typing import Dict

import boto3
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_aws.llms.sagemaker_endpoint import LLMContentHandler
from langchain_aws.llms.sagemaker_endpoint import SagemakerEndpoint
from langchain_core.prompts import PromptTemplate


class ContentHandler(LLMContentHandler):
    content_type = "application/json"
    accepts = "application/json"

    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
        input_str = json.dumps({"inputs": prompt, "parameters": model_kwargs})
        return input_str.encode("utf-8")

    def transform_output(self, output: bytes) -> str:
        response_json = json.loads(output.read().decode("utf-8"))
        return response_json[0]["generated_text"]


region = "<<region>>"
session = boto3.session.Session()
sagemaker_client = boto3.client("sagemaker-runtime", region_name=region)
endpoint_name = "<<endpoint-name>>"
conversation_memory = ConversationBufferMemory(
    memory_key="history", input_key="input", human_prefix="Human", ai_prefix="AI"
)
content_handler = ContentHandler()

llm = SagemakerEndpoint(
    endpoint_name=endpoint_name,
    client=sagemaker_client,
    content_handler=content_handler,
    model_kwargs={"temperature": 0.1, "max_new_tokens": 1000},
    streaming=False,  # changing this to True is an issue right now.
)
prompt_data = "{history} <s>[INST] {input} [/INST]"
question = "what is the recipe of mayonnaise?"
prompt_template = PromptTemplate(template=prompt_data)
conversation = ConversationChain(llm=llm, verbose=True, memory=conversation_memory, prompt=prompt_template)

response = conversation.invoke({"input": question, "history": conversation_memory.chat_memory.messages})
print("Model response::", response["response"])

---------------------------------------------------------------------------
StopIteration                             Traceback (most recent call last)
File [.../site-packages/langchain_aws/llms/sagemaker_endpoint.py:68](.../site-packages/langchain_aws/llms/sagemaker_endpoint.py#line=67), in LineIterator.__next__(self)
     67 try:
---> 68     chunk = next(self.byte_iterator)
     69 except StopIteration:

StopIteration: 

During handling of the above exception, another exception occurred:

KeyboardInterrupt                         Traceback (most recent call last)
Cell In[7], line 48
     45 prompt_template = PromptTemplate(template=prompt_data)
     46 conversation = ConversationChain(llm=llm, verbose=True, memory=conversation_memory, prompt=prompt_template)
---> 48 response = conversation.invoke({"input": question, "history": conversation_memory.chat_memory.messages})
     49 print("Model response::", response["response"])

File [.../site-packages/langchain/chains/base.py:166](.../site-packages/langchain/chains/base.py#line=165), in Chain.invoke(self, input, config, **kwargs)
    164 except BaseException as e:
    165     run_manager.on_chain_error(e)
--> 166     raise e
    167 run_manager.on_chain_end(outputs)
    169 if include_run_info:

File [.../site-packages/langchain/chains/base.py:156](.../site-packages/langchain/chains/base.py#line=155), in Chain.invoke(self, input, config, **kwargs)
    153 try:
    154     self._validate_inputs(inputs)
    155     outputs = (
--> 156         self._call(inputs, run_manager=run_manager)
    157         if new_arg_supported
    158         else self._call(inputs)
    159     )
    161     final_outputs: Dict[str, Any] = self.prep_outputs(
    162         inputs, outputs, return_only_outputs
    163     )
    164 except BaseException as e:

File [.../site-packages/langchain/chains/llm.py:126](.../site-packages/langchain/chains/llm.py#line=125), in LLMChain._call(self, inputs, run_manager)
    121 def _call(
    122     self,
    123     inputs: Dict[str, Any],
    124     run_manager: Optional[CallbackManagerForChainRun] = None,
    125 ) -> Dict[str, str]:
--> 126     response = self.generate([inputs], run_manager=run_manager)
    127     return self.create_outputs(response)[0]

File [.../site-packages/langchain/chains/llm.py:138](.../site-packages/langchain/chains/llm.py#line=137), in LLMChain.generate(self, input_list, run_manager)
    136 callbacks = run_manager.get_child() if run_manager else None
    137 if isinstance(self.llm, BaseLanguageModel):
--> 138     return self.llm.generate_prompt(
    139         prompts,
    140         stop,
    141         callbacks=callbacks,
    142         **self.llm_kwargs,
    143     )
    144 else:
    145     results = self.llm.bind(stop=stop, **self.llm_kwargs).batch(
    146         cast(List, prompts), {"callbacks": callbacks}
    147     )

File [.../site-packages/langchain_core/language_models/llms.py:633](.../site-packages/langchain_core/language_models/llms.py#line=632), in BaseLLM.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    625 def generate_prompt(
    626     self,
    627     prompts: List[PromptValue],
   (...)
    630     **kwargs: Any,
    631 ) -> LLMResult:
    632     prompt_strings = [p.to_string() for p in prompts]
--> 633     return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)

File [.../site-packages/langchain_core/language_models/llms.py:803](.../site-packages/langchain_core/language_models/llms.py#line=802), in BaseLLM.generate(self, prompts, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    788 if (self.cache is None and get_llm_cache() is None) or self.cache is False:
    789     run_managers = [
    790         callback_manager.on_llm_start(
    791             dumpd(self),
   (...)
    801         )
    802     ]
--> 803     output = self._generate_helper(
    804         prompts, stop, run_managers, bool(new_arg_supported), **kwargs
    805     )
    806     return output
    807 if len(missing_prompts) > 0:

File [.../site-packages/langchain_core/language_models/llms.py:670](.../site-packages/langchain_core/language_models/llms.py#line=669), in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
    668     for run_manager in run_managers:
    669         run_manager.on_llm_error(e, response=LLMResult(generations=[]))
--> 670     raise e
    671 flattened_outputs = output.flatten()
    672 for manager, flattened_output in zip(run_managers, flattened_outputs):

File [.../site-packages/langchain_core/language_models/llms.py:657](.../site-packages/langchain_core/language_models/llms.py#line=656), in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
    647 def _generate_helper(
    648     self,
    649     prompts: List[str],
   (...)
    653     **kwargs: Any,
    654 ) -> LLMResult:
    655     try:
    656         output = (
--> 657             self._generate(
    658                 prompts,
    659                 stop=stop,
    660                 # TODO: support multiple run managers
    661                 run_manager=run_managers[0] if run_managers else None,
    662                 **kwargs,
    663             )
    664             if new_arg_supported
    665             else self._generate(prompts, stop=stop)
    666         )
    667     except BaseException as e:
    668         for run_manager in run_managers:

File [.../site-packages/langchain_core/language_models/llms.py:1317](.../site-packages/langchain_core/language_models/llms.py#line=1316), in LLM._generate(self, prompts, stop, run_manager, **kwargs)
   1314 new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
   1315 for prompt in prompts:
   1316     text = (
-> 1317         self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
   1318         if new_arg_supported
   1319         else self._call(prompt, stop=stop, **kwargs)
   1320     )
   1321     generations.append([Generation(text=text)])
   1322 return LLMResult(generations=generations)

File [.../site-packages/langchain_aws/llms/sagemaker_endpoint.py:373](.../site-packages/langchain_aws/llms/sagemaker_endpoint.py#line=372), in SagemakerEndpoint._call(self, prompt, stop, run_manager, **kwargs)
    371 if self.streaming and run_manager:
    372     completion: str = ""
--> 373     for chunk in self._stream(prompt, stop, run_manager, **kwargs):
    374         completion += chunk.text
    375     return completion

File [.../site-packages/langchain_aws/llms/sagemaker_endpoint.py:327](.../site-packages/langchain_aws/llms/sagemaker_endpoint.py#line=326), in SagemakerEndpoint._stream(self, prompt, stop, run_manager, **kwargs)
    319 resp = self.client.invoke_endpoint_with_response_stream(
    320     EndpointName=self.endpoint_name,
    321     Body=self.content_handler.transform_input(prompt, _model_kwargs),
    322     ContentType=self.content_handler.content_type,
    323     **_endpoint_kwargs,
    324 )
    325 iterator = LineIterator(resp["Body"])
--> 327 for line in iterator:
    328     text = self.content_handler.transform_output(line)
    330     if stop is not None:

File [.../site-packages/langchain_aws/llms/sagemaker_endpoint.py:70](.../site-packages/langchain_aws/llms/sagemaker_endpoint.py#line=69), in LineIterator.__next__(self)
     68     chunk = next(self.byte_iterator)
     69 except StopIteration:
---> 70     if self.read_pos < self.buffer.getbuffer().nbytes:
     71         continue
     72     raise

KeyboardInterrupt:

  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 432, in _stream
    result = self._generate(
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 501, in _generate
    for chunk in self._stream(messages, stop, run_manager, **kwargs):
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 432, in _stream
    result = self._generate(
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 501, in _generate
    for chunk in self._stream(messages, stop, run_manager, **kwargs):
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 432, in _stream
    result = self._generate(
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 501, in _generate
    for chunk in self._stream(messages, stop, run_manager, **kwargs):
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\chat_models\bedrock.py", line 427, in _stream
    provider = self._get_provider()
  File "C:\Users\..\AppData\Local\pypoetry\Cache\virtualenvs\..\lib\site-packages\langchain_aws\llms\bedrock.py", line 570, in _get_provider
    if self.model_id.startswith("arn"):
RecursionError: maximum recursion depth exceeded while calling a Python object

def _stream(
...

        if "claude-3" in self._get_model():
            if _tools_in_params({**kwargs}):
                result = self._generate(
                    messages, stop=stop, run_manager=run_manager, **kwargs
                )

    def _generate(
...
        if self.streaming:
            response_metadata: List[Dict[str, Any]] = []
            for chunk in self._stream(messages, stop, run_manager, **kwargs):
                completion += chunk.text
                response_metadata.append(chunk.message.response_metadata)
                if "tool_calls" in chunk.message.additional_kwargs.keys():
                    tool_calls = chunk.message.additional_kwargs["tool_calls"]
            llm_output = _combine_generation_info_for_llm_result(
                response_metadata, provider_stop_reason_code
            )

ValueError: Error raised by bedrock service: 'stop_reason'

model_id = "meta.llama2-70b-chat-v1"
model_kwargs = {
    "temperature": 0,
    "max_gen_len": 2048,
}

llm = BedrockChat(
    model_id=model_id,
    model_kwargs=model_kwargs
)

llm.invoke("show me the weather in sf", stop=["Humidity"])

ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: Malformed input request: extraneous key [stop_sequences] is not permitted, please reformat your input and try again.

        bedrock_session = boto3.Session(
                            region_name="us-east-1"
                      )

        bedrock_client = bedrock_session.client("bedrock-runtime")
            
        llm = ChatBedrockConverse(
                client=bedrock_client,
                model_id=llm_model, #"meta.llama3-70b-instruct-v1:0" or "anthropic.claude-3-5-sonnet-20240620-v1:0"
                verbose=verbose,
                callbacks=callbacks
            )
       tools = [...] #A list of BaseTool

        agent = create_tool_calling_agent(llm, tools, prompt)

        langchain_agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

        user_message = {
            "message": message,
        }

        if new_files:
            user_message["files"] = str(new_files)

        response = langchain_agent_executor.invoke({
            "input": json.dumps(user_message),
            "chat_history": self.memory.buffer_as_messages,
        })

[{'type': 'text', 'text': '[...]', 'index': 0}]

Arguments: (ValidationException("An error occurred (ValidationException) when calling the ConverseStream operation: This model doesn't support tool use in streaming mode."),)

[
  {
    "role": "user",
    "content": "REDACTED"
  },
  {
    "role": "assistant",
    "content": "REDACTED"
  },
  {
    "role": "assistant",
    "content": "REDACTED"
  }
]

Traceback (most recent call last):
  File "/usr/local/lib/python3.11/site-packages/langchain_aws/llms/bedrock.py", line 628, in _prepare_input_and_invoke
    response = self.client.invoke_model(**request_options)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/botocore/client.py", line 565, in _api_call
    return self._make_api_call(operation_name, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/botocore/client.py", line 1021, in _make_api_call
    raise error_class(parsed_response, operation_name)
botocore.errorfactory.ValidationException: An error occurred (ValidationException) when calling the InvokeModel operation: messages: roles must alternate between "user" and "assistant", but found multiple "assistant" roles in a row

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.11/site-packages/uvicorn/protocols/http/httptools_impl.py", line 399, in run_asgi
    result = await app(  # type: ignore[func-returns-value]
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 70, in __call__
    return await self.app(scope, receive, send)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/applications.py", line 123, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 186, in __call__
    raise exc
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 164, in __call__
    await self.app(scope, receive, _send)
  File "/usr/local/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 756, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 776, in app
    await route.handle(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 297, in handle
    await self.app(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 77, in app
    await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 72, in app
    response = await func(request)
               ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 278, in app
    raw_response = await run_endpoint_function(
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/fastapi/routing.py", line 191, in run_endpoint_function
    return await dependant.call(**values)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/routers/spel.py", line 28, in spel_invoke
    content = chain.handle_query(message.input)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/services/ai_components.py", line 289, in handle_query
    return self.graph.invoke(
           ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 1333, in invoke
    for chunk in self.stream(
  File "/usr/local/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 876, in stream
    _panic_or_proceed(done, inflight, step)
  File "/usr/local/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 1422, in _panic_or_proceed
    raise exc
  File "/usr/local/lib/python3.11/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langgraph/pregel/retry.py", line 66, in run_with_retry
    task.proc.invoke(task.input, task.config)
  File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 2399, in invoke
    input = step.invoke(
            ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langgraph/utils.py", line 95, in invoke
    ret = context.run(self.func, input, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/services/ai_components.py", line 315, in agent_node
    result = agent.invoke(state)
             ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 2399, in invoke
    input = step.invoke(
            ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 170, in invoke
    self.generate_prompt(
  File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 599, in generate_prompt
    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 456, in generate
    raise e
  File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 446, in generate
    self._generate_with_cache(
  File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 671, in _generate_with_cache
    result = self._generate(
             ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langchain_aws/chat_models/bedrock.py", line 440, in _generate
    completion, llm_output = self._prepare_input_and_invoke(
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/site-packages/langchain_aws/llms/bedrock.py", line 635, in _prepare_input_and_invoke
    raise ValueError(f"Error raised by bedrock service: {e}")
ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: messages: roles must alternate between "user" and "assistant", but found multiple "assistant" roles in a row

Traceback (most recent call last):
  File "/Users/<REDACTED>/test/genai/.venv/lib/python3.12/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 600, in _run_script
    exec(code, module.__dict__)
  File "/Users/<REDACTED>/test/genai/chat.py", line 40, in <module>
    for chunk in chat.stream(messages):
  File "/Users/<REDACTED>/test/genai/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 265, in stream
    raise e
  File "/Users/<REDACTED>/test/genai/.venv/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 256, in stream
    generation += chunk
  File "/Users/<REDACTED>/test/genai/.venv/lib/python3.12/site-packages/langchain_core/outputs/chat_generation.py", line 79, in __add__
    message=self.message + other.message,
            ~~~~~~~~~~~~~^~~~~~~~~~~~~~~
  File "/Users/<REDACTED>/test/genai/.venv/lib/python3.12/site-packages/langchain_core/messages/ai.py", line 224, in __add__
    response_metadata = merge_dicts(
                        ^^^^^^^^^^^^
  File "/Users/<REDACTED>/test/genai/.venv/lib/python3.12/site-packages/langchain_core/utils/_merge.py", line 40, in merge_dicts
    raise TypeError(
TypeError: Additional kwargs key generation_token_count already exists in left dict and value has unsupported type <class 'int'>.

from langchain_aws import ChatBedrock
from langchain_core.messages import HumanMessage

prompt = "Tell me a joke"

chat = ChatBedrock(
    region_name="us-east-1",
    model_id="meta.llama3-8b-instruct-v1:0",
    model_kwargs={"temperature": 0.8},
    streaming=True,
)

messages = [HumanMessage(content=prompt)]

for chunk in chat.stream(messages):
    print(chunk.content, end="", flush=True)

# requirements.txt

langchain==0.2.6
langchain_aws==0.1.8
langchain_community==0.2.6
langchain_core==0.2.10
streamlit==1.35.0
unstructured==0.14.7

from langchain_aws import BedrockEmbeddings

# The options set here are overwritten
model_kwargs = {
    "dimensions": 256,
    "normalize": False
}

embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0",
                               region_name="eu-central-1", model_kwargs=model_kwargs)

text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua."
embedded = embeddings.embed_documents(texts=[text])

print(embedded[0])         # Vector is normalized because of the norm = True default in embed_documents()
print(len(embedded[0]))    # Prints a length of 1024 because of the dim = 1024 default in embed_documents()

from langchain_aws import BedrockEmbeddings
from langchain_postgres.vectorstores import PGVector
import os

DB_USER = os.getenv('PGUSER')
DB_PASSWORD = os.getenv('PGPASSWORD')
DB_HOST = os.getenv('PGHOST')
DB_PORT = os.getenv('PGPORT')
DB_NAME = os.getenv('PGDATABASE')

model_kwargs = {
    "dimensions": 256,
    "normalize": False
}

connection = f"postgresql+psycopg://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}"

text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua."

embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0",
                               region_name="eu-central-1", model_kwargs=model_kwargs)

pgvector_store = PGVector(
    connection=connection, embeddings=embeddings)

pgvector_store.add_texts(texts=[text])   # add_texts() calls embed_documents() and model_kwargs are overwritten again

embedded_texts = embeddings.embed_documents(
        texts=texts, dim=512)

vector_store.add_embeddings(
        texts=texts,
        embeddings=embedded_texts
)

retriever = vector_store.as_retriever()
    chain = (
        {
            "context": RunnableLambda(get_retriever_query_dict) | retriever,
            "question": itemgetter("question")
        }
        | prompt
        | model
        | StrOutputParser()
    )
chain.invoke({"question": question})

[SQL: SELECT langchain_pg_embedding.id AS langchain_pg_embedding_id, langchain_pg_embedding.collection_id AS langchain_pg_embedding_collection_id, langchain_pg_embedding.embedding AS langchain_pg_embedding_embedding, langchain_pg_embedding.document AS langchain_pg_embedding_document, langchain_pg_embedding.cmetadata AS langchain_pg_embedding_cmetadata, langchain_pg_embedding.embedding <=> %(embedding_1)s AS distance

sqlalchemy.exc.DataError: (psycopg.errors.DataException) different vector dimensions 512 and 1024

ConversationChain(
        llm=ss.llm,
        verbose=True,
        memory=ConversationBufferWindowMemory(
            k=ss.configs['models']['llm'][model]['memory_window'],
            ai_prefix="Assistant",
            chat_memory=StreamlitChatMessageHistory(),
        ),
        prompt=llm_prompt,
    ).predict(
        input=input_text, callbacks=[StreamHandler(st.empty())]
    )

Claude v3 models are not supported by this LLM.Please use `from langchain_community.chat_models import BedrockChat` instead. (type=value_error)

from langchain_core.messages import (
BaseMessage,
HumanMessage,
ToolMessage,
)
from inputs import description,links
import operator
from typing import Annotated, Sequence, TypedDict
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from tools.uber_suggest import search_seo_keywords
from tools.scraper import scrape
from tools.serper import get_serper_results
from langgraph.graph import END, StateGraph, START
from langgraph.prebuilt import ToolNode
from langchain_aws import ChatBedrock,ChatBedrockConverse
import os

llm = ChatBedrockConverse(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
temperature=0,
credentials_profile_name="bedrock",
region_name='us-west-2'
)

def create__generation_agent(llm, tools, system_message: str):
"""Create an agent."""
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK, another assistant with different tools "
" will help where you left off. Execute what you can to make progress."
" If you or any of the other assistants have the final answer or deliverable,"
" prefix your response with FINAL ANSWER so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
return prompt | llm.bind_tools(tools)

def create_reviewer_agent(llm, tools, system_message: str):
"""Create an agent."""
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK, another assistant with different tools "
" will help where you left off. Execute what you can to make progress."
" prefix your response with FINAL REVIEW or CONTINUE so the team knows to stop or continue."
" prefix will be FINAL REVIEW if nothing major to review."
" prefix will be CONTINUE if you have a critique"
" You have access to the following tools: {tool_names}.\n{system_message}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
return prompt | llm.bind_tools(tools)

This defines the object that is passed between each node
in the graph. We will create different nodes for each agent and tool
class AgentState(TypedDict):
messages: Annotated[Sequence[BaseMessage], operator.add]
sender: str

import functools

from langchain_core.messages import AIMessage

Helper function to create a node for a given agent
def agent_node(state, agent, name):
result = agent.invoke(state)

if result.tool_calls:
pass
else:
result = AIMessage(**result.dict(exclude={"type", "name"}), name=name)
return {
"messages": [result],
"sender": name,
}

Research agent and node
generator_agent = create__generation_agent(
llm,
tools,
system_message="""
""",
)
generator_node = functools.partial(agent_node, agent=generator_agent, name="generator")

chart_generator
reviewer_agent = create_reviewer_agent(
llm,
tools,
system_message="""""",
)
reviewer_node = functools.partial(agent_node, agent=reviewer_agent, name="reviewer")
tool_node = ToolNode(tools)

from typing import Literal

def router(state) -> Literal["call_tool", "end", "continue"]:

messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:

return "call_tool"
if "FINAL ANSWER" in last_message.content:
return "continue"
elif "FINAL REVIEW" in last_message.content:
return "end"
else:
return "continue"

workflow = StateGraph(AgentState)

workflow.add_node("generator", generator_node)
workflow.add_node("reviewer", reviewer_node)
workflow.add_node("call_tool", tool_node)

workflow.add_conditional_edges(
"generator",
router,
{"continue": "reviewer", "call_tool": "call_tool", "end": END},
)
workflow.add_conditional_edges(
"reviewer",
router,
{"continue": "generator", "call_tool": "call_tool", "end": END},
)

workflow.add_conditional_edges(
"call_tool",

lambda x: x["sender"],
{
"generator": "generator",
"reviewer": "reviewer",
},
)
workflow.add_edge(START, "generator")
graph = workflow.compile()

events = graph.stream(
{
"description":description,
"links":links,
"messages": [
HumanMessage(
content=f"""""
)
],
},

{"recursion_limit": 150},
)
for s in events:
print(s)
print("----")

response = client.invoke_model(
    body=b'bytes'|file,
    contentType='string',
    accept='string',
    modelId='string',
    trace='ENABLED'|'DISABLED',
    guardrailIdentifier='string',
    guardrailVersion='string'
)

import boto3 

session = boto3.Session(region_name=region)
boto3_bedrock = session.client(service_name="bedrock-runtime")

llm = BedrockLLM(region_name="us-east-1", model_id="cohere.command-r-v1:0" client=boto3_bedrock, verbose=verbose)
llm.invoke("Hey") # Fails

# ----------------------------------

prompt = ChatPromptTemplate.from_messages(
        [
            ("system", "You are a greeting chatbot, greet users only"),
            ("human", "{greeting}"),
        ]
    )
chain = (prompt | llm)

chain.invoke({"greeting": "Hey"}) # Fails

@classmethod
def prepare_input(
    cls,
    provider: str,
    model_kwargs: Dict[str, Any],
    prompt: Optional[str] = None,
    system: Optional[str] = None,
    messages: Optional[List[Dict]] = None,
) -> Dict[str, Any]:
    input_body = {**model_kwargs}
    if provider == "anthropic":
        if messages:
            input_body["anthropic_version"] = "bedrock-2023-05-31"
            input_body["messages"] = messages
            if system:
                input_body["system"] = system
            if "max_tokens" not in input_body:
                input_body["max_tokens"] = 1024
        if prompt:
            input_body["prompt"] = _human_assistant_format(prompt)
            if "max_tokens_to_sample" not in input_body:
                input_body["max_tokens_to_sample"] = 1024
    elif provider == "cohere"):
        input_body["message"] = prompt
    elif provider in ("ai21", "meta", "mistral"):
        input_body["prompt"] = prompt
    elif provider == "amazon":
        input_body = dict()
        input_body["inputText"] = prompt
        input_body["textGenerationConfig"] = {**model_kwargs}
    else:
        input_body["inputText"] = prompt

    return input_body