Model Error Debugging

python3 main.py TOOL CODE: from smolagents import Tool from huggingface_hub import InferenceClient class TextToImageTool(Tool): description = "This tool creates an image according to a prompt, which is a text descripti on." name = "image_generator" inputs = {"prompt": {"type": "string", "description": "The image generator prompt. Don't h esitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}} output_type = "image" model_sdxl = "black-forest-labs/FLUX.1-schnell" client = InferenceClient(model_sdxl) def forward(self, prompt): return self.client.text_to_image(prompt) ╭───────────────────────────────────────── New run ──────────────────────────────────────────╮ │ │ │ Write a longform blog post inspired by https://danielkliewer.com │ │ │ ╰─ OllamaModel - ───────────────────────────────────────────────────────────────────────────╯ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ DEBUG - formatted_messages: [{'role': <MessageRole.SYSTEM: 'system'>, 'content': 'You are an e xpert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of \'Thought:\', \'Code:\', and \'Observation:\' sequences.\n\nAt each step, in the \'Thought:\' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\nThen in the \'Code:\' sequence, you should write the code in simple Python. The code sequence must end with \'<end_code>\' sequence.\nDuring each intermediate step, you can use \'print()\' to save whatever important information you will then need.\nThese print outputs will then appear in the \'Observation:\' field, which will be available as input for the next step.\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = document_qa(document=document, question="Who is the oldest person mentioned?")\nprint(answer)\n```<end_code>\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\nThought: I will now generate an image showcasing the oldest person.\nCode:\n```py\nimage = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")\nfinal_answer(image)\n```<end_code>\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool\nCode:\n```py\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n```<end_code>\n\n---\nTask:\n"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\nYou have been provided with these additional arguments, that you can access using the keys as variables in your python code:\n{\'question\': \'Quel est l\'animal sur l\'image?\', \'image\': \'path/to/image.jpg\'}"\n\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\nCode:\n```py\ntranslated_question = translator(question=question, src_lang="French", tgt_lang="English")\nprint(f"The translated question is {translated_question}.")\nanswer = image_qa(image=image, question=translated_question)\nfinal_answer(f"The answer is {answer}")\n```<end_code>\n\n---\nTask:\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\n\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\nCode:\n```py\npages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")\nprint(pages)\n```<end_code>\nObservation:\nNo result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".\n\nThought: The query was maybe too restrictive and did not find any results. Let\'s try again with a broader query.\nCode:\n```py\npages = search(query="1979 interview Stanislaus Ulam")\nprint(pages)\n```<end_code>\nObservation:\nFound 6 pages:\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\n\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\n\n(truncated)\n\nThought: I will read the first 2 pages to know more.\nCode:\n```py\nfor url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:\n whole_page = visit_webpage(url)\n print(whole_page)\n print("\n" + "="*80 + "\n") # Print separator between pages\n```<end_code>\nObservation:\nManhattan Project Locations:\nLos Alamos, NM\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\n(truncated)\n\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let\'s answer in one word.\nCode:\n```py\nfinal_answer("diminished")\n```<end_code>\n\n---\nTask: "Which city has the highest population: Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nCode:\n```py\nfor city in ["Guangzhou", "Shanghai"]:\n print(f"Population {city}:", search(f"{city} population")\n```<end_code>\nObservation:\nPopulation Guangzhou: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\nPopulation Shanghai: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has the highest population.\nCode:\n```py\nfinal_answer("Shanghai")\n```<end_code>\n\n---\nTask: "What is the current age of the pope, raised to the power 0.36?"\n\nThought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.\nCode:\n```py\npope_age_wiki = wiki(query="current pope age")\nprint("Pope age as per wikipedia:", pope_age_wiki)\npope_age_search = web_search(query="current pope age")\nprint("Pope age as per google search:", pope_age_search)\n```<end_code>\nObservation:\nPope age: "The pope Francis is currently 88 years old."\n\nThought: I know that the pope is 88 years old. Let\'s compute the result using python code.\nCode:\n```py\npope_current_age = 88 ** 0.36\nfinal_answer(pope_current_age)\n```<end_code>\n\nAbove example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:\n\n\n- web_search: Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\n Takes inputs: {\'query\': {\'type\': \'string\', \'description\': \'The search query to perform.\'}}\n Returns an output of type: string\n\n- image_generator: This tool creates an image according to a prompt, which is a text description.\n Takes inputs: {\'prompt\': {\'type\': \'string\', \'description\': "The image generator prompt. Don\'t hesitate to add details in the prompt to make the image look better, like \'high-res, photorealistic\', etc."}}\n Returns an output of type: image\n\n- final_answer: Provides a final answer to the given problem.\n Takes inputs: {\'answer\': {\'type\': \'any\', \'description\': \'The final answer to the problem\'}}\n Returns an output of type: any\n\n\n\nHere are the rules you should always follow to solve your task:\n1. Always provide a \'Thought:\' sequence, and a \'Code:\n```py\' sequence ending with \'```<end_code>\' sequence, else you will fail.\n2. Use only variables that you have defined!\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in \'answer = wiki({\'query\': "What is the place where James Bond lives?"})\', but use the arguments directly as in \'answer = wiki(query="What is the place where James Bond lives?")\'.\n4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\n5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\n6. Don\'t name any new variable with the same name as a tool: for instance don\'t name a variable \'final_answer\'.\n7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\n8. You can use imports in your code, but only from the following list of modules: [\'math\', \'queue\', \'datetime\', \'unicodedata\', \'re\', \'time\', \'random\', \'stat\', \'statistics\', \'itertools\', \'collections\']\n9. The state persists between code executions: so if in one step you\'ve created variables or imported modules, these will all persist.\n10. Don\'t give up! You\'re in charge of solving the task, not providing directions to solve it.\n\nNow Begin! If you solve the task correctly, you will receive a reward of $1,000,000.'}, {'role': <MessageRole.USER: 'user'>, 'content': 'New task:\nWrite a longform blog post inspired by https://danielkliewer.com'}] Error in generating model output: 'str' object has no attribute 'content' [Step 0: Duration 99.82 seconds] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ DEBUG - formatted_messages: [{'role': <MessageRole.SYSTEM: 'system'>, 'content': 'You are an e xpert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of \'Thought:\', \'Code:\', and \'Observation:\' sequences.\n\nAt each step, in the \'Thought:\' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\nThen in the \'Code:\' sequence, you should write the code in simple Python. The code sequence must end with \'<end_code>\' sequence.\nDuring each intermediate step, you can use \'print()\' to save whatever important information you will then need.\nThese print outputs will then appear in the \'Observation:\' field, which will be available as input for the next step.\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = document_qa(document=document, question="Who is the oldest person mentioned?")\nprint(answer)\n```<end_code>\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\nThought: I will now generate an image showcasing the oldest person.\nCode:\n```py\nimage = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")\nfinal_answer(image)\n```<end_code>\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool\nCode:\n```py\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n```<end_code>\n\n---\nTask:\n"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\nYou have been provided with these additional arguments, that you can access using the keys as variables in your python code:\n{\'question\': \'Quel est l\'animal sur l\'image?\', \'image\': \'path/to/image.jpg\'}"\n\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\nCode:\n```py\ntranslated_question = translator(question=question, src_lang="French", tgt_lang="English")\nprint(f"The translated question is {translated_question}.")\nanswer = image_qa(image=image, question=translated_question)\nfinal_answer(f"The answer is {answer}")\n```<end_code>\n\n---\nTask:\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\n\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\nCode:\n```py\npages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")\nprint(pages)\n```<end_code>\nObservation:\nNo result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".\n\nThought: The query was maybe too restrictive and did not find any results. Let\'s try again with a broader query.\nCode:\n```py\npages = search(query="1979 interview Stanislaus Ulam")\nprint(pages)\n```<end_code>\nObservation:\nFound 6 pages:\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\n\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\n\n(truncated)\n\nThought: I will read the first 2 pages to know more.\nCode:\n```py\nfor url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:\n whole_page = visit_webpage(url)\n print(whole_page)\n print("\n" + "="*80 + "\n") # Print separator between pages\n```<end_code>\nObservation:\nManhattan Project Locations:\nLos Alamos, NM\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\n(truncated)\n\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let\'s answer in one word.\nCode:\n```py\nfinal_answer("diminished")\n```<end_code>\n\n---\nTask: "Which city has the highest population: Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nCode:\n```py\nfor city in ["Guangzhou", "Shanghai"]:\n print(f"Population {city}:", search(f"{city} population")\n```<end_code>\nObservation:\nPopulation Guangzhou: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\nPopulation Shanghai: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has the highest population.\nCode:\n```py\nfinal_answer("Shanghai")\n```<end_code>\n\n---\nTask: "What is the current age of the pope, raised to the power 0.36?"\n\nThought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.\nCode:\n```py\npope_age_wiki = wiki(query="current pope age")\nprint("Pope age as per wikipedia:", pope_age_wiki)\npope_age_search = web_search(query="current pope age")\nprint("Pope age as per google search:", pope_age_search)\n```<end_code>\nObservation:\nPope age: "The pope Francis is currently 88 years old."\n\nThought: I know that the pope is 88 years old. Let\'s compute the result using python code.\nCode:\n```py\npope_current_age = 88 ** 0.36\nfinal_answer(pope_current_age)\n```<end_code>\n\nAbove example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:\n\n\n- web_search: Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\n Takes inputs: {\'query\': {\'type\': \'string\', \'description\': \'The search query to perform.\'}}\n Returns an output of type: string\n\n- image_generator: This tool creates an image according to a prompt, which is a text description.\n Takes inputs: {\'prompt\': {\'type\': \'string\', \'description\': "The image generator prompt. Don\'t hesitate to add details in the prompt to make the image look better, like \'high-res, photorealistic\', etc."}}\n Returns an output of type: image\n\n- final_answer: Provides a final answer to the given problem.\n Takes inputs: {\'answer\': {\'type\': \'any\', \'description\': \'The final answer to the problem\'}}\n Returns an output of type: any\n\n\n\nHere are the rules you should always follow to solve your task:\n1. Always provide a \'Thought:\' sequence, and a \'Code:\n```py\' sequence ending with \'```<end_code>\' sequence, else you will fail.\n2. Use only variables that you have defined!\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in \'answer = wiki({\'query\': "What is the place where James Bond lives?"})\', but use the arguments directly as in \'answer = wiki(query="What is the place where James Bond lives?")\'.\n4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\n5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\n6. Don\'t name any new variable with the same name as a tool: for instance don\'t name a variable \'final_answer\'.\n7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\n8. You can use imports in your code, but only from the following list of modules: [\'math\', \'queue\', \'datetime\', \'unicodedata\', \'re\', \'time\', \'random\', \'stat\', \'statistics\', \'itertools\', \'collections\']\n9. The state persists between code executions: so if in one step you\'ve created variables or imported modules, these will all persist.\n10. Don\'t give up! You\'re in charge of solving the task, not providing directions to solve it.\n\nNow Begin! If you solve the task correctly, you will receive a reward of $1,000,000.'}, {'role': <MessageRole.USER: 'user'>, 'content': 'New task:\nWrite a longform blog post inspired by https://danielkliewer.com'}, {'role': <MessageRole.TOOL_RESPONSE: 'tool-response'>, 'content': "Error:\nError in generating model output:\n'str' object has no attribute 'content'\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\n"}] Ollama Error: 1 validation error for Message role Input should be 'user', 'assistant', 'system' or 'tool' [type=literal_error, input_value=<Me ssageRole.TOOL_RESPONSE: 'tool-response'>, input_type=MessageRole] For further information visit https://errors.pydantic.dev/2.10/v/literal_error Error in generating model output: 'str' object has no attribute 'content' [Step 1: Duration 0.00 seconds] DEBUG - formatted_messages: [{'role': <MessageRole.SYSTEM: 'system'>, 'content': '\nYou are a world expert at gathering known and unknown facts based on a conversation.\nBelow you will find a task, and a history of attempts made to solve the task. You will have to produce a list of these:\n### 1. Facts given in the task\n### 2. Facts that we have learned\n### 3. Facts still to look up\n### 4. Facts still to derive\nFind the task and history below.'}, {'role': <MessageRole.SYSTEM: 'system'>, 'content': 'You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of \'Thought:\', \'Code:\', and \'Observation:\' sequences.\n\nAt each step, in the \'Thought:\' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\nThen in the \'Code:\' sequence, you should write the code in simple Python. The code sequence must end with \'<end_code>\' sequence.\nDuring each intermediate step, you can use \'print()\' to save whatever important information you will then need.\nThese print outputs will then appear in the \'Observation:\' field, which will be available as input for the next step.\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = document_qa(document=document, question="Who is the oldest person mentioned?")\nprint(answer)\n```<end_code>\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\nThought: I will now generate an image showcasing the oldest person.\nCode:\n```py\nimage = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")\nfinal_answer(image)\n```<end_code>\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool\nCode:\n```py\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n```<end_code>\n\n---\nTask:\n"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\nYou have been provided with these additional arguments, that you can access using the keys as variables in your python code:\n{\'question\': \'Quel est l\'animal sur l\'image?\', \'image\': \'path/to/image.jpg\'}"\n\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\nCode:\n```py\ntranslated_question = translator(question=question, src_lang="French", tgt_lang="English")\nprint(f"The translated question is {translated_question}.")\nanswer = image_qa(image=image, question=translated_question)\nfinal_answer(f"The answer is {answer}")\n```<end_code>\n\n---\nTask:\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\n\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\nCode:\n```py\npages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")\nprint(pages)\n```<end_code>\nObservation:\nNo result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".\n\nThought: The query was maybe too restrictive and did not find any results. Let\'s try again with a broader query.\nCode:\n```py\npages = search(query="1979 interview Stanislaus Ulam")\nprint(pages)\n```<end_code>\nObservation:\nFound 6 pages:\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\n\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\n\n(truncated)\n\nThought: I will read the first 2 pages to know more.\nCode:\n```py\nfor url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:\n whole_page = visit_webpage(url)\n print(whole_page)\n print("\n" + "="*80 + "\n") # Print separator between pages\n```<end_code>\nObservation:\nManhattan Project Locations:\nLos Alamos, NM\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\n(truncated)\n\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let\'s answer in one word.\nCode:\n```py\nfinal_answer("diminished")\n```<end_code>\n\n---\nTask: "Which city has the highest population: Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nCode:\n```py\nfor city in ["Guangzhou", "Shanghai"]:\n print(f"Population {city}:", search(f"{city} population")\n```<end_code>\nObservation:\nPopulation Guangzhou: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\nPopulation Shanghai: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has the highest population.\nCode:\n```py\nfinal_answer("Shanghai")\n```<end_code>\n\n---\nTask: "What is the current age of the pope, raised to the power 0.36?"\n\nThought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.\nCode:\n```py\npope_age_wiki = wiki(query="current pope age")\nprint("Pope age as per wikipedia:", pope_age_wiki)\npope_age_search = web_search(query="current pope age")\nprint("Pope age as per google search:", pope_age_search)\n```<end_code>\nObservation:\nPope age: "The pope Francis is currently 88 years old."\n\nThought: I know that the pope is 88 years old. Let\'s compute the result using python code.\nCode:\n```py\npope_current_age = 88 ** 0.36\nfinal_answer(pope_current_age)\n```<end_code>\n\nAbove example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:\n\n\n- web_search: Performs a duckduckgo web search based on your query (think a Google search) then returns the top search results.\n Takes inputs: {\'query\': {\'type\': \'string\', \'description\': \'The search query to perform.\'}}\n Returns an output of type: string\n\n- image_generator: This tool creates an image according to a prompt, which is a text description.\n Takes inputs: {\'prompt\': {\'type\': \'string\', \'description\': "The image generator prompt. Don\'t hesitate to add details in the prompt to make the image look better, like \'high-res, photorealistic\', etc."}}\n Returns an output of type: image\n\n- final_answer: Provides a final answer to the given problem.\n Takes inputs: {\'answer\': {\'type\': \'any\', \'description\': \'The final answer to the problem\'}}\n Returns an output of type: any\n\n\n\nHere are the rules you should always follow to solve your task:\n1. Always provide a \'Thought:\' sequence, and a \'Code:\n```py\' sequence ending with \'```<end_code>\' sequence, else you will fail.\n2. Use only variables that you have defined!\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in \'answer = wiki({\'query\': "What is the place where James Bond lives?"})\', but use the arguments directly as in \'answer = wiki(query="What is the place where James Bond lives?")\'.\n4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\n5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\n6. Don\'t name any new variable with the same name as a tool: for instance don\'t name a variable \'final_answer\'.\n7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\n8. You can use imports in your code, but only from the following list of modules: [\'math\', \'queue\', \'datetime\', \'unicodedata\', \'re\', \'time\', \'random\', \'stat\', \'statistics\', \'itertools\', \'collections\']\n9. The state persists between code executions: so if in one step you\'ve created variables or imported modules, these will all persist.\n10. Don\'t give up! You\'re in charge of solving the task, not providing directions to solve it.\n\nNow Begin! If you solve the task correctly, you will receive a reward of $1,000,000.'}, {'role': <MessageRole.USER: 'user'>, 'content': 'New task:\nWrite a longform blog post inspired by https://danielkliewer.com'}, {'role': <MessageRole.TOOL_RESPONSE: 'tool-response'>, 'content': "Error:\nError in generating model output:\n'str' object has no attribute 'content'\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\n"}, {'role': <MessageRole.TOOL_RESPONSE: 'tool-response'>, 'content': "Error:\nError in generating model output:\n'str' object has no attribute 'content'\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\n"}, {'role': <MessageRole.USER: 'user'>, 'content': "Earlier we've built a list of facts.\nBut since in your previous steps you may have learned useful new facts or invalidated some false ones.\nPlease update your list of facts based on the previous history, and provide these headings:\n### 1. Facts given in the task\n### 2. Facts that we have learned\n### 3. Facts still to look up\n### 4. Facts still to derive\n\nNow write your new list of facts below."}] Ollama Error: 1 validation error for Message role Input should be 'user', 'assistant', 'system' or 'tool' [type=literal_error, input_value=<Me ssageRole.TOOL_RESPONSE: 'tool-response'>, input_type=MessageRole] For further information visit https://errors.pydantic.dev/2.10/v/literal_error [Step 2: Duration 0.00 seconds] Execution failed: 'str' object has no attribute 'content' from smolagents import load_tool, CodeAgent, DuckDuckGoSearchTool import ollama class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): try: formatted_messages = [] for msg in messages: # If msg is a string, treat it as a user message with that string as content if isinstance(msg, str): formatted_messages.append({ "role": "user", "content": msg }) # If msg is a dictionary, extract 'role' and 'content' elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") # Handle any lists in content if isinstance(content, list): content = " ".join( part.get("text", "") for part in content if isinstance(part, dict) and "text" in part ) formatted_messages.append({ "role": role, "content": content }) else: # Handle any unexpected type formatted_messages.append({ "role": "user", "content": str(msg) }) print("DEBUG - formatted_messages:", formatted_messages) # Get response from Ollama response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) return response.get("message", {}).get("content", "") except Exception as e: print(f"Ollama Error: {str(e)}") return "" # Initialize tools and agent image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() agent = CodeAgent( tools=[search_tool, image_generation_tool], model=OllamaModel("mistral-small:24b-instruct-2501-q8_0"), planning_interval=3, ) # Run with error handling try: result = agent.run( "Write a longform blog post inspired by https://danielkliewer.com", ) print(result) except Exception as e: print(f"Execution failed: {str(e)}")

It seems there is an error in generating the model's output due to an issue with how the content is being formatted and passed. The error occurs when trying to send a message that is not properly structured, leading to an exception. The issue may be with the way input messages are being handled or the model's response formatting. To resolve this, I recommend ensuring that all messages are correctly structured, as the model expects either `role` and `content` in a dictionary or just a string that it can process. Also, make sure that if there are any lists in the message content, they are properly joined. Would you like assistance in debugging or fixing this code?

'str' object has no attribute 'content'

The error you're encountering—`'str' object has no attribute 'content'`—suggests that the code is attempting to treat a string as a dictionary, which leads to the error when it tries to access the `content` attribute that is expected to exist on dictionaries, not strings. This typically happens when the code passes a string directly to the `messages` list instead of a dictionary that includes both the `role` and `content`. ### Suggested Fix: In the part of the code where you are building the `formatted_messages` list, ensure that you are properly wrapping each string in a dictionary. Here's how you can adjust that portion of the code: ```python for msg in messages: if isinstance(msg, str): formatted_messages.append({ "role": "user", # or "system" if that's the intended role "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") if isinstance(content, list): content = " ".join( part.get("text", "") for part in content if isinstance(part, dict) and "text" in part ) formatted_messages.append({ "role": role, "content": content }) else: formatted_messages.append({ "role": "user", "content": str(msg) }) ``` This way, the message is correctly formatted with both a `role` and `content`, even if it’s just a simple string. This should fix the `'str' object has no attribute 'content'` error. Let me know if you'd like further clarification or help with additional parts of the code!

python3 main.py TOOL CODE: from smolagents import Tool from huggingface_hub import InferenceClient class TextToImageTool(Tool): description = "This tool creates an image according to a prompt, which is a text description." name = "image_generator" inputs = {"prompt": {"type": "string", "description": "The image generato r prompt. Don't hesitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}} output_type = "image" model_sdxl = "black-forest-labs/FLUX.1-schnell" client = InferenceClient(model_sdxl) def forward(self, prompt): return self.client.text_to_image(prompt) ╭───────────────────────────────── New run ─────────────────────────────────╮ │ │ │ Write a longform blog post inspired by https://danielkliewer.com │ │ │ ╰─ OllamaModel - ──────────────────────────────────────────────────────────╯ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ INFO:httpx:HTTP Request: POST http://127.0.0.1:11434/api/chat "HTTP/1.1 200 O K" Error in generating model output: 'str' object has no attribute 'content' [Step 0: Duration 27.02 seconds] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ERROR:__main__:Ollama Error: 1 validation error for Message role Input should be 'user', 'assistant', 'system' or 'tool' [type=literal_error , input_value=<MessageRole.TOOL_RESPONSE: 'tool-response'>, input_type=MessageRole] For further information visit https://errors.pydantic.dev/2.10/v/literal_ error Error in generating model output: 'str' object has no attribute 'content' [Step 1: Duration 0.00 seconds] ERROR:__main__:Ollama Error: 1 validation error for Message role Input should be 'user', 'assistant', 'system' or 'tool' [type=literal_error , input_value=<MessageRole.TOOL_RESPONSE: 'tool-response'>, input_type=MessageRole] For further information visit https://errors.pydantic.dev/2.10/v/literal_ error [Step 2: Duration 0.00 seconds] Execution failed: 'str' object has no attribute 'content'

The error you are seeing—`'str' object has no attribute 'content'`—is related to how the message is being passed in your chat request. Specifically, when you are passing a message to the Ollama model, it expects a certain structure, but it seems like a `str` (a string) is being used directly, where a more complex structure is needed. It seems the model or function expects a message with both `role` and `content` attributes, but it is receiving just a string, which doesn't include `role`. ### Solution: When building the message list for Ollama, ensure each message has the `role` and `content` keys, even if it’s just a string. The `role` should be one of `'user'`, `'assistant'`, or `'system'`. Here’s an updated version of your message handling: ### Updated Message Formatting Code: ```python # Example code to fix the issue def format_messages(messages): formatted_messages = [] for msg in messages: if isinstance(msg, str): formatted_messages.append({ "role": "user", # Default role can be 'user' "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") # If content is a list, convert it to a single string if isinstance(content, list): content = " ".join( part.get("text", "") for part in content if isinstance(part, dict) and "text" in part ) formatted_messages.append({ "role": role, "content": content }) else: formatted_messages.append({ "role": "user", # Default role for unexpected types "content": str(msg) }) return formatted_messages # Example usage messages = ["Write a longform blog post inspired by https://danielkliewer.com"] formatted_messages = format_messages(messages) print("Formatted Messages:", formatted_messages) # Now pass `formatted_messages` to the model call response = ollama_model.chat(formatted_messages) ``` ### Key Changes: - Each string is wrapped in a dictionary with `role` and `content`. - The `role` is set to `"user"` by default. - If content is a list (like in some structured formats), it is joined into a single string. This should resolve the `'str' object has no attribute 'content'` error, as it ensures the expected structure of the message. Let me know if you'd like more help with this!

this is how it should work, but I want to use ollama : from smolagents import load_tool, CodeAgent, HfApiModel, DuckDuckGoSearchTool from dotenv import load_dotenv load_dotenv() # Import tool from Hub image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() agent = CodeAgent( tools=[search_tool, image_generation_tool], model=HfApiModel("Qwen/Qwen2.5-72B-Instruct"), planning_interval=3 # This is where you activate planning! ) # Run it! result = agent.run( "How long would a cheetah at full speed take to run the length of Pont Alexandre III?", )

It looks like you're trying to use the `smolagents` framework with a model from Hugging Face (`Qwen/Qwen2.5-72B-Instruct`) and integrate a code agent with tools like `DuckDuckGoSearchTool` and `image_generation_tool`. To get everything working properly with Ollama, you’ll need to ensure that the agent is set up correctly, and the tools you’re loading are integrated seamlessly. Here's an updated version of your script that should work with Ollama using the same logic but properly handling the integration: ```python from smolagents import load_tool, CodeAgent, OllamaModel, DuckDuckGoSearchTool from dotenv import load_dotenv # Load environment variables load_dotenv() # Define tools image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() # Define Ollama model ollama_model = OllamaModel("mistral-small:24b-instruct-2501-q8_0") # Create agent with tools and Ollama model agent = CodeAgent( tools=[search_tool, image_generation_tool], model=ollama_model, # Use the Ollama model here planning_interval=3 # Activates planning ) # Run the agent with your query result = agent.run( "How long would a cheetah at full speed take to run the length of Pont Alexandre III?" ) # Output the result print(result) ``` ### Key Points: 1. **Ollama Model**: Instead of `HfApiModel`, you're using `OllamaModel` to handle the communication with the Ollama model (e.g., `mistral-small:24b-instruct-2501-q8_0`). 2. **Agent Setup**: You integrate both the `DuckDuckGoSearchTool` and `image_generation_tool` into the agent. 3. **Planning**: The agent has a `planning_interval` set, so it will go through its planning phase before executing. Make sure you have the `smolagents`, `duckduckgo`, and other required dependencies installed, and ensure your environment is set up properly (e.g., `.env` file for any necessary tokens). Let me know if you'd like more assistance or run into any issues!

python3 main.py Traceback (most recent call last): File "/Users/danielkliewer/Think/main.py", line 1, in <module> from smolagents import load_tool, CodeAgent, OllamaModel, DuckDuckGoSearc hTool ImportError: cannot import name 'OllamaModel' from 'smolagents' (/Users/danie lkliewer/Think/venv/lib/python3.13/site-packages/smolagents/__init__.py)

The error you're encountering—`ImportError: cannot import name 'OllamaModel' from 'smolagents'`—suggests that `OllamaModel` is not part of the `smolagents` package you are using. It seems the `OllamaModel` was not defined in `smolagents`, so you'll need to either: 1. **Use a Custom Ollama Model Class**: You can create your own class that integrates with Ollama, or 2. **Switch to a Different Model**: Use another model available in `smolagents`, such as `HfApiModel`. ### Option 1: Use a Custom Ollama Model Class Here’s how you can manually create a wrapper for the Ollama model: ```python from smolagents import load_tool, CodeAgent, DuckDuckGoSearchTool from dotenv import load_dotenv import ollama # Load environment variables load_dotenv() # Define a custom class to use Ollama class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [] for msg in messages: if isinstance(msg, str): formatted_messages.append({ "role": "user", "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") if isinstance(content, list): content = " ".join(part.get("text", "") for part in content if isinstance(part, dict) and "text" in part) formatted_messages.append({ "role": role, "content": content }) else: formatted_messages.append({ "role": "user", "content": str(msg) }) response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) return response.get("message", {}).get("content", "") # Define tools image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() # Define the custom Ollama model ollama_model = OllamaModel("mistral-small:24b-instruct-2501-q8_0") # Create the agent agent = CodeAgent( tools=[search_tool, image_generation_tool], model=ollama_model, planning_interval=3 ) # Run the agent result = agent.run( "How long would a cheetah at full speed take to run the length of Pont Alexandre III?" ) # Output the result print(result) ``` This custom `OllamaModel` class handles communication with the Ollama model and ensures the messages are correctly formatted for the API. ### Option 2: Use a Model from `smolagents` If you don't want to create a custom model class, you can use the available models from `smolagents`, such as `HfApiModel`. Here’s how you can modify your script: ```python from smolagents import load_tool, CodeAgent, HfApiModel, DuckDuckGoSearchTool from dotenv import load_dotenv load_dotenv() # Define tools image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() # Use an existing model from smolagents model = HfApiModel("Qwen/Qwen2.5-72B-Instruct") # Create the agent agent = CodeAgent( tools=[search_tool, image_generation_tool], model=model, planning_interval=3 ) # Run the agent result = agent.run( "How long would a cheetah at full speed take to run the length of Pont Alexandre III?" ) # Output the result print(result) ``` ### Which Option Should You Choose? - If you want to use **Ollama** specifically, go with **Option 1** and implement a custom class. - If using **another available model** from `smolagents` like `HfApiModel` is acceptable, go with **Option 2**. Let me know if you'd like more assistance!

python3 main.py TOOL CODE: from smolagents import Tool from huggingface_hub import InferenceClient class TextToImageTool(Tool): description = "This tool creates an image according to a prompt, which is a text description." name = "image_generator" inputs = {"prompt": {"type": "string", "description": "The image generato r prompt. Don't hesitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}} output_type = "image" model_sdxl = "black-forest-labs/FLUX.1-schnell" client = InferenceClient(model_sdxl) def forward(self, prompt): return self.client.text_to_image(prompt) ╭───────────────────────────────── New run ─────────────────────────────────╮ │ │ │ Write a longform blog post inspired by https://danielkliewer.com │ │ │ ╰─ OllamaModel - ──────────────────────────────────────────────────────────╯ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Error in generating model output: 'str' object has no attribute 'content' [Step 0: Duration 94.81 seconds] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Error in generating model output: 1 validation error for Message role Input should be 'user', 'assistant', 'system' or 'tool' For further information visit https://errors.pydantic.dev/2.10/v/literal_error [Step 1: Duration 0.00 seconds] [Step 2: Duration 0.00 seconds] Traceback (most recent call last): File "/Users/danielkliewer/Think/main.py", line 61, in <module> result = agent.run( "Write a longform blog post inspired by https://danielkliewer.com" ) File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/smolagen ts/agents.py", line 419, in run return deque(self._run(task=self.task, images=images), maxlen=1)[0] ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/smolagen ts/agents.py", line 440, in _run self.planning_step( ~~~~~~~~~~~~~~~~~~^ task, ^^^^^ is_first_step=(self.step_number == 0), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ step=self.step_number, ^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/smolagen ts/agents.py", line 577, in planning_step chat_message_facts: ChatMessage = self.model(input_messages) ~~~~~~~~~~^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/Think/main.py", line 38, in __call__ response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/ollama/_ client.py", line 339, in chat messages=[message for message in _copy_messages(messages)], ~~~~~~~~~~~~~~^^^^^^^^^^ File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/ollama/_ client.py", line 1135, in _copy_messages yield Message.model_validate( ~~~~~~~~~~~~~~~~~~~~~~^ {k: [image for image in _copy_images(v)] if k == 'images' else v for k, v in dict(message).items() if v}, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/pydantic /main.py", line 627, in model_validate return cls.__pydantic_validator__.validate_python( ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ obj, strict=strict, from_attributes=from_attributes, context=context ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ pydantic_core._pydantic_core.ValidationError: 1 validation error for Message role Input should be 'user', 'assistant', 'system' or 'tool' [type=literal_error , input_value=<MessageRole.TOOL_RESPONSE: 'tool-response'>, input_type=MessageRole] For further information visit https://errors.pydantic.dev/2.10/v/literal_ error https://huggingface.co/docs/smolagents/reference/models

The error you're encountering suggests that the `role` field in your messages isn't set correctly. Specifically, the error mentions that the role should be one of `'user'`, `'assistant'`, `'system'`, or `'tool'`, but it's encountering a value that is not valid. From the trace, it appears the issue occurs when the code passes a response with an invalid `role` (`MessageRole.TOOL_RESPONSE`) instead of one of the valid roles. ### Solution: The problem seems to be happening in the code where you're passing messages to the Ollama model. You need to make sure that each message passed to the model contains a valid `role`. To fix this, modify your `OllamaModel` class to ensure that all `messages` contain one of the valid roles (`user`, `assistant`, `system`, or `tool`). ### Fixing the `OllamaModel` class: ```python class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [] for msg in messages: if isinstance(msg, str): # Make sure each message has a valid role formatted_messages.append({ "role": "user", # Role should be 'user' "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") # Default to 'user' content = msg.get("content", "") if isinstance(content, list): content = " ".join(part.get("text", "") for part in content if isinstance(part, dict) and "text" in part) formatted_messages.append({ "role": role if role in ['user', 'assistant', 'system', 'tool'] else 'user', # Ensure valid role "content": content }) else: formatted_messages.append({ "role": "user", # Default to 'user' for unexpected types "content": str(msg) }) response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) return response.get("message", {}).get("content", "") ``` ### Changes Made: - **Validation of `role`**: I added a validation check to make sure that the `role` is one of the following valid values: `'user'`, `'assistant'`, `'system'`, or `'tool'`. If an invalid `role` is found, it defaults to `'user'`. - **Default role handling**: If no `role` is provided, it will default to `'user'`. ### Why This Fix Works: - **Role Handling**: The error occurs because the `role` was invalid when passing the message. By ensuring that each message has a valid `role` (and defaulting to `'user'` if necessary), the `pydantic` validation error can be avoided. ### Next Steps: - Update the `OllamaModel` class with this fix. - Test again to ensure that messages are correctly passed with the valid `role` field. Let me know if this resolves the issue!

python3 main.py TOOL CODE: from smolagents import Tool from huggingface_hub import InferenceClient class TextToImageTool(Tool): description = "This tool creates an image according to a prompt, which is a text description." name = "image_generator" inputs = {"prompt": {"type": "string", "description": "The image generato r prompt. Don't hesitate to add details in the prompt to make the image look better, like 'high-res, photorealistic', etc."}} output_type = "image" model_sdxl = "black-forest-labs/FLUX.1-schnell" client = InferenceClient(model_sdxl) def forward(self, prompt): return self.client.text_to_image(prompt) ╭───────────────────────────────── New run ─────────────────────────────────╮ │ │ │ Write a longform blog post inspired by https://danielkliewer.com │ │ │ ╰─ OllamaModel - ──────────────────────────────────────────────────────────╯ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Error in generating model output: 'str' object has no attribute 'content' [Step 0: Duration 91.93 seconds] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Error in generating model output: 'str' object has no attribute 'content' [Step 1: Duration 33.12 seconds] [Step 2: Duration 37.51 seconds] Traceback (most recent call last): File "/Users/danielkliewer/Think/main.py", line 61, in <module> result = agent.run( "Write a longform blog post inspired by https://danielkliewer.com" ) File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/smolagen ts/agents.py", line 419, in run return deque(self._run(task=self.task, images=images), maxlen=1)[0] ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/smolagen ts/agents.py", line 440, in _run self.planning_step( ~~~~~~~~~~~~~~~~~~^ task, ^^^^^ is_first_step=(self.step_number == 0), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ step=self.step_number, ^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/Users/danielkliewer/Think/venv/lib/python3.13/site-packages/smolagen ts/agents.py", line 578, in planning_step facts_update = chat_message_facts.content ^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'str' object has no attribute 'content'

The error you're encountering (`'str' object has no attribute 'content'`) happens because the `chat_message_facts` object is expected to be an object with a `content` attribute, but it's receiving a string instead. This issue is causing the `AttributeError` when the code tries to access `.content`. ### Why is this happening? The error originates from the following part of the code: ```python facts_update = chat_message_facts.content ``` Here, `chat_message_facts` should be an object that contains a `content` field, but it seems like a string is being passed instead. This might be a result of how the messages are being formatted or how the model response is being processed. ### Potential Fix: We need to ensure that `chat_message_facts` is an object that has a `content` attribute. It’s possible that a string is being used directly, instead of wrapping the string in the expected structure. #### Step 1: Check and Format the Response When formatting the messages to be passed to the model, ensure that each message is properly structured, and the response from the model is correctly processed. Here’s a potential fix for how the response is handled in your code: ### Updated `OllamaModel` Class: ```python class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [] for msg in messages: if isinstance(msg, str): # Ensure that the message is formatted correctly formatted_messages.append({ "role": "user", "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") if isinstance(content, list): content = " ".join(part.get("text", "") for part in content if isinstance(part, dict) and "text" in part) formatted_messages.append({ "role": role if role in ['user', 'assistant', 'system', 'tool'] else 'user', "content": content }) else: formatted_messages.append({ "role": "user", # Default role for unexpected types "content": str(msg) }) response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) # Ensure response is formatted correctly message_content = response.get("message", {}).get("content", "") if isinstance(message_content, str): # Wrap string responses in an object with a 'content' field return {"content": message_content} return response ``` ### Key Changes: - **Message Formatting**: Ensured that all messages passed to `OllamaModel` are properly structured with both `role` and `content`. - **Response Handling**: When the response is returned, it’s checked for the `content` field. If the `content` is a string, it's wrapped in a dictionary with the `content` key. This ensures the response is structured as expected. ### Step 2: Test the Solution Now that we've updated the response formatting, try running your code again to see if this resolves the `AttributeError`. The fix ensures that the `content` attribute is always available when the response is processed. Let me know if this works or if you need further assistance!

from smolagents import load_tool, CodeAgent, DuckDuckGoSearchTool from dotenv import load_dotenv import ollama # Load environment variables load_dotenv() # Define a custom class to use Ollama class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [] response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) return response # Define tools image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() # Define the custom Ollama model ollama_model = OllamaModel("mistral-small:24b-instruct-2501-q8_0") # Create the agent agent = CodeAgent( tools=[search_tool, image_generation_tool], model=ollama_model, planning_interval=3 ) # Run the agent result = agent.run( "Write a longform blog post inspired by https://danielkliewer.com" ) # Output the result print(result)

It looks like you have a clean setup for using a custom `OllamaModel` with `smolagents`, but there’s an important detail missing in the `OllamaModel` class: the message formatting and passing the messages correctly to the `ollama.Client`. In your current implementation, `formatted_messages` is an empty list, which means no messages are being passed to the `ollama` API, and that's why you don't get the expected response. To fix this, you need to ensure that the messages are properly formatted before being passed to `ollama.Client`. Here's the updated code: ### Updated `OllamaModel` Class: ```python from smolagents import load_tool, CodeAgent, DuckDuckGoSearchTool from dotenv import load_dotenv import ollama # Load environment variables load_dotenv() # Define a custom class to use Ollama class OllamaModel: def __init__(self, model_name): self.model_name = model_name self.client = ollama.Client() def __call__(self, messages, **kwargs): formatted_messages = [] # Ensure messages are correctly formatted for msg in messages: if isinstance(msg, str): formatted_messages.append({ "role": "user", # Default to 'user' for plain strings "content": msg }) elif isinstance(msg, dict): role = msg.get("role", "user") content = msg.get("content", "") if isinstance(content, list): content = " ".join(part.get("text", "") for part in content if isinstance(part, dict) and "text" in part) formatted_messages.append({ "role": role if role in ['user', 'assistant', 'system', 'tool'] else 'user', "content": content }) else: formatted_messages.append({ "role": "user", # Default role for unexpected types "content": str(msg) }) response = self.client.chat( model=self.model_name, messages=formatted_messages, options={'temperature': 0.7, 'stream': False} ) # Return the response in the format expected by smolagents return response.get("message", {}).get("content", "") # Define tools image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True) search_tool = DuckDuckGoSearchTool() # Define the custom Ollama model ollama_model = OllamaModel("mistral-small:24b-instruct-2501-q8_0") # Create the agent agent = CodeAgent( tools=[search_tool, image_generation_tool], model=ollama_model, planning_interval=3 ) # Run the agent result = agent.run( "Write a longform blog post inspired by https://danielkliewer.com" ) # Output the result print(result) ``` ### Key Changes: 1. **Message Formatting**: The messages are now properly formatted before being passed to the `ollama.Client`. The `formatted_messages` list will now correctly include messages with the `role` and `content` fields. 2. **Response Handling**: After sending the request to `ollama.Client`, we check for the content in the response and return the message content as expected. ### Next Steps: - This fix should now ensure that the model receives the properly formatted messages. - Run the script again and see if it now returns the expected result. Let me know if this works or if you encounter any other issues!