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[ { "attributes": { "agent.type": null, "input.mime_type": null, "input.value": "{\"task\": \"What's the weather in Paris, France?\", \"stream\": true, \"reset\": true, \"images\": null, \"additional_args\": null, \"max_steps\": 20, \"return_full_result\": null}", "llm.input_messages.0.message.contents.0.message_content.text": null, "llm.input_messages.0.message.contents.0.message_content.type": null, "llm.input_messages.0.message.role": null, "llm.input_messages.1.message.contents.0.message_content.text": null, "llm.input_messages.1.message.contents.0.message_content.type": null, "llm.input_messages.1.message.role": null, "llm.input_messages.2.message.contents.0.message_content.text": null, "llm.input_messages.2.message.contents.0.message_content.type": null, "llm.input_messages.2.message.role": null, "llm.input_messages.3.message.contents.0.message_content.text": null, "llm.input_messages.3.message.contents.0.message_content.type": null, "llm.input_messages.3.message.role": null, "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": null, "llm.model_name": null, "llm.output_messages.0.message.content": null, "llm.output_messages.0.message.role": null, "llm.token_count.completion": "0", "llm.token_count.prompt": "0", "llm.token_count.total": "0", "openinference.span.kind": "AGENT", "output.mime_type": null, "output.value": "<generator object MultiStepAgent._run_stream at 0x000001B3BF2DC040>", "prompt": null, "smolagents.max_steps": "6", "smolagents.task": null, "smolagents.tools_names": "('get_weather', 'calculator', 'get_current_time', 'web_search', 'final_answer')", "test.difficulty": null, "test.id": null, "tests.steps": null, "tests.tool_calls": null, "tool.description": null, "tool.name": null, "tool.parameters": null }, "duration_ms": 7.0117, "end_time": 1761160661972695000, "events": [], "kind": "SpanKind.INTERNAL", "name": "ToolCallingAgent.run", "parent_span_id": "0x35162caec186eb40", "resource": { "attributes": { "service.name": "genai-test-app", "telemetry.sdk.language": "python", "telemetry.sdk.name": "opentelemetry", "telemetry.sdk.version": "1.38.0" } }, "span_id": "0xe5ae7dcbfc1360b9", "start_time": 1761160661965683200, "status": { "code": 1, "description": null }, "tool_output": null, "total_tokens": 0, "trace_id": "0x69b05d628d2a4adf88b16162d1f77510" }, { "attributes": { "agent.type": null, "input.mime_type": "application/json", "input.value": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat's the weather in Paris, France?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": null, "llm.input_messages.2.message.contents.0.message_content.type": null, "llm.input_messages.2.message.role": null, "llm.input_messages.3.message.contents.0.message_content.text": null, "llm.input_messages.3.message.contents.0.message_content.type": null, "llm.input_messages.3.message.role": null, "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}], \"stop\": [\"Observation:\", \"Calling tools:\"], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Gets the current weather for a given location. Returns temperature and conditions.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city and country, e.g. 'Paris, France'\"}}, \"required\": [\"location\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"calculator\", \"description\": \"Performs basic math calculations. 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Returns temperature and conditions.", "tool.name": "get_weather", "tool.parameters": "{\"location\": {\"type\": \"string\", \"description\": \"The city and country, e.g. 'Paris, France'\"}}" }, "duration_ms": 0, "end_time": 1761160666783141000, "events": [], "kind": "SpanKind.INTERNAL", "name": "WeatherTool", "parent_span_id": "0x35162caec186eb40", "resource": { "attributes": { "service.name": "genai-test-app", "telemetry.sdk.language": "python", "telemetry.sdk.name": "opentelemetry", "telemetry.sdk.version": "1.38.0" } }, "span_id": "0x73338017f8a4ed02", "start_time": 1761160666783141000, "status": { "code": 1, "description": null }, "tool_output": "20°C, Partly Cloudy", "total_tokens": null, "trace_id": "0x69b05d628d2a4adf88b16162d1f77510" }, { "attributes": { "agent.type": null, "input.mime_type": "application/json", "input.value": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Calling tools:\\n[{'id': 'I2qDFqxaH', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': {'location': 'Paris, France'}}}]\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Observation:\\n20°C, Partly Cloudy\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat's the weather in Paris, France?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": "Calling tools:\n[{'id': 'I2qDFqxaH', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': {'location': 'Paris, France'}}}]", "llm.input_messages.2.message.contents.0.message_content.type": "text", "llm.input_messages.2.message.role": "assistant", "llm.input_messages.3.message.contents.0.message_content.text": "Observation:\n20°C, Partly Cloudy", "llm.input_messages.3.message.contents.0.message_content.type": "text", "llm.input_messages.3.message.role": "user", "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Calling tools:\\n[{'id': 'I2qDFqxaH', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': {'location': 'Paris, France'}}}]\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Observation:\\n20°C, Partly Cloudy\"}]}], \"stop\": [\"Observation:\", \"Calling tools:\"], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Gets the current weather for a given location. 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You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat's the weather in Paris, France?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": null, "llm.input_messages.2.message.contents.0.message_content.type": null, "llm.input_messages.2.message.role": null, "llm.input_messages.3.message.contents.0.message_content.text": null, "llm.input_messages.3.message.contents.0.message_content.type": null, "llm.input_messages.3.message.role": null, "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}], \"stop\": [\"Observation:\", \"Calling tools:\"], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Gets the current weather for a given location. Returns temperature and conditions.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city and country, e.g. 'Paris, France'\"}}, \"required\": [\"location\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"calculator\", \"description\": \"Performs basic math calculations. 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You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat's the weather in Paris, France?\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Calling tools:\\n[{'id': '5AKLn1bqz', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': {'location': 'Paris, France'}}}]\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Observation:\\n20°C, Partly Cloudy\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat's the weather in Paris, France?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": "Calling tools:\n[{'id': '5AKLn1bqz', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': {'location': 'Paris, France'}}}]", "llm.input_messages.2.message.contents.0.message_content.type": "text", "llm.input_messages.2.message.role": "assistant", "llm.input_messages.3.message.contents.0.message_content.text": "Observation:\n20°C, Partly Cloudy", "llm.input_messages.3.message.contents.0.message_content.type": "text", "llm.input_messages.3.message.role": "user", "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. 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Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. 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You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat time is it in UTC?\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat time is it in UTC?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": null, "llm.input_messages.2.message.contents.0.message_content.type": null, "llm.input_messages.2.message.role": null, "llm.input_messages.3.message.contents.0.message_content.text": null, "llm.input_messages.3.message.contents.0.message_content.type": null, "llm.input_messages.3.message.role": null, "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat time is it in UTC?\"}]}], \"stop\": [\"Observation:\", \"Calling tools:\"], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Gets the current weather for a given location. Returns temperature and conditions.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city and country, e.g. 'Paris, France'\"}}, \"required\": [\"location\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"calculator\", \"description\": \"Performs basic math calculations. 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Defaults to UTC.\", \"nullable\": true}}" }, "duration_ms": 0.9996, "end_time": 1761160669726249500, "events": [], "kind": "SpanKind.INTERNAL", "name": "TimeTool", "parent_span_id": "0xaf26166d178c92bd", "resource": { "attributes": { "service.name": "genai-test-app", "telemetry.sdk.language": "python", "telemetry.sdk.name": "opentelemetry", "telemetry.sdk.version": "1.38.0" } }, "span_id": "0x6061feeb32388575", "start_time": 1761160669725249800, "status": { "code": 1, "description": null }, "tool_output": "Current time in UTC: 2025-10-23 00:47:49", "total_tokens": null, "trace_id": "0x89010ea6f2b7d4d0704dfb1858e8be1e" }, { "attributes": { "agent.type": null, "input.mime_type": "application/json", "input.value": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat time is it in UTC?\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Calling tools:\\n[{'id': 'O2FiHz7qS', 'type': 'function', 'function': {'name': 'get_current_time', 'arguments': {}}}]\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Observation:\\nCurrent time in UTC: 2025-10-23 00:47:49\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat time is it in UTC?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": "Calling tools:\n[{'id': 'O2FiHz7qS', 'type': 'function', 'function': {'name': 'get_current_time', 'arguments': {}}}]", "llm.input_messages.2.message.contents.0.message_content.type": "text", "llm.input_messages.2.message.role": "assistant", "llm.input_messages.3.message.contents.0.message_content.text": "Observation:\nCurrent time in UTC: 2025-10-23 00:47:49", "llm.input_messages.3.message.contents.0.message_content.type": "text", "llm.input_messages.3.message.role": "user", "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. 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You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat time is it in UTC?\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!", "llm.input_messages.0.message.contents.0.message_content.type": "text", "llm.input_messages.0.message.role": "system", "llm.input_messages.1.message.contents.0.message_content.text": "New task:\nWhat time is it in UTC?", "llm.input_messages.1.message.contents.0.message_content.type": "text", "llm.input_messages.1.message.role": "user", "llm.input_messages.2.message.contents.0.message_content.text": null, "llm.input_messages.2.message.contents.0.message_content.type": null, "llm.input_messages.2.message.role": null, "llm.input_messages.3.message.contents.0.message_content.text": null, "llm.input_messages.3.message.contents.0.message_content.type": null, "llm.input_messages.3.message.role": null, "llm.input_messages.4.message.contents.0.message_content.text": null, "llm.input_messages.4.message.contents.0.message_content.type": null, "llm.input_messages.4.message.role": null, "llm.input_messages.5.message.contents.0.message_content.text": null, "llm.input_messages.5.message.contents.0.message_content.type": null, "llm.input_messages.5.message.role": null, "llm.invocation_parameters": "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat time is it in UTC?\"}]}], \"stop\": [\"Observation:\", \"Calling tools:\"], \"tools\": [{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Gets the current weather for a given location. Returns temperature and conditions.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"The city and country, e.g. 'Paris, France'\"}}, \"required\": [\"location\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"calculator\", \"description\": \"Performs basic math calculations. 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You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": {\\\"answer\\\": \\\"insert your final answer here\\\"}\\n}\\n\\n\\nHere are a few examples using notional tools:\\n---\\nTask: \\\"Generate an image of the oldest person in this document.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"document_qa\\\",\\n \\\"arguments\\\": {\\\"document\\\": \\\"document.pdf\\\", \\\"question\\\": \\\"Who is the oldest person mentioned?\\\"}\\n}\\nObservation: \\\"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_generator\\\",\\n \\\"arguments\\\": {\\\"prompt\\\": \\\"A portrait of John Doe, a 55-year-old man living in Canada.\\\"}\\n}\\nObservation: \\\"image.png\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"image.png\\\"\\n}\\n\\n---\\nTask: \\\"What is the result of the following operation: 5 + 3 + 1294.678?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"python_interpreter\\\",\\n \\\"arguments\\\": {\\\"code\\\": \\\"5 + 3 + 1294.678\\\"}\\n}\\nObservation: 1302.678\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"1302.678\\\"\\n}\\n\\n---\\nTask: \\\"Which city has the highest population , Guangzhou or Shanghai?\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Guangzhou\\\"\\n}\\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\\n\\n\\nAction:\\n{\\n \\\"name\\\": \\\"web_search\\\",\\n \\\"arguments\\\": \\\"Population Shanghai\\\"\\n}\\nObservation: '26 million (2019)'\\n\\nAction:\\n{\\n \\\"name\\\": \\\"final_answer\\\",\\n \\\"arguments\\\": \\\"Shanghai\\\"\\n}\\n\\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\\n Takes inputs: {'location': {'type': 'string', 'description': \\\"The city and country, e.g. 'Paris, France'\\\"}}\\n Returns an output of type: string\\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\\n Returns an output of type: string\\n- get_current_time: Gets the current time in a specific timezone or UTC.\\n Takes inputs: {'timezone': {'type': 'string', 'description': \\\"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. Never re-do a tool call that you previously did with the exact same parameters.\\n\\nNow Begin!\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"New task:\\nWhat time is it in UTC?\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"Calling tools:\\n[{'id': 'SrlRJlgXA', 'type': 'function', 'function': {'name': 'get_current_time', 'arguments': {}}}]\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Observation:\\nCurrent time in UTC: 2025-10-23 00:47:56\"}]}]}", "llm.input_messages.0.message.contents.0.message_content.text": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n \"name\": \"image_transformer\",\n \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n \"name\": \"document_qa\",\n \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n \"name\": \"image_generator\",\n \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n \"name\": \"python_interpreter\",\n \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n \"name\": \"web_search\",\n \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n \"name\": \"final_answer\",\n \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n- get_weather: Gets the current weather for a given location. Returns temperature and conditions.\n Takes inputs: {'location': {'type': 'string', 'description': \"The city and country, e.g. 'Paris, France'\"}}\n Returns an output of type: string\n- calculator: Performs basic math calculations. Supports +, -, *, /, and parentheses.\n Takes inputs: {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate'}}\n Returns an output of type: string\n- get_current_time: Gets the current time in a specific timezone or UTC.\n Takes inputs: {'timezone': {'type': 'string', 'description': \"The timezone, e.g. 'UTC', 'EST', 'PST'. Defaults to UTC.\", 'nullable': True}}\n Returns an output of type: string\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- 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\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. 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You will be given a task to solve as best you can.\\nTo do so, you have been given access to some tools.\\n\\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \\\"observation\\\".\\nThis Action/Observation can repeat N times, you should take several steps when needed.\\n\\nYou can use the result of the previous action as input for the next action.\\nThe observation will always be a string: it can represent a file, like \\\"image_1.jpg\\\".\\nThen you can use it as input for the next action. You can do it for instance as follows:\\n\\nObservation: \\\"image_1.jpg\\\"\\n\\nAction:\\n{\\n \\\"name\\\": \\\"image_transformer\\\",\\n \\\"arguments\\\": {\\\"image\\\": \\\"image_1.jpg\\\"}\\n}\\n\\nTo provide the final answer to the task, use an action blob with \\\"name\\\": \\\"final_answer\\\" tool. It is the only way to complete the task, else you will be stuck on a loop. 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Defaults to UTC.\\\", 'nullable': True}}\\n Returns an output of type: string\\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- 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\\nHere are the rules you should always follow to solve your task:\\n1. ALWAYS provide a tool call, else you will fail.\\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\\n4. 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