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9a6e12d7edd9003e

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9a6e12d7edd9003e

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9a6e12d7edd9003e

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(this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: What is the sin of 0.47 radians, divided by the cube root of 27?\nThought: I need to calculate the sin of 0.47 radians and the cube root of 27.\nAction: Calculator\nAction Input: sin(0.47)\nObservation: Answer: 0.4528862853790683\nThought:"}, "outputs": {"gen_0": " I need to divide this answer by the cube root of 27.\nAction: Calculator\nAction Input: 3\u221a27"}}], "child_spans": null, "span_kind": "LLM"}], "span_kind": "CHAIN"}, {"span_id": null, "name": "Calculator", "start_time_ms": 1681920602275, "end_time_ms": 1681920603900, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false, "color": "green", "llm_prefix": "Thought:", "observation_prefix": "Observation: "}, "execution_order": 10, "action": "{'name': 'Calculator', 'description': 'Useful for when you need to answer questions about math.', '_self': Tool(name='Calculator', description='Useful for when you need to answer questions about math.', args_schema=None, return_direct=False, verbose=False, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef169b20>, func=<bound method Chain.run of LLMMathChain(memory=None, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef13e7c0>, verbose=False, llm=OpenAI(cache=None, verbose=False, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef13ee80>, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\\'s numexpr library. 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9a6e12d7edd9003e

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(this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: What happens when you take 15 and divide it by 5?\nThought:"}, "outputs": {"gen_0": " I need to divide 15 by 5\nAction: Calculator\nAction Input: 15/5"}}], "child_spans": null, "span_kind": "LLM"}], "span_kind": "CHAIN"}, {"span_id": null, "name": "Calculator", "start_time_ms": 1681920606594, "end_time_ms": 1681920608003, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false, "color": "green", "llm_prefix": "Thought:", "observation_prefix": "Observation: "}, "execution_order": 4, "action": "{'name': 'Calculator', 'description': 'Useful for when you need to answer questions about math.', '_self': Tool(name='Calculator', description='Useful for when you need to answer questions about math.', args_schema=None, return_direct=False, verbose=False, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef169b20>, func=<bound method Chain.run of LLMMathChain(memory=None, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef13e7c0>, verbose=False, llm=OpenAI(cache=None, verbose=False, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef13ee80>, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\\'s numexpr library. Use the output of running this code to answer the question.\\n\\nQuestion: ${{Question with math problem.}}\\n```text\\n${{single line mathematical expression that solves the problem}}\\n```\\n...numexpr.evaluate(text)...\\n```output\\n${{Output of running the code}}\\n```\\nAnswer: ${{Answer}}\\n\\nBegin.\\n\\nQuestion: What is 37593 * 67?\\n\\n```text\\n37593 * 67\\n```\\n...numexpr.evaluate(\"37593 * 67\")...\\n```output\\n2518731\\n```\\nAnswer: 2518731\\n\\nQuestion: {question}\\n', template_format='f-string', validate_template=True), input_key='question', output_key='answer')>, coroutine=<bound method Chain.arun of LLMMathChain(memory=None, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef13e940>, verbose=False, llm=OpenAI(cache=None, verbose=False, callback_manager=<wandb.integration.langchain.monkeypatch._CallbackManagerOnStartProxy object at 0x7fa1ef13ee80>, client=<class 'openai.api_resources.completion.Completion'>, model_name='text-davinci-003', temperature=0.0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0, n=1, best_of=1, model_kwargs={}, openai_api_key=None, openai_api_base=None, openai_organization=None, batch_size=20, request_timeout=None, logit_bias={}, max_retries=6, streaming=False, allowed_special=set(), disallowed_special='all'), prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\\'s numexpr library. Use the output of running this code to answer the question.\\n\\nQuestion: ${{Question with math problem.}}\\n```text\\n${{single line mathematical expression that solves the problem}}\\n```\\n...numexpr.evaluate(text)...\\n```output\\n${{Output of running the code}}\\n```\\nAnswer: ${{Answer}}\\n\\nBegin.\\n\\nQuestion: What is 37593 * 67?\\n\\n```text\\n37593 * 67\\n```\\n...numexpr.evaluate(\"37593 * 67\")...\\n```output\\n2518731\\n```\\nAnswer: 2518731\\n\\nQuestion: {question}\\n', template_format='f-string', validate_template=True), input_key='question', output_key='answer')>)}"}, "results": [{"inputs": {"input": "15/5"}, "outputs": {"output": "Answer: 3.0"}}], "child_spans": [{"span_id": null, "name": "LLMMathChain", "start_time_ms": 1681920606594, "end_time_ms": 1681920608003, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false}, "execution_order": 5}, "results": [{"inputs": {"question": "15/5"}, "outputs": {"answer": "Answer: 3.0"}}], "child_spans": [{"span_id": null, "name": "LLMChain", "start_time_ms": 1681920606594, "end_time_ms": 1681920608002, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false}, "execution_order": 6}, "results": [{"inputs": {"question": "15/5", "stop": ["```output"]}, "outputs": {"text": "\n```text\n15/5\n```\n...numexpr.evaluate(\"15/5\")...\n"}}], "child_spans": [{"span_id": null, "name": "OpenAI", "start_time_ms": 1681920606595, "end_time_ms": 1681920608002, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false}, "execution_order": 7, "llm_output": {"token_usage": {"total_tokens": 179, "prompt_tokens": 155, "completion_tokens": 24}, "model_name": "text-davinci-003"}}, "results": [{"inputs": {"prompt": "Translate a math problem into a expression that can be executed using Python's numexpr library. 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9a6e12d7edd9003e

{"span_id": null, "name": "AgentExecutor", "start_time_ms": 1681920609649, "end_time_ms": 1681920610964, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false}, "execution_order": 1}, "results": [{"inputs": {"input": "How many legs does a horse have?"}, "outputs": {"output": "4"}}], "child_spans": [{"span_id": null, "name": "LLMChain", "start_time_ms": 1681920609650, "end_time_ms": 1681920610964, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false}, "execution_order": 2}, "results": [{"inputs": {"input": "How many legs does a horse have?", "agent_scratchpad": "", "stop": ["\nObservation:", "\n\tObservation:"]}, "outputs": {"text": " I need to know the number of legs a horse has.\nAction: None\nFinal Answer: 4"}}], "child_spans": [{"span_id": null, "name": "OpenAI", "start_time_ms": 1681920609650, "end_time_ms": 1681920610964, "status_code": "SUCCESS", "status_message": null, "attributes": {"kwargs": {"verbose": false}, "execution_order": 3, "llm_output": {"token_usage": {"total_tokens": 179, "prompt_tokens": 158, "completion_tokens": 21}, "model_name": "text-davinci-003"}}, "results": [{"inputs": {"prompt": "Answer the following questions as best you can. You have access to the following tools:\n\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: How many legs does a horse have?\nThought:"}, "outputs": {"gen_0": " I need to know the number of legs a horse has.\nAction: None\nFinal Answer: 4"}}], "child_spans": null, "span_kind": "LLM"}], "span_kind": "CHAIN"}], "span_kind": "CHAIN"}