From 16b0ab137328f1e1d06f2ed6b9158c52b7ad4730 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Sun, 25 Aug 2024 15:33:53 +0000 Subject: [PATCH 1/4] Supply prompt prefix as a list of messages --- motleycrew/agents/langchain/langchain.py | 7 +++++- .../agents/langchain/tool_calling_react.py | 25 ++++++++++++++++--- .../langchain/tool_calling_react_prompts.py | 6 ++--- motleycrew/agents/parent.py | 15 ++++++++--- motleycrew/common/defaults.py | 1 + motleycrew/common/utils.py | 7 +++--- 6 files changed, 46 insertions(+), 15 deletions(-) diff --git a/motleycrew/agents/langchain/langchain.py b/motleycrew/agents/langchain/langchain.py index 33a8510d..cfd394e1 100644 --- a/motleycrew/agents/langchain/langchain.py +++ b/motleycrew/agents/langchain/langchain.py @@ -26,6 +26,7 @@ def __init__( tools: Sequence[MotleySupportedTool] | None = None, output_handler: MotleySupportedTool | None = None, chat_history: bool | GetSessionHistoryCallable = True, + input_as_messages: bool = False, verbose: bool = False, ): """ @@ -65,6 +66,8 @@ def __init__( See :class:`langchain_core.runnables.history.RunnableWithMessageHistory` for more details. + input_as_messages: Whether the agent expects a list of messages as input instead of a single string. + verbose: Whether to log verbose output. """ super().__init__( @@ -85,6 +88,8 @@ def __init__( else: self.get_session_history_callable = chat_history + self.input_as_messages = input_as_messages + def materialize(self): """Materialize the agent and wrap it in RunnableWithMessageHistory if needed.""" if self.is_materialized: @@ -141,7 +146,7 @@ def invoke( config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Any: - prompt = self.prepare_for_invocation(input=input) + prompt = self.prepare_for_invocation(input=input, prompt_as_messages=self.input_as_messages) config = add_default_callbacks_to_langchain_config(config) if self.get_session_history_callable: diff --git a/motleycrew/agents/langchain/tool_calling_react.py b/motleycrew/agents/langchain/tool_calling_react.py index 3d4a7efd..579cc415 100644 --- a/motleycrew/agents/langchain/tool_calling_react.py +++ b/motleycrew/agents/langchain/tool_calling_react.py @@ -1,16 +1,18 @@ from __future__ import annotations -from typing import Sequence, Optional +from typing import Sequence, Optional, Callable from langchain.agents import AgentExecutor from langchain.agents.format_scratchpad.tools import format_to_tool_messages from langchain.agents.output_parsers.tools import ToolsAgentOutputParser from langchain_core.language_models import BaseChatModel from langchain_core.prompts.chat import ChatPromptTemplate -from langchain_core.runnables import Runnable, RunnablePassthrough +from langchain_core.runnables import Runnable, RunnablePassthrough, RunnableLambda from langchain_core.runnables.history import GetSessionHistoryCallable from langchain_core.tools import BaseTool +from motleycrew.common.utils import print_passthrough + try: from langchain_anthropic import ChatAnthropic except ImportError: @@ -21,7 +23,7 @@ ToolCallingReActPromptsForOpenAI, ToolCallingReActPromptsForAnthropic, ) -from motleycrew.common import LLMFramework +from motleycrew.common import LLMFramework, Defaults from motleycrew.common import MotleySupportedTool from motleycrew.common.llms import init_llm from motleycrew.tools import MotleyTool @@ -63,6 +65,7 @@ def create_tool_calling_react_agent( tools: Sequence[BaseTool], prompt: ChatPromptTemplate, output_handler: BaseTool | None = None, + intermediate_steps_processor: Callable | None = None, ) -> Runnable: prompt = prompt.partial( tools=render_text_description(list(tools)), @@ -76,12 +79,18 @@ def create_tool_calling_react_agent( llm_with_tools = llm.bind_tools(tools=tools_for_llm) + if not intermediate_steps_processor: + intermediate_steps_processor = lambda x: x + agent = ( RunnablePassthrough.assign( - agent_scratchpad=lambda x: format_to_tool_messages(x["intermediate_steps"]), + agent_scratchpad=lambda x: format_to_tool_messages( + intermediate_steps_processor(x["intermediate_steps"]) + ), additional_notes=lambda x: x.get("additional_notes") or [], ) | prompt + | RunnableLambda(print_passthrough) | llm_with_tools | ToolsAgentOutputParser() ) @@ -108,6 +117,8 @@ def __init__( handle_parsing_errors: bool = True, handle_tool_errors: bool = True, llm: BaseChatModel | None = None, + max_iterations: int | None = Defaults.DEFAULT_REACT_AGENT_MAX_ITERATIONS, + intermediate_steps_processor: Callable | None = None, verbose: bool = False, ): """ @@ -128,6 +139,9 @@ def __init__( handle_tool_errors: Whether to handle tool errors. If True, `handle_tool_error` and `handle_validation_error` in all tools are set to True. + max_iterations: The maximum number of agent iterations. + intermediate_steps_processor: Function that modifies the intermediate steps array + in some way before each agent iteration. llm: Language model to use. verbose: Whether to log verbose output. @@ -162,6 +176,7 @@ def agent_factory( tools=tools_for_langchain, prompt=prompt, output_handler=output_handler_for_langchain, + intermediate_steps_processor=intermediate_steps_processor, ) if output_handler_for_langchain: @@ -177,6 +192,7 @@ def agent_factory( tools=tools_for_langchain, handle_parsing_errors=handle_parsing_errors, verbose=verbose, + max_iterations=max_iterations, ) return agent_executor @@ -188,5 +204,6 @@ def agent_factory( tools=tools, output_handler=output_handler, chat_history=chat_history, + input_as_messages=True, verbose=verbose, ) diff --git a/motleycrew/agents/langchain/tool_calling_react_prompts.py b/motleycrew/agents/langchain/tool_calling_react_prompts.py index 4f92296d..40be639b 100644 --- a/motleycrew/agents/langchain/tool_calling_react_prompts.py +++ b/motleycrew/agents/langchain/tool_calling_react_prompts.py @@ -25,7 +25,7 @@ def __init__(self): MessagesPlaceholder(variable_name="chat_history", optional=True), ("system", self.main_instruction), MessagesPlaceholder(variable_name="example_messages", optional=True), - ("user", "{input}"), + MessagesPlaceholder(variable_name="input"), MessagesPlaceholder(variable_name="agent_scratchpad"), MessagesPlaceholder(variable_name="additional_notes", optional=True), ] @@ -64,14 +64,14 @@ class ToolCallingReActPromptsForOpenAI(ToolCallingReActPrompts): Begin! """ - output_instruction_with_output_handler = """ + output_instruction_without_output_handler = """ If you have sufficient information to answer the question, your reply must look like ``` Final Answer: [the final answer to the original input question] ``` but without the backticks.""" - output_instruction_without_output_handler = """ + output_instruction_with_output_handler = """ If you have sufficient information to answer the question, you must call the output handler tool. NEVER return the final answer directly, but always do it by CALLING this tool: diff --git a/motleycrew/agents/parent.py b/motleycrew/agents/parent.py index b492bcba..5b7d4ced 100644 --- a/motleycrew/agents/parent.py +++ b/motleycrew/agents/parent.py @@ -10,6 +10,7 @@ Union, ) +from langchain_core.messages import BaseMessage from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessage, SystemMessage from langchain_core.runnables import RunnableConfig from langchain_core.tools import StructuredTool @@ -108,13 +109,14 @@ def __str__(self): return self.__repr__() def compose_prompt( - self, input_dict: dict, prompt: ChatPromptTemplate | str - ) -> Union[str, ChatPromptTemplate]: + self, input_dict: dict, prompt: ChatPromptTemplate | str, as_messages: bool = False + ) -> Union[str, list[BaseMessage]]: """Compose the agent's prompt from the prompt prefix and the provided prompt. Args: input_dict: The input dictionary to the agent. prompt: The prompt to be added to the agent's prompt. + as_messages: Whether the prompt should be returned as a Langchain messages list instead of a single string. Returns: The composed prompt. @@ -145,6 +147,9 @@ def compose_prompt( else: raise ValueError("Prompt must be a string or a ChatPromptTemplate") + if as_messages: + return prompt_messages + # TODO: pass the unformatted messages list to agents that can handle it prompt = "\n\n".join([m.content for m in prompt_messages]) + "\n" return prompt @@ -237,13 +242,15 @@ def materialize(self): else: self._agent = self.agent_factory(tools=self.tools) - def prepare_for_invocation(self, input: dict) -> str: + def prepare_for_invocation(self, input: dict, prompt_as_messages: bool = False) -> str: """Prepare the agent for invocation by materializing it and composing the prompt. Should be called in the beginning of the agent's invoke method. Args: input: the input to the agent + prompt_as_messages: Whether the prompt should be returned as a Langchain messages list + instead of a single string. Returns: str: the composed prompt @@ -254,7 +261,7 @@ def prepare_for_invocation(self, input: dict) -> str: self.output_handler.agent = self self.output_handler.agent_input = input - prompt = self.compose_prompt(input, input.get("prompt")) + prompt = self.compose_prompt(input, input.get("prompt"), as_messages=prompt_as_messages) return prompt def add_tools(self, tools: Sequence[MotleySupportedTool]): diff --git a/motleycrew/common/defaults.py b/motleycrew/common/defaults.py index 8e8849cc..5081336f 100644 --- a/motleycrew/common/defaults.py +++ b/motleycrew/common/defaults.py @@ -5,6 +5,7 @@ class Defaults: """Default values for various settings.""" + DEFAULT_REACT_AGENT_MAX_ITERATIONS = 15 DEFAULT_LLM_FAMILY = LLMFamily.OPENAI DEFAULT_LLM_NAME = "gpt-4o" DEFAULT_LLM_TEMPERATURE = 0.0 diff --git a/motleycrew/common/utils.py b/motleycrew/common/utils.py index bb96fca6..e8de4027 100644 --- a/motleycrew/common/utils.py +++ b/motleycrew/common/utils.py @@ -1,8 +1,10 @@ """Various helpers and utility functions used throughout the project.""" + +import hashlib import sys from typing import Optional, Sequence -import hashlib from urllib.parse import urlparse + from langchain_core.messages import BaseMessage from motleycrew.common.exceptions import ModuleNotInstalled @@ -47,9 +49,8 @@ def generate_hex_hash(data: str, length: Optional[int] = None): def print_passthrough(x): """A helper function useful for debugging LCEL chains. It just returns the input value. - You can put a breakpoint in this function to debug the chain. + You can put a breakpoint in this function to debug a chain. 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zG;Sh$=1DoIP~h1yVG22ZtLIG3F+G#w8F@-6)*s91R8yo;A}(bg5X&$fa?F=f8sfQy ztSJ4gxr8#d3)EvOo>A;q&mm!q_;Tu(V2s!o6w^|j1+5-ym*ti(I;bWf)&;s&Iq|8` zjSPoKA#{w=CG-)8B%x!bQzc1AhoIu*0(nd}vTovo(=;UE6Dl9SE;@V9oG0=%07qhzuqqRMxlVO%RAFpy73K4Qb)m zMRA6Cg7dxVWv{MWrk=cSb63Idwp5g_t774z{?#`N#l`HDl+Kib;kYU&mMkY9;TR(R zsjjbQ{x4$i#ui#o%9qHj=+`Fw_NRVn(t7zFNO%3>;a9Kj=0mq+n6N%5+C_>WHy4jRAfb>4mHXU9SB=_fBU_xqQRlA|YIuKf6I`Sm?~-PDnnmES<5 zX|DObdAH=m#oQa}Zkl)N8Onbg-oK|OzQf>6dB{~By+& arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n swapped = True\n if not swapped:\n break\n return arr\n\n# Test the bubble sort function\nsample_list = [64, 34, 25, 12, 22, 11, 90]\nsorted_list = bubble_sort(sample_list)\nprint(sorted_list)\n\nThe `bubble_sort` function sorts a list by repeatedly swapping adjacent elements if they are in the wrong order. The outer loop runs `n` times, where `n` is the length of the list. The inner loop runs `n-i-1` times, where `i` is the current iteration of the outer loop, to avoid re-checking the already sorted elements at the end of the list. An optimization is added with a `swapped` flag to break out of the loop early if no elements were swapped in an inner loop iteration, indicating that the list is already sorted. The test case demonstrates the function by sorting a sample list `[64, 34, 25, 12, 22, 11, 90]`, and the output is `[11, 12, 22, 25, 34, 64, 90]`. The time complexity of bubble sort is O(n^2) in the worst and average cases, but the early exit optimization can improve performance on nearly sorted lists." \ No newline at end of file +"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr\n\n# Test the bubble sort implementation\nsample_list = [64, 34, 25, 12, 22, 11, 90]\nsorted_list = bubble_sort(sample_list)\nprint('Sorted list:', sorted_list)\n\nThe bubble_sort function sorts a list using the bubble sort algorithm. It repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. This process is repeated until the list is sorted. The outer loop runs n times, and the inner loop runs n-i-1 times, where n is the length of the list and i is the current iteration of the outer loop. The test case demonstrates sorting a sample list." \ No newline at end of file diff --git a/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json b/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json index 85f00865..ee20f4b6 100644 --- a/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json +++ b/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json @@ -1 +1 @@ -"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe first solve for \\( x \\) and \\( y \\).\n\nUsing the sympy library, we find:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\nNext, we calculate \\( x - y \\):\n\n\\[ x - y = -23 - 25 = -48 \\]\n\nThus, the values are:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\\[ x - y = -48 \\]" \ No newline at end of file +"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe can use the method of solving simultaneous equations. Using sympy, we find the values of \\( x \\) and \\( y \\) as follows:\n\nFirst, we represent the system of equations in matrix form and solve it:\n\n\\[\n\\begin{cases}\n725x + 727y = 1500 \\\\\n729x + 731y = 1508\n\\end{cases}\n\\]\n\nUsing sympy, we find:\n\n\\[\nx = -23, \\quad y = 25\n\\]\n\nNext, we calculate \\( x - y \\):\n\n\\[\nx - y = -23 - 25 = -48\n\\]\n\nThus, the values are:\n\n\\[\nx = -23, \\quad y = 25, \\quad x - y = -48\n\\]" \ No newline at end of file diff --git a/tests/itest_golden_data/multi_step_research_agent_ipynb.json b/tests/itest_golden_data/multi_step_research_agent_ipynb.json index 8f9d272b..cdab45c7 100644 --- a/tests/itest_golden_data/multi_step_research_agent_ipynb.json +++ b/tests/itest_golden_data/multi_step_research_agent_ipynb.json @@ -1 +1 @@ -"Why did Arjuna kill Karna, his half-brother?\n\nArjuna killed Karna, his half-brother, primarily due to Krishna's influence and command. During their duel, Karna's chariot got stuck in the mud, and he appealed to Arjuna and Krishna for a pause to fix it, invoking the concept of honor. Krishna responded by questioning the honor in Karna's past actions, specifically mentioning the humiliation of Draupadi and the killing of Abhimanyu. This reminder served to highlight Karna's dishonorable deeds. Despite Arjuna's hesitation upon seeing Karna defenseless, Krishna commanded him to \"Kill him!\" Arjuna obeyed Krishna's command, and his arrow struck and killed Karna." \ No newline at end of file +"Why did Arjuna kill Karna, his half-brother?\n\nArjuna killed Karna, his half-brother, primarily due to Krishna's influence and command. During their battle, Karna's chariot got stuck in the mud, and he called out to Arjuna and Krishna, claiming they were honor-bound to let him fix it. Krishna retorted by questioning the honor in Karna's past actions, such as the humiliation of Draupadi and the killing of Abhimanyu. When Karna attempted to use the Brahmastra but forgot the mantra, Krishna saw an opportunity and shouted to Arjuna, \"Kill him!\" Despite seeing Karna defenseless and hesitating, Arjuna ultimately obeyed Krishna's command and shot an arrow that killed Karna." \ No newline at end of file diff --git a/tests/itest_golden_data/validating_agent_output_ipynb.json b/tests/itest_golden_data/validating_agent_output_ipynb.json index c0815f6f..7723f968 100644 --- a/tests/itest_golden_data/validating_agent_output_ipynb.json +++ b/tests/itest_golden_data/validating_agent_output_ipynb.json @@ -1 +1 @@ -"Comprehensive Analysis of AI Advancements in 2024\n\nKey Trends in AI 2024:\n1. Generative AI Maturation: Generative AI continues to evolve, becoming more useful for everyday applications and business operations.\n2. Integration into Business Operations: AI is increasingly being integrated into various business processes, enhancing efficiency and decision-making.\n3. Multimodal AI: The development of AI models that can process and understand multiple types of data (e.g., text, images, audio) simultaneously.\n4. Model Optimization and Accessibility: Advances in model optimization are making AI more accessible and cost-effective for businesses.\n5. AI for Social Good: AI is being leveraged for social impact, including climate research and healthcare improvements.\n\nBreakthrough Technologies in AI 2024:\n1. Advanced Generative Models: New generative AI models are being developed for more complex and diverse applications.\n2. AI-Driven Drug Discovery: AI is revolutionizing drug discovery processes, making them faster and more efficient.\n3. AI in Manufacturing: AI technologies are significantly improving manufacturing processes, leading to cost reductions and increased productivity.\n4. Virtual Agents and Assistants: Enhanced virtual agents and assistants are becoming more prevalent in customer service and personal assistance.\n5. Customized Local Models: Development of AI models tailored to specific local needs and data pipelines.\n\nPotential Industry Impacts:\n1. Manufacturing: AI is expected to drive significant financial benefits and operational efficiencies in the manufacturing sector.\n2. Healthcare: AI-driven drug discovery and diagnostic tools are transforming healthcare delivery and patient outcomes.\n3. Retail: AI is enhancing customer experiences and optimizing supply chain management in the retail industry.\n4. Finance: AI is improving fraud detection, risk management, and personalized financial services.\n5. Climate Research: AI is being used to advance climate research, providing more accurate predictions and solutions for environmental challenges.\n\nAI Applications in Physics:\n1. Phase Transition Classification: Generative AI is being used to classify phase transitions in materials more efficiently than traditional methods.\n2. Global Forecasts: AI models are being developed to improve global weather and climate forecasts.\n3. Phonon Dispersion Prediction: Machine-learning frameworks are predicting phonon dispersion relations much faster and with greater accuracy than previous techniques." \ No newline at end of file +"### Comprehensive Analysis of AI Advancements in 2024\n\n#### 1. Key Trends in AI 2024\n\n1. **Generative AI**: Generative AI continues to evolve and become more accessible to the general public. Tools like ChatGPT have reached mass adoption, and 2024 is expected to see more people experimenting with various AI models. This trend is driving significant changes in how businesses and individuals interact with technology.\n\n2. **Multimodal AI**: Multimodal AI, which processes multiple types of data (e.g., text, images, audio) simultaneously, is gaining traction. This advancement allows for more sophisticated and comprehensive AI applications, enhancing user experiences and expanding the potential use cases of AI.\n\n3. **Ethics and Safety**: There is a growing emphasis on the ethical and safe deployment of AI technologies. Organizations are increasingly focusing on developing AI systems that are transparent, fair, and aligned with regulatory standards to mitigate risks and build trust with users.\n\n4. **Integration of AI in Daily Life**: AI is becoming seamlessly integrated into daily life, with applications ranging from personal assistants to smart home devices. This trend is expected to continue, making AI an indispensable part of everyday activities.\n\n#### 2. Breakthrough AI Technologies in 2024\n\n1. **AI-Powered Scientific Discovery**: AI is revolutionizing scientific research by accelerating the discovery process. AI algorithms are being used to analyze vast amounts of data, identify patterns, and generate new hypotheses, leading to breakthroughs in various scientific fields.\n\n2. **Elastocalorics**: This emerging technology involves materials that change temperature when subjected to mechanical stress. AI is being used to optimize these materials for applications in energy storage and cooling systems, contributing to more efficient and sustainable technologies.\n\n3. **State-of-the-Art AI Models**: Advanced AI models are being developed to tackle complex problems in areas such as healthcare, finance, and manufacturing. These models leverage deep learning, reinforcement learning, and other cutting-edge techniques to deliver unprecedented performance and accuracy.\n\n4. **AI in Physics**: AI is making significant strides in the field of physics. Researchers are using generative AI to develop physics-informed techniques for classifying phase transitions in materials, which is more efficient than existing methods. Additionally, AI is being coupled with fundamental physics to accelerate calculations and uncover new mechanisms in physical systems.\n\n#### 3. Potential Industry Impacts of AI Advancements in 2024\n\n1. **Manufacturing**: The manufacturing industry is expected to see the largest financial impact from AI. AI-driven automation, predictive maintenance, and quality control are enhancing productivity and reducing costs, leading to significant economic benefits.\n\n2. **Healthcare**: AI is transforming healthcare by enabling personalized medicine, improving diagnostic accuracy, and streamlining administrative processes. These advancements are improving patient outcomes and reducing healthcare costs.\n\n3. **Finance**: In the finance sector, AI is being used for fraud detection, risk management, and algorithmic trading. These applications are enhancing the efficiency and security of financial transactions, driving growth and innovation in the industry.\n\n4. **Energy**: AI is playing a crucial role in optimizing energy consumption and integrating renewable energy sources. AI-powered systems are being used to manage energy grids, predict energy demand, and improve the efficiency of energy storage solutions.\n\n### Conclusion\n\nThe advancements in AI in 2024 are poised to bring transformative changes across various industries. Key trends such as generative AI, multimodal AI, and a focus on ethics and safety are shaping the future of AI. Breakthrough technologies like AI-powered scientific discovery, elastocalorics, and AI applications in physics are driving innovation, while the potential industry impacts highlight the significant economic and societal benefits of AI. As AI continues to evolve, it will be essential for organizations to stay informed and adapt to these changes to harness the full potential of AI technologies." \ No newline at end of file From a8c01c82e41ef94d251641f752d103b3d40ebf59 Mon Sep 17 00:00:00 2001 From: whimo Date: Sun, 25 Aug 2024 20:10:38 +0400 Subject: [PATCH 3/4] Fix tests --- tests/test_agents/test_langchain_output_handler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_agents/test_langchain_output_handler.py b/tests/test_agents/test_langchain_output_handler.py index 5f0cfe6f..4008bb60 100644 --- a/tests/test_agents/test_langchain_output_handler.py +++ b/tests/test_agents/test_langchain_output_handler.py @@ -58,7 +58,7 @@ def agent(): @pytest.fixture def run_kwargs(agent): - agent_executor = agent.agent.bound.bound.last.default.bound + agent_executor = agent.agent.bound.bound.last.bound.deps[0].bound run_kwargs = { "name_to_tool_map": {tool.name: tool for tool in agent_executor.tools}, 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zZ`29l=c+TOTHnu9<4(;k06S3!A*?IX?B}i1(gZdSv#5eoU@eh~#$!?HN<|$|*pcOO zUP(>B8ETt1xaO@F3$(P^!4rT7NgV-HeuHIC&WlPcn3O4^!eUe@pT=4geF=6gB`_-I zhOIQ*k4O@&mGxgyR}dsyS;jdLJ7Cn`c(pDXPS8wAo$m^Zn{dU%P06sXw~#a(C-eNR%*{Fo609H=l5g+S`$seWaE z$z_=~J|ev$t<|b=t~yC+u$Ro8z=z4qhID8dM%|qLOT$z)P#jgOt5sQf@85J0SewAo5o+5 zG;Sh$=1DoIP~h1yVG22ZtLIG3F+G#w8F@-6)*s91R8yo;A}(bg5X&$fa?F=f8sfQy ztSJ4gxr8#d3)EvOo>A;q&mm!q_;Tu(V2s!o6w^|j1+5-ym*ti(I;bWf)&;s&Iq|8` zjSPoKA#{w=CG-)8B%x!bQzc1AhoIu*0(nd}vTovo(=;UE6Dl9SE;@V9oG0=%07qhzuqqRMxlVO%RAFpy73K4Qb)m zMRA6Cg7dxVWv{MWrk=cSb63Idwp5g_t774z{?#`N#l`HDl+Kib;kYU&mMkY9;TR(R zsjjbQ{x4$i#ui#o%9qHj=+`Fw_NRVn(t7zFNO%3>;a9Kj=0mq+n6N%5+C_>WHy4jRAfb>4mHXU9SB=_fBU_xqQRlA|YIuKf6I`Sm?~-PDnnmES<5 zX|DObdAH=m#oQa}Zkl)N8Onbg-oK|OzQf>6dB{~By+& arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr\n\n# Test the bubble sort implementation\nsample_list = [64, 34, 25, 12, 22, 11, 90]\nsorted_list = bubble_sort(sample_list)\nprint('Sorted list:', sorted_list)\n\nThe bubble_sort function sorts a list using the bubble sort algorithm. It repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. This process is repeated until the list is sorted. The outer loop runs n times, and the inner loop runs n-i-1 times, where n is the length of the list and i is the current iteration of the outer loop. The test case demonstrates sorting a sample list." \ No newline at end of file +"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr\n\n# Test the bubble sort implementation\nsample_list = [64, 34, 25, 12, 22, 11, 90]\nsorted_list = bubble_sort(sample_list)\nprint(sorted_list)\n\nThe bubble sort algorithm repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted.\n\nExplanation:\n1. The `bubble_sort` function takes a list `arr` as input.\n2. The variable `n` is set to the length of the list.\n3. The outer loop runs `n` times, where `i` ranges from 0 to `n-1`.\n4. The inner loop runs from 0 to `n-i-1`, comparing each pair of adjacent elements.\n5. If the element at position `j` is greater than the element at position `j+1`, they are swapped.\n6. This process is repeated until the list is sorted.\n7. The sorted list is returned." \ No newline at end of file diff --git a/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json b/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json index ee20f4b6..7ba567ce 100644 --- a/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json +++ b/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json @@ -1 +1 @@ -"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe can use the method of solving simultaneous equations. Using sympy, we find the values of \\( x \\) and \\( y \\) as follows:\n\nFirst, we represent the system of equations in matrix form and solve it:\n\n\\[\n\\begin{cases}\n725x + 727y = 1500 \\\\\n729x + 731y = 1508\n\\end{cases}\n\\]\n\nUsing sympy, we find:\n\n\\[\nx = -23, \\quad y = 25\n\\]\n\nNext, we calculate \\( x - y \\):\n\n\\[\nx - y = -23 - 25 = -48\n\\]\n\nThus, the values are:\n\n\\[\nx = -23, \\quad y = 25, \\quad x - y = -48\n\\]" \ No newline at end of file +"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe use the sympy library to find the values of \\( x \\) and \\( y \\).\n\nFirst, we define the equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nUsing sympy to solve these equations, we find:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\nTo find \\( x - y \\):\n\n\\[ x - y = -23 - 25 = -48 \\]\n\nThus, the values are:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\\[ x - y = -48 \\]" \ No newline at end of file diff --git a/tests/itest_golden_data/multi_step_research_agent_ipynb.json b/tests/itest_golden_data/multi_step_research_agent_ipynb.json index cdab45c7..09f281ec 100644 --- a/tests/itest_golden_data/multi_step_research_agent_ipynb.json +++ b/tests/itest_golden_data/multi_step_research_agent_ipynb.json @@ -1 +1 @@ -"Why did Arjuna kill Karna, his half-brother?\n\nArjuna killed Karna, his half-brother, primarily due to Krishna's influence and command. During their battle, Karna's chariot got stuck in the mud, and he called out to Arjuna and Krishna, claiming they were honor-bound to let him fix it. Krishna retorted by questioning the honor in Karna's past actions, such as the humiliation of Draupadi and the killing of Abhimanyu. When Karna attempted to use the Brahmastra but forgot the mantra, Krishna saw an opportunity and shouted to Arjuna, \"Kill him!\" Despite seeing Karna defenseless and hesitating, Arjuna ultimately obeyed Krishna's command and shot an arrow that killed Karna." \ No newline at end of file +"Why did Arjuna kill Karna, his half-brother?\n\nKarna's past actions, such as the humiliation of Draupadi and the killing of Abhimanyu, played a significant role in Arjuna's decision to kill him, primarily through Krishna's influence. During the battle, when Karna's chariot got stuck in the mud, he appealed to Arjuna and Krishna to honorably allow him to fix it. Krishna responded by questioning the honor in Karna's past deeds, specifically mentioning the humiliation of Draupadi and the butchering of Abhimanyu. This reminder of Karna's dishonorable actions served to justify the killing in the context of the war's moral framework. When Karna attempted to use the Brahmastra but forgot the mantra, Krishna commanded Arjuna to kill him. Despite Arjuna's initial hesitation, seeing Karna defenseless, he ultimately obeyed Krishna's command and killed Karna. Thus, Krishna's invocation of Karna's past misdeeds and his direct order were crucial in Arjuna's decision to kill Karna." \ No newline at end of file diff --git a/tests/itest_golden_data/validating_agent_output_ipynb.json b/tests/itest_golden_data/validating_agent_output_ipynb.json index 7723f968..e8321a8b 100644 --- a/tests/itest_golden_data/validating_agent_output_ipynb.json +++ b/tests/itest_golden_data/validating_agent_output_ipynb.json @@ -1 +1 @@ -"### Comprehensive Analysis of AI Advancements in 2024\n\n#### 1. Key Trends in AI 2024\n\n1. **Generative AI**: Generative AI continues to evolve and become more accessible to the general public. Tools like ChatGPT have reached mass adoption, and 2024 is expected to see more people experimenting with various AI models. This trend is driving significant changes in how businesses and individuals interact with technology.\n\n2. **Multimodal AI**: Multimodal AI, which processes multiple types of data (e.g., text, images, audio) simultaneously, is gaining traction. This advancement allows for more sophisticated and comprehensive AI applications, enhancing user experiences and expanding the potential use cases of AI.\n\n3. **Ethics and Safety**: There is a growing emphasis on the ethical and safe deployment of AI technologies. Organizations are increasingly focusing on developing AI systems that are transparent, fair, and aligned with regulatory standards to mitigate risks and build trust with users.\n\n4. **Integration of AI in Daily Life**: AI is becoming seamlessly integrated into daily life, with applications ranging from personal assistants to smart home devices. This trend is expected to continue, making AI an indispensable part of everyday activities.\n\n#### 2. Breakthrough AI Technologies in 2024\n\n1. **AI-Powered Scientific Discovery**: AI is revolutionizing scientific research by accelerating the discovery process. AI algorithms are being used to analyze vast amounts of data, identify patterns, and generate new hypotheses, leading to breakthroughs in various scientific fields.\n\n2. **Elastocalorics**: This emerging technology involves materials that change temperature when subjected to mechanical stress. AI is being used to optimize these materials for applications in energy storage and cooling systems, contributing to more efficient and sustainable technologies.\n\n3. **State-of-the-Art AI Models**: Advanced AI models are being developed to tackle complex problems in areas such as healthcare, finance, and manufacturing. These models leverage deep learning, reinforcement learning, and other cutting-edge techniques to deliver unprecedented performance and accuracy.\n\n4. **AI in Physics**: AI is making significant strides in the field of physics. Researchers are using generative AI to develop physics-informed techniques for classifying phase transitions in materials, which is more efficient than existing methods. Additionally, AI is being coupled with fundamental physics to accelerate calculations and uncover new mechanisms in physical systems.\n\n#### 3. Potential Industry Impacts of AI Advancements in 2024\n\n1. **Manufacturing**: The manufacturing industry is expected to see the largest financial impact from AI. AI-driven automation, predictive maintenance, and quality control are enhancing productivity and reducing costs, leading to significant economic benefits.\n\n2. **Healthcare**: AI is transforming healthcare by enabling personalized medicine, improving diagnostic accuracy, and streamlining administrative processes. These advancements are improving patient outcomes and reducing healthcare costs.\n\n3. **Finance**: In the finance sector, AI is being used for fraud detection, risk management, and algorithmic trading. These applications are enhancing the efficiency and security of financial transactions, driving growth and innovation in the industry.\n\n4. **Energy**: AI is playing a crucial role in optimizing energy consumption and integrating renewable energy sources. AI-powered systems are being used to manage energy grids, predict energy demand, and improve the efficiency of energy storage solutions.\n\n### Conclusion\n\nThe advancements in AI in 2024 are poised to bring transformative changes across various industries. Key trends such as generative AI, multimodal AI, and a focus on ethics and safety are shaping the future of AI. Breakthrough technologies like AI-powered scientific discovery, elastocalorics, and AI applications in physics are driving innovation, while the potential industry impacts highlight the significant economic and societal benefits of AI. As AI continues to evolve, it will be essential for organizations to stay informed and adapt to these changes to harness the full potential of AI technologies." \ No newline at end of file +"### Comprehensive Analysis of AI Advancements in 2024\n\n#### Key Trends\n1. **Generative AI Integration**: Generative AI, which gained significant attention in previous years, is now becoming more useful for the general public. Tools like ChatGPT have reached mass adoption, and organizations are deriving substantial business value from these technologies.\n\n2. **Multimodal AI**: This trend involves AI systems that can process and integrate multiple types of data (e.g., text, images, audio) simultaneously. This capability enhances the versatility and applicability of AI across various domains.\n\n3. **Ethics and Safety**: There is a growing emphasis on the ethical deployment of AI and ensuring safety in AI applications. This includes addressing biases, ensuring transparency, and complying with evolving regulatory landscapes.\n\n4. **AI Democratization**: More people are experimenting with AI models, leading to a proliferation of small, specialized AI applications. This democratization is making AI accessible to non-tech individuals and small businesses.\n\n5. **Consolidation of Data and AI Leadership**: Organizations are streamlining their technology and data leadership roles, reducing the number of chief data and analytics officers to create more integrated and efficient leadership structures.\n\n#### Breakthrough Technologies\n1. **AI-Powered Scientific Discovery**: AI is being used to accelerate scientific research and discovery, particularly in fields like drug development and materials science.\n\n2. **Elastocalorics**: This emerging technology involves materials that can change temperature under mechanical stress, offering potential applications in energy-efficient cooling systems.\n\n3. **AI in Clean Energy**: AI systems are being developed to store clean energy as heat, which could significantly contribute to decarbonizing industries.\n\n4. **Advanced Large Language Models**: Continued advancements in large language models are enabling more sophisticated natural language processing capabilities, enhancing applications in customer service, content creation, and more.\n\n5. **Cutting-Edge Robotics**: Innovations in robotics, powered by AI, are leading to more autonomous and capable machines that can perform complex tasks in manufacturing, healthcare, and other sectors.\n\n#### Potential Industry Impacts\n1. **Manufacturing**: The manufacturing sector is expected to see the largest financial impact from AI, with significant improvements in efficiency, predictive maintenance, and supply chain optimization.\n\n2. **Healthcare**: AI is revolutionizing healthcare through advancements in diagnostics, personalized medicine, and robotic surgery, leading to better patient outcomes and reduced costs.\n\n3. **Finance**: The finance industry is leveraging AI for fraud detection, algorithmic trading, and personalized financial services, enhancing security and customer experience.\n\n4. **Retail**: AI is transforming retail by enabling personalized shopping experiences, optimizing inventory management, and improving customer service through chatbots and recommendation systems.\n\n5. **Energy**: AI is playing a crucial role in the energy sector by optimizing energy consumption, integrating renewable energy sources, and improving grid management.\n\n6. **Physics**: AI is being used to uncover mechanisms in physics, such as classifying phase transitions in materials and enhancing computational efficiency by coupling AI with fundamental physics. These applications are driving new paradigms in AI discovery and promoting advancements in theoretical and applied physics.\n\nIn conclusion, 2024 is poised to be a pivotal year for AI, with significant advancements and widespread adoption across various industries. The focus on ethical AI, multimodal capabilities, and breakthrough technologies will drive innovation and create substantial economic and societal impacts." \ No newline at end of file

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