.

#agent.pmpt.txt#

@@ -1,35 +0,0 @@
-Assistant is a large language model trained by OpenAI.
-
-Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-
-Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
-
-Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
-
-TOOLS:
-------
-
-Assistant has access to the following tools:
-
-{{tools_names}}
-
-To use a tool, please use the following format:
-
-```
-Thought: Do I need to use a tool? Yes
-Action: the action to take, should be one of [{{tool_names}}]
-Action Input: the input to the action
-Observation: the result of the action
-```
-
-When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
-
-```
-Thought: Do I need to use a tool? No
-AI: [your response here]
-```
-
-Begin!
-
-New input: {{input}}
-{{agent_scratchpad}}

#agent.py#

@@ -1,46 +0,0 @@
-# + tags=["hide_inp"]
-
-desc = """
-### Gradio Tool
-
-Chain that ask for a command-line question and then runs the bash command. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/bash.ipynb)
-
-(Adapted from LangChain [BashChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_bash.html))
-"""
-# -
-
-# $
-
-from minichain import show, prompt, OpenAI
-from gradio_tools.tools import StableDiffusionTool, ImageCaptioningTool
-
-class Tool1:
-    description: str = "Tool 1"
-    def run(self, prompt):
-        return prompt
-
-class Tool2:
-    description: str = "Tool 2"
-    def run(self, prompt):
-        return prompt
-
-
-@prompt(OpenAI())
-def gen(model, query):
-    return model(query)
-
-def gradio_example(query):
-    return gen(query)
-
-
-# $
-
-gradio = show(gradio_example,
-              subprompts=[caption],
-              examples=['/home/srush/Projects/MiniChain/examples/63dd90c7-9b8d-4ba4-bc07-a378fd932304/tmph3xi9ylr.jpg', 'Make me a flower'],
-              out_type="markdown",
-              description=desc
-              )
-if __name__ == "__main__":
-    gradio.launch()
-

#base.sh#

@@ -1,2 +0,0 @@
-export OPENAI_API_KEY="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"
-export SERP_KEY="593a073fa4c730efe918e592a538b36e80841bc8f8dd4070c1566920f75ba140"

#chat.py#

@@ -1,76 +0,0 @@
-# + tags=["hide_inp"]
-desc = """
-### Chat
-
-A chat-like example for multi-turn chat with state. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/chat.ipynb)
-
-(Adapted from [LangChain](https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html)'s version of this [blog post](https://www.engraved.blog/building-a-virtual-machine-inside/).)
-
-"""
-# -
-
-
-# $
-
-from dataclasses import dataclass, replace
-from typing import List, Tuple
-from minichain import OpenAI, prompt, show, transform, Mock
-
-# Generic stateful Memory
-
-MEMORY = 2
-
-@dataclass
-class State:
-    memory: List[Tuple[str, str]]
-    human_input: str = ""
-
-    def push(self, response: str) -> "State":
-        memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]
-        return State(memory + [(self.human_input, response)])
-
-    def __str__(self):
-        return self.memory[-1][-1]
-
-# Chat prompt with memory
-
-@prompt(OpenAI(), template_file="chat.pmpt.tpl")
-def chat_response(model, state: State) -> State:
-    return model.stream(state)
-
-@transform()
-def update(state, chat_output):
-    result = chat_output.split("Assistant:")[-1]
-    return state.push(result)
-
-
-def chat(command, state):
-    state = replace(state, human_input=command)
-    return update(state, chat_response(state))
-    
-# $
-
-examples = [
-    "ls ~",
-    "cd ~",
-    "{Please make a file jokes.txt inside and put some jokes inside}",
-    """echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""",
-    """echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""",
-    """echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image""",
-    "nvidia-smi"
-]
-
-print(chat("ls", State([])).run())
-
-gradio = show(chat,
-              initial_state=State([]),
-              subprompts=[chat_response],
-              examples=examples,
-              out_type="json",
-              description=desc,
-              code=open("chat.py", "r").read().split("$")[1].strip().strip("#").strip(),
-)
-if __name__ == "__main__":
-    gradio.queue().launch()
-
-

#chatgpt.py#

@@ -1,78 +0,0 @@
-# + tags=["hide_inp"]
-
-desc = """
-# ChatGPT
-
-"ChatGPT" like examples.  Adapted from
-[LangChain](https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html)'s
-version of this [blog post](https://www.engraved.blog/building-a-virtual-machine-inside/).
-"""
-# -
-
-import warnings
-from dataclasses import dataclass
-from typing import List, Tuple
-from IPython.display import Markdown, display
-import minichain
-
-# + tags=["hide_inp"]
-warnings.filterwarnings("ignore")
-# -
-
-
-# Generic stateful Memory
-
-MEMORY = 2
-
-@dataclass
-class State:
-    memory: List[Tuple[str, str]]
-    human_input: str = ""
-
-    def push(self, response: str) -> "State":
-        memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]
-        return State(memory + [(self.human_input, response)])
-
-# Chat prompt with memory
-
-class ChatPrompt(minichain.TemplatePrompt):
-    template_file = "chatgpt.pmpt.tpl"
-    def parse(self, out: str, inp: State) -> State:
-        result = out.split("Assistant:")[-1]
-        return inp.push(result)
-
-class Human(minichain.Prompt):
-    def parse(self, out: str, inp: State) -> State:
-        return inp.human_input = out
-    
-    
-fake_human = [
-    "I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.",
-    "ls ~",
-    "cd ~",
-    "{Please make a file jokes.txt inside and put some jokes inside}",
-    """echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""",
-    """echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""",
-    """echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image""",
-    "nvidia-smi"
-]
-
-with minichain.start_chain("chatgpt") as backend:
-    prompt = ChatPrompt(backend.OpenAI())
-    human = Human(backend.Mock(fake_human))
-    state = State([])
-    for i in range(len(fake_human)):
-        human.chain(prompt)
-        # display(Markdown(f'**Human:** <span style="color: blue">{t}</span>'))
-        # display(Markdown(f'**Assistant:** {state.memory[-1][1]}'))
-        # display(Markdown(f'--------------'))
-
-
-# + tags=["hide_inp"]
-ChatPrompt().show(State([("human 1", "output 1"), ("human 2", "output 2") ], "cd ~"),
-                    "Text Assistant: Hello")
-# -
-
-# View the run log.
-
-minichain.show_log("chatgpt.log")

#math_demo.py#

@@ -1,27 +0,0 @@
-# Notebook to answer a math problem with code.
-# Adapted from Dust [maths-generate-code](https://dust.tt/spolu/a/d12ac33169)
-
-import minichain
-
-# Prompt that asks LLM for code from math.
-
-class MathPrompt(minichain.TemplatePrompt[str]):
-    template_file = "math.pmpt.tpl"
-
-# Ask a question and run it as python code.
-
-with minichain.start_chain("math") as backend:
-    question = "What is the sum of the powers of 3 (3^i) that are smaller than 100?"
-    prompt = MathPrompt(backend.OpenAI()).chain(minichain.SimplePrompt(backend.Python()))
-    result = prompt({"question": question})
-    print(result)
-
-# View the prompt
-    
-# + tags=["hide_inp"]
-MathPrompt().show({"question": "What is 10 + 12?"}, "10 + 12")
-# -
-
-# View the log
-
-minichain.show_log("math.log")

#ner.py#

@@ -1,46 +0,0 @@
-# + tags=["hide_inp"]
-
-desc = """
-### Named Entity Recognition
-
-Chain that does named entity recognition with arbitrary labels. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/ner.ipynb)
-
-(Adapted from [promptify](https://github.com/promptslab/Promptify/blob/main/promptify/prompts/nlp/templates/ner.jinja)).
-"""
-# -
-
-# $
-
-from minichain import prompt, transform, show, OpenAI
-import json
-
-@prompt(OpenAI(), template_file = "ner.pmpt.tpl")
-def ner_extract(model, kwargs):
-    return model(kwargs)
-
-@transform()
-def to_json(chat_output):
-    return json.loads(chat_output)
-
-@prompt(OpenAI())
-def team_describe(model, inp):
-    query = "Can you describe these basketball teams? " + \
-        " ".join([i["E"] for i in inp if i["T"] =="Team"])
-    return model(query)
-
-
-def ner(text_input, labels, domain):
-    extract = to_json(ner_extract(dict(text_input=text_input, labels=labels, domain=domain)))
-    return team_describe(extract)
-
-# $
-
-gradio = show(ner,
-              examples=[["An NBA playoff pairing a year ago, the 76ers (39-20) meet the Miami Heat (32-29) for the first time this season on Monday night at home.", "Team, Date", "Sports"]],
-              description=desc,
-              subprompts=[ner_extract, team_describe],
-              code=open("ner.py", "r").read().split("$")[1].strip().strip("#").strip(),
-              )
-
-if __name__ == "__main__":
-    gradio.queue().launch()

#qa.py#

@@ -1,62 +0,0 @@
-# + tags=["hide_inp"]
-desc = """
-### Question Answering with Retrieval
-
-Chain that answers questions with embeedding based retrieval. [[Code](https://github.com/srush/MiniChain/blob/main/examples/qa.py)]
-
-(Adapted from [OpenAI Notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).)
-"""
-# -
-
-import datasets
-import numpy as np
-
-from minichain import EmbeddingPrompt, TemplatePrompt, show_log, start_chain
-
-# We use Hugging Face Datasets as the database by assigning
-# a FAISS index.
-
-olympics = datasets.load_from_disk("olympics.data")
-olympics.add_faiss_index("embeddings")
-
-
-# Fast KNN retieval prompt
-
-
-class KNNPrompt(EmbeddingPrompt):
-    def find(self, out, inp):
-        res = olympics.get_nearest_examples("embeddings", np.array(out), 3)
-        return {"question": inp, "docs": res.examples["content"]}
-
-
-# QA prompt to ask question with examples
-
-
-class QAPrompt(TemplatePrompt):
-    template_file = "qa.pmpt.tpl"
-
-
-with start_chain("qa") as backend:
-    prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
-    
-questions = ["Who won the 2020 Summer Olympics men's high jump?",
-             "Why was the 2020 Summer Olympics originally postponed?",
-             "In the 2020 Summer Olympics, how many gold medals did the country which won the most medals win?",
-             "What is the total number of medals won by France?",
-             "What is the tallest mountain in the world?"]
-
-gradio = prompt.to_gradio(fields=["query"],
-                          examples=questions,
-                          description=desc)
-if __name__ == "__main__":
-    gradio.launch()
-
-
-
-# # + tags=["hide_inp"]
-# QAPrompt().show(
-#     {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
-# )
-# # -
-
-# show_log("qa.log")

#selfask.py#

@@ -1,64 +0,0 @@
-
-desc = """
-### Self-Ask
-
- Notebook implementation of the self-ask + Google tool use prompt.
-
- (Adapted from [Self-Ask repo](https://github.com/ofirpress/self-ask))
-"""
-
-# $
-
-from dataclasses import dataclass, replace
-from typing import Optional
-from minichain import prompt, show, OpenAI, Google
-
-
-@dataclass
-class State:
-    question: str
-    history: str =  ""
-    next_query: Optional[str] = None
-    final_answer: Optional[str] = None
-
-
-@prompt(OpenAI(),
-        template_file = "selfask.pmpt.tpl",
-        stop_template = "\nIntermediate answer:")
-def self_ask(model, state):
-    out = model(state)
-    res = out.split(":", 1)[1]
-    if out.startswith("Follow up:"):
-        return replace(state, next_query=res)
-    elif out.startswith("So the final answer is:"):
-        return replace(state, final_answer=res)
-    
-@prompt(Google())
-def google(model, state):
-    if state.next_query is None:
-        return state
-    
-    result = model(state.next_query)
-    return State(state.question,
-                 state.history + "\nIntermediate answer: " + result + "\n")
-
-def selfask(question):
-    state = State(question)
-    for i in range(3):
-        state = self_ask(state)
-        state = google(state)
-    return state
-
-# $
-
-gradio = show(selfask,
-              examples=["What is the zip code of the city where George Washington was born?"],
-              subprompts=[self_ask, google] * 3,
-              description=desc,
-              code=open("selfask.py", "r").read().split("$")[1].strip().strip("#").strip(),
-              out_type="json"
-              )
-if __name__ == "__main__":
-    gradio.launch()
-
-

README.md

@@ -4,7 +4,7 @@ emoji: ⛓️
 colorFrom: purple
 colorTo: blue
 sdk: gradio
-sdk_version: 3.26.0
+sdk_version: 4.7.0
 app_file: app.py
 pinned: true
 python_version: 3.9.0

README.md~

@@ -1,12 +0,0 @@
----
-title: Minichain Math
-emoji: 📈
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

agent.pmpt.tpl~

@@ -1,38 +0,0 @@
-Assistant is a large language model trained by OpenAI.
-
-Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-
-Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
-
-Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
-
-TOOLS:
-------
-
-Assistant has access to the following tools:
-
-{{tools_names}}
-
-To use a tool, please use the following format:
-
-```
-Thought: Do I need to use a tool? Yes
-Action: the action to take, should be one of [{{tool_names}}]
-Action Input: the input to the action
-Observation: the result of the action
-```
-
-When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
-
-```
-Thought: Do I need to use a tool? No
-AI: [your response here]
-```
-
-Begin!
-
-Previous conversation history:
-{{chat_history}}
-
-New input: {{input}}
-{{agent_scratchpad}}

agent.pmpt.txt

@@ -1,35 +0,0 @@
-Assistant is a large language model trained by OpenAI.
-
-Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-
-Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
-
-Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
-
-TOOLS:
-------
-
-Assistant has access to the following tools:
-
-{{tools_names}}
-
-To use a tool, please use the following format:
-
-```
-Thought: Do I need to use a tool? Yes
-Action: the action to take, should be one of [{{tool_names}}]
-Action Input: the input to the action
-Observation: the result of the action
-```
-
-When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
-
-```
-Thought: Do I need to use a tool? No
-AI: [your response here]
-```
-
-Begin!
-
-New input: {{input}}
-{{agent_scratchpad}}

agent.pmpt.txt~

@@ -1,38 +0,0 @@
-Assistant is a large language model trained by OpenAI.
-
-Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-
-Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
-
-Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
-
-TOOLS:
-------
-
-Assistant has access to the following tools:
-
-{{tools_names}}
-
-To use a tool, please use the following format:
-
-```
-Thought: Do I need to use a tool? Yes
-Action: the action to take, should be one of [{{tool_names}}]
-Action Input: the input to the action
-Observation: the result of the action
-```
-
-When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
-
-```
-Thought: Do I need to use a tool? No
-AI: [your response here]
-```
-
-Begin!
-
-Previous conversation history:
-{{chat_history}}
-
-New input: {{input}}
-{{agent_scratchpad}}

agent.py

@@ -21,7 +21,7 @@ from gradio_tools.tools import StableDiffusionTool, ImageCaptioningTool, ImageTo
 
 #     description = "Image Captioning"
 
-tools = [StableDiffusionTool(), ImageCaptioningTool()]
+tools = [StableDiffusionTool(), ImageCaptioningTool(), ImageToMusicTool()]
 
 
 @prompt(OpenAI(stop=["Observation:"]),

agent.py~

@@ -1,75 +0,0 @@
-# + tags=["hide_inp"]
-
-desc = """
-### Gradio Tool
-
-Chain that ask for a command-line question and then runs the bash command. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/bash.ipynb)
-
-(Adapted from LangChain [BashChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_bash.html))
-"""
-# -
-
-# $
-
-from minichain import Id, prompt, OpenAIStream
-from gradio_tools.tools import StableDiffusionTool, ImageCaptioningTool
-
-    
-
-@prompt(StableDiffusionTool())
-def gen(model, query):
-    return model(query)
-
-@prompt(ImageCaptioningTool())
-def caption(model, img_src):
-    return model(img_src)
-
-tools = [gen, caption]
-
-@prompt(Id(),
-        #OpenAIStream(), stream=True,
-        template_file="agent.pmpt.tpl")
-def agent(model, query):
-    print(model(dict(tools=[(str(tool.backend.__class__), tool.backend.description)
-                            for tool in tools],
-                     input=query
-                     )))
-    return ("StableDiffusionTool", "Draw a flower")
-    # out = ""
-    # for t in model.stream(dict(tools=[(str(tool.backend.__class__), tool.backend.description)
-    #                                   for tool in tools],
-    #                       input=query
-    #                       )):
-    #     out += t
-    #     yield out
-    # lines = out.split("\n")
-    # response = lines[0].split("?")[1].strip()
-    # if response == "Yes":
-    #     tool = lines[1].split(":")[1].strip()
-    #     yield tool
-
-@prompt(dynamic=tools)
-def selector(model, input):
-    selector, input = input
-    if selector == "StableDiffusionTool":
-        return model.tool(input, tool_num=0)
-    else:
-        return model.tool(input, tool_num=1)
-        
-
-def run(query):
-    select_input = agent(query)
-    return selector(select_input)
-
-run("make a pic").run()
-# $
-
-gradio = show(run,
-              subprompts=[agent, selector],
-              examples=['Draw me a flower'],
-              out_type="markdown",
-              description=desc
-              )
-if __name__ == "__main__":
-    gradio.launch()
-

app.ipynb

@@ -1,106 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0e8d6177",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -q git+https://github.com/srush/MiniChain\n",
-    "!git clone https://github.com/srush/MiniChain; cp -fr MiniChain/examples/* . "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be91fdf2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "from chat import gradio as chat\n",
-    "from ner import gradio as ner\n",
-    "from math_demo import gradio as math_demo\n",
-    "from bash import gradio as bash\n",
-    "from pal import gradio as pal\n",
-    "from gatsby import gradio as gatsby\n",
-    "from qa import gradio as qa\n",
-    "from stats import gradio as stats\n",
-    "from selfask import gradio as selfask\n",
-    "from backtrack import gradio as backtrack"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b618caa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "CSS = \"\"\"\n",
-    "#clean div.form {border: 0px} \n",
-    "#response {border: 0px; background: #ffeec6} \n",
-    "#prompt {border: 0px;background: aliceblue} \n",
-    "#json {border: 0px} \n",
-    "#result {border: 0px; background: #c5e0e5} \n",
-    "#inner {margin: 10px; padding: 10px; font-size: 20px; } \n",
-    "#inner textarea {border: 0px}\n",
-    "div.gradio-container {color: black}\n",
-    "span.head {font-size: 60pt; font-family: cursive;}\n",
-    "body {\n",
-    "  --text-sm: 15px;\n",
-    "  --text-md: 18px;\n",
-    "  --text-lg: 20px;\n",
-    "  --input-text-size: 20px;\n",
-    "  --section-text-size: 20px;\n",
-    "}\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30e34bad",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7cb4d4d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with gr.Blocks(css=CSS, theme=gr.themes.Monochrome()) as demo:\n",
-    "    gr.HTML(\"<center style='background:#B6B7BA'> <span class='head'>Mini</span><img src='https://user-images.githubusercontent.com/35882/227017900-0cacdfb7-37e2-47b1-9347-a233810d3544.png' width='20%' style='display:inline'><span class='head'>Chain</span></center><center> <br><a href='https://github.com/srush/minichain'>[library]</a> </center>\")\n",
-    "\n",
-    "    gr.TabbedInterface([math_demo, qa,  chat, gatsby,  ner, bash, pal, stats, selfask, backtrack],\n",
-    "                       [\"Math\", \"QA\", \"Chat\", \"Book\", \"NER\", \"Bash\", \"PAL\", \"Stats\", \"SelfAsk\", \"Backtrack\"],\n",
-    "                       css = CSS)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57ec58c2",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "demo.launch()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

app.py

@@ -9,8 +9,6 @@ from qa import gradio as qa
 from stats import gradio as stats
 from selfask import gradio as selfask
 from table import gradio as table
-from agent import gradio as agent
-from gradio_example import gradio as gradio_example
 
 CSS = """
 #clean div.form {border: 0px}

app.py~

@@ -1,40 +0,0 @@
-import gradio as gr
-from chat import gradio as chat
-from ner import gradio as ner
-from math_demo import gradio as math_demo
-from bash import gradio as bash
-from pal import gradio as pal
-from gatsby import gradio as gatsby
-from qa import gradio as qa
-from stats import gradio as stats
-from selfask import gradio as selfask
-from backtrack import gradio as backtrack
-from table import gradio as table
-from gradio_example import gradio as gradio_example
-
-CSS = """
-#clean div.form {border: 0px} 
-#response {border: 0px; background: #ffeec6} 
-#prompt {border: 0px;background: aliceblue} 
-div.gradio-container {color: black}
-span.head {font-size: 60pt; font-family: cursive;}
-body {
-  --text-sm: 12px;
-  --text-md: 16px;
-  --text-lg: 18px;
-  --input-text-size: 16px;
-  --section-text-size: 16px;
-}
-"""
-
-
-
-with gr.Blocks(css=CSS, theme=gr.themes.Monochrome()) as demo:
-    gr.HTML("<center style='background:#B6B7BA'> <span class='head'>Mini</span><img src='https://user-images.githubusercontent.com/35882/227017900-0cacdfb7-37e2-47b1-9347-a233810d3544.png' width='20%' style='display:inline'><span class='head'>Chain</span></center><center> <br><a href='https://github.com/srush/minichain'>[library]</a> </center>")
-
-    gr.TabbedInterface([math_demo, qa,  chat, gatsby,  ner, bash, pal, table, gradio_example, stats, selfask, backtrack],
-                       ["Math", "QA", "Chat", "Book", "NER", "Bash", "PAL", "Table", "Gradio", "Stats", "SelfAsk", "Backtrack"],
-                       css = CSS)
-
-demo.queue().launch()
-

backtrack.py

@@ -1,42 +0,0 @@
-# + tags=["hide_inp"]
-desc = """
-### Backtrack on Failure
-
-Chain that backtracks on failure. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/backtrack.ipynb)
-
-"""
-# -
-
-# $
-from minichain import prompt, Mock, show, OpenAI
-import minichain
-
-@prompt(Mock(["dog", "blue", "cat"]), stream=True)
-def prompt_generation(model):
-    out = ""
-    for token in model.stream(""):
-        out += token
-        yield out
-    yield out
-
-@prompt(Mock(["No", "Yes"]), template="Answer 'yes' is {{query}} is a color. Answer:", stream=False)
-def prompt_validation(model, x):
-    out = model(dict(query=x))
-    if out.strip().lower().startswith("yes"):
-        return x
-    return model.fail(1)
-    
-def run():
-    x = prompt_generation()
-    return prompt_validation(x)
-# $
-
-gradio = show(run,
-            examples = [],
-            subprompts=[prompt_generation, prompt_validation],
-            code=open("backtrack.py", "r").read().split("$")[1].strip().strip("#").strip(),
-            out_type="markdown"
-            )
-
-if __name__ == "__main__":
-    gradio.queue().launch()

backtrack.py~

@@ -1,38 +0,0 @@
-# + tags=["hide_inp"]
-desc = """
-### Backtrack on Failure
-
-Chain that backtracks on failure. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/backtrack.ipynb)
-
-"""
-# -
-
-# $
-from minichain import prompt, Mock, show, OpenAI
-import minichain
-
-@prompt(Mock(["dog", "blue", "cat"]))
-def prompt_generation(model):
-    return model("")
-
-@prompt(OpenAI(), template="Answer 'yes' is {{query}} is a color. Answer:")
-def prompt_validation(model, x):
-    out = model(dict(query=x))
-    if out.strip().lower().startswith("yes"):
-        return x
-    return model.fail(1)
-    
-def run():
-    x = prompt_generation()
-    return prompt_validation(x)
-# $
-
-gradio = show(run,
-            examples = [],
-            subprompts=[prompt_generation, prompt_validation],
-            code=open("backtrack.py", "r").read().split("$")[1].strip().strip("#").strip(),
-            out_type="markdown"
-            )
-
-if __name__ == "__main__":
-    gradio.launch()

base.ipynb

@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04b70841",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -q git+https://github.com/srush/MiniChain\n",
-    "!git clone https://github.com/srush/MiniChain; cp -fr MiniChain/examples/* . "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8ea4879",
-   "metadata": {},
-   "source": [
-    "Prompt from ...\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0370e1bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = \"\"\"\n",
-    "Question: Who lived longer, Muhammad Ali or Alan Turing?\n",
-    "Are follow up questions needed here: Yes.\n",
-    "Follow up: How old was Muhammad Ali when he died?\n",
-    "Intermediate answer: Muhammad Ali was 74 years old when he died.\n",
-    "Follow up: How old was Alan Turing when he died?\n",
-    "Intermediate answer: Alan Turing was 41 years old when he died.\n",
-    "So the final answer is: Muhammad Ali\n",
-    "\n",
-    "Question: When was the founder of craigslist born?\n",
-    "Are follow up questions needed here: Yes.\n",
-    "Follow up: Who was the founder of craigslist?\n",
-    "Intermediate answer: Craigslist was founded by Craig Newmark.\n",
-    "Follow up: When was Craig Newmark born?\n",
-    "Intermediate answer: Craig Newmark was born on December 6, 1952.\n",
-    "So the final answer is: December 6, 1952\n",
-    "\n",
-    "Question: Who was the maternal grandfather of George Washington?\n",
-    "Are follow up questions needed here: Yes.\n",
-    "Follow up: Who was the mother of George Washington?\n",
-    "Intermediate answer: The mother of George Washington was Mary Ball Washington.\n",
-    "Follow up: Who was the father of Mary Ball Washington?\n",
-    "Intermediate answer: The father of Mary Ball Washington was Joseph Ball.\n",
-    "So the final answer is: Joseph Ball\n",
-    "\n",
-    "Question: Are both the directors of Jaws and Casino Royale from the same country?\n",
-    "Are follow up questions needed here: Yes.\n",
-    "Follow up: Who is the director of Jaws?\n",
-    "Intermediate answer: The director of Jaws is Steven Spielberg.\n",
-    "Follow up: Where is Steven Spielberg from?\n",
-    "Intermediate answer: The United States.\n",
-    "Follow up: Who is the director of Casino Royale?\n",
-    "Intermediate answer: The director of Casino Royale is Martin Campbell.\n",
-    "Follow up: Where is Martin Campbell from?\n",
-    "Intermediate answer: New Zealand.\n",
-    "So the final answer is: No\n",
-    "\n",
-    "Question: {{input}}\n",
-    "Are followup questions needed here: {% if agent_scratchpad %}Yes{%else%}No{% endif %}.\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82a93b59",
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "import jinja2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4e5105b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class SelfAsk:\n",
-    "    def render(self, input: str, agent_scratchpad: bool):\n",
-    "        return jinja.render(prompt, dict(input=input,\n",
-    "                                         agent_scatchpad=agent_scratchpad))\n",
-    "\n",
-    "    def parse(self, response: str):\n",
-    "        pass\n",
-    "\n",
-    "\n",
-    "    def stop(self):\n",
-    "        return []"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

base.py

@@ -1,57 +0,0 @@
-# Prompt from ...
-#
-
-prompt = """
-Question: Who lived longer, Muhammad Ali or Alan Turing?
-Are follow up questions needed here: Yes.
-Follow up: How old was Muhammad Ali when he died?
-Intermediate answer: Muhammad Ali was 74 years old when he died.
-Follow up: How old was Alan Turing when he died?
-Intermediate answer: Alan Turing was 41 years old when he died.
-So the final answer is: Muhammad Ali
-
-Question: When was the founder of craigslist born?
-Are follow up questions needed here: Yes.
-Follow up: Who was the founder of craigslist?
-Intermediate answer: Craigslist was founded by Craig Newmark.
-Follow up: When was Craig Newmark born?
-Intermediate answer: Craig Newmark was born on December 6, 1952.
-So the final answer is: December 6, 1952
-
-Question: Who was the maternal grandfather of George Washington?
-Are follow up questions needed here: Yes.
-Follow up: Who was the mother of George Washington?
-Intermediate answer: The mother of George Washington was Mary Ball Washington.
-Follow up: Who was the father of Mary Ball Washington?
-Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
-So the final answer is: Joseph Ball
-
-Question: Are both the directors of Jaws and Casino Royale from the same country?
-Are follow up questions needed here: Yes.
-Follow up: Who is the director of Jaws?
-Intermediate answer: The director of Jaws is Steven Spielberg.
-Follow up: Where is Steven Spielberg from?
-Intermediate answer: The United States.
-Follow up: Who is the director of Casino Royale?
-Intermediate answer: The director of Casino Royale is Martin Campbell.
-Follow up: Where is Martin Campbell from?
-Intermediate answer: New Zealand.
-So the final answer is: No
-
-Question: {{input}}
-Are followup questions needed here: {% if agent_scratchpad %}Yes{%else%}No{% endif %}.
-"""
-
-import jinja2
-
-class SelfAsk:
-    def render(self, input: str, agent_scratchpad: bool):
-        return jinja.render(prompt, dict(input=input,
-                                         agent_scatchpad=agent_scratchpad))
-
-    def parse(self, response: str):
-        pass
-
-
-    def stop(self):
-        return []

base.sh

@@ -1,2 +0,0 @@
-export OPENAI_API_KEY="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"
-export SERP_KEY="593a073fa4c730efe918e592a538b36e80841bc8f8dd4070c1566920f75ba140"

base.sh~

@@ -1,2 +0,0 @@
-export OPENAI_KEY="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"
-export SERP_KEY="593a073fa4c730efe918e592a538b36e80841bc8f8dd4070c1566920f75ba140"

bash.log

@@ -1,16 +1,18 @@
-{"action_status": "started", "timestamp": 1678804794.3736765, "task_uuid": "b91a9977-eb2c-4ee4-8081-2c9c11c5c84b", "action_type": "bash", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.3737779, "task_uuid": "b91a9977-eb2c-4ee4-8081-2c9c11c5c84b", "action_type": "bash", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.407293, "task_uuid": "a24f4aa6-8e24-4d6b-aa12-0c326d842918", "action_type": "pal", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.4074926, "task_uuid": "a24f4aa6-8e24-4d6b-aa12-0c326d842918", "action_type": "pal", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.616296, "task_uuid": "45a836db-19bb-4bc5-a29b-d9a3933dd175", "action_type": "gatsby", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.616455, "task_uuid": "45a836db-19bb-4bc5-a29b-d9a3933dd175", "action_type": "gatsby", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.7338963, "task_uuid": "dc9993f0-9c8a-42a8-8f7e-bc4b40be1a1e", "action_type": "qa", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.7341123, "task_uuid": "dc9993f0-9c8a-42a8-8f7e-bc4b40be1a1e", "action_type": "qa", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.8375309, "task_uuid": "dd1251af-7770-460d-ac61-85db28fd24df", "action_type": "stats", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.8376832, "task_uuid": "dd1251af-7770-460d-ac61-85db28fd24df", "action_type": "stats", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804807.8764367, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "<class 'math_demo.MathPrompt'>", "task_level": [1]}
-{"input": {"question": "What is the sum of the powers of 3 (3^i) that are smaller than 100?"}, "action_status": "started", "timestamp": 1678804807.876612, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Input Function", "task_level": [2, 1]}
-{"action_status": "succeeded", "timestamp": 1678804807.8774285, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "#### Question:\n\n* What is 37593 * 67?\n\n#### Code:\n\n```python\nprint(37593 * 67)\n```\n\n#### Question:\n\n* Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?\n\n#### Code:\n\n```python\nprint((16-3-4)*2)\n```\n\n#### Question:\n\n* How many of the integers between 0 and 99 inclusive are divisible by 8?\n\n#### Code:\n\n```python\ncount = 0\nfor i in range(0, 99+1):\n    if i % 8 == 0: count += 1\nprint(count)\n```\n\n#### Question:\n\n* A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\n\n#### Code:\n\n```python\nprint(2 + 2/2)\n```\n\n#### Question:\n\n* What is the sum of the powers of 3 (3^i) that are smaller than 100?\n\n#### Code:", "action_status": "started", "timestamp": 1678804807.877532, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Prompted", "task_level": [3, 1]}
-{"exception": "builtins.AssertionError", "reason": "Need an OPENAI_KEY. Get one here https://openai.com/api/", "action_status": "failed", "timestamp": 1678804807.8874092, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Prompted", "task_level": [3, 2]}
-{"exception": "builtins.AssertionError", "reason": "Need an OPENAI_KEY. Get one here https://openai.com/api/", "action_status": "failed", "timestamp": 1678804807.8875778, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "<class 'math_demo.MathPrompt'>", "task_level": [4]}
+{"action_status": "started", "timestamp": 1678722984.131601, "task_uuid": "b5da4857-2f0d-45f2-9c52-902cb1cbc204", "action_type": "bash", "task_level": [1]}
+{"action_status": "succeeded", "timestamp": 1678722984.131747, "task_uuid": "b5da4857-2f0d-45f2-9c52-902cb1cbc204", "action_type": "bash", "task_level": [2]}
+{"action_status": "started", "timestamp": 1678722991.9121287, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "<class '__main__.CLIPrompt'>", "task_level": [1]}
+{"input": {"question": "Go up one directory, and then into the minichain directory,and list the files in the directory"}, "action_status": "started", "timestamp": 1678722991.912252, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "Input Function", "task_level": [2, 1]}
+{"action_status": "succeeded", "timestamp": 1678722991.9134278, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "Input Function", "task_level": [2, 2]}
+{"prompt": "If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n\nQuestion: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n\nI need to take the following actions:\n- List all files in the directory\n- Create a new directory\n- Copy the files from the first directory into the second directory\n```bash\nls\nmkdir myNewDirectory\ncp -r target/* myNewDirectory\n```\n\nThat is the format. Begin!\n\nQuestion: \"Go up one directory, and then into the minichain directory,and list the files in the directory\"", "action_status": "started", "timestamp": 1678722991.9135177, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "Prompted", "task_level": [3, 1]}
+{"action_status": "succeeded", "timestamp": 1678722992.8956733, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "Prompted", "task_level": [3, 2]}
+{"result": "\n\n```bash\ncd ..\ncd minichain\nls\n```", "action_status": "started", "timestamp": 1678722992.895786, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "Result", "task_level": [4, 1]}
+{"action_status": "succeeded", "timestamp": 1678722992.895828, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "Result", "task_level": [4, 2]}
+{"action_status": "succeeded", "timestamp": 1678722992.8958883, "task_uuid": "79c36874-bc57-4f5c-99c8-a376d9a35996", "action_type": "<class '__main__.CLIPrompt'>", "task_level": [5]}
+{"action_status": "started", "timestamp": 1678722992.8959506, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "<class '__main__.BashPrompt'>", "task_level": [1]}
+{"input": ["cd ..", "cd minichain", "ls"], "action_status": "started", "timestamp": 1678722992.8959756, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "Input Function", "task_level": [2, 1]}
+{"action_status": "succeeded", "timestamp": 1678722992.8960001, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "Input Function", "task_level": [2, 2]}
+{"prompt": "cd ..;cd minichain;ls", "action_status": "started", "timestamp": 1678722992.8960173, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "Prompted", "task_level": [3, 1]}
+{"action_status": "succeeded", "timestamp": 1678722992.9118538, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "Prompted", "task_level": [3, 2]}
+{"result": "backend.py\nbase.py\n__init__.py\nprompts.py\n__pycache__\ntemplates\n", "action_status": "started", "timestamp": 1678722992.9120688, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "Result", "task_level": [4, 1]}
+{"action_status": "succeeded", "timestamp": 1678722992.9121852, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "Result", "task_level": [4, 2]}
+{"action_status": "succeeded", "timestamp": 1678722992.9122384, "task_uuid": "8b4569fb-f15b-4e15-a857-00ddbf7756ac", "action_type": "<class '__main__.BashPrompt'>", "task_level": [5]}

bash.py~

@@ -1,21 +0,0 @@
-
-class Bash(JinjaPrompt[]):
-    prompt_template = """If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:
-
-Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"
-
-I need to take the following actions:
-- List all files in the directory
-- Create a new directory
-- Copy the files from the first directory into the second directory
-```bash
-ls
-mkdir myNewDirectory
-cp -r target/* myNewDirectory
-```
-
-That is the format. Begin!
-
-Question: {question}"""
-
-    

chat.log

@@ -1,22 +0,0 @@
-{"action_status": "started", "timestamp": 1678804794.0478587, "task_uuid": "ee9d703e-b2a7-49c8-bb88-44836746c9aa", "action_type": "chat", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.0480256, "task_uuid": "ee9d703e-b2a7-49c8-bb88-44836746c9aa", "action_type": "chat", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.3142183, "task_uuid": "2447e362-ebdb-4151-bb2b-70ada0c012fa", "action_type": "ner", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.314325, "task_uuid": "2447e362-ebdb-4151-bb2b-70ada0c012fa", "action_type": "ner", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.3484201, "task_uuid": "051d71c8-5062-4319-a533-fdf97e822b41", "action_type": "math", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.3485305, "task_uuid": "051d71c8-5062-4319-a533-fdf97e822b41", "action_type": "math", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.3736765, "task_uuid": "b91a9977-eb2c-4ee4-8081-2c9c11c5c84b", "action_type": "bash", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.3737779, "task_uuid": "b91a9977-eb2c-4ee4-8081-2c9c11c5c84b", "action_type": "bash", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.407293, "task_uuid": "a24f4aa6-8e24-4d6b-aa12-0c326d842918", "action_type": "pal", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.4074926, "task_uuid": "a24f4aa6-8e24-4d6b-aa12-0c326d842918", "action_type": "pal", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.616296, "task_uuid": "45a836db-19bb-4bc5-a29b-d9a3933dd175", "action_type": "gatsby", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.616455, "task_uuid": "45a836db-19bb-4bc5-a29b-d9a3933dd175", "action_type": "gatsby", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.7338963, "task_uuid": "dc9993f0-9c8a-42a8-8f7e-bc4b40be1a1e", "action_type": "qa", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.7341123, "task_uuid": "dc9993f0-9c8a-42a8-8f7e-bc4b40be1a1e", "action_type": "qa", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804794.8375309, "task_uuid": "dd1251af-7770-460d-ac61-85db28fd24df", "action_type": "stats", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678804794.8376832, "task_uuid": "dd1251af-7770-460d-ac61-85db28fd24df", "action_type": "stats", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678804807.8764367, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "<class 'math_demo.MathPrompt'>", "task_level": [1]}
-{"input": {"question": "What is the sum of the powers of 3 (3^i) that are smaller than 100?"}, "action_status": "started", "timestamp": 1678804807.876612, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Input Function", "task_level": [2, 1]}
-{"action_status": "succeeded", "timestamp": 1678804807.8774285, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "#### Question:\n\n* What is 37593 * 67?\n\n#### Code:\n\n```python\nprint(37593 * 67)\n```\n\n#### Question:\n\n* Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?\n\n#### Code:\n\n```python\nprint((16-3-4)*2)\n```\n\n#### Question:\n\n* How many of the integers between 0 and 99 inclusive are divisible by 8?\n\n#### Code:\n\n```python\ncount = 0\nfor i in range(0, 99+1):\n    if i % 8 == 0: count += 1\nprint(count)\n```\n\n#### Question:\n\n* A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\n\n#### Code:\n\n```python\nprint(2 + 2/2)\n```\n\n#### Question:\n\n* What is the sum of the powers of 3 (3^i) that are smaller than 100?\n\n#### Code:", "action_status": "started", "timestamp": 1678804807.877532, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Prompted", "task_level": [3, 1]}
-{"exception": "builtins.AssertionError", "reason": "Need an OPENAI_KEY. Get one here https://openai.com/api/", "action_status": "failed", "timestamp": 1678804807.8874092, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "Prompted", "task_level": [3, 2]}
-{"exception": "builtins.AssertionError", "reason": "Need an OPENAI_KEY. Get one here https://openai.com/api/", "action_status": "failed", "timestamp": 1678804807.8875778, "task_uuid": "3130f0e2-8d6f-4992-9e12-8f90e9aa96c4", "action_type": "<class 'math_demo.MathPrompt'>", "task_level": [4]}

chat.py~

@@ -1,67 +0,0 @@
-
-
-import warnings
-from dataclasses import dataclass
-from typing import List, Tuple
-from IPython.display import Markdown, display
-import minichain
-
-# + tags=["hide_inp"]
-warnings.filterwarnings("ignore")
-# -
-
-
-# Generic stateful Memory
-
-MEMORY = 2
-
-@dataclass
-class State:
-    memory: List[Tuple[str, str]]
-    human_input: str = ""
-
-    def push(self, response: str) -> "State":
-        memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]
-        return State(memory + [(self.human_input, response)])
-
-# Chat prompt with memory
-
-class ChatPrompt(minichain.TemplatePrompt):
-    template_file = "chatgpt.pmpt.tpl"
-    def parse(self, out: str, inp: State) -> State:
-        result = out.split("Assistant:")[-1]
-        return inp.push(result)
-
-class Human(minichain.Prompt):
-    def parse(self, out: str, inp: State) -> State:
-        return inp.human_input = out
-    
-
-with minichain.start_chain("chat") as backend:
-    prompt = ChatPrompt(backend.OpenAI())
-    state = State([])
-
-gradio = prompt.to_gradio(fields= ["question"],
-                          state= {"state", state},
-                          examples=['Go up one directory, and then into the minichain directory,'
-                                    'and list the files in the directory'],
-                          out_type="markdown"
-)
-if __name__ == "__main__":
-    gradio.launch()
-
-# for i in range(len(fake_human)):
-#     human.chain(prompt)
-
-    
-fake_human = [
-    "I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.",
-    "ls ~",
-    "cd ~",
-    "{Please make a file jokes.txt inside and put some jokes inside}",
-    """echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""",
-    """echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""",
-    """echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image""",
-    "nvidia-smi"
-]
-

chatgpt.ipynb

@@ -1,1099 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3a1d57c7",
-   "metadata": {},
-   "source": [
-    "# ChatGPT"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4751e660",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "source": [
-    "\"ChatGPT\" like examples.  Adapted from\n",
-    "[LangChain](https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html)'s\n",
-    "version of this [blog\n",
-    "post](https://www.engraved.blog/building-a-virtual-machine-inside/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0acb2e7c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.736481Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.736154Z",
-     "iopub.status.idle": "2023-02-27T16:23:05.928562Z",
-     "shell.execute_reply": "2023-02-27T16:23:05.927370Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "from dataclasses import dataclass\n",
-    "from typing import List, Tuple\n",
-    "from IPython.display import Markdown, display\n",
-    "import minichain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "53e77d82",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.933659Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.932423Z",
-     "iopub.status.idle": "2023-02-27T16:23:05.937782Z",
-     "shell.execute_reply": "2023-02-27T16:23:05.937143Z"
-    },
-    "lines_to_next_cell": 2,
-    "tags": [
-     "hide_inp"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4fbd918",
-   "metadata": {},
-   "source": [
-    "Generic stateful Memory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e2c75e33",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.942500Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.941348Z",
-     "iopub.status.idle": "2023-02-27T16:23:05.946048Z",
-     "shell.execute_reply": "2023-02-27T16:23:05.945445Z"
-    },
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "MEMORY = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4bb6e612",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.950846Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.949709Z",
-     "iopub.status.idle": "2023-02-27T16:23:05.956371Z",
-     "shell.execute_reply": "2023-02-27T16:23:05.955692Z"
-    },
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "@dataclass\n",
-    "class State:\n",
-    "    memory: List[Tuple[str, str]]\n",
-    "    human_input: str = \"\"\n",
-    "\n",
-    "    def push(self, response: str) -> \"State\":\n",
-    "        memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]\n",
-    "        return State(memory + [(self.human_input, response)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b958184",
-   "metadata": {},
-   "source": [
-    "Chat prompt with memory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "651c6a01",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.961370Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.960177Z",
-     "iopub.status.idle": "2023-02-27T16:23:05.965742Z",
-     "shell.execute_reply": "2023-02-27T16:23:05.965181Z"
-    },
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "class ChatPrompt(minichain.TemplatePrompt):\n",
-    "    template_file = \"chatgpt.pmpt.tpl\"\n",
-    "    def parse(self, out: str, inp: State) -> State:\n",
-    "        result = out.split(\"Assistant:\")[-1]\n",
-    "        return inp.push(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2594b95f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.968305Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.967862Z",
-     "iopub.status.idle": "2023-02-27T16:23:05.971369Z",
-     "shell.execute_reply": "2023-02-27T16:23:05.970873Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "fake_human = [\n",
-    "    \"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\",\n",
-    "    \"ls ~\",\n",
-    "    \"cd ~\",\n",
-    "    \"{Please make a file jokes.txt inside and put some jokes inside}\",\n",
-    "    \"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\",\n",
-    "    \"\"\"echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\"\"\",\n",
-    "    \"\"\"echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\\nCOPY entrypoint.sh entrypoint.sh\\nENTRYPOINT [\\\"/bin/sh\\\",\\\"entrypoint.sh\\\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\"\"\",\n",
-    "    \"nvidia-smi\"\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "200f6f4f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:05.973893Z",
-     "iopub.status.busy": "2023-02-27T16:23:05.973504Z",
-     "iopub.status.idle": "2023-02-27T16:23:39.934620Z",
-     "shell.execute_reply": "2023-02-27T16:23:39.932059Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "```\n",
-       "$ pwd\n",
-       "/\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">ls ~</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "```\n",
-       "$ ls ~\n",
-       "Desktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">cd ~</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:**  \n",
-       "```\n",
-       "$ cd ~\n",
-       "$ pwd\n",
-       "/home/username\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">{Please make a file jokes.txt inside and put some jokes inside}</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "\n",
-       "```\n",
-       "$ touch jokes.txt\n",
-       "$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n",
-       "$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n",
-       "$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "\n",
-       "```\n",
-       "$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n",
-       "$ python3 run.py\n",
-       "Result: 33\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "\n",
-       "```\n",
-       "$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n",
-       "$ python3 run.py\n",
-       "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\n",
-       "COPY entrypoint.sh entrypoint.sh\n",
-       "ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "\n",
-       "```\n",
-       "$ echo -e \"echo 'Hello from Docker\" > entrypoint.sh\n",
-       "$ echo -e \"FROM ubuntu:20.04\n",
-       "COPY entrypoint.sh entrypoint.sh\n",
-       "ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\n",
-       "$ docker build . -t my_docker_image\n",
-       "$ docker run -t my_docker_image\n",
-       "Hello from Docker\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Human:** <span style=\"color: blue\">nvidia-smi</span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Assistant:** \n",
-       "\n",
-       "```\n",
-       "$ nvidia-smi\n",
-       "Sat May 15 21:45:02 2021       \n",
-       "+-----------------------------------------------------------------------------+\n",
-       "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
-       "|-------------------------------+----------------------+----------------------+\n",
-       "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
-       "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
-       "|===============================+======================+======================|\n",
-       "|   0  GeForce RTX 208...  Off  | 00000000:01:00.0 Off |                  N/A |\n",
-       "| N/A   45C    P0    28W /  N/A |    590MiB /  7979MiB |      0%      Default |\n",
-       "+-------------------------------+----------------------+----------------------+\n",
-       "                                                                               \n",
-       "+-----------------------------------------------------------------------------+\n",
-       "| Processes:                                                       GPU Memory |\n",
-       "|  GPU       PID   Type   Process name                             Usage      |\n",
-       "|=============================================================================|\n",
-       "|"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "--------------"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with minichain.start_chain(\"chatgpt\") as backend:\n",
-    "    prompt = ChatPrompt(backend.OpenAI())\n",
-    "    state = State([])\n",
-    "    for t in fake_human:\n",
-    "        state.human_input = t\n",
-    "        display(Markdown(f'**Human:** <span style=\"color: blue\">{t}</span>'))\n",
-    "        state = prompt(state)\n",
-    "        display(Markdown(f'**Assistant:** {state.memory[-1][1]}'))\n",
-    "        display(Markdown(f'--------------'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "49506eaa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:39.944473Z",
-     "iopub.status.busy": "2023-02-27T16:23:39.943436Z",
-     "iopub.status.idle": "2023-02-27T16:23:40.011091Z",
-     "shell.execute_reply": "2023-02-27T16:23:40.010474Z"
-    },
-    "tags": [
-     "hide_inp"
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<!-- <link rel=\"stylesheet\" href=\"https://cdn.rawgit.com/Chalarangelo/mini.css/v3.0.1/dist/mini-default.min.css\"> -->\n",
-       " <main class=\"container\">\n",
-       "\n",
-       "<h3>ChatPrompt</h3>\n",
-       "\n",
-       "<dl>\n",
-       "  <dt>Input:</dt>\n",
-       "  <dd>\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"n\">State</span><span class=\"p\">(</span><span class=\"n\">memory</span><span class=\"o\">=</span><span class=\"p\">[(</span><span class=\"s1\">&#39;human 1&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;output 1&#39;</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"s1\">&#39;human 2&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;output 2&#39;</span><span class=\"p\">)],</span> <span class=\"n\">human_input</span><span class=\"o\">=</span><span class=\"s1\">&#39;cd ~&#39;</span><span class=\"p\">)</span>\n",
-       "</pre></div>\n",
-       "\n",
-       "\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt> Full Prompt: </dt>\n",
-       "  <dd>\n",
-       "    <details>\n",
-       "      <summary>Prompt</summary>\n",
-       "      <p>Assistant is a large language model trained by OpenAI.<br><br>Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.<br><br>Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.<br><br>Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.<br><br><br>Human: human 1<br>AI: output 1<br><br>Human: human 2<br>AI: output 2<br><br><br>Human: <div style='color:red'>cd ~</div><br>Assistant:</p>\n",
-       "    </details>\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt> Response: </dt>\n",
-       "  <dd>\n",
-       "    Text Assistant: Hello\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt>Value:</dt>\n",
-       "  <dd>\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"n\">State</span><span class=\"p\">(</span><span class=\"n\">memory</span><span class=\"o\">=</span><span class=\"p\">[(</span><span class=\"s1\">&#39;human 2&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;output 2&#39;</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"s1\">&#39;cd ~&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39; Hello&#39;</span><span class=\"p\">)],</span> <span class=\"n\">human_input</span><span class=\"o\">=</span><span class=\"s1\">&#39;&#39;</span><span class=\"p\">)</span>\n",
-       "</pre></div>\n",
-       "\n",
-       "  </dd>\n",
-       "</main>\n"
-      ],
-      "text/plain": [
-       "HTML(html='\\n<!-- <link rel=\"stylesheet\" href=\"https://cdn.rawgit.com/Chalarangelo/mini.css/v3.0.1/dist/mini-default.min.css\"> -->\\n <main class=\"container\">\\n\\n<h3>ChatPrompt</h3>\\n\\n<dl>\\n  <dt>Input:</dt>\\n  <dd>\\n<div class=\"highlight\"><pre><span></span><span class=\"n\">State</span><span class=\"p\">(</span><span class=\"n\">memory</span><span class=\"o\">=</span><span class=\"p\">[(</span><span class=\"s1\">&#39;human 1&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;output 1&#39;</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"s1\">&#39;human 2&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;output 2&#39;</span><span class=\"p\">)],</span> <span class=\"n\">human_input</span><span class=\"o\">=</span><span class=\"s1\">&#39;cd ~&#39;</span><span class=\"p\">)</span>\\n</pre></div>\\n\\n\\n  </dd>\\n\\n  <dt> Full Prompt: </dt>\\n  <dd>\\n    <details>\\n      <summary>Prompt</summary>\\n      <p>Assistant is a large language model trained by OpenAI.<br><br>Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.<br><br>Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.<br><br>Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.<br><br><br>Human: human 1<br>AI: output 1<br><br>Human: human 2<br>AI: output 2<br><br><br>Human: <div style=\\'color:red\\'>cd ~</div><br>Assistant:</p>\\n    </details>\\n  </dd>\\n\\n  <dt> Response: </dt>\\n  <dd>\\n    Text Assistant: Hello\\n  </dd>\\n\\n  <dt>Value:</dt>\\n  <dd>\\n<div class=\"highlight\"><pre><span></span><span class=\"n\">State</span><span class=\"p\">(</span><span class=\"n\">memory</span><span class=\"o\">=</span><span class=\"p\">[(</span><span class=\"s1\">&#39;human 2&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;output 2&#39;</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"s1\">&#39;cd ~&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39; Hello&#39;</span><span class=\"p\">)],</span> <span class=\"n\">human_input</span><span class=\"o\">=</span><span class=\"s1\">&#39;&#39;</span><span class=\"p\">)</span>\\n</pre></div>\\n\\n  </dd>\\n</main>\\n')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ChatPrompt().show(State([(\"human 1\", \"output 1\"), (\"human 2\", \"output 2\") ], \"cd ~\"),\n",
-    "                    \"Text Assistant: Hello\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c8c967b",
-   "metadata": {},
-   "source": [
-    "View the run log."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7884cf9d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T16:23:40.013468Z",
-     "iopub.status.busy": "2023-02-27T16:23:40.013285Z",
-     "iopub.status.idle": "2023-02-27T16:23:40.076730Z",
-     "shell.execute_reply": "2023-02-27T16:23:40.076097Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[38;5;15mbe24331e-675b-4c1f-aa66-65b84a7602e3\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5mchatgpt\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:05Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m33.953s\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5mchatgpt\u001b[0m/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:39Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15m60ca1813-07db-499b-b10b-d5b64709303f\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m3.264s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m\n",
-      "    │   ���── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": 'State(memory=[(\\'cd ~\\', \\' \\\\n```\\\\n$ cd ~\\\\n$ pwd\\\\n/home/username\\\\n```\\'), (\\'{Please make a file jokes.txt inside and put some jokes inside}\\', \\'\\\\n\\\\n```\\\\n$ touch jokes.txt\\\\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\\\\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\\\\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\\\\n```\\')], human_input=\\'echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py && python3 run.py\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514995.8653514', \"'task_uuid'\": \"'60ca1813-07db-499b-b10b-d5b64709303f'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m3.259s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: cd ~⏎\n",
-      "    │   │   AI:  ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ cd ~⏎\n",
-      "    │   │   $ pwd⏎\n",
-      "    │   │   /home/username⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: {Please make a file jokes.txt inside and put some jokes inside}⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ touch jokes.txt⏎\n",
-      "    │   │   $ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt⏎\n",
-      "    │   │   $ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt⏎\n",
-      "    │   │   $ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py⏎\n",
-      "    │   │   $ python3 run.py⏎\n",
-      "    │   │   Result: 33⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15mc2062d1e-37fb-44e9-9e3f-35fd33720af5\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:06Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.477s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:06Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:06Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": \"State(memory=[], human_input='I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514986.2875783', \"'task_uuid'\": \"'c2062d1e-37fb-44e9-9e3f-35fd33720af5'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:06Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.472s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ pwd⏎\n",
-      "    │   │   /⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15mea339a52-82af-4c19-8ee6-1d2f081bc437\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m11.867s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": 'State(memory=[(\\'echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\\\\n$ python3 run.py\\\\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\\\\n```\\'), (\\'echo -e \"echo \\\\\\'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\\\\nCOPY entrypoint.sh entrypoint.sh\\\\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"echo \\\\\\'Hello from Docker\" > entrypoint.sh\\\\n$ echo -e \"FROM ubuntu:20.04\\\\nCOPY entrypoint.sh entrypoint.sh\\\\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\\\\n$ docker build . -t my_docker_image\\\\n$ docker run -t my_docker_image\\\\nHello from Docker\\\\n```\\')], human_input=\\'nvidia-smi\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677515008.0539572', \"'task_uuid'\": \"'ea339a52-82af-4c19-8ee6-1d2f081bc437'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m11.858s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py⏎\n",
-      "    │   │   $ python3 run.py⏎\n",
-      "    │   │   [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04⏎\n",
-      "    │   │   COPY entrypoint.sh entrypoint.sh⏎\n",
-      "    │   │   ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"echo 'Hello from Docker\" > entrypoint.sh⏎\n",
-      "    │   │   $ echo -e \"FROM ubuntu:20.04⏎\n",
-      "    │   │   COPY entrypoint.sh entrypoint.sh⏎\n",
-      "    │   │   ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile⏎\n",
-      "    │   │   $ docker build . -t my_docker_image⏎\n",
-      "    │   │   $ docker run -t my_docker_image⏎\n",
-      "    │   │   Hello from Docker⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: nvidia-smi⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:39Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:39Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ nvidia-smi⏎\n",
-      "    │   │   Sat May 15 21:45:02 2021       ⏎\n",
-      "    │   │   +-----------------------------------------------------------------------------+⏎\n",
-      "    │   │   | NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |⏎\n",
-      "    │   │   |-------------------------------+----------------------+----------------------+⏎\n",
-      "    │   │   | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |⏎\n",
-      "    │   │   | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |⏎\n",
-      "    │   │   |===============================+======================+======================|⏎\n",
-      "    │   │   |   0  GeForce RTX 208...  Off  | 00000000:01:00.0 Off |                  N/A |⏎\n",
-      "    │   │   | N/A   45C    P0    28W /  N/A |    590MiB /  7979MiB |      0%      Default |⏎\n",
-      "    │   │   +-------------------------------+----------------------+----------------------+⏎\n",
-      "    │   │                                                                                  ⏎\n",
-      "    │   │   +-----------------------------------------------------------------------------+⏎\n",
-      "    │   │   | Processes:                                                       GPU Memory |⏎\n",
-      "    │   │   |  GPU       PID   Type   Process name                             Usage      |⏎\n",
-      "    │   │   |=============================================================================|⏎\n",
-      "    │   │   |\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:39Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:39Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15m5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m4.063s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": 'State(memory=[(\\'{Please make a file jokes.txt inside and put some jokes inside}\\', \\'\\\\n\\\\n```\\\\n$ touch jokes.txt\\\\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\\\\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\\\\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\\\\n```\\'), (\\'echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py\\\\n$ python3 run.py\\\\nResult: 33\\\\n```\\')], human_input=\\'echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514999.1337094', \"'task_uuid'\": \"'5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m4.061s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: {Please make a file jokes.txt inside and put some jokes inside}⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ touch jokes.txt⏎\n",
-      "    │   │   $ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt⏎\n",
-      "    │   │   $ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt⏎\n",
-      "    │   │   $ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py⏎\n",
-      "    │   │   $ python3 run.py⏎\n",
-      "    │   │   Result: 33⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py⏎\n",
-      "    │   │   $ python3 run.py⏎\n",
-      "    │   │   [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15m8d9f2eb7-c022-48e6-bdeb-c35b8852c934\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.437s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": \"State(memory=[('I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.', '\\\\n```\\\\n$ pwd\\\\n/\\\\n```'), ('ls ~', '\\\\n```\\\\n$ ls ~\\\\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\\\\n```')], human_input='cd ~')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514989.5083773', \"'task_uuid'\": \"'8d9f2eb7-c022-48e6-bdeb-c35b8852c934'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.428s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ pwd⏎\n",
-      "    │   │   /⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: ls ~⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ ls ~⏎\n",
-      "    │   │   Desktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: cd ~⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m:  ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ cd ~⏎\n",
-      "    │   │   $ pwd⏎\n",
-      "    │   │   /home/username⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15m6a51f09a-2d2a-4883-a864-3bb66ba6215b\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.718s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": \"State(memory=[('I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.', '\\\\n```\\\\n$ pwd\\\\n/\\\\n```')], human_input='ls ~')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514987.7763977', \"'task_uuid'\": \"'6a51f09a-2d2a-4883-a864-3bb66ba6215b'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:07Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.710s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ pwd⏎\n",
-      "    │   │   /⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: ls ~⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ ls ~⏎\n",
-      "    │   │   Desktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:09Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15m00a84ff6-6fc1-4afc-88b1-e82a893855d3\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m4.841s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": 'State(memory=[(\\'echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py\\\\n$ python3 run.py\\\\nResult: 33\\\\n```\\'), (\\'echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\\\\n$ python3 run.py\\\\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\\\\n```\\')], human_input=\\'echo -e \"echo \\\\\\'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\\\\nCOPY entrypoint.sh entrypoint.sh\\\\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677515003.1996367', \"'task_uuid'\": \"'00a84ff6-6fc1-4afc-88b1-e82a893855d3'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:23Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m4.838s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py⏎\n",
-      "    │   │   $ python3 run.py⏎\n",
-      "    │   │   Result: 33⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py⏎\n",
-      "    │   │   $ python3 run.py⏎\n",
-      "    │   │   [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04⏎\n",
-      "    │   │   COPY entrypoint.sh entrypoint.sh⏎\n",
-      "    │   │   ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ echo -e \"echo 'Hello from Docker\" > entrypoint.sh⏎\n",
-      "    │   │   $ echo -e \"FROM ubuntu:20.04⏎\n",
-      "    │   │   COPY entrypoint.sh entrypoint.sh⏎\n",
-      "    │   │   ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile⏎\n",
-      "    │   │   $ docker build . -t my_docker_image⏎\n",
-      "    │   │   $ docker run -t my_docker_image⏎\n",
-      "    │   │   Hello from Docker⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:28Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15mb0225ad7-9ac0-4735-b895-3fa45eeef9db\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m4.909s\u001b[2m\u001b[0m\n",
-      "    ├── <unnamed>\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5meliot:destination_failure\u001b[0m/3\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mexception\u001b[0m: builtins.AttributeError\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mmessage\u001b[0m: {\"'input'\": \"State(memory=[('ls ~', '\\\\n```\\\\n$ ls ~\\\\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\\\\n```'), ('cd ~', ' \\\\n```\\\\n$ cd ~\\\\n$ pwd\\\\n/home/username\\\\n```')], human_input='{Please make a file jokes.txt inside and put some jokes inside}')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514990.9499614', \"'task_uuid'\": \"'b0225ad7-9ac0-4735-b895-3fa45eeef9db'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}\u001b[0m\n",
-      "    │   └── \u001b[38;5;4mreason\u001b[0m: module 'numpy' has no attribute 'bool'.⏎\n",
-      "    │       `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.⏎\n",
-      "    │       The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:⏎\n",
-      "    │           https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:10Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m4.906s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: Assistant is a large language model trained by OpenAI.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: ls ~⏎\n",
-      "    │   │   AI: ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ ls ~⏎\n",
-      "    │   │   Desktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: cd ~⏎\n",
-      "    │   │   AI:  ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ cd ~⏎\n",
-      "    │   │   $ pwd⏎\n",
-      "    │   │   /home/username⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Human: {Please make a file jokes.txt inside and put some jokes inside}⏎\n",
-      "    │   │   Assistant:\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/5/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   $ touch jokes.txt⏎\n",
-      "    │   │   $ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt⏎\n",
-      "    │   │   $ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt⏎\n",
-      "    │   │   $ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/5/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.ChatPrompt'>\u001b[0m/6\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 16:23:15Z\u001b[2m\u001b[0m\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "minichain.show_log(\"chatgpt.log\")"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "tags,-all"
-  },
-  "kernelspec": {
-   "display_name": "minichain",
-   "language": "python",
-   "name": "minichain"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

chatgpt.log

@@ -1,66 +0,0 @@
-{"action_status": "started", "timestamp": 1677514985.975952, "task_uuid": "be24331e-675b-4c1f-aa66-65b84a7602e3", "action_type": "chatgpt", "task_level": [1]}
-{"action_status": "started", "timestamp": 1677514986.2875004, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [1]}
-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": \"State(memory=[], human_input='I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514986.2875783', \"'task_uuid'\": \"'c2062d1e-37fb-44e9-9e3f-35fd33720af5'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677514986.287665, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677514986.2912104, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\n\nHuman: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\nAssistant:", "action_status": "started", "timestamp": 1677514986.2913702, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677514987.7635746, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n```\n$ pwd\n/\n```", "action_status": "started", "timestamp": 1677514987.763787, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677514987.763915, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "Result", "task_level": [5, 2]}
-{"action_status": "succeeded", "timestamp": 1677514987.7640302, "task_uuid": "c2062d1e-37fb-44e9-9e3f-35fd33720af5", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "started", "timestamp": 1677514987.7762234, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [1]}
-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": \"State(memory=[('I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.', '\\\\n```\\\\n$ pwd\\\\n/\\\\n```')], human_input='ls ~')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514987.7763977', \"'task_uuid'\": \"'6a51f09a-2d2a-4883-a864-3bb66ba6215b'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677514987.776682, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677514987.7836592, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\nAI: \n```\n$ pwd\n/\n```\n\n\nHuman: ls ~\nAssistant:", "action_status": "started", "timestamp": 1677514987.7838352, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677514989.494076, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n```\n$ ls ~\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\n```", "action_status": "started", "timestamp": 1677514989.4943125, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677514989.4944377, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "Result", "task_level": [5, 2]}
-{"action_status": "succeeded", "timestamp": 1677514989.4945207, "task_uuid": "6a51f09a-2d2a-4883-a864-3bb66ba6215b", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "started", "timestamp": 1677514989.5081844, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [1]}
-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": \"State(memory=[('I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.', '\\\\n```\\\\n$ pwd\\\\n/\\\\n```'), ('ls ~', '\\\\n```\\\\n$ ls ~\\\\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\\\\n```')], human_input='cd ~')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514989.5083773', \"'task_uuid'\": \"'8d9f2eb7-c022-48e6-bdeb-c35b8852c934'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677514989.5086985, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677514989.5162013, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\nAI: \n```\n$ pwd\n/\n```\n\nHuman: ls ~\nAI: \n```\n$ ls ~\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\n```\n\n\nHuman: cd ~\nAssistant:", "action_status": "started", "timestamp": 1677514989.516413, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677514990.9448535, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": " \n```\n$ cd ~\n$ pwd\n/home/username\n```", "action_status": "started", "timestamp": 1677514990.9449363, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677514990.9449837, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "Result", "task_level": [5, 2]}
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-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": \"State(memory=[('ls ~', '\\\\n```\\\\n$ ls ~\\\\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\\\\n```'), ('cd ~', ' \\\\n```\\\\n$ cd ~\\\\n$ pwd\\\\n/home/username\\\\n```')], human_input='{Please make a file jokes.txt inside and put some jokes inside}')\", \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514990.9499614', \"'task_uuid'\": \"'b0225ad7-9ac0-4735-b895-3fa45eeef9db'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677514990.9501207, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677514990.9532957, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: ls ~\nAI: \n```\n$ ls ~\nDesktop/ Documents/ Downloads/ Music/ Pictures/ Public/ Templates/ Videos/\n```\n\nHuman: cd ~\nAI:  \n```\n$ cd ~\n$ pwd\n/home/username\n```\n\n\nHuman: {Please make a file jokes.txt inside and put some jokes inside}\nAssistant:", "action_status": "started", "timestamp": 1677514990.9533696, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677514995.858929, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n\n```\n$ touch jokes.txt\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n```", "action_status": "started", "timestamp": 1677514995.8590267, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677514995.8590858, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "action_type": "Result", "task_level": [5, 2]}
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-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": 'State(memory=[(\\'cd ~\\', \\' \\\\n```\\\\n$ cd ~\\\\n$ pwd\\\\n/home/username\\\\n```\\'), (\\'{Please make a file jokes.txt inside and put some jokes inside}\\', \\'\\\\n\\\\n```\\\\n$ touch jokes.txt\\\\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\\\\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\\\\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\\\\n```\\')], human_input=\\'echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py && python3 run.py\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514995.8653514', \"'task_uuid'\": \"'60ca1813-07db-499b-b10b-d5b64709303f'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677514995.8655088, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677514995.8692126, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: cd ~\nAI:  \n```\n$ cd ~\n$ pwd\n/home/username\n```\n\nHuman: {Please make a file jokes.txt inside and put some jokes inside}\nAI: \n\n```\n$ touch jokes.txt\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n```\n\n\nHuman: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\nAssistant:", "action_status": "started", "timestamp": 1677514995.8693106, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677514999.1287231, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n\n```\n$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n$ python3 run.py\nResult: 33\n```", "action_status": "started", "timestamp": 1677514999.1288157, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677514999.1288638, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "action_type": "Result", "task_level": [5, 2]}
-{"action_status": "succeeded", "timestamp": 1677514999.1288946, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
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-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": 'State(memory=[(\\'{Please make a file jokes.txt inside and put some jokes inside}\\', \\'\\\\n\\\\n```\\\\n$ touch jokes.txt\\\\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\\\\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\\\\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\\\\n```\\'), (\\'echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py\\\\n$ python3 run.py\\\\nResult: 33\\\\n```\\')], human_input=\\'echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677514999.1337094', \"'task_uuid'\": \"'5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677514999.1337867, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677514999.135381, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: {Please make a file jokes.txt inside and put some jokes inside}\nAI: \n\n```\n$ touch jokes.txt\n$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n```\n\nHuman: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\nAI: \n\n```\n$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n$ python3 run.py\nResult: 33\n```\n\n\nHuman: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\nAssistant:", "action_status": "started", "timestamp": 1677514999.1354215, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677515003.1960745, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n\n```\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n$ python3 run.py\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n```", "action_status": "started", "timestamp": 1677515003.1962907, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677515003.1963375, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "action_type": "Result", "task_level": [5, 2]}
-{"action_status": "succeeded", "timestamp": 1677515003.1963675, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "started", "timestamp": 1677515003.1995413, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [1]}
-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": 'State(memory=[(\\'echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"x=lambda y:y*5+3;print(\\\\\\'Result:\\\\\\' + str(x(6)))\" > run.py\\\\n$ python3 run.py\\\\nResult: 33\\\\n```\\'), (\\'echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\\\\n$ python3 run.py\\\\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\\\\n```\\')], human_input=\\'echo -e \"echo \\\\\\'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\\\\nCOPY entrypoint.sh entrypoint.sh\\\\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677515003.1996367', \"'task_uuid'\": \"'00a84ff6-6fc1-4afc-88b1-e82a893855d3'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677515003.1997197, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677515003.201452, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\nAI: \n\n```\n$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n$ python3 run.py\nResult: 33\n```\n\nHuman: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\nAI: \n\n```\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n$ python3 run.py\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n```\n\n\nHuman: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\nAssistant:", "action_status": "started", "timestamp": 1677515003.2015123, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677515008.0399287, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n\n```\n$ echo -e \"echo 'Hello from Docker\" > entrypoint.sh\n$ echo -e \"FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\n$ docker build . -t my_docker_image\n$ docker run -t my_docker_image\nHello from Docker\n```", "action_status": "started", "timestamp": 1677515008.0401833, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677515008.040339, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "Result", "task_level": [5, 2]}
-{"action_status": "succeeded", "timestamp": 1677515008.040439, "task_uuid": "00a84ff6-6fc1-4afc-88b1-e82a893855d3", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "started", "timestamp": 1677515008.053587, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [1]}
-{"reason": "module 'numpy' has no attribute 'bool'.\n`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "exception": "builtins.AttributeError", "message": "{\"'input'\": 'State(memory=[(\\'echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\\\\n$ python3 run.py\\\\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\\\\n```\\'), (\\'echo -e \"echo \\\\\\'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\\\\nCOPY entrypoint.sh entrypoint.sh\\\\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\\', \\'\\\\n\\\\n```\\\\n$ echo -e \"echo \\\\\\'Hello from Docker\" > entrypoint.sh\\\\n$ echo -e \"FROM ubuntu:20.04\\\\nCOPY entrypoint.sh entrypoint.sh\\\\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\\\\n$ docker build . -t my_docker_image\\\\n$ docker run -t my_docker_image\\\\nHello from Docker\\\\n```\\')], human_input=\\'nvidia-smi\\')', \"'action_status'\": \"'started'\", \"'timestamp'\": '1677515008.0539572', \"'task_uuid'\": \"'ea339a52-82af-4c19-8ee6-1d2f081bc437'\", \"'action_type'\": \"'Input Function'\", \"'task_level'\": '[2, 1]'}", "timestamp": 1677515008.0543113, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "task_level": [3], "message_type": "eliot:destination_failure"}
-{"action_status": "succeeded", "timestamp": 1677515008.0610886, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "Input Function", "task_level": [2, 2]}
-{"prompt": "Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\n\nHuman: echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\nAI: \n\n```\n$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n$ python3 run.py\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n```\n\nHuman: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\nAI: \n\n```\n$ echo -e \"echo 'Hello from Docker\" > entrypoint.sh\n$ echo -e \"FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\n$ docker build . -t my_docker_image\n$ docker run -t my_docker_image\nHello from Docker\n```\n\n\nHuman: nvidia-smi\nAssistant:", "action_status": "started", "timestamp": 1677515008.0612898, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "Prompted", "task_level": [4, 1]}
-{"action_status": "succeeded", "timestamp": 1677515019.9196224, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "Prompted", "task_level": [4, 2]}
-{"result": "\n\n```\n$ nvidia-smi\nSat May 15 21:45:02 2021       \n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n|-------------------------------+----------------------+----------------------+\n| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n|===============================+======================+======================|\n|   0  GeForce RTX 208...  Off  | 00000000:01:00.0 Off |                  N/A |\n| N/A   45C    P0    28W /  N/A |    590MiB /  7979MiB |      0%      Default |\n+-------------------------------+----------------------+----------------------+\n                                                                               \n+-----------------------------------------------------------------------------+\n| Processes:                                                       GPU Memory |\n|  GPU       PID   Type   Process name                             Usage      |\n|=============================================================================|\n|", "action_status": "started", "timestamp": 1677515019.919849, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "Result", "task_level": [5, 1]}
-{"action_status": "succeeded", "timestamp": 1677515019.920001, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "Result", "task_level": [5, 2]}
-{"action_status": "succeeded", "timestamp": 1677515019.9200878, "task_uuid": "ea339a52-82af-4c19-8ee6-1d2f081bc437", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "succeeded", "timestamp": 1677515019.9287043, "task_uuid": "be24331e-675b-4c1f-aa66-65b84a7602e3", "action_type": "chatgpt", "task_level": [2]}