.

#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")

#qa.py#

@@ -1,45 +0,0 @@
-# # QA
-
-# Questions answering with embeddings.  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:
-    question = "Who won the 2020 Summer Olympics men's high jump?"
-    prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
-    result = prompt(question)
-    print(result)
-
-# + tags=["hide_inp"]
-QAPrompt().show(
-    {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
-)
-# -
-
-show_log("qa.log")

#selfask.py#

@@ -1,83 +0,0 @@
-# Notebook implementation of the self-ask + Google tool use prompt.
-# Adapted from https://github.com/ofirpress/self-ask
-
-from dataclasses import dataclass
-
-from parsita import *
-
-import minichain
-
-# Define the state of the bot.
-
-@dataclass
-class IntermediateState:
-    s: str
-
-@dataclass
-class FinalState:
-    s: str
-
-@dataclass
-class Out:
-    echo: str
-    state: FinalState | IntermediateState
-
-
-# Self Ask Prompt
-
-class SelfAsk(minichain.TemplatePrompt[Out]):
-    template_file = "selfask.pmpt.tpl"
-    stop_template = "\nIntermediate answer:"
-
-    # Parsita parser.
-    class Parser(TextParsers):
-        follow = (lit("Follow up:") >> reg(r".*")) > IntermediateState
-        finish = (lit("So the final answer is: ") >> reg(r".*")) > FinalState
-        response = follow | finish
-
-    def parse(self, response: str, inp) -> Out:
-        return Out(
-            self.prompt(inp).prompt + response,
-            self.Parser.response.parse(response).or_die(),
-        )
-
-# Runtime loop
-
-def selfask(inp: str, openai, google) -> str:
-    prompt1 = SelfAsk(openai)
-    prompt2 = minichain.SimplePrompt(google)
-    suffix = ""
-    for i in range(3):
-        out = prompt1(dict(input=inp, suffix=suffix, agent_scratchpad=True))
-
-        if isinstance(out.state, FinalState):
-            break
-        suffix += out.echo
-        out2 = prompt2(out.state.s)
-        suffix += "\nIntermediate answer: " + out2 + "\n"
-    return out.state.s
-
-
-with minichain.start_chain("selfask") as backend:
-    result = selfask(
-        "What is the zip code of the city where George Washington was born?",
-        backend.OpenAI(),
-        backend.Google(),
-    )
-    print(result)
-
-# View prompt examples.
-
-# + tags=["hide_inp"]
-SelfAsk().show(
-    {
-        "input": "What is the zip code of the city where George Washington was born?",
-        "agent_scratchpad": True,
-    },
-    "Follow up: Where was George Washington born?",
-)
-# -
-
-# View log.
-
-minichain.show_log("selfask.log")

README.md

@@ -1,12 +0,0 @@
----
-title: Minichain
-emoji: 🐠
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

all/#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")

all/#qa.py#

@@ -1,45 +0,0 @@
-# # QA
-
-# Questions answering with embeddings.  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:
-    question = "Who won the 2020 Summer Olympics men's high jump?"
-    prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
-    result = prompt(question)
-    print(result)
-
-# + tags=["hide_inp"]
-QAPrompt().show(
-    {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
-)
-# -
-
-show_log("qa.log")

all/#selfask.py#

@@ -1,83 +0,0 @@
-# Notebook implementation of the self-ask + Google tool use prompt.
-# Adapted from https://github.com/ofirpress/self-ask
-
-from dataclasses import dataclass
-
-from parsita import *
-
-import minichain
-
-# Define the state of the bot.
-
-@dataclass
-class IntermediateState:
-    s: str
-
-@dataclass
-class FinalState:
-    s: str
-
-@dataclass
-class Out:
-    echo: str
-    state: FinalState | IntermediateState
-
-
-# Self Ask Prompt
-
-class SelfAsk(minichain.TemplatePrompt[Out]):
-    template_file = "selfask.pmpt.tpl"
-    stop_template = "\nIntermediate answer:"
-
-    # Parsita parser.
-    class Parser(TextParsers):
-        follow = (lit("Follow up:") >> reg(r".*")) > IntermediateState
-        finish = (lit("So the final answer is: ") >> reg(r".*")) > FinalState
-        response = follow | finish
-
-    def parse(self, response: str, inp) -> Out:
-        return Out(
-            self.prompt(inp).prompt + response,
-            self.Parser.response.parse(response).or_die(),
-        )
-
-# Runtime loop
-
-def selfask(inp: str, openai, google) -> str:
-    prompt1 = SelfAsk(openai)
-    prompt2 = minichain.SimplePrompt(google)
-    suffix = ""
-    for i in range(3):
-        out = prompt1(dict(input=inp, suffix=suffix, agent_scratchpad=True))
-
-        if isinstance(out.state, FinalState):
-            break
-        suffix += out.echo
-        out2 = prompt2(out.state.s)
-        suffix += "\nIntermediate answer: " + out2 + "\n"
-    return out.state.s
-
-
-with minichain.start_chain("selfask") as backend:
-    result = selfask(
-        "What is the zip code of the city where George Washington was born?",
-        backend.OpenAI(),
-        backend.Google(),
-    )
-    print(result)
-
-# View prompt examples.
-
-# + tags=["hide_inp"]
-SelfAsk().show(
-    {
-        "input": "What is the zip code of the city where George Washington was born?",
-        "agent_scratchpad": True,
-    },
-    "Follow up: Where was George Washington born?",
-)
-# -
-
-# View log.
-
-minichain.show_log("selfask.log")

all/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 []

all/base.sh

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

all/base.sh~

@@ -1 +0,0 @@
-export OPENAI_KEY="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"

all/bash.ipynb

@@ -1,412 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "27962df3",
-   "metadata": {},
-   "source": [
-    "Notebook to generate and run a bash command.\n",
-    "Adapted from LangChain\n",
-    "[BashChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_bash.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e01d45bc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:17.548344Z",
-     "iopub.status.busy": "2023-02-27T14:12:17.547695Z",
-     "iopub.status.idle": "2023-02-27T14:12:17.767947Z",
-     "shell.execute_reply": "2023-02-27T14:12:17.767208Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import minichain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2caf597",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "source": [
-    "Prompt that asks LLM to produce a bash command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8669ca4e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:17.773257Z",
-     "iopub.status.busy": "2023-02-27T14:12:17.771880Z",
-     "iopub.status.idle": "2023-02-27T14:12:17.778057Z",
-     "shell.execute_reply": "2023-02-27T14:12:17.777432Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "class CLIPrompt(minichain.TemplatePrompt):\n",
-    "    template_file = \"bash.pmpt.tpl\"\n",
-    "\n",
-    "    def parse(self, out: str, inp):\n",
-    "        out = out.strip()\n",
-    "        assert out.startswith(\"```bash\")\n",
-    "        return out.split(\"\\n\")[1:-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79cc544e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "source": [
-    "Prompt that runs the bash command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "db1e09b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:17.782732Z",
-     "iopub.status.busy": "2023-02-27T14:12:17.781591Z",
-     "iopub.status.idle": "2023-02-27T14:12:17.787303Z",
-     "shell.execute_reply": "2023-02-27T14:12:17.786651Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class BashPrompt(minichain.Prompt):\n",
-    "    def prompt(self, inp) -> str:\n",
-    "        return \";\".join(inp).replace(\"\\n\", \"\")\n",
-    "\n",
-    "    def parse(self, out: str, inp) -> str:\n",
-    "        return out"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b993ae8",
-   "metadata": {},
-   "source": [
-    "Generate and run bash command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "17572fff",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:17.792430Z",
-     "iopub.status.busy": "2023-02-27T14:12:17.791251Z",
-     "iopub.status.idle": "2023-02-27T14:12:19.652953Z",
-     "shell.execute_reply": "2023-02-27T14:12:19.650586Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#backend.py#\n",
-      "backend.py\n",
-      "base.py\n",
-      "__init__.py\n",
-      "lang.py\n",
-      "prompts.py\n",
-      "__pycache__\n",
-      "templates\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "with minichain.start_chain(\"bash\") as backend:\n",
-    "    question = (\n",
-    "        '\"go up one directory, and then into the minichain directory,'\n",
-    "        'and list the files in the directory\"'\n",
-    "    )\n",
-    "    prompt = CLIPrompt(backend.OpenAI()).chain(BashPrompt(backend.BashProcess()))\n",
-    "    result = prompt({\"question\": question})\n",
-    "    print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aadedf29",
-   "metadata": {},
-   "source": [
-    "View the prompts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0e1c2f1a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:19.662941Z",
-     "iopub.status.busy": "2023-02-27T14:12:19.661254Z",
-     "iopub.status.idle": "2023-02-27T14:12:19.738758Z",
-     "shell.execute_reply": "2023-02-27T14:12:19.738197Z"
-    },
-    "lines_to_next_cell": 2,
-    "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>CLIPrompt</h3>\n",
-       "\n",
-       "<dl>\n",
-       "  <dt>Input:</dt>\n",
-       "  <dd>\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"p\">{</span><span class=\"s1\">&#39;question&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;list the files in the directory&#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>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:<br><br>Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"<br><br>I need to take the following actions:<br>- List all files in the directory<br>- Create a new directory<br>- Copy the files from the first directory into the second directory<br>```bash<br>ls<br>mkdir myNewDirectory<br>cp -r target/* myNewDirectory<br>```<br><br>That is the format. Begin!<br><br>Question: <div style='color:red'>list the files in the directory</div></p>\n",
-       "    </details>\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt> Response: </dt>\n",
-       "  <dd>\n",
-       "    ```bash<br>ls<br>```\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt>Value:</dt>\n",
-       "  <dd>\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"p\">[</span><span class=\"s1\">&#39;ls&#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>CLIPrompt</h3>\\n\\n<dl>\\n  <dt>Input:</dt>\\n  <dd>\\n<div class=\"highlight\"><pre><span></span><span class=\"p\">{</span><span class=\"s1\">&#39;question&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;list the files in the directory&#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>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:<br><br>Question: \"copy the files in the directory named \\'target\\' into a new directory at the same level as target called \\'myNewDirectory\\'\"<br><br>I need to take the following actions:<br>- List all files in the directory<br>- Create a new directory<br>- Copy the files from the first directory into the second directory<br>```bash<br>ls<br>mkdir myNewDirectory<br>cp -r target/* myNewDirectory<br>```<br><br>That is the format. Begin!<br><br>Question: <div style=\\'color:red\\'>list the files in the directory</div></p>\\n    </details>\\n  </dd>\\n\\n  <dt> Response: </dt>\\n  <dd>\\n    ```bash<br>ls<br>```\\n  </dd>\\n\\n  <dt>Value:</dt>\\n  <dd>\\n<div class=\"highlight\"><pre><span></span><span class=\"p\">[</span><span class=\"s1\">&#39;ls&#39;</span><span class=\"p\">]</span>\\n</pre></div>\\n\\n  </dd>\\n</main>\\n')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CLIPrompt().show(\n",
-    "    {\"question\": \"list the files in the directory\"}, \"\"\"```bash\\nls\\n```\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "79cf9c84",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:19.740976Z",
-     "iopub.status.busy": "2023-02-27T14:12:19.740788Z",
-     "iopub.status.idle": "2023-02-27T14:12:19.745592Z",
-     "shell.execute_reply": "2023-02-27T14:12:19.745136Z"
-    },
-    "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>BashPrompt</h3>\n",
-       "\n",
-       "<dl>\n",
-       "  <dt>Input:</dt>\n",
-       "  <dd>\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"p\">[</span><span class=\"s1\">&#39;ls&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;cat file.txt&#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>ls;cat file.txt</p>\n",
-       "    </details>\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt> Response: </dt>\n",
-       "  <dd>\n",
-       "    hello\n",
-       "  </dd>\n",
-       "\n",
-       "  <dt>Value:</dt>\n",
-       "  <dd>\n",
-       "<div class=\"highlight\"><pre><span></span><span class=\"n\">hello</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>BashPrompt</h3>\\n\\n<dl>\\n  <dt>Input:</dt>\\n  <dd>\\n<div class=\"highlight\"><pre><span></span><span class=\"p\">[</span><span class=\"s1\">&#39;ls&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;cat file.txt&#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>ls;cat file.txt</p>\\n    </details>\\n  </dd>\\n\\n  <dt> Response: </dt>\\n  <dd>\\n    hello\\n  </dd>\\n\\n  <dt>Value:</dt>\\n  <dd>\\n<div class=\"highlight\"><pre><span></span><span class=\"n\">hello</span>\\n</pre></div>\\n\\n  </dd>\\n</main>\\n')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "BashPrompt().show([\"ls\", \"cat file.txt\"], \"hello\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89c7bc6c",
-   "metadata": {},
-   "source": [
-    "View the run log."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7dbcec08",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-02-27T14:12:19.748151Z",
-     "iopub.status.busy": "2023-02-27T14:12:19.747899Z",
-     "iopub.status.idle": "2023-02-27T14:12:19.768720Z",
-     "shell.execute_reply": "2023-02-27T14:12:19.768248Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[38;5;15mfc682a98-f50a-4837-8b6d-87e843c19732\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.CLIPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:18Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.531s\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mInput Function\u001b[0m/2/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:18Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.003s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4minput\u001b[0m: \u001b[0m\n",
-      "    │   │   └── \u001b[38;5;4mquestion\u001b[0m: \"go up one directory, and then into the minichain directory,and list the files in the directory\"\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:18Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/3/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:18Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.528s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: 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",
-      "    │   │   ⏎\n",
-      "    │   │   Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   I 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⏎\n",
-      "    │   │   ls⏎\n",
-      "    │   │   mkdir myNewDirectory⏎\n",
-      "    │   │   cp -r target/* myNewDirectory⏎\n",
-      "    │   │   ```⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   That is the format. Begin!⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   Question: \"go up one directory, and then into the minichain directory,and list the files in the directory\"\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/3/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: ⏎\n",
-      "    │   │   ⏎\n",
-      "    │   │   ```bash⏎\n",
-      "    │   │   cd ..⏎\n",
-      "    │   │   cd minichain⏎\n",
-      "    │   │   ls⏎\n",
-      "    │   │   ```\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.CLIPrompt'>\u001b[0m/5\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15m328e2368-6c0f-4a03-ae78-26aa43a517e3\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5m<class '__main__.BashPrompt'>\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.006s\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mInput Function\u001b[0m/2/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4minput\u001b[0m: \u001b[0m\n",
-      "    │   │   ├── \u001b[38;5;4m0\u001b[0m: cd ..\u001b[0m\n",
-      "    │   │   ├── \u001b[38;5;4m1\u001b[0m: cd minichain\u001b[0m\n",
-      "    │   │   └── \u001b[38;5;4m2\u001b[0m: ls\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mInput Function\u001b[0m/2/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mPrompted\u001b[0m/3/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.005s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mprompt\u001b[0m: cd ..;cd minichain;ls\u001b[0m\n",
-      "    │   └── \u001b[38;5;5mPrompted\u001b[0m/3/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "    ├── \u001b[38;5;5mResult\u001b[0m/4/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m0.000s\u001b[2m\u001b[0m\n",
-      "    │   ├── \u001b[38;5;4mresult\u001b[0m: #backend.py#⏎\n",
-      "    │   │   backend.py⏎\n",
-      "    │   │   base.py⏎\n",
-      "    │   │   __init__.py⏎\n",
-      "    │   │   lang.py⏎\n",
-      "    │   │   prompts.py⏎\n",
-      "    │   │   __pycache__⏎\n",
-      "    │   │   templates⏎\n",
-      "    │   │   \u001b[0m\n",
-      "    │   └── \u001b[38;5;5mResult\u001b[0m/4/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5m<class '__main__.BashPrompt'>\u001b[0m/5\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "\n",
-      "\u001b[38;5;15mca5f4b25-55ca-441d-a0d2-f39ad0bca2d0\u001b[1m\u001b[0m\n",
-      "└── \u001b[38;5;5mbash\u001b[0m/1\u001b[0m ⇒ \u001b[38;5;2mstarted\u001b[0m \u001b[38;5;15m2023-02-27 14:12:17Z\u001b[2m\u001b[0m ⧖ \u001b[38;5;4m1.850s\u001b[2m\u001b[0m\n",
-      "    └── \u001b[38;5;5mbash\u001b[0m/2\u001b[0m ⇒ \u001b[38;5;2msucceeded\u001b[0m \u001b[38;5;15m2023-02-27 14:12:19Z\u001b[2m\u001b[0m\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "minichain.show_log(\"bash.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
-}

all/bash.log

@@ -1,8 +0,0 @@
-{"action_status": "started", "timestamp": 1678759606.621539, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.6216574, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}

all/bash.pmpt.tpl

@@ -1,17 +0,0 @@
-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}}"

all/bash.py

@@ -1,64 +0,0 @@
-# Notebook to generate and run a bash command.
-# Adapted from LangChain
-# [BashChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_bash.html)
-
-import minichain
-
-# Prompt that asks LLM to produce a bash command.
-
-
-class CLIPrompt(minichain.TemplatePrompt):
-    template_file = "bash.pmpt.tpl"
-
-    def parse(self, out: str, inp):
-        out = out.strip()
-        assert out.startswith("```bash")
-        return out.split("\n")[1:-1]
-
-
-# Prompt that runs the bash command.
-
-
-class BashPrompt(minichain.Prompt):
-    def prompt(self, inp) -> str:
-        return ";".join(inp).replace("\n", "")
-
-    def parse(self, out: str, inp) -> str:
-        return out
-
-
-# Generate and run bash command.
-
-with minichain.start_chain("bash") as backend:
-    question = (
-
-    )
-    prompt = CLIPrompt(backend.OpenAI()).chain(BashPrompt(backend.BashProcess()))
-
-gradio = prompt.to_gradio(fields =["question"],
-                 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()
-
-
-    
-# View the prompts.
-
-# + tags=["hide_inp"]
-# CLIPrompt().show(
-#     {"question": "list the files in the directory"}, """```bash\nls\n```"""
-# )
-# # -
-
-
-# # + tags=["hide_inp"]
-# BashPrompt().show(["ls", "cat file.txt"], "hello")
-# # -
-
-# # View the run log.
-
-# minichain.show_log("bash.log")

all/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}"""
-
-    

all/chat.log

@@ -1,14 +0,0 @@
-{"action_status": "started", "timestamp": 1678759606.424852, "task_uuid": "0b226ec0-566b-4e36-af45-25c0881e5228", "action_type": "chat", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.4249172, "task_uuid": "0b226ec0-566b-4e36-af45-25c0881e5228", "action_type": "chat", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.5748045, "task_uuid": "a2181bdc-7e7a-47a7-9a58-6abf2c2e10cd", "action_type": "ner", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.5748866, "task_uuid": "a2181bdc-7e7a-47a7-9a58-6abf2c2e10cd", "action_type": "ner", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.5984528, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.5985456, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.621539, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.6216574, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
-{"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
-{"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}

all/chat.pmpt.tpl

@@ -1,15 +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.
-
-{% for d in memory %}
-Human: {{d[0]}}
-AI: {{d[1]}}
-{% endfor %}
-
-Human: {{human_input}}
-Assistant:

all/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([])
-
-    
-examples = [
-    "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"
-]
-
-gradio = prompt.to_gradio(fields= ["human_input"],
-                          initial_state= state,
-                          examples=examples,
-                          out_type="json"
-)
-if __name__ == "__main__":
-    gradio.launch()
-
-# for i in range(len(fake_human)):
-#     human.chain(prompt)
-
-    

all/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"
-]
-

all/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
-}

all/chatgpt.log

@@ -1,66 +0,0 @@
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-{"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]}
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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=[('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]}
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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=[('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]}
-{"action_status": "succeeded", "timestamp": 1677514990.9450145, "task_uuid": "8d9f2eb7-c022-48e6-bdeb-c35b8852c934", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "started", "timestamp": 1677514990.949691, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "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=[('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]}
-{"action_status": "succeeded", "timestamp": 1677514995.859122, "task_uuid": "b0225ad7-9ac0-4735-b895-3fa45eeef9db", "action_type": "<class '__main__.ChatPrompt'>", "task_level": [6]}
-{"action_status": "started", "timestamp": 1677514995.86515, "task_uuid": "60ca1813-07db-499b-b10b-d5b64709303f", "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=[(\\'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]}
-{"action_status": "started", "timestamp": 1677514999.1336586, "task_uuid": "5a8d829e-f545-4f75-b1b3-efb6cdb5d8e4", "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=[(\\'{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]}

all/chatgpt.pmpt.tpl

@@ -1,15 +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.
-
-{% for d in memory %}
-Human: {{d[0]}}
-AI: {{d[1]}}
-{% endfor %}
-
-Human: {{human_input}}
-Assistant:

all/chatgpt.pmpt.tpl~

@@ -1,15 +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.
-
-{% for d in memory %}
-Human: {{d.human}}
-AI: {{d.ai}}
-{% endfor %}
-
-Human: {{human_input}}
-Assistant:

all/chatgpt.py

@@ -1,75 +0,0 @@
-# # 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")

all/chatgpt.py~

@@ -1,9 +0,0 @@
-
-
-
-
-
-
-
-
-output = chatgpt_chain.predict(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.")

all/color.py

@@ -1,26 +0,0 @@
-# Answer a math problem with code.
-# Adapted from Dust [maths-generate-code](https://dust.tt/spolu/a/d12ac33169)
-
-from minichain import Backend, JinjaPrompt, Prompt, start_chain, SimplePrompt, show_log
-
-
-# Prompt that asks LLM for code from math.
-
-class ColorPrompt(Prompt[str, bool]):
-    def parse(inp: str) -> str:
-        return f"Answer 'Yes' if this is a color, {inp}. Answer:"
-
-    def parse(out: str, inp) -> bool:
-        # Encode the parsing logic
-        return out.strip() == "Yes"  
-ColorPrompt().show({"inp": "dog"}, "No")
-
-
-with start_chain("color") as backend:
-    question = 'What is the sum of the powers of 3 (3^i) that are smaller than 100?'
-    prompt = MathPrompt(backend.OpenAI()).chain(SimplePrompt(backend.Python()))
-    result = prompt({"question": question})
-    print(result)
-
-    
-show_log("math.log")

all/color.py~

@@ -1,26 +0,0 @@
-# Answer a math problem with code.
-# Adapted from Dust [maths-generate-code](https://dust.tt/spolu/a/d12ac33169)
-
-from minichain import Backend, JinjaPrompt, Prompt, start_chain, SimplePrompt, show_log
-
-
-# Prompt that asks LLM for code from math.
-
-class ColorPrompt(Prompt[str, bool]):
-    def parse(inp: str) -> str:
-        return f"Answer 'Yes' if this is a color, {inp}. Answer:"
-
-    def parse(out: str, inp) -> bool:
-        # Encode the parsing logic
-        return out.strip() == "Yes"  
-ColorPrompt().show({"inp": "dog"}, "No")
-
-
-with 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(SimplePrompt(backend.Python()))
-    result = prompt({"question": question})
-    print(result)
-
-    
-show_log("math.log")

all/examples.py

@@ -1,20 +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 stats import gradio as stats
-
-css = "#clean div.form {border: 0px} #response {border: 0px; background: #ffeec6} #prompt {border: 0px;background: aliceblue} #json {border: 0px} #result {border: 0px; background: #c5e0e5} #inner {padding: 20px} #inner textarea {border: 0px} .tabs div.tabitem {border: 0px}"
-
-with gr.Blocks(css=css) as demo:
-    gr.HTML("<center> <img width='10%' style='display:inline; padding: 5px' src='https://user-images.githubusercontent.com/35882/218286642-67985b6f-d483-49be-825b-f62b72c469cd.png'> <h1 style='display:inline'> Mini-Chain </h1> <img width='10%' style='display:inline;padding: 5px' src='https://avatars.githubusercontent.com/u/25720743?s=200&v=4'> </center><br><center><a href='https://github.com/srush/minichain'>[code]</a>  <a href='https://user-images.githubusercontent.com/35882/218286642-67985b6f-d483-49be-825b-f62b72c469cd.png'>[docs]</a></center>")
-
-    gr.TabbedInterface([chat, gatsby,  math_demo, ner,  bash, pal, stats],
-                              ["Chat",  "QA",  "Math", "NER", "Bash", "PAL", "Stats"],
-                              css= css)
-
-demo.launch()
-

all/examples.py~

@@ -1,2 +0,0 @@
-from ner import gradio as ner
-from math import gradio as math