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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e9c5f0df",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [],
"source": [
"!pip install -q git+https://github.com/srush/MiniChain\n",
"!git clone https://github.com/srush/MiniChain; cp -fr MiniChain/examples/* . "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56932926",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [],
"source": [
"desc = \"\"\"\n",
"### Self-Ask\n",
"\n",
" Notebook implementation of the self-ask + Google tool use prompt. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/selfask.ipynb)\n",
"\n",
" (Adapted from [Self-Ask repo](https://github.com/ofirpress/self-ask))\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43d24799",
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass, replace\n",
"from typing import Optional\n",
"from minichain import prompt, show, OpenAI, Google"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "506b8e41",
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class State:\n",
" question: str\n",
" history: str = \"\"\n",
" next_query: Optional[str] = None\n",
" final_answer: Optional[str] = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb5fdf8e",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"@prompt(OpenAI(),\n",
" template_file = \"selfask.pmpt.tpl\",\n",
" stop_template = \"\\nIntermediate answer:\")\n",
"def self_ask(model, state):\n",
" out = model(state)\n",
" res = out.split(\":\", 1)[1]\n",
" if out.startswith(\"Follow up:\"):\n",
" return replace(state, next_query=res)\n",
" elif out.startswith(\"So the final answer is:\"):\n",
" return replace(state, final_answer=res)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2d66476",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [],
"source": [
"@prompt(Google())\n",
"def google(model, state):\n",
" if state.next_query is None:\n",
" return state\n",
"\n",
" result = model(state.next_query)\n",
" return State(state.question,\n",
" state.history + \"\\nIntermediate answer: \" + result + \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e41d7a75",
"metadata": {},
"outputs": [],
"source": [
"def selfask(question):\n",
" state = State(question)\n",
" for i in range(3):\n",
" state = self_ask(state)\n",
" state = google(state)\n",
" return state"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6e4b06f",
"metadata": {},
"outputs": [],
"source": [
"gradio = show(selfask,\n",
" examples=[\"What is the zip code of the city where George Washington was born?\"],\n",
" subprompts=[self_ask, google] * 3,\n",
" description=desc,\n",
" out_type=\"json\"\n",
" )\n",
"if __name__ == \"__main__\":\n",
" gradio.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6afd60de",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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