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harrison/p
| Author | SHA1 | Date |
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Harrison Chase
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0d0d3f122a | 2 years ago |
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Harrison Chase
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bf3a9973f0 | 2 years ago |
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Harrison Chase
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c28d5ec3ba | 2 years ago |
@ -0,0 +1,410 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "43fb16cb",
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"metadata": {},
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"source": [
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"# Prompt Walkthrough\n",
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"\n",
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"An overview of the different types of prompts in LangChain and how to use them"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cddb465e",
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"metadata": {},
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"source": [
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"### Basic Prompt\n",
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"\n",
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"The most simple type of prompt - a string template that takes any number of input variables. The template should be formatted as a Python f-string."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "094229f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import Prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ab46bd2a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Tell me a joke.'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# An example prompt with no input variables\n",
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"no_input_prompt = Prompt(input_variables=[], template=\"Tell me a joke.\")\n",
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"no_input_prompt.format()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c3ad0fa8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Tell me a funny joke.'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# An example prompt with one input variable\n",
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"no_input_prompt = Prompt(input_variables=[\"adjective\"], template=\"Tell me a {adjective} joke.\")\n",
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"no_input_prompt.format(adjective=\"funny\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "ba577dcf",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Tell me a funny joke about chickens.'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# An example prompt with multiple input variables\n",
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"no_input_prompt = Prompt(input_variables=[\"adjective\", \"content\"], template=\"Tell me a {adjective} joke about {content}.\")\n",
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"no_input_prompt.format(adjective=\"funny\", content=\"chickens\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d27b1824",
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"metadata": {},
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"source": [
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"### Examples\n",
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"Examples are datapoints that can be used to show the model how to produce results. They can be either strings, or dictionaries that are then turned into strings by an example prompt itself."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2c00e965",
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"metadata": {},
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"outputs": [],
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"source": [
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"string_examples = [\"Input: happy\\nOutput: sad\", \"Input: tall\\nOutput: short\"]\n",
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"dict_examples = [{\"input\": \"happy\", \"output\": \"sad\"}, {\"input\": \"tall\", \"output\": \"short\"}]\n",
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"example_prompt = Prompt(input_variables=[\"input\",\"output\"], template=\"Input: {input}\\nOutput: {output}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1492b49d",
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"metadata": {},
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"source": [
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"### Simple Prompt with examples\n",
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"\n",
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"We can then use these examples to construct prompts."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "1a5a686d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give the antonym of every input\n",
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"\n",
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"Input: happy\n",
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"Output: sad\n",
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"\n",
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"Input: tall\n",
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"Output: short\n",
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"\n",
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"Input: big\n",
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"Output:\n"
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]
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}
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],
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"source": [
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"prompt_from_string_examples = Prompt.from_examples(\n",
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" string_examples, \n",
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" prefix=\"Give the antonym of every input\",\n",
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" suffix=\"Input: {adjective}\\nOutput:\", \n",
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" input_variables=[\"adjective\"],\n",
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")\n",
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"print(prompt_from_string_examples.format(adjective=\"big\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "7931e5f2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give the antonym of every input\n",
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"\n",
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"Input: happy\n",
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"Output: sad\n",
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"\n",
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"Input: tall\n",
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"Output: short\n",
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"\n",
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"Input: big\n",
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"Output:\n"
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]
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}
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],
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"source": [
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"prompt_from_string_examples = Prompt.from_structured_examples(\n",
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" dict_examples,\n",
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" example_prompt,\n",
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" prefix=\"Give the antonym of every input\",\n",
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" suffix=\"Input: {adjective}\\nOutput:\", \n",
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" input_variables=[\"adjective\"],\n",
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")\n",
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"print(prompt_from_string_examples.format(adjective=\"big\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "861a4d1f",
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"metadata": {},
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"source": [
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"### Dynamic Prompt\n",
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"\n",
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"We also do more clever things with prompts - for example, only select a certain number of examples in order to limit the size of the text passed in. This will vary with the input text size."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "7c469c95",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import DynamicPrompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "207e55f7",
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"metadata": {},
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"outputs": [],
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"source": [
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"dynamic_prompt = DynamicPrompt.from_structured_examples(\n",
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" dict_examples,\n",
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" example_prompt,\n",
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" prefix=\"Give the antonym of every input\",\n",
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" suffix=\"Input: {adjective}\\nOutput:\", \n",
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" input_variables=[\"adjective\"],\n",
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" max_length=20,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "d00b4385",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give the antonym of every input\n",
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"\n",
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"Input: happy\n",
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"Output: sad\n",
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"\n",
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"Input: tall\n",
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"Output: short\n",
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"\n",
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"Input: big\n",
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"Output:\n"
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]
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}
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],
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"source": [
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"# An example with small input, so it selects both examples.\n",
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"print(dynamic_prompt.format(adjective=\"big\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "878bcde9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give the antonym of every input\n",
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"\n",
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"Input: happy\n",
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"Output: sad\n",
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"\n",
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"Input: big and huge and massive\n",
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"Output:\n"
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]
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}
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],
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"source": [
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"# An example with long input, so it selects only one example.\n",
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"print(dynamic_prompt.format(adjective=\"big and huge and massive\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2d007b0a",
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"metadata": {},
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"source": [
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"# Optimized Prompt\n",
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"\n",
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"Besides selecting a variable number of examples to show, we can also select examples that most closely match the user input. This is done by creating embeddings of the user input and comparing it embeddings of the examples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "241bfe80",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts.optimized import OptimizedPrompt\n",
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"from langchain.vectorstores import FAISS\n",
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"from langchain.embeddings import OpenAIEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "50d0a701",
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"metadata": {},
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"outputs": [],
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"source": [
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"optimized_prompt = OptimizedPrompt.from_structured_examples(\n",
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" dict_examples,\n",
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" example_prompt,\n",
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" prefix=\"Give the antonym of every input\",\n",
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" suffix=\"Input: {adjective}\\nOutput:\", \n",
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" input_variables=[\"adjective\"],\n",
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" embeddings=OpenAIEmbeddings(),\n",
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" vectorstore_cls=FAISS\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "4c8fdf45",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give the antonym of every input\n",
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"\n",
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"Input: happy\n",
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"Output: sad\n",
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"\n",
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"Input: worried\n",
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"Output:\n"
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]
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}
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],
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"source": [
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"# Input is a feeling, so should select the happy/sad example\n",
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"print(optimized_prompt.format(adjective=\"worried\", k=1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "829af21a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give the antonym of every input\n",
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"\n",
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"Input: tall\n",
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"Output: short\n",
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"\n",
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"Input: fat\n",
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"Output:\n"
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]
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}
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],
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"source": [
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"# Input is a measurment, so should select the tall/short example\n",
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"print(optimized_prompt.format(adjective=\"fat\", k=1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "76a1065d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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