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Merged
merged 3 commits into from
Jan 4, 2025
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[E-2] 03-OutputParser / 06-DatetimeOutputParser #96

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6dddde4
DatetimeOutputParser modified load_dotenv() parmeter
stsr1284 Jan 1, 2025
2411ffb
오타수정
stsr1284 Jan 2, 2025
d27c87f
fix conflict
stsr1284 Jan 3, 2025
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305 changes: 305 additions & 0 deletions 03-OutputParser/06-DatetimeOutputParser.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "635d8ebb",
"metadata": {},
"source": [
"# DatetimeOutputParser\n",
"\n",
"- Author: [Donghak Lee](https://github.com/stsr1284)\n",
"- Design: []()\n",
"- Peer Review: []()\n",
"- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n",
"\n",
"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain-academy/blob/main/module-4/sub-graph.ipynb) [![Open in LangChain Academy](https://cdn.prod.website-files.com/65b8cd72835ceeacd4449a53/66e9eba12c7b7688aa3dbb5e_LCA-badge-green.svg)](https://academy.langchain.com/courses/take/intro-to-langgraph/lessons/58239937-lesson-2-sub-graphs)\n",
"\n",
"## Overview\n",
"\n",
"The `DatetimeOutputParser` is an output parser that generates structured outputs in the form of `datetime` objects.\n",
"\n",
"By converting the outputs of LLMs into `datetime` objects, it enables more systematic and consistent processing of date and time data, making it useful for data processing and analysis.\n",
"\n",
"This tutorial demonstrates how to use the `DatetimeOutputParser` to:\n",
"1. Set up and initialize the parser for datetime generation\n",
"2. Convert a datetime object to a string\n",
"\n",
"### Table of Contents\n",
"\n",
"- [Overview](#overview)\n",
"- [Environement Setup](#environment-setup)\n",
"- [Implementing the Datetime Output Parser](#implementing-the-datetime-output-parser)\n",
"\n",
"\n",
"### References\n",
"\n",
"- [LangChain DatetimeOutputParser](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html)\n",
"- [LangChain ChatOpenAI API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html)\n",
"----"
]
},
{
"cell_type": "markdown",
"id": "c6c7aba4",
"metadata": {},
"source": [
"## Environment Setup\n",
"\n",
"Set up the environment. You may refer to [Environment Setup](https://wikidocs.net/257836) for more details.\n",
"\n",
"**[Note]**\n",
"- `langchain-opentutorial` is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials. \n",
"- You can checkout the [`langchain-opentutorial`](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "21943adb",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"!pip install langchain-opentutorial"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "f25ec196",
"metadata": {},
"outputs": [],
"source": [
"# Install required packages\n",
"from langchain_opentutorial import package\n",
"\n",
"package.install(\n",
" [\n",
" \"langsmith\",\n",
" \"langchain\",\n",
" \"langchain_core\",\n",
" \"langchain_openai\",\n",
" ],\n",
" verbose=False,\n",
" upgrade=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7f9065ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Environment variables have been set successfully.\n"
]
}
],
"source": [
"# Set environment variables\n",
"from langchain_opentutorial import set_env\n",
"\n",
"set_env(\n",
" {\n",
" \"OPENAI_API_KEY\": \"\",\n",
" \"LANGCHAIN_API_KEY\": \"\",\n",
" \"LANGCHAIN_TRACING_V2\": \"true\",\n",
" \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n",
" \"LANGCHAIN_PROJECT\": \"06-DatetimeOutputParser\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "690a9ae0",
"metadata": {},
"source": [
"You can alternatively set `OPENAI_API_KEY` in .env file and load it.\n",
"\n",
"[Note] This is not necessary if you've already set `OPENAI_API_KEY` in previous steps."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "4f99b5b6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "markdown",
"id": "c9760b5f",
"metadata": {},
"source": [
"## Implementing the Datetime Output Parser\n",
"If you need to generate output in the form of a date or time, LangChain's `DatetimeOutputParser` simplifies the process.\n",
"\n",
"The `format` of the `DatetimeOutputParser` can be specified by referring to the table below.\n",
"| Format Code | Description | Example |\n",
"|--------------|-----------------------|----------------------|\n",
"| %Y | 4-digit year | 2024 |\n",
"| %y | 2-digit year | 24 |\n",
"| %m | 2-digit month | 07 |\n",
"| %d | 2-digit day | 04 |\n",
"| %H | 24-hour format hour | 14 |\n",
"| %I | 12-hour format hour | 02 |\n",
"| %p | AM or PM | PM |\n",
"| %M | 2-digit minute | 45 |\n",
"| %S | 2-digit second | 08 |\n",
"| %f | Microsecond (6 digits)| 000123 |\n",
"| %z | UTC offset | +0900 |\n",
"| %Z | Timezone name | KST |\n",
"| %a | Abbreviated weekday | Thu |\n",
"| %A | Full weekday name | Thursday |\n",
"| %b | Abbreviated month | Jul |\n",
"| %B | Full month name | July |\n",
"| %c | Full date and time | Thu Jul 4 14:45:08 2024 |\n",
"| %x | Full date | 07/04/24 |\n",
"| %X | Full time | 14:45:08 |\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "69cb77da",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Write a datetime string that matches the following pattern: '%Y-%m-%d'.\n",
"\n",
"Examples: 0727-11-27, 0177-08-19, 0383-11-24\n",
"\n",
"Return ONLY this string, no other words!\n",
"-----------------------------------------------\n",
"\n",
"input_variables=['question'] input_types={} partial_variables={'format_instructions': \"Write a datetime string that matches the following pattern: '%Y-%m-%d'.\\n\\nExamples: 0727-11-27, 0177-08-19, 0383-11-24\\n\\nReturn ONLY this string, no other words!\"} template='Answer the users question:\\n\\n#Format Instructions: \\n{format_instructions}\\n\\n#Question: \\n{question}\\n\\n#Answer:'\n"
]
}
],
"source": [
"from langchain.output_parsers import DatetimeOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"# Initialize the output parser\n",
"output_parser = DatetimeOutputParser()\n",
"\n",
"# Specify date format\n",
"date_format = \"%Y-%m-%d\"\n",
"output_parser.format = date_format\n",
"\n",
"# Get format instructions\n",
"format_instructions = output_parser.get_format_instructions()\n",
"\n",
"# Create answer template for user questions\n",
"template = \"\"\"Answer the users question:\\n\\n#Format Instructions: \\n{format_instructions}\\n\\n#Question: \\n{question}\\n\\n#Answer:\"\"\"\n",
"\n",
"# Create a prompt from the template\n",
"prompt = PromptTemplate.from_template(\n",
" template,\n",
" partial_variables={\n",
" \"format_instructions\": format_instructions,\n",
" }, # Use parser's format instructions\n",
")\n",
"\n",
"print(format_instructions)\n",
"print(\"-----------------------------------------------\\n\")\n",
"print(prompt)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "4fc39071",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1998-09-04 00:00:00\n",
"<class 'datetime.datetime'>\n"
]
}
],
"source": [
"from langchain_openai import ChatOpenAI\n",
"\n",
"# Combine the prompt, chat model, and output parser into a chain\n",
"chain = prompt | ChatOpenAI() | output_parser\n",
"\n",
"# Call the chain to get an answer to the question\n",
"output = chain.invoke({\"question\": \"The year Google was founded\"})\n",
"\n",
"print(output)\n",
"print(type(output))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "6cf66464",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1998-09-04'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert the result to a string\n",
"output.strftime(date_format)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "langchain-opentutorial-uPwUb4lu-py3.11",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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