The DatetimeOutputParser is an output parser that generates structured outputs in the form of datetime objects.
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.
This tutorial demonstrates how to use the DatetimeOutputParser to:
Set up and initialize the parser for datetime generation
You can alternatively set OPENAI_API_KEY in .env file and load it.
[Note] This is not necessary if you've already set OPENAI_API_KEY in previous steps.
from dotenv import load_dotenvload_dotenv(override=True)
False
Using the Datetime Output Parser
If you need to generate output in the form of a date or time, the DatetimeOutputParser from LangChain simplifies the process.
The format of the DatetimeOutputParser can be specified by referring to the table below.
Format Code
Description
Example
%Y
4-digit year
2024
%y
2-digit year
24
%m
2-digit month
07
%d
2-digit day
04
%H
24-hour format hour
14
%I
12-hour format hour
02
%p
AM or PM
PM
%M
2-digit minute
45
%S
2-digit second
08
%f
Microsecond (6 digits)
000123
%z
UTC offset
+0900
%Z
Timezone name
KST
%a
Abbreviated weekday
Thu
%A
Full weekday name
Thursday
%b
Abbreviated month
Jul
%B
Full month name
July
%c
Full date and time
Thu Jul 4 14:45:08 2024
%x
Full date
07/04/24
%X
Full time
14:45:08
from langchain.output_parsers import DatetimeOutputParserfrom langchain_core.prompts import PromptTemplate# Initialize the output parseroutput_parser =DatetimeOutputParser()# Specify date formatdate_format ="%Y-%m-%d"output_parser.format = date_format# Get format instructionsformat_instructions = output_parser.get_format_instructions()# Create answer template for user questionstemplate ="""Answer the users question:\n\n#Format Instructions: \n{format_instructions}\n\n#Question: \n{question}\n\n#Answer:"""# Create a prompt from the templateprompt = PromptTemplate.from_template( template, partial_variables={"format_instructions": format_instructions, }, # Use parser's format instructions)print(format_instructions)print("-----------------------------------------------\n")print(prompt)
Write a datetime string that matches the following pattern: '%Y-%m-%d'.
Examples: 0594-05-12, 0088-08-25, 0371-10-02
Return ONLY this string, no other words!
-----------------------------------------------
input_variables=['question'] input_types={} partial_variables={'format_instructions': "Write a datetime string that matches the following pattern: '%Y-%m-%d'.\n\nExamples: 0594-05-12, 0088-08-25, 0371-10-02\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:'
from langchain_openai import ChatOpenAImodel =ChatOpenAI(temperature=0, model_name="gpt-4o-mini")# Combine the prompt, chat model, and output parser into a chainchain = prompt | model | output_parser# Call the chain to get an answer to the questionoutput = chain.invoke({"question": "The year Google was founded"})print(output)print(type(output))
1998-09-04 00:00:00
# Convert the result to a stringoutput.strftime(date_format)
Let's create a simple example that converts astream output to datetime objects using a generator function.
from langchain_core.output_parsers.string import StrOutputParserfrom langchain.output_parsers.datetime import DatetimeOutputParserfrom langchain_core.prompts.prompt import PromptTemplatefrom langchain_openai.chat_models.base import ChatOpenAIimport datetimefrom typing import AsyncIterator, List# Initialize the output parseroutput_parser =DatetimeOutputParser()# Specify date formatdate_format ="%Y-%m-%d"output_parser.format = date_format# Get format instructionsformat_instructions = output_parser.get_format_instructions()# Create answer template for user questionstemplate = ("Answer the users question:\n\n""#Format Instructions: \n{format_instructions}\n""Write a comma-separated list of 5 founding years of companies similar to: {company}")# Create a prompt from the templateprompt = PromptTemplate.from_template( template, partial_variables={"format_instructions": format_instructions},)# Initialize the ChatOpenAI model with temperature set to 0.0model =ChatOpenAI(temperature=0.0, model_name="gpt-4o-mini")# Create a chain combining the prompt, model, and string output parserstr_chain = prompt | model |StrOutputParser()# Define an asynchronous function to convert strings to datetime objectsasyncdefconvert_strings_to_datetime(input: AsyncIterator[str],) -> AsyncIterator[List[datetime.datetime]]: buffer =""asyncfor chunk ininput: buffer += chunkwhile","in buffer: comma_index = buffer.index(",") date_str = buffer[:comma_index].strip() date_obj = output_parser.parse(date_str)# Convert to datetime objectyield [date_obj] buffer = buffer[comma_index +1:] date_str = buffer.strip()if date_str: date_obj = output_parser.parse( date_str )# Convert remaining buffer to datetime objectyield [date_obj]# Connect the str_chain and convert_strings_to_datetime in a pipelinealist_chain = str_chain | convert_strings_to_datetime
# Use async for loop to stream data.asyncfor chunk in alist_chain.astream({"company": "Google"}):# Print each chunk and flush the buffer.print(chunk, flush=True)
