martin/linkedIn_auto_jobs_applier_with_AI
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Merge pull request #1 from parth-20-07/bug/rate-limit-error

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Add Sleep Timer for OpenAI Rate Limit Error
This commit is contained in:
Parth Patel 2024-09-03 11:54:08 -04:00
commit c06801ddba
2 changed files with 75 additions and 23 deletions
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1
.gitignore vendored
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@ -11,4 +11,5 @@ generated_cv*
.vscode
chrome_profile
answers.json
virtual/*
data*

97
src/gpt.py
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@ -3,6 +3,7 @@ import os
import re
import textwrap
from datetime import datetime
import time
from typing import Dict, List
from pathlib import Path
from dotenv import load_dotenv
@ -12,14 +13,14 @@ from langchain_core.prompt_values import StringPromptValue
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from Levenshtein import distance
from openai import RateLimitError # Make sure to import the necessary exception class
import src.strings as strings
load_dotenv()
class LLMLogger:
def __init__(self, llm: ChatOpenAI):
self.llm = llm
@ -83,7 +84,27 @@ class LoggerChatModel:
def __call__(self, messages: List[Dict[str, str]]) -> str:
# Call the LLM with the provided messages and log the response.
reply = self.llm(messages)
while True:
try:
# Try to get the reply
reply = self.llm(messages)
break # Break the loop if successful
except RateLimitError as e:
# Handle the rate limit error specifically
if "429" in str(e):
# print(f"Rate Limit Error: {e}")
print("Sleeping for 450 seconds before retrying...")
time.sleep(450) # Sleep for 7 minutes and 30 seconds
else:
# Handle any other RateLimitError that doesn't match the 429 code
print(f"An unexpected RateLimitError occurred: {e}")
reply = None
break # Exit loop or handle accordingly
except Exception as e:
# Handle other generic exceptions
print(f"An unexpected error occurred: {e}")
reply = None
break # Exit loop or handle accordingly
parsed_reply = self.parse_llmresult(reply)
LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)
return reply
@ -114,9 +135,8 @@ class LoggerChatModel:
class GPTAnswerer:
def __init__(self, openai_api_key):
self.llm_cheap = LoggerChatModel(
ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4)
)
self.llm_cheap = LoggerChatModel(ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4))
@property
def job_description(self):
return self.job.description
@ -144,11 +164,13 @@ class GPTAnswerer:
def set_job(self, job):
self.job = job
self.job.set_summarize_job_description(self.summarize_job_description(self.job.description))
self.job.set_summarize_job_description(
self.summarize_job_description(self.job.description)
)
def set_job_application_profile(self, job_application_profile):
self.job_application_profile = job_application_profile
def summarize_job_description(self, text: str) -> str:
strings.summarize_prompt_template = self._preprocess_template_string(
strings.summarize_prompt_template
@ -157,23 +179,33 @@ class GPTAnswerer:
chain = prompt | self.llm_cheap | StrOutputParser()
output = chain.invoke({"text": text})
return output
def _create_chain(self, template: str):
prompt = ChatPromptTemplate.from_template(template)
return prompt | self.llm_cheap | StrOutputParser()
def answer_question_textual_wide_range(self, question: str) -> str:
# Define chains for each section of the resume
chains = {
"personal_information": self._create_chain(strings.personal_information_template),
"self_identification": self._create_chain(strings.self_identification_template),
"legal_authorization": self._create_chain(strings.legal_authorization_template),
"personal_information": self._create_chain(
strings.personal_information_template
),
"self_identification": self._create_chain(
strings.self_identification_template
),
"legal_authorization": self._create_chain(
strings.legal_authorization_template
),
"work_preferences": self._create_chain(strings.work_preferences_template),
"education_details": self._create_chain(strings.education_details_template),
"experience_details": self._create_chain(strings.experience_details_template),
"experience_details": self._create_chain(
strings.experience_details_template
),
"projects": self._create_chain(strings.projects_template),
"availability": self._create_chain(strings.availability_template),
"salary_expectations": self._create_chain(strings.salary_expectations_template),
"salary_expectations": self._create_chain(
strings.salary_expectations_template
),
"certifications": self._create_chain(strings.certifications_template),
"languages": self._create_chain(strings.languages_template),
"interests": self._create_chain(strings.interests_template),
@ -273,21 +305,38 @@ class GPTAnswerer:
section_name = output.lower().replace(" ", "_")
if section_name == "cover_letter":
chain = chains.get(section_name)
output = chain.invoke({"resume": self.resume, "job_description": self.job_description})
output = chain.invoke(
{"resume": self.resume, "job_description": self.job_description}
)
return output
resume_section = getattr(self.resume, section_name, None) or getattr(self.job_application_profile, section_name, None)
resume_section = getattr(self.resume, section_name, None) or getattr(
self.job_application_profile, section_name, None
)
if resume_section is None:
raise ValueError(f"Section '{section_name}' not found in either resume or job_application_profile.")
raise ValueError(
f"Section '{section_name}' not found in either resume or job_application_profile."
)
chain = chains.get(section_name)
if chain is None:
raise ValueError(f"Chain not defined for section '{section_name}'")
return chain.invoke({"resume_section": resume_section, "question": question})
def answer_question_numeric(self, question: str, default_experience: int = 3) -> int:
func_template = self._preprocess_template_string(strings.numeric_question_template)
def answer_question_numeric(
self, question: str, default_experience: int = 3
) -> int:
func_template = self._preprocess_template_string(
strings.numeric_question_template
)
prompt = ChatPromptTemplate.from_template(func_template)
chain = prompt | self.llm_cheap | StrOutputParser()
output_str = chain.invoke({"resume_educations": self.resume.education_details,"resume_jobs": self.resume.experience_details,"resume_projects": self.resume.projects , "question": question})
output_str = chain.invoke(
{
"resume_educations": self.resume.education_details,
"resume_jobs": self.resume.experience_details,
"resume_projects": self.resume.projects,
"question": question,
}
)
try:
output = self.extract_number_from_string(output_str)
except ValueError:
@ -305,10 +354,12 @@ class GPTAnswerer:
func_template = self._preprocess_template_string(strings.options_template)
prompt = ChatPromptTemplate.from_template(func_template)
chain = prompt | self.llm_cheap | StrOutputParser()
output_str = chain.invoke({"resume": self.resume, "question": question, "options": options})
output_str = chain.invoke(
{"resume": self.resume, "question": question, "options": options}
)
best_option = self.find_best_match(output_str, options)
return best_option
def resume_or_cover(self, phrase: str) -> str:
# Define the prompt template
prompt_template = """