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Merge pull request #333 from blackms/v3

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Fix method invocation in LoggerChatModel
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Federico 2024-09-10 00:04:38 +02:00 • committed by GitHub
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3 changed files with 214 additions and 46 deletions
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166
.gitignore vendored
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@ -1,15 +1,155 @@
*.csv
__pycache__/**
.idea/**
open_ai_calls.log
test*
openaiSelenium*
open_ai_calls.json
_*
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv’s dependency resolution may lead to different
# Pipfile.lock files generated on each colleague’s machine.
# Thus, uncomment the following line if the pipenv environment is expected to be identical
# across all environments.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
generated_cv*
.vscode
chrome_profile
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
# PyCharm and all JetBrains IDEs
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
.idea/
*.iml
# Visual Studio Code
.vscode/
# Visual Studio 2015/2017/2019/2022
.vs/
*.opendb
*.VC.db
# User-specific files
*.suo
*.user
*.userosscache
*.sln.docstates
# Mono Auto Generated Files
mono_crash.*
# Project Specific
data_folder/output/*
generated_cv/*
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answers.json
data*
*virtual

90
src/gpt.py
View file

@ -70,7 +70,8 @@ class AIAdapter:
llm_model_type = config['llm_model_type']
llm_model = config['llm_model']
llm_api_url = config['llm_api_url']
print('Using {0} with {1} from {2}'.format(llm_model_type, llm_model, llm_api_url))
print('Using {0} with {1} from {2}'.format(
llm_model_type, llm_model, llm_api_url))
if llm_model_type == "openai":
return OpenAIModel(api_key, llm_model, llm_api_url)
@ -99,7 +100,8 @@ class LLMLogger:
logger.debug("Parsed reply received: %s", parsed_reply)
try:
calls_log = os.path.join(Path("data_folder/output"), "open_ai_calls.json")
calls_log = os.path.join(
Path("data_folder/output"), "open_ai_calls.json")
logger.debug("Logging path determined: %s", calls_log)
except Exception as e:
logger.error("Error determining the log path: %s", str(e))
@ -118,18 +120,22 @@ class LLMLogger:
}
logger.debug("Prompts converted to dictionary: %s", prompts)
except Exception as e:
logger.error("Error converting prompts to dictionary: %s", str(e))
logger.error(
"Error converting prompts to dictionary: %s", str(e))
raise
else:
logger.debug("Prompts are of unknown type, attempting default conversion")
logger.debug(
"Prompts are of unknown type, attempting default conversion")
try:
prompts = {
f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages)
}
logger.debug("Prompts converted to dictionary using default method: %s", prompts)
logger.debug(
"Prompts converted to dictionary using default method: %s", prompts)
except Exception as e:
logger.error("Error converting prompts using default method: %s", str(e))
logger.error(
"Error converting prompts using default method: %s", str(e))
raise
try:
@ -144,7 +150,8 @@ class LLMLogger:
output_tokens = token_usage["output_tokens"]
input_tokens = token_usage["input_tokens"]
total_tokens = token_usage["total_tokens"]
logger.debug("Token usage - Input: %d, Output: %d, Total: %d", input_tokens, output_tokens, total_tokens)
logger.debug("Token usage - Input: %d, Output: %d, Total: %d",
input_tokens, output_tokens, total_tokens)
except KeyError as e:
logger.error("KeyError in parsed_reply structure: %s", str(e))
raise
@ -159,7 +166,8 @@ class LLMLogger:
try:
prompt_price_per_token = 0.00000015
completion_price_per_token = 0.0000006
total_cost = (input_tokens * prompt_price_per_token) + (output_tokens * completion_price_per_token)
total_cost = (input_tokens * prompt_price_per_token) + \
(output_tokens * completion_price_per_token)
logger.debug("Total cost calculated: %f", total_cost)
except Exception as e:
logger.error("Error calculating total cost: %s", str(e))
@ -178,12 +186,14 @@ class LLMLogger:
}
logger.debug("Log entry created: %s", log_entry)
except KeyError as e:
logger.error("Error creating log entry: missing key %s in parsed_reply", str(e))
logger.error(
"Error creating log entry: missing key %s in parsed_reply", str(e))
raise
try:
with open(calls_log, "a", encoding="utf-8") as f:
json_string = json.dumps(log_entry, ensure_ascii=False, indent=4)
json_string = json.dumps(
log_entry, ensure_ascii=False, indent=4)
f.write(json_string + "\n")
logger.debug("Log entry written to file: %s", calls_log)
except Exception as e:
@ -194,23 +204,24 @@ class LLMLogger:
class LoggerChatModel:
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
self.llm = llm
logger.debug("LoggerChatModel successfully initialized with LLM: %s", llm)
logger.debug(
"LoggerChatModel successfully initialized with LLM: %s", llm)
def __call__(self, messages: List[Dict[str, str]]) -> str:
logger.debug("Entering __call__ method with messages: %s", messages)
while True:
try:
logger.debug("Attempting to call the LLM with messages")
reply = self.llm.invoke(messages)
logger.debug("LLM response received: %s", reply)
parsed_reply = self.parse_llmresult(reply)
logger.debug("Parsed LLM reply: %s", parsed_reply)
LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)
LLMLogger.log_request(
prompts=messages, parsed_reply=parsed_reply)
logger.debug("Request successfully logged")
return reply
@ -246,7 +257,8 @@ class LoggerChatModel:
except Exception as e:
logger.error("Unexpected error occurred: %s", str(e))
logger.info("Waiting for 30 seconds before retrying due to an unexpected error.")
logger.info(
"Waiting for 30 seconds before retrying due to an unexpected error.")
time.sleep(30)
continue
@ -279,11 +291,13 @@ class LoggerChatModel:
return parsed_result
except KeyError as e:
logger.error("KeyError while parsing LLM result: missing key %s", str(e))
logger.error(
"KeyError while parsing LLM result: missing key %s", str(e))
raise
except Exception as e:
logger.error("Unexpected error while parsing LLM result: %s", str(e))
logger.error(
"Unexpected error while parsing LLM result: %s", str(e))
raise
@ -299,7 +313,8 @@ class GPTAnswerer:
@staticmethod
def find_best_match(text: str, options: list[str]) -> str:
logger.debug("Finding best match for text: '%s' in options: %s", text, options)
logger.debug(
"Finding best match for text: '%s' in options: %s", text, options)
distances = [
(option, distance(text.lower(), option.lower())) for option in options
]
@ -325,10 +340,12 @@ class GPTAnswerer:
def set_job(self, job):
logger.debug("Setting job: %s", 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):
logger.debug("Setting job application profile: %s", job_application_profile)
logger.debug("Setting job application profile: %s",
job_application_profile)
self.job_application_profile = job_application_profile
def summarize_job_description(self, text: str) -> str:
@ -336,7 +353,8 @@ class GPTAnswerer:
strings.summarize_prompt_template = self._preprocess_template_string(
strings.summarize_prompt_template
)
prompt = ChatPromptTemplate.from_template(strings.summarize_prompt_template)
prompt = ChatPromptTemplate.from_template(
strings.summarize_prompt_template)
chain = prompt | self.llm_cheap | StrOutputParser()
output = chain.invoke({"text": text})
logger.debug("Summary generated: %s", output)
@ -460,31 +478,37 @@ class GPTAnswerer:
r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education Details|Experience Details|Projects|Availability|Salary Expectations|Certifications|Languages|Interests|Cover letter)",
output, re.IGNORECASE)
if not match:
raise ValueError("Could not extract section name from the response.")
raise ValueError(
"Could not extract section name from the response.")
section_name = match.group(1).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})
logger.debug("Cover letter generated: %s", output)
return output
resume_section = getattr(self.resume, section_name, None) or getattr(self.job_application_profile, section_name,
None)
if resume_section is None:
logger.error("Section '%s' not found in either resume or job_application_profile.", section_name)
raise ValueError(f"Section '{section_name}' not found in either resume or job_application_profile.")
logger.error(
"Section '%s' not found in either resume or job_application_profile.", section_name)
raise ValueError(f"Section '{
section_name}' not found in either resume or job_application_profile.")
chain = chains.get(section_name)
if chain is None:
logger.error("Chain not defined for section '%s'", section_name)
raise ValueError(f"Chain not defined for section '{section_name}'")
output = chain.invoke({"resume_section": resume_section, "question": question})
output = chain.invoke(
{"resume_section": resume_section, "question": question})
logger.debug("Question answered: %s", output)
return output
def answer_question_numeric(self, question: str, default_experience: int = 3) -> int:
logger.debug("Answering numeric question: %s", question)
func_template = self._preprocess_template_string(strings.numeric_question_template)
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(
@ -495,7 +519,8 @@ class GPTAnswerer:
output = self.extract_number_from_string(output_str)
logger.debug("Extracted number: %d", output)
except ValueError:
logger.warning("Failed to extract number, using default experience: %d", default_experience)
logger.warning(
"Failed to extract number, using default experience: %d", default_experience)
output = default_experience
return output
@ -511,17 +536,20 @@ class GPTAnswerer:
def answer_question_from_options(self, question: str, options: list[str]) -> str:
logger.debug("Answering question from options: %s", question)
func_template = self._preprocess_template_string(strings.options_template)
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})
logger.debug("Raw output for options question: %s", output_str)
best_option = self.find_best_match(output_str, options)
logger.debug("Best option determined: %s", best_option)
return best_option
def resume_or_cover(self, phrase: str) -> str:
logger.debug("Determining if phrase refers to resume or cover letter: %s", phrase)
logger.debug(
"Determining if phrase refers to resume or cover letter: %s", phrase)
prompt_template = """
Given the following phrase, respond with only 'resume' if the phrase is about a resume, or 'cover' if it's about a cover letter.
If the phrase contains only one word 'upload', consider it as 'cover'.