PROD.2026/RekomenciBackend
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fix merge conflict

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This commit is contained in:
ivankirpichnikov
2025-11-23 05:28:35 +03:00
parent c2fe8a9d83 f92e3d3372
commit f07ef3ce60
26 changed files with 478 additions and 269 deletions
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src/dataset/__main__.py
-49
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@@ -1,49 +0,0 @@
import json
import logging
import urllib.parse
from collections.abc import Sequence
from pathlib import Path
from typing import Final
from adaptix import DebugTrail, NameStyle, Retort, name_mapping
from dataset.data_structures import DataSetLine, Salary
from dataset.upload_key_skills import upload_key_kills
DATASET_PATH: Final = Path("hh_ru_vacancies.jsonlines")
BASE_URL: Final = "https://team-39-alpha-gm5qjkou.hack.prodcontest.ru"
UPLOAD_KEY_SKILLS: Final = urllib.parse.urljoin(BASE_URL, "key_skills")
logger = logging.getLogger(__name__)
def parse_dataset(file_path: Path) -> Sequence[DataSetLine]:
retort = Retort(
recipe=[
name_mapping(Salary, name_style=NameStyle.CAMEL),
],
debug_trail=DebugTrail.DISABLE,
strict_coercion=False,
)
raw_lines = []
with file_path.open("r", encoding="utf-8") as f:
raw_lines = map(json.loads, f.readlines())
return retort.load(raw_lines, Sequence[DataSetLine])
def main() -> None:
logging.basicConfig(level=logging.INFO)
logger.info("Parsing dataset...")
dataset = parse_dataset(DATASET_PATH)
upload_key_kills(dataset, UPLOAD_KEY_SKILLS)
logger.info("finished script")
if __name__ == "__main__":
main()
src/dataset/load_vacancies.py
+1 -1
View File
@@ -41,7 +41,7 @@ async def main() -> None:
ml_container = make_ml_ioc(ml_configuration)
csv_path = Path("filtered_vacancies.csv")
max_records = 51
max_records = 1000
try:
async with backend_container() as backend_request_container, ml_container() as ml_request_container:
src/dataset/upload_key_skills.py
-37
View File
@@ -1,37 +0,0 @@
import logging
from collections.abc import Sequence
from requests import Session
from dataset.data_structures import DataSetLine
logger = logging.getLogger(__name__)
def upload_key_kills(
dataset: Sequence[DataSetLine],
upload_endpoint: str,
max_upload_count: int | None = None
) -> None:
session = Session()
key_skills = []
for count, line in enumerate(dataset):
if max_upload_count is not None and count >= max_upload_count:
break
key_skills.extend(line.key_skills)
logger.info("Upload skills %r", key_skills)
response = session.post(
upload_endpoint,
json={
"key_skills": key_skills
}
)
if response.status_code != 200:
logger.warning("Doesn't upload skills. Status code %r", response.status_code)
else:
logger.info("Upload skills %r. Status code %r", key_skills, response.status_code)
key_skills = []
src/template_project/adapters/data_gateways/resume.py
+8 -9
View File
@@ -26,7 +26,6 @@ from template_project.application.resume.entity import (
ResumePrediction,
ResumeProject,
)
from template_project.application.resume.errors import ResumeNotFoundError
from template_project.application.user.entity import UserId
@@ -35,12 +34,8 @@ class DefaultResumeDataGateway(ResumeDataGateway):
self._session = session
@override
async def load(self, resume_id: ResumeId) -> Resume:
resume = await self._session.get(Resume, resume_id)
if resume is None:
raise ResumeNotFoundError(resume_id=resume_id)
return resume
async def load_by_resume_id(self, resume_id: ResumeId) -> Resume | None:
return await self._session.get(Resume, resume_id)
@override
async def list_by_user_id(self, user_id: UserId, limit: int, offset: int) -> Sequence[Resume]:
@@ -64,11 +59,15 @@ class DefaultResumeDataGateway(ResumeDataGateway):
async def get_history(self, resume_id: ResumeId) -> Sequence[Resume]:
# TODO: N+1
history: list[Resume] = []
current_resume = await self.load(resume_id)
current_resume = await self.load_by_resume_id(resume_id)
if current_resume is None:
return history
history.append(current_resume)
while current_resume.down_resume_id is not None:
current_resume = await self.load(current_resume.down_resume_id)
current_resume = await self.load_by_resume_id(current_resume.down_resume_id)
if current_resume is None:
break
history.append(current_resume)
return history
src/template_project/adapters/data_gateways/tables.py
+30 -1
View File
@@ -70,6 +70,34 @@ class StringArrayType(TypeDecorator[list[str]]):
return []
class ExperienceTypeType(TypeDecorator[ExperienceType]):
impl: Any = String
cache_ok: bool | None = True
@override
def process_bind_param(self, value: Any, dialect: Any) -> Any:
if value is None:
return None
if isinstance(value, ExperienceType):
return value.value
if isinstance(value, str):
return value
return None
@override
def process_result_value(self, value: Any, dialect: Any) -> ExperienceType:
if value is None:
raise ValueError("experience_type cannot be None")
if isinstance(value, ExperienceType):
return value
if isinstance(value, str):
try:
return ExperienceType(value)
except ValueError:
raise ValueError(f"Invalid experience_type value: {value}")
raise ValueError(f"Cannot convert {type(value)} to ExperienceType")
user_table: Final = Table(
"users",
meta_data,
@@ -138,7 +166,7 @@ resume_table: Final = Table(
Column("location", String, nullable=False),
Column("about_me", String, nullable=False),
Column("key_skills", StringArrayType(), nullable=False, server_default=text("'[]'::jsonb")),
Column("experience_type", String, nullable=False),
Column("experience_type", ExperienceTypeType(), nullable=False),
Column("down_resume_id", UUID, ForeignKey("resume.id", ondelete="CASCADE"), nullable=True, default=None),
Column("up_resume_id", UUID, ForeignKey("resume.id", ondelete="CASCADE"), nullable=True, default=None),
)
@@ -241,6 +269,7 @@ mapper_registry.map_imperatively(
resume_table,
properties={
"key_skills": resume_table.c.key_skills,
"experience_type": resume_table.c.experience_type,
},
)
mapper_registry.map_imperatively(ResumeEmbedding, resume_embedding_table)
src/template_project/adapters/data_gateways/vacancy.py
+2
View File
@@ -20,6 +20,8 @@ class DefaultVacancyDataGateway(VacancyDataGateway):
select(Vacancy, label("resume_similarity", vacancy_embedding_table.c.vector.cosine_distance(vector)))
.join(VacancyEmbedding, vacancy_embedding_table.c.vacancy_id == vacancy_table.c.id)
.where(vacancy_embedding_table.c.vector.cosine_distance(vector) > 0.5)
.order_by(vacancy_embedding_table.c.vector.cosine_distance(vector).asc())
.limit(100)
)
result = await self._session.execute(statement)
return [
src/template_project/adapters/generators/__init__.py
View File
src/template_project/adapters/generators/resume_embedding_vector.py
+2 -21
View File
@@ -1,16 +1,8 @@
from typing import Final, override
from typing import override
from template_project.adapters.ml_api_gateway import MlApiGateway
from template_project.application.common.enums import ExperienceType
from template_project.application.resume.vector_generator import ResumeEmbeddingVectorGenerator
EMBEDDING_TEXT_TEMPLATE: Final = """
Позиция: {position}
Опыт: {experience_type}
Ключевые навыки: {key_skills}
Описание: {about_me}
"""
class DefaultResumeEmbeddingVectorGenerator(ResumeEmbeddingVectorGenerator):
def __init__(self, ml_api_gateway: MlApiGateway) -> None:
@@ -19,17 +11,6 @@ class DefaultResumeEmbeddingVectorGenerator(ResumeEmbeddingVectorGenerator):
@override
async def generate(
self,
position: str,
about_me: str,
experience_type: ExperienceType,
key_skills: list[str],
text: str,
) -> list[float]:
text = EMBEDDING_TEXT_TEMPLATE.format_map(
{
"position": position,
"experience_type": experience_type,
"key_skills": ", ".join(key_skills),
"about_me": about_me,
}
)
return await self._ml_api_gateway.generate_embedding(text)
src/template_project/adapters/generators/resume_prediction.py
+3 -3
View File
@@ -15,12 +15,12 @@ class DefaultResumePredictionGenerator(ResumePredictionGenerator):
async def generate(
self,
resume: Resume,
suituble_vacancies: Sequence[SuitableVacancy],
suitable_vacancies: Sequence[SuitableVacancy],
) -> ResumePrediction:
response = await self._ml_api_gateway.generate_resume_prediction(
resume_id=resume.id,
key_skills=resume.key_skills,
suituble_vacancies=[
suitable_vacancies=[
SuitableVacancyDs(
vacancy_id=str(suituble_vacancy.vacancy.id),
from_salary=suituble_vacancy.vacancy.from_salary,
@@ -28,7 +28,7 @@ class DefaultResumePredictionGenerator(ResumePredictionGenerator):
key_skills=suituble_vacancy.vacancy.key_skills,
resume_similarity=suituble_vacancy.resume_similarity,
)
for suituble_vacancy in suituble_vacancies
for suitable_vacancy in suitable_vacancies
],
)
return ResumePrediction.factory(
src/template_project/adapters/ml_api_gateway.py
+10 -9
View File
@@ -36,7 +36,7 @@ class MlApiGateway:
self,
resume_id: ResumeId,
key_skills: list[str],
suituble_vacancies: Sequence[SuitableVacancyDs],
suitable_vacancies: Sequence[SuitableVacancyDs],
) -> GenerateResumePredictionResponse:
response = await self._client.post(
"/predict",
@@ -45,12 +45,13 @@ class MlApiGateway:
"key_skills": key_skills,
"vacancies": [
{
"vacancy_id": suituble_vacancy.vacancy_id,
"from_salary": suituble_vacancy.from_salary,
"to_salary": suituble_vacancy.to_salary,
"key_skills": suituble_vacancy.key_skills,
"resume_similarity": suituble_vacancy.resume_similarity,
} for suituble_vacancy in suituble_vacancies
"vacancy_id": str(suitable_vacancy.vacancy_id),
"from_salary": str(suitable_vacancy.from_salary),
"to_salary": str(suitable_vacancy.to_salary),
"key_skills": suitable_vacancy.key_skills,
"resume_similarity": suitable_vacancy.resume_similarity,
}
for suitable_vacancy in suitable_vacancies
],
},
timeout=100,
@@ -58,7 +59,7 @@ class MlApiGateway:
response_json = response.json()
return GenerateResumePredictionResponse(
salary_from=response_json["salary_from"],
salary_to=response_json["salary_to"],
salary_from=Decimal(str(response_json["salary_from"])),
salary_to=Decimal(str(response_json["salary_to"])),
recommended_skills=response_json["recommended_skills"],
)
src/template_project/application/resume/data_gateway.py
+1 -1
View File
@@ -15,7 +15,7 @@ from template_project.application.user.entity import UserId
class ResumeDataGateway(Protocol):
@abstractmethod
async def load(self, resume_id: ResumeId) -> Resume:
async def load_by_resume_id(self, resume_id: ResumeId) -> Resume | None:
raise NotImplementedError
@abstractmethod
src/template_project/application/resume/interactors/add.py
-5
View File
@@ -10,7 +10,6 @@ from template_project.application.resume.entity import (
ResumeId,
ResumeProject,
)
from template_project.application.resume.interactors.resume_embedding import ResumeEmbeddingInteractor
@to_data_structure
@@ -38,8 +37,6 @@ class ProjectInput:
class AddResumeInteractor:
unit_of_work: UnitOfWork
identity_provider: IdentityProvider
# TODO: переделать в фоновую таску
resume_embedding_interactor: ResumeEmbeddingInteractor
async def execute(
self,
@@ -97,8 +94,6 @@ class AddResumeInteractor:
)
await self.unit_of_work.add(resume_project)
await self.resume_embedding_interactor.run(resume)
await self.unit_of_work.commit()
return resume.id
src/template_project/application/resume/interactors/edit.py
+4 -2
View File
@@ -16,7 +16,7 @@ from template_project.application.resume.entity import (
ResumeId,
ResumeProject,
)
from template_project.application.resume.errors import ResumeDoesBelongUserError
from template_project.application.resume.errors import ResumeDoesBelongUserError, ResumeNotFoundError
@to_data_structure
@@ -96,7 +96,9 @@ class EditResumeInteractor:
projects: list[ProjectInput] | None = None,
) -> EditResumeResponse:
user = await self.identity_provider.get_current_user()
old_resume = await self.resume_data_gateway.load(resume_id)
old_resume = await self.resume_data_gateway.load_by_resume_id(resume_id)
if old_resume is None:
raise ResumeNotFoundError(resume_id=resume_id)
if old_resume.user_id != user.id:
raise ResumeDoesBelongUserError
src/template_project/application/resume/interactors/get.py
+123 -23
View File
@@ -12,7 +12,7 @@ from template_project.application.resume.data_gateway import (
ResumeProjectDataGateway,
)
from template_project.application.resume.entity import ResumeId
from template_project.application.resume.errors import ResumeDoesBelongUserError
from template_project.application.resume.errors import ResumeDoesBelongUserError, ResumeNotFoundError
@to_data_structure
@@ -72,7 +72,9 @@ class GetResumeInteractor:
) -> GetResumeResponse:
user = await self.identity_provider.get_current_user()
resume = await self.resume_data_gateway.load(resume_id)
resume = await self.resume_data_gateway.load_by_resume_id(resume_id)
if resume is None:
raise ResumeNotFoundError(resume_id=resume_id)
if resume.user_id != user.id:
raise ResumeDoesBelongUserError
@@ -134,53 +136,151 @@ class ResumeListItemResponse:
about_me: str
key_skills: list[str]
experience_type: ExperienceType
experience: list[ExperienceItemResponse]
education: list[EducationItemResponse]
projects: list[ProjectItemResponse]
prediction: ResumePredictionResponse | None
@to_interactor
class GetResumeListInteractor:
identity_provider: IdentityProvider
resume_data_gateway: ResumeDataGateway
resume_prediction_data_gateway: ResumePredictionDataGateway
resume_experience_data_gateway: ResumeExperienceDataGateway
resume_education_data_gateway: ResumeEducationDataGateway
resume_project_data_gateway: ResumeProjectDataGateway
async def execute(self, limit: int, offset: int) -> list[ResumeListItemResponse]:
user = await self.identity_provider.get_current_user()
resumes = await self.resume_data_gateway.list_latest_by_user_id(user.id, limit=limit, offset=offset)
return [
ResumeListItemResponse(
id=r.id,
position=r.position,
location=r.location,
about_me=r.about_me,
key_skills=r.key_skills,
experience_type=r.experience_type,
result = []
for r in resumes:
resume_prediction = await self.resume_prediction_data_gateway.load_by_resume_id(r.id)
if resume_prediction is not None:
prediction = ResumePredictionResponse(
from_salary=resume_prediction.from_salary,
to_salary=resume_prediction.to_salary,
recommended_skills=resume_prediction.recommended_skills,
)
else:
prediction = None
experiences = await self.resume_experience_data_gateway.load_by_resume_id(r.id)
educations = await self.resume_education_data_gateway.load_by_resume_id(r.id)
projects = await self.resume_project_data_gateway.load_by_resume_id(r.id)
result.append(
ResumeListItemResponse(
id=r.id,
position=r.position,
location=r.location,
about_me=r.about_me,
key_skills=r.key_skills,
experience_type=r.experience_type,
experience=[
ExperienceItemResponse(
place=exp.place,
description=exp.description,
months_duration=exp.months_duration,
)
for exp in experiences
],
education=[
EducationItemResponse(
place=edu.place,
grade=edu.grade,
specialization=edu.specialization,
description=edu.description,
)
for edu in educations
],
projects=[
ProjectItemResponse(
name=proj.name,
description=proj.description,
)
for proj in projects
],
prediction=prediction,
)
)
for r in resumes
]
return result
@to_interactor
class GetResumeHistoryInteractor:
identity_provider: IdentityProvider
resume_data_gateway: ResumeDataGateway
resume_prediction_data_gateway: ResumePredictionDataGateway
resume_experience_data_gateway: ResumeExperienceDataGateway
resume_education_data_gateway: ResumeEducationDataGateway
resume_project_data_gateway: ResumeProjectDataGateway
async def execute(self, resume_id: ResumeId) -> list[ResumeListItemResponse]:
user = await self.identity_provider.get_current_user()
resume = await self.resume_data_gateway.load(resume_id)
resume = await self.resume_data_gateway.load_by_resume_id(resume_id)
if resume is None:
raise ResumeNotFoundError(resume_id=resume_id)
if resume.user_id != user.id:
raise ResumeDoesBelongUserError
history = await self.resume_data_gateway.get_history(resume_id)
return [
ResumeListItemResponse(
id=r.id,
position=r.position,
location=r.location,
about_me=r.about_me,
key_skills=r.key_skills,
experience_type=r.experience_type,
result = []
for r in history:
resume_prediction = await self.resume_prediction_data_gateway.load_by_resume_id(r.id)
if resume_prediction is not None:
prediction = ResumePredictionResponse(
from_salary=resume_prediction.from_salary,
to_salary=resume_prediction.to_salary,
recommended_skills=resume_prediction.recommended_skills,
)
else:
prediction = None
experiences = await self.resume_experience_data_gateway.load_by_resume_id(r.id)
educations = await self.resume_education_data_gateway.load_by_resume_id(r.id)
projects = await self.resume_project_data_gateway.load_by_resume_id(r.id)
result.append(
ResumeListItemResponse(
id=r.id,
position=r.position,
location=r.location,
about_me=r.about_me,
key_skills=r.key_skills,
experience_type=r.experience_type,
experience=[
ExperienceItemResponse(
place=exp.place,
description=exp.description,
months_duration=exp.months_duration,
)
for exp in experiences
],
education=[
EducationItemResponse(
place=edu.place,
grade=edu.grade,
specialization=edu.specialization,
description=edu.description,
)
for edu in educations
],
projects=[
ProjectItemResponse(
name=proj.name,
description=proj.description,
)
for proj in projects
],
prediction=prediction,
)
)
for r in history
]
return result
src/template_project/application/resume/interactors/predict_salary.py → src/template_project/application/resume/interactors/predict_model.py
+1 -1
View File
@@ -33,7 +33,7 @@ class PredictSalaryResponse:
@to_interactor
class PredictSalaryInteractor:
class PredictModelInteractor:
async def execute(self, request: PredictSalaryRequest) -> PredictSalaryResponse:
salary_from, salary_to = self._predict_salary(request.vacancies, request.key_skills)
recommended_skills = self._recommend_skills(request.vacancies, request.key_skills)
src/template_project/application/resume/interactors/prediction_pipeline.py
+100
View File
@@ -0,0 +1,100 @@
from typing import Final
from Levenshtein import ratio
from template_project.application.common.data_structure import to_data_structure
from template_project.application.common.interactor import to_interactor
from template_project.application.common.unit_of_work import UnitOfWork
from template_project.application.resume.data_gateway import ResumeDataGateway
from template_project.application.resume.entity import Resume, ResumeEmbedding, ResumeId
from template_project.application.resume.resume_prediction_generator import ResumePredictionGenerator
from template_project.application.resume.vector_generator import ResumeEmbeddingVectorGenerator
from template_project.application.vacancy.data_gateway import VacancyDataGateway
from template_project.application.vacancy.data_structure import SuitableVacancy
EMBEDDING_TEXT_TEMPLATE: Final = """
Позиция: {position}
Опыт: {experience_type}
Ключевые навыки: {key_skills}
Описание: {about_me}
"""
def _calculate_skills_matching(resume_skills: list[str], vacancy_skills: list[str]) -> float:
count_skills = 0
ratio_skill_sum = 0.0
for resume_key_skill in resume_skills:
for vacancy_key_skill in vacancy_skills:
ratio_skill = ratio(resume_key_skill, vacancy_key_skill)
if ratio_skill != 0:
count_skills += 1
ratio_skill_sum += ratio_skill
try:
return ratio_skill_sum / count_skills
except ZeroDivisionError:
return 0.0
def _filter_and_sort_vacancies(
resume: Resume,
suitable_vacancies: list[SuitableVacancy],
limit: int = 50,
) -> list[SuitableVacancy]:
def calculate_priority(vacancy: SuitableVacancy) -> float:
priority = vacancy.resume_similarity
if resume.experience_type == vacancy.vacancy.experience_type:
priority += 0.1
if resume.key_skills:
skills_matching = _calculate_skills_matching(resume.key_skills, vacancy.vacancy.key_skills)
priority += skills_matching * 0.2
return priority
sorted_vacancies = sorted(suitable_vacancies, key=calculate_priority, reverse=True)
return sorted_vacancies[:limit]
@to_data_structure
class PredictResumeRequest:
resume_id: ResumeId
@to_interactor
class ResumePredictionInteractor:
unit_of_work: UnitOfWork
resume_data_gateway: ResumeDataGateway
vacancy_data_gateway: VacancyDataGateway
vector_generator: ResumeEmbeddingVectorGenerator
resume_prediction_generator: ResumePredictionGenerator
async def execute(self, request: PredictResumeRequest) -> None:
resume = await self.resume_data_gateway.load_by_resume_id(request.resume_id)
if resume is None:
return
embedding_text = EMBEDDING_TEXT_TEMPLATE.format_map({
"position": resume.position,
"experience_type": resume.experience_type.value,
"key_skills": ", ".join(resume.key_skills),
"about_me": resume.about_me,
})
vector = await self.vector_generator.generate(embedding_text)
resume_embedding = ResumeEmbedding.factory(
resume_id=resume.id,
vector=vector,
)
suitable_vacancies_list = list(await self.vacancy_data_gateway.get_suitable(resume_embedding.vector))
suitable_vacancies_filtered = _filter_and_sort_vacancies(resume, suitable_vacancies_list, limit=50)
resume_prediction = await self.resume_prediction_generator.generate(
resume=resume,
suitable_vacancies=suitable_vacancies_filtered,
)
await self.unit_of_work.add(resume_embedding, resume_prediction)
await self.unit_of_work.commit()
src/template_project/application/resume/interactors/resume_embedding.py
-76
View File
@@ -1,76 +0,0 @@
from collections.abc import Callable
from Levenshtein import ratio
from template_project.application.common.unit_of_work import UnitOfWork
from template_project.application.resume.entity import Resume, ResumeEmbedding
from template_project.application.resume.resume_prediction_generator import ResumePredictionGenerator
from template_project.application.resume.vector_generator import ResumeEmbeddingVectorGenerator
from template_project.application.vacancy.data_gateway import VacancyDataGateway
from template_project.application.vacancy.data_structure import SuitableVacancy
def suitable_vacancies_key(
resume: Resume,
) -> Callable[[SuitableVacancy], tuple[bool, bool]]:
def wrapper(suitable_vacancy: SuitableVacancy) -> tuple[bool, bool]:
count_skills = 0
ratio_skill_sum = 0.0
for resum_key_skill in resume.key_skills:
for suitable_resume_key_skill in suitable_vacancy.vacancy.key_skills:
ratio_skill = ratio(resum_key_skill, suitable_resume_key_skill)
if ratio_skill != 0:
count_skills += 1
ratio_skill_sum += ratio_skill
try:
matching_skills = ratio_skill_sum / count_skills
except ZeroDivisionError:
matching_skills = 0
return resume.experience_type == suitable_vacancy.vacancy.experience_type, matching_skills >= 50
return wrapper
class ResumeEmbeddingInteractor:
def __init__(
self,
unit_of_work: UnitOfWork,
vacancy_data_gateway: VacancyDataGateway,
vector_generator: ResumeEmbeddingVectorGenerator,
resume_prediction_generator: ResumePredictionGenerator,
) -> None:
self.unit_of_work = unit_of_work
self.vector_generator = vector_generator
self.vacancy_data_gateway = vacancy_data_gateway
self.resume_prediction_generator = resume_prediction_generator
async def run(
self,
resume: Resume,
) -> None:
vector = await self.vector_generator.generate(
position=resume.position,
about_me=resume.about_me,
key_skills=resume.key_skills,
experience_type=resume.experience_type,
)
resume_embedding = ResumeEmbedding.factory(
resume_id=resume.id,
vector=vector,
)
suitable_vacancies = await self.vacancy_data_gateway.get_suitable(resume_embedding.vector)
suitable_vacancies_filtered = sorted(
suitable_vacancies,
key=suitable_vacancies_key(resume),
)[:50]
resume_prediction = await self.resume_prediction_generator.generate(
resume=resume,
suituble_vacancies=suitable_vacancies_filtered,
)
await self.unit_of_work.add(resume_embedding, resume_prediction)
await self.unit_of_work.commit()
src/template_project/application/resume/resume_prediction_generator.py
+1 -1
View File
@@ -11,6 +11,6 @@ class ResumePredictionGenerator(Protocol):
async def generate(
self,
resume: Resume,
suituble_vacancies: Sequence[SuitableVacancy],
suitable_vacancies: Sequence[SuitableVacancy],
) -> ResumePrediction:
raise NotImplementedError
src/template_project/application/resume/vector_generator.py
+1 -6
View File
@@ -1,15 +1,10 @@
from abc import abstractmethod
from template_project.application.common.enums import ExperienceType
class ResumeEmbeddingVectorGenerator:
@abstractmethod
async def generate(
self,
position: str,
about_me: str,
experience_type: ExperienceType,
key_skills: list[str],
text: str,
) -> list[float]:
raise NotImplementedError
src/template_project/ml/ioc/interactor.py
+2 -2
View File
@@ -1,11 +1,11 @@
from dishka import BaseScope, Provider, Scope, provide_all
from template_project.application.resume.interactors.predict_salary import PredictSalaryInteractor
from template_project.application.resume.interactors.predict_model import PredictModelInteractor
class InteractorProvider(Provider):
scope: BaseScope | None = Scope.REQUEST
interactors = provide_all(
PredictSalaryInteractor,
PredictModelInteractor,
)
src/template_project/ml/routes/predict.py
+3 -3
View File
@@ -6,8 +6,8 @@ from fastapi import APIRouter
from pydantic import BaseModel, Field
from template_project.application.resume.entity import ResumeId
from template_project.application.resume.interactors.predict_salary import (
PredictSalaryInteractor,
from template_project.application.resume.interactors.predict_model import (
PredictModelInteractor,
PredictSalaryRequest,
VacancyInput,
)
@@ -94,7 +94,7 @@ class PredictSalaryResponseModel(BaseModel):
)
async def predict(
request: PredictSalaryRequestModel,
interactor: FromDishka[PredictSalaryInteractor],
interactor: FromDishka[PredictModelInteractor],
) -> PredictSalaryResponseModel:
vacancy_inputs = [
VacancyInput(
src/template_project/web_api/ioc/connection.py
+3 -2
View File
@@ -2,7 +2,7 @@ from collections.abc import AsyncIterable
from aioboto3.session import Session
from dishka import Provider, Scope, provide
from httpx import AsyncClient
from httpx import AsyncClient, Timeout
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, create_async_engine
from template_project.adapters.ml_api_gateway import MlApiGateway
@@ -40,5 +40,6 @@ class ConnectionProvider(Provider):
@provide(scope=Scope.APP)
async def ml_api_gateway(self, config: MlApiConfiguration) -> AsyncIterable[MlApiGateway]:
async with AsyncClient(base_url=config.url) as client:
timeout = Timeout(30.0, read=30.0)
async with AsyncClient(base_url=config.url, timeout=timeout) as client:
yield MlApiGateway(client)
src/template_project/web_api/ioc/interactor.py
+2 -2
View File
@@ -13,7 +13,7 @@ from template_project.application.resume.interactors.get import (
GetResumeInteractor,
GetResumeListInteractor,
)
from template_project.application.resume.interactors.resume_embedding import ResumeEmbeddingInteractor
from template_project.application.resume.interactors.prediction_pipeline import ResumePredictionInteractor
from template_project.application.user.profile.interactors.get_profile import GetProfileInteractor
from template_project.application.user.profile.interactors.patch_profile import PatchProfileInteractor
@@ -33,5 +33,5 @@ class InteractorProvider(Provider):
GetResumeHistoryInteractor,
AddResumeInteractor,
EditResumeInteractor,
ResumeEmbeddingInteractor,
ResumePredictionInteractor,
)
src/template_project/web_api/routes/resume.py
+164 -2
View File
@@ -2,9 +2,9 @@ from decimal import Decimal
from http import HTTPStatus
from typing import Annotated
from dishka import FromDishka
from dishka import AsyncContainer, FromDishka
from dishka.integrations.fastapi import DishkaRoute
from fastapi import APIRouter, Depends, HTTPException, Query
from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query, Request
from fastapi.security import HTTPBearer
from pydantic import BaseModel, Field
@@ -28,6 +28,10 @@ from template_project.application.resume.interactors.get import (
GetResumeInteractor,
GetResumeListInteractor,
)
from template_project.application.resume.interactors.prediction_pipeline import (
PredictResumeRequest,
ResumePredictionInteractor,
)
security = HTTPBearer()
router = APIRouter(route_class=DishkaRoute, tags=["Resume"], dependencies=[Depends(security)])
@@ -223,6 +227,10 @@ class ResumeListItem(BaseModel):
about_me: str = Field(description="About me section")
key_skills: list[str] = Field(description="List of key skills")
experience_type: ExperienceType = Field(description="Experience type")
experience: list[ExperienceItem] = Field(description="Work experience list")
education: list[EducationItem] = Field(description="Education list")
projects: list[ProjectItem] = Field(description="Projects list")
prediction: SalaryPrediction | None = Field(None, description="Salary prediction (can be null)")
model_config = {
"json_schema_extra": {
@@ -232,6 +240,27 @@ class ResumeListItem(BaseModel):
"about_me": "Experienced Python developer with 5 years of experience",
"key_skills": ["Python", "FastAPI", "PostgreSQL"],
"experience_type": "between3And6",
"experience": [
{
"place": "T-bank",
"description": "some description lorem ipsum",
"months_duration": 12,
}
],
"education": [
{
"place": "Central university",
"grade": "bachelor",
"specialization": "IT guy",
"description": "optional field, if user want add something",
}
],
"projects": [
{
"name": "Rekomenci fluon",
"description": "fucking shit",
}
],
}
}
}
@@ -251,9 +280,31 @@ class GetResumeListResponse(BaseModel):
"resumes": [
{
"position": "Python Developer",
"location": "Moscow",
"about_me": "Experienced Python developer",
"key_skills": ["Python", "FastAPI"],
"experience_type": "between3And6",
"experience": [
{
"place": "T-bank",
"description": "some description lorem ipsum",
"months_duration": 12,
}
],
"education": [
{
"place": "Central university",
"grade": "bachelor",
"specialization": "IT guy",
"description": "optional field, if user want add something",
}
],
"projects": [
{
"name": "Rekomenci fluon",
"description": "fucking shit",
}
],
}
]
}
@@ -275,9 +326,31 @@ class GetResumeHistoryResponse(BaseModel):
"resumes": [
{
"position": "Python Developer",
"location": "Moscow",
"about_me": "Experienced Python developer",
"key_skills": ["Python", "FastAPI"],
"experience_type": "between3And6",
"experience": [
{
"place": "T-bank",
"description": "some description lorem ipsum",
"months_duration": 12,
}
],
"education": [
{
"place": "Central university",
"grade": "bachelor",
"specialization": "IT guy",
"description": "optional field, if user want add something",
}
],
"projects": [
{
"name": "Rekomenci fluon",
"description": "fucking shit",
}
],
}
]
}
@@ -296,6 +369,8 @@ class GetResumeHistoryResponse(BaseModel):
)
async def create_resume(
request: CreateResumeRequest,
background_tasks: BackgroundTasks,
fastapi_request: Request,
interactor: FromDishka[AddResumeInteractor],
) -> CreateResumeResponse:
experience = (
@@ -332,6 +407,18 @@ async def create_resume(
education=education,
projects=projects,
)
async def run_prediction(resume_id: ResumeId, container: AsyncContainer) -> None:
async with container() as request_container:
prediction_interactor = await request_container.get(ResumePredictionInteractor)
await prediction_interactor.execute(PredictResumeRequest(resume_id=resume_id))
background_tasks.add_task(
run_prediction,
interactor_response,
fastapi_request.app.state.dishka_container,
)
return CreateResumeResponse(
resume_id=interactor_response,
)
@@ -361,6 +448,37 @@ async def get_resume_list(
about_me=r.about_me,
key_skills=r.key_skills,
experience_type=r.experience_type,
experience=[
ExperienceItem(
place=exp.place,
description=exp.description,
months_duration=exp.months_duration,
)
for exp in r.experience
],
education=[
EducationItem(
place=edu.place,
grade=edu.grade,
specialization=edu.specialization,
description=edu.description,
)
for edu in r.education
],
projects=[
ProjectItem(
name=proj.name,
description=proj.description,
)
for proj in r.projects
],
prediction=SalaryPrediction(
from_salary=r.prediction.from_salary,
to_salary=r.prediction.to_salary,
recommended_skills=r.prediction.recommended_skills,
)
if r.prediction is not None
else None,
)
for r in interactor_response
]
@@ -570,6 +688,37 @@ async def get_resume_history(
about_me=r.about_me,
key_skills=r.key_skills,
experience_type=r.experience_type,
experience=[
ExperienceItem(
place=exp.place,
description=exp.description,
months_duration=exp.months_duration,
)
for exp in r.experience
],
education=[
EducationItem(
place=edu.place,
grade=edu.grade,
specialization=edu.specialization,
description=edu.description,
)
for edu in r.education
],
projects=[
ProjectItem(
name=proj.name,
description=proj.description,
)
for proj in r.projects
],
prediction=SalaryPrediction(
from_salary=r.prediction.from_salary,
to_salary=r.prediction.to_salary,
recommended_skills=r.prediction.recommended_skills,
)
if r.prediction is not None
else None,
)
for r in interactor_response
]
@@ -590,6 +739,8 @@ async def get_resume_history(
async def patch_resume(
resume_id: ResumeId,
request: PatchResumeRequest,
background_tasks: BackgroundTasks,
fastapi_request: Request,
interactor: FromDishka[EditResumeInteractor],
) -> PatchResumeResponse:
try:
@@ -628,6 +779,17 @@ async def patch_resume(
education=education,
projects=projects,
)
async def run_prediction(resume_id: ResumeId, container: AsyncContainer) -> None:
async with container() as request_container:
prediction_interactor = await request_container.get(ResumePredictionInteractor)
await prediction_interactor.execute(PredictResumeRequest(resume_id=resume_id))
background_tasks.add_task(
run_prediction,
interactor_response.id,
fastapi_request.app.state.dishka_container,
)
except ResumeDoesBelongUserError as error:
raise HTTPException(
status_code=HTTPStatus.FORBIDDEN,