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478dec6 f3bdba1 478dec6 f3bdba1 478dec6 f3bdba1 478dec6 e4b8ed7 478dec6 abc3feb 478dec6 6a0f2f3 b0853c5 6a0f2f3 b0853c5 6a0f2f3 b0853c5 478dec6 6a0f2f3 b0853c5 478dec6 6a0f2f3 478dec6 6a0f2f3 478dec6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | from config.get_config import master_config
from datetime import datetime
from pydantic import Field, BaseModel
from typing import Optional, List, Literal, Dict
from typing_extensions import TypedDict
from uuid import uuid4
tzinfo = master_config.tzinfo
LOGIC_NUMERIC = Literal["greater than",
"less than",
"equal",
"greater than or equal",
"less than or equal"
]
LOGIC_CATEGORICAL = Literal[
"equal",
"similar",
"not similar"]
class RawProfile(TypedDict):
profile_id: str
content_type: Literal["pdf", "docx", "txt"] = "pdf"
filename: str
content: str
class AIProfile(TypedDict):
fullname: str = Field(description="Fullname of the candidate", default="-")
# gender: str = Field(description="Gender of the candidate, if available", default="null")
# age: int = Field(description="Age in number")
gpa_edu_1: float = Field(description="""GPA of candidate's bachelor degree (same like sarjana, s1, undergradute), if exists.""", default=0)
univ_edu_1: str = Field(description="""University where candidate take bachelor degree, if exists.""", default="-")
major_edu_1: str = Field(description="""Major of candidate's bachelor degree, if exists.""", default="-")
gpa_edu_2: float = Field(description="""GPA of candidate's master degree (same like master, s2, postgraduate), if exists.""", default=0)
univ_edu_2: str = Field(description="""University where candidate take master degree, if exists.""", default="-")
major_edu_2: str = Field(description="""Major of candidate's master degree, if exists.""", default="-")
gpa_edu_3: float = Field(description="""GPA of candidate's doctoral or phd degree (same like phd, s3, doctoral), if exists.""", default=0)
univ_edu_3: str = Field(description="""University where candidate take doctoral or phd degree, if exists.""", default="-")
major_edu_3: str = Field(description="""Major of candidate's doctoral or phd degree, if exists.""", default="-")
domicile: str = Field(description="Current domicile of the candidate", default="-")
yoe: float = Field(description="The candidate's total years of experience (as an float)", default=0)
hardskills: Optional[List[str]] = Field(description="List of the candidate's hard skills",default_factory=list)
softskills: Optional[List[str]] = Field(description="List of the candidate's soft skills", default_factory=list)
certifications: Optional[List[str]] = Field(description="List of the candidate's certifications", default_factory=list)
business_domain: Optional[List[str]] = Field(description="List of the candidate's business domain experience based on working experience or project", default_factory=list)
class AIProfileTbScore(TypedDict):
fullname: str = Field(description="Fullname of the candidate", default="-")
# gender: str = Field(description="Gender of the candidate, if available", default="null")
# age: int = Field(description="Age in number")
gpa_edu_1: float = Field(description="""GPA of candidate's bachelor degree (same like sarjana, s1, undergradute), if exists.""", default=0)
univ_edu_1: list = Field(description="""University where candidate take bachelor degree, if exists.""", default="-")
major_edu_1: list = Field(description="""Major of candidate's bachelor degree, if exists.""", default="-")
gpa_edu_2: float = Field(description="""GPA of candidate's master degree (same like master, s2, postgraduate), if exists.""", default=0)
univ_edu_2: list = Field(description="""University where candidate take master degree, if exists.""", default="-")
major_edu_2: list = Field(description="""Major of candidate's master degree, if exists.""", default="-")
gpa_edu_3: float = Field(description="""GPA of candidate's doctoral or phd degree (same like phd, s3, doctoral), if exists.""", default=0)
univ_edu_3: list = Field(description="""University where candidate take doctoral or phd degree, if exists.""", default="-")
major_edu_3: list = Field(description="""Major of candidate's doctoral or phd degree, if exists.""", default="-")
domicile: str = Field(description="Current domicile of the candidate", default="-")
yoe: float = Field(description="The candidate's total years of experience (as an float)", default=0)
hardskills: Optional[List[str]] = Field(description="List of the candidate's hard skills",default_factory=list)
softskills: Optional[List[str]] = Field(description="List of the candidate's soft skills", default_factory=list)
certifications: Optional[List[str]] = Field(description="List of the candidate's certifications", default_factory=list)
business_domain: Optional[List[str]] = Field(description="List of the candidate's business domain experience based on working experience or project", default_factory=list)
class Profile(AIProfile):
profile_id: str
created_at: datetime = datetime.now().replace(tzinfo=tzinfo)
class Profiles(TypedDict):
profiles: List[Profile]
class Criteria(TypedDict):
gpa_edu_1: Optional[float] = 0
univ_edu_1: Optional[List] = []
major_edu_1: Optional[List] = []
gpa_edu_2: Optional[float] = 0
univ_edu_2: Optional[List] = []
major_edu_2: Optional[List] = []
gpa_edu_3: Optional[float] = 0
univ_edu_3: Optional[List] = []
major_edu_3: Optional[List] = []
domicile: Optional[str] = None
yoe: Optional[int] = 0
hardskills: Optional[List] = []
softskills: Optional[List] = []
certifications: Optional[List] = []
business_domain: Optional[List] = []
class CriteriaWeight(TypedDict):
gpa_edu_1: Optional[float] = 0
univ_edu_1: Optional[float] = 0
major_edu_1: Optional[float] = 0
gpa_edu_2: Optional[float] = 0
univ_edu_2: Optional[float] = 0
major_edu_2: Optional[float] = 0
gpa_edu_3: Optional[float] = 0
univ_edu_3: Optional[float] = 0
major_edu_3: Optional[float] = 0
domicile: Optional[float] = 0
yoe: Optional[float] = 0
hardskills: Optional[float] = 0
softskills: Optional[float] = 0
certifications: Optional[float] = 0
business_domain: Optional[float] = 0
# class InputScoring(AIProfile):
# profile_id: str = Field(description="profile id")
# criteria: Criteria = Field(description="Criteria to be matched with the profile")
# criteria_weight: CriteriaWeight = Field(description="Criteria weight to be applied when profile matching")
class InputScoring(TypedDict):
profile_id: str = Field(description="profile id")
weight_id: str = Field(description="weight id")
class InputScoringBulk(TypedDict): #TODO: USE THIS ON /v2/calculate_score
criteria: Criteria = Field(description="Criteria to be matched with the profile")
criteria_weight: CriteriaWeight = Field(description="Criteria weight to be applied when profile matching")
class PayloadExtractOne(TypedDict):
profile_id: str = str(uuid4())
filename: str
content_type: Literal["pdf", "docx", "txt"] = "pdf"
content: str
class DataResponseExtractOne(TypedDict):
profile_id: Optional[str]
class ResponseExtractOne(TypedDict):
status: Literal["success", "failed", "canceled"]
message: Optional[str] = "empty"
data: Optional[DataResponseExtractOne] = None
# EXTRACT PROFILE BULK
class DataResponseExtractBulk(TypedDict):
status_ids: Dict
criteria_id: str
class PayloadExtractBulk(TypedDict):
profile_id: str
content_type: Literal["pdf", "docx", "txt"] = "pdf"
content: str
class ResponseExtractBulk(TypedDict):
status: Literal["success", "partial-success", "failed", "canceled"]
message: Optional[str]
data: Optional[DataResponseExtractBulk] = None
# MATCH PROFILE ONE
class PayloadMatchOne(TypedDict):
profile_id: str
criteria: Criteria
criteria_weight: CriteriaWeight
class Score(TypedDict):
profile_id: str
score: float
class DataResponseMatchOne(TypedDict):
profile_id: str
criteria_id: Optional[str] = None
matching_id: Optional[str] = None
scoring_id: Optional[str] = None
score: Optional[float] = None
class ResponseMatchOne(Score):
status: Literal["success", "failed", "canceled"]
message: Optional[str] = None
data: Optional[DataResponseMatchOne] = None
# MATCH PROFILE BULK
class DataResponseMatchBulk(TypedDict):
status_ids: Dict
criteria_id: str
class PayloadMatchBulk(TypedDict):
filter: List[str]
criteria: Criteria
criteria_weight: CriteriaWeight
class ResponseMatchBulk(TypedDict):
status: Literal["success", "partial-success", "failed", "canceled"]
message: Optional[str]
data: Optional[DataResponseExtractBulk] = None
# class DataResponseExtractBulk(TypedDict):
# status_ids: Dict
# criteria_id: str
# class PayloadExtractBulk(TypedDict):
# profile_id: str
# content_type: Literal["pdf", "docx", "txt"] = "pdf"
# content: str
# class ResponseExtractBulk(TypedDict):
# status: Literal["success", "partial-success", "failed", "canceled"]
# message: Optional[str]
# data: Optional[DataResponseExtractBulk] = None
desc_AIMatchProfile = "choose 1 if match else 0"
class AIMatchProfile(TypedDict):
gpa_edu_1: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
univ_edu_1: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
major_edu_1: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
gpa_edu_2: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
univ_edu_2: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
major_edu_2: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
gpa_edu_3: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
univ_edu_3: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
major_edu_3: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
domicile: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
yoe: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
hardskills: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
softskills: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
certifications: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
business_domain: Optional[Literal[1, 0]] = Field(description=desc_AIMatchProfile, default=None)
class OutProfile(BaseModel):
fullname: str = Field(description="Fullname of the candidate", default="-")
# gender: str = Field(description="Gender of the candidate, if available", default="null")
# age: int = Field(description="Age in number")
high_edu_univ_1: str = Field(description="""University where candidate take bachelor degree, if exists.""", default="-")
high_edu_major_1: str = Field(description="""Major of candidate's bachelor degree, if exists.""", default="-")
high_edu_gpa_1: float = Field(description="""GPA of candidate's bachelor degree, if exists.""", default=0)
high_edu_univ_2: str = Field(description="""University where candidate take master degree, if exists.""", default="-")
high_edu_major_2: str = Field(description="""Major of candidate's master degree, if exists.""", default="-")
high_edu_gpa_2: float = Field(description="""GPA of candidate's master degree, if exists.""", default=0)
high_edu_univ_3: str = Field(description="""University where candidate take doctoral or phd degree, if exists.""", default="-")
high_edu_major_3: str = Field(description="""Major of candidate's doctoral or phd degree, if exists.""", default="-")
high_edu_gpa_3: float = Field(description="""GPA of candidate's doctoral or phd degree, if exists.""", default=0)
domicile: str = Field(description="Current domicile of the candidate", default="-")
yoe: float = Field(description="The candidate's total years of experience (as an float)", default=0)
hardskills: Optional[List[str]] = Field(description="List of the candidate's hard skills", default=[])
softskills: Optional[List[str]] = Field(description="List of the candidate's soft skills", default=[])
certifications: Optional[List[str]] = Field(description="List of the candidate's certifications", default=[])
business_domain_experiences: Optional[List[str]] = Field(description="List of the candidate's business domain experience based on working experience or project", default=[])
class OutMatching(BaseModel):
score: int = Field(description="Score of profile matching, in range 0-100. If profile and criteria is closed then will give higher score.", default=0)
reason: str = Field(description="Reason behind why you give that such score to current profile.", default="-")
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