File size: 7,115 Bytes
02acac5 | 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 | import os
import json
import google.generativeai as genai
from typing import List, Dict, Any
import time
# 1. Configure Gemini SDK
API_KEY = os.getenv("GOOGLE_API_KEY")
if not API_KEY:
raise ValueError("Set GOOGLE_API_KEY in env before running.")
genai.configure(api_key=API_KEY) # type: ignore
# 2. File paths
DATA_DIR = "data"
PROFILE_IN = os.path.join(DATA_DIR, "onboarding_profiles.jsonl")
JOB_IN = os.path.join(DATA_DIR, "job_listings.jsonl")
PROFILE_OUT = os.path.join(DATA_DIR, "embeddings_profiles.jsonl")
JOB_OUT = os.path.join(DATA_DIR, "embeddings_jobs.jsonl")
def get_embedding(text: str, model: str = "models/text-embedding-004", task_type: str = "retrieval_document") -> List[float]:
"""
Get embedding for a single text using Gemini API.
Args:
text: Text to embed
model: Embedding model to use
task_type: Task type for the embedding
Returns:
List of floats representing the embedding vector
"""
try:
# Use the updated API for google-generativeai >= 0.8.0
response = genai.embed_content( # type: ignore
model=model,
content=text,
task_type=task_type,
title=None # Optional title for the content
)
return response['embedding']
except Exception as e:
print(f"Error getting embedding: {e}")
# Retry once after a short delay
time.sleep(1)
try:
response = genai.embed_content( # type: ignore
model=model,
content=text,
task_type=task_type
)
return response['embedding']
except Exception as e2:
print(f"Retry failed: {e2}")
raise e2
def create_profile_text(record: Dict[str, Any]) -> str:
"""Create a comprehensive text representation of a profile."""
text_parts = []
if record.get('name'):
text_parts.append(f"Name: {record['name']}")
if record.get('role'):
text_parts.append(f"Role: {record['role']}")
if record.get('skills'):
skills = record['skills']
if isinstance(skills, list):
text_parts.append(f"Skills: {', '.join(skills)}")
else:
text_parts.append(f"Skills: {skills}")
if record.get('experience'):
text_parts.append(f"Experience: {record['experience']}")
if record.get('location'):
text_parts.append(f"Location: {record['location']}")
return ". ".join(text_parts) + "."
def create_job_text(record: Dict[str, Any]) -> str:
"""Create a comprehensive text representation of a job listing."""
text_parts = []
if record.get('title'):
text_parts.append(f"Title: {record['title']}")
if record.get('company'):
text_parts.append(f"Company: {record['company']}")
if record.get('type'):
text_parts.append(f"Type: {record['type']}")
if record.get('skills'):
skills = record['skills']
if isinstance(skills, list):
text_parts.append(f"Required Skills: {', '.join(skills)}")
else:
text_parts.append(f"Required Skills: {skills}")
if record.get('description'):
text_parts.append(f"Description: {record['description']}")
if record.get('location'):
text_parts.append(f"Location: {record['location']}")
return ". ".join(text_parts) + "."
def embed_and_write(in_path: str, out_path: str, is_profile: bool = True,
embed_model: str = "models/text-embedding-004"):
"""
Read JSONL from in_path, generate embeddings, and write to out_path.
Args:
in_path: Input JSONL file path
out_path: Output JSONL file path
is_profile: Whether processing profiles (True) or jobs (False)
embed_model: Embedding model to use
"""
if not os.path.exists(in_path):
print(f"Input file not found: {in_path}")
return
processed_count = 0
error_count = 0
with open(in_path, "r", encoding="utf-8") as f_in, \
open(out_path, "w", encoding="utf-8") as f_out:
for line_num, line in enumerate(f_in, 1):
try:
record = json.loads(line.strip())
doc_id = record.get("id", f"unknown_{line_num}")
# Create text representation
if is_profile:
text = create_profile_text(record)
task_type = "retrieval_document"
else:
text = create_job_text(record)
task_type = "retrieval_document"
# Get embedding
embedding = get_embedding(text, embed_model, task_type)
# Create output object
out_obj = {
"id": doc_id,
"text": text,
"embedding": embedding,
"original_data": record # Keep original data for reference
}
# Write to output file
f_out.write(json.dumps(out_obj) + "\n")
processed_count += 1
print(f"✓ Embedded {('profile' if is_profile else 'job')} {doc_id} "
f"(line {line_num})")
# Small delay to avoid rate limiting
time.sleep(0.1)
except json.JSONDecodeError as e:
error_count += 1
print(f"✗ JSON decode error on line {line_num}: {e}")
continue
except Exception as e:
error_count += 1
print(f"✗ Error processing line {line_num}: {e}")
continue
print(f"\nProcessed: {processed_count}, Errors: {error_count}")
def main():
"""Main function to process both profiles and jobs."""
# Create data directory if it doesn't exist
os.makedirs(DATA_DIR, exist_ok=True)
print("Starting embedding generation...")
print(f"Using embedding model: models/text-embedding-004")
# Process profiles
if os.path.exists(PROFILE_IN):
print(f"\n📋 Processing profiles from {PROFILE_IN}")
embed_and_write(PROFILE_IN, PROFILE_OUT, is_profile=True)
print(f"✓ Profile embeddings saved to {PROFILE_OUT}")
else:
print(f"⚠️ Profile file not found: {PROFILE_IN}")
# Process job listings
if os.path.exists(JOB_IN):
print(f"\n💼 Processing jobs from {JOB_IN}")
embed_and_write(JOB_IN, JOB_OUT, is_profile=False)
print(f"✓ Job embeddings saved to {JOB_OUT}")
else:
print(f"⚠️ Job file not found: {JOB_IN}")
print("\n🎉 Embedding generation completed!")
if __name__ == "__main__":
main() |