curl --request POST \
--url https://api.hirebase.org/v2/resumes/embed \
--header 'Content-Type: application/json' \
--data '{}'{
"resume": {
"personal_information.data": {
"full_name": "<string>",
"email": "<string>",
"phone_number": "<string>",
"address": {
"street": "<string>",
"city": "<string>",
"state": "<string>",
"zip_code": "<string>",
"country": "<string>"
},
"links": [
"<string>"
]
},
"summary_or_objective": "<string>",
"skills": [
{
"category": "<string>",
"details": [
"<string>"
]
}
],
"work_experience": [
{
"title": "<string>",
"company": "<string>",
"location": "<string>",
"start_date": "<string>",
"end_date": "<string>",
"responsibilities": [
"<string>"
],
"achievements": [
"<string>"
]
}
],
"education": [
{
"degree": "<string>",
"institution": "<string>",
"location": "<string>",
"start_date": "<string>",
"end_date": "<string>",
"GPA": "<string>",
"relevant_courses": [
"<string>"
],
"thesis_or_project": "<string>"
}
],
"certifications": [
{}
],
"projects": [
{
"name": "<string>",
"description": "<string>",
"technologies": [
"<string>"
],
"industry": "<string>",
"link": "<string>",
"images_or_links": [
"<string>"
]
}
],
"languages": [
"<string>"
],
"awards": [
{}
],
"volunteer_experience": [
{}
],
"hobbies_and_interests": [
"<string>"
],
"custom_sections": [
{}
],
"other_fields": {},
"version": "<string>"
},
"result": {
"embedding": [
123
],
"dtype": "<string>",
"dim": 123,
"model_name": "<string>",
"model_version": "<string>"
}
}Upload a resume file, parse its contents, and generate vector embeddings for semantic matching.
curl --request POST \
--url https://api.hirebase.org/v2/resumes/embed \
--header 'Content-Type: application/json' \
--data '{}'{
"resume": {
"personal_information.data": {
"full_name": "<string>",
"email": "<string>",
"phone_number": "<string>",
"address": {
"street": "<string>",
"city": "<string>",
"state": "<string>",
"zip_code": "<string>",
"country": "<string>"
},
"links": [
"<string>"
]
},
"summary_or_objective": "<string>",
"skills": [
{
"category": "<string>",
"details": [
"<string>"
]
}
],
"work_experience": [
{
"title": "<string>",
"company": "<string>",
"location": "<string>",
"start_date": "<string>",
"end_date": "<string>",
"responsibilities": [
"<string>"
],
"achievements": [
"<string>"
]
}
],
"education": [
{
"degree": "<string>",
"institution": "<string>",
"location": "<string>",
"start_date": "<string>",
"end_date": "<string>",
"GPA": "<string>",
"relevant_courses": [
"<string>"
],
"thesis_or_project": "<string>"
}
],
"certifications": [
{}
],
"projects": [
{
"name": "<string>",
"description": "<string>",
"technologies": [
"<string>"
],
"industry": "<string>",
"link": "<string>",
"images_or_links": [
"<string>"
]
}
],
"languages": [
"<string>"
],
"awards": [
{}
],
"volunteer_experience": [
{}
],
"hobbies_and_interests": [
"<string>"
],
"custom_sections": [
{}
],
"other_fields": {},
"version": "<string>"
},
"result": {
"embedding": [
123
],
"dtype": "<string>",
"dim": 123,
"model_name": "<string>",
"model_version": "<string>"
}
}Upload a resume document in PDF, Word, text, or HTML format. The service parses structured data (personal info, experience, skills, etc.) and returns both the parsed resume and its vector embedding.Documentation Index
Fetch the complete documentation index at: https://www.hirebase.org/docs/llms.txt
Use this file to discover all available pages before exploring further.
POST /v2/resumes/embed
multipart/form-data
Show child attributes
Show child attributes
curl -X POST "https://api.hirebase.org/v2/resumes/embed" \
-H "x-api-key: YOUR_API_KEY" \
-F "file=@/path/to/resume.pdf"
{
"resume": {
"personal_information": {
"data": {
"full_name": "Your Name",
"email": "youremail@gmail.com",
"phone_number": "(123) 456-7890",
"address": {
"street": "",
"city": "123 smith street",
"state": "Ohio",
"zip_code": "",
"country": "USA"
},
"links": ["https://yourportfolio.site/"]
}
},
"summary_or_objective": "Summary taken from the resume",
"skills": [
{
"category": "Programming Languages",
"details": ["Python", "C & C++", "Java", "Rust", "..."]
}
],
"work_experience": [
{
"title": "ML Engineer",
"company": "Huggingface",
"location": "Remote, USA",
"start_date": "June 2024",
"end_date": "Present",
"responsibilities": ["Make GenAI models", "..."],
"achievements": ["Scaled to millions of images/day."]
}
],
"education": [
{
"degree": "B.S. in Computer Science",
"institution": "University of Toronto",
"location": "Toronto, Canada",
"start_date": "Fall 2020",
"end_date": "Spring 2024",
"GPA": "3.9/4.0",
"relevant_courses": [],
"thesis_or_project": ""
}
],
"projects": [
{
"name": "Amazon Product Review Sentiment Analysis",
"description": "Enter a review of Amazon product. Predicts whether the review is positive or negative.",
"technologies": [],
"industry": "AI & ML",
"link": "https://github.com/your-github-profile/ai-sentiment-analysis.git",
"images_or_links": []
}
],
"hobbies_and_interests": ["Language Models", "GenAI"],
"version": "v1"
},
"result": {
"embedding": [-0.0294, 0.0613, -0.0413, 0.0187, -0.0822, 0.0154],
"dtype": "resume",
"dim": 768,
"model_name": "socrates",
"model_version": "v2"
}
}
embedding contains 768 floats — only the first six are shown above for brevity.400 Bad Request
500 Internal Server Error