resumereaderapi (Python)

Resume parser client for Python. Hand it a PDF, a DOCX, a scan or raw text and get structured candidate data back as a dictionary.

Needs requests. Python 3.7 and later.

pip install resumereaderapi

Why use a hosted resume parser

Writing regular expressions for CVs is a trap. Layouts vary, languages vary, and half the documents are scans. A hosted resume parser gives you one stable shape no matter what the candidate uploaded.

The service behind this package is the resume parser from Alpha Quantum. It returns 114 fields and marks missing facts as None instead of guessing.

Parse in three lines

from resumereaderapi import ResumeReader

rr = ResumeReader("YOUR_API_KEY")
data = rr.parse_file("cv.pdf")

print(data["resume"]["contact"]["emails"])
print(data["remaining_credits"])

Calls

rr.parse_text(text, field_names=None, exclude_sensitive=False, anonymize=False,
              max_pages=None, sections=None, language=None)
rr.parse_file(path, ...same options...)
rr.parse_url(https_url, ...same options...)

rr.normalize_titles(["Sr. SWE II"])
rr.normalize_skills(["K8s", "JS"])
rr.normalize_locations(["NYC"])

Normalizer methods batch in groups of 100 and return {"results": [...], "credits_used": float, "remaining_credits": float}.

Skill normalization in practice

Candidates write the same skill ten ways. JS, Javascript and java script should be one filter value.

raw = ["JS", "Javascript", "Python 3.11", "py", "MS Excel", "n/a"]
out = rr.normalize_skills(raw)
for item in out["results"]:
    print(item["input"], "->", item["normalized_skill"], item["skill_type"])

Junk entries such as "n/a" come back with normalized_skill set to None. See the skill normalization page for fifty real examples.

Anonymize before a human sees the CV

data = rr.parse_file(
    "cv.docx",
    anonymize=True,
    exclude_sensitive=True,
)

Names, contact values and sensitive fields become placeholders such as [NAME] and [EMAIL]. This is the base of a blind review process, covered under CV anonymization.

Handle errors

from resumereaderapi import ResumeReaderError

try:
    rr.parse_file(path)
except ResumeReaderError as exc:
    if exc.status == 402:
        print("buy more credits")
    elif exc.status in (413, 422):
        print("bad document:", exc)
    else:
        raise

Example: into a DataFrame

import pandas as pd
from pathlib import Path

rows = []
for p in Path("cvs").glob("*.pdf"):
    r = rr.parse_file(str(p))["resume"]
    rows.append({
        "name": r["contact"]["full_name"],
        "city": r["contact"]["address"]["city"],
        "title": (r["career"]["current_position"] or {}).get("title"),
        "years": r["career"]["total_experience_years"],
        "skills": ", ".join(r["skills"]["technical"]),
    })

pd.DataFrame(rows).to_csv("candidates.csv", index=False)

Example: Flask upload endpoint

from flask import Flask, request, jsonify
import tempfile, os

app = Flask(__name__)

@app.post("/parse")
def parse():
    f = request.files["cv"]
    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(f.filename)[1]) as tmp:
        f.save(tmp.name)
    try:
        return jsonify(rr.parse_file(tmp.name)["resume"])
    finally:
        os.unlink(tmp.name)

Questions

What file types? PDF, DOCX, TXT, RTF, HTML, ODT, spreadsheets and images including scanned PDFs.

Is there a limit per call? 10 MB, 20 pages and about 30,000 tokens.

Does it work in French? Pass field_names="fr" for French keys. Values keep the language of the resume.

MIT license. Contact: info@alpha-quantum.com