Transformer API Python SDK
Official Python client for the Transformer API — career-graph extraction, job matching, and career intelligence over raw text, with real provenance on every derived fact.
Install
pip install careerstudiomax-transformer
Quickstart
from transformer import TransformerClient
client = TransformerClient(api_key="csk_live_your_key_here")
result = client.extract("Led backend team at Acme, hired by a recruiter for a Senior Engineer role, $145k")
print(result["data"]["entities"])
What you can do
| Method | Description |
|---|---|
client.extract(text) |
Extract career entities and relationships from raw text |
client.career_graph(text=...) |
Build a knowledge graph, with real provenance on every node/edge |
client.career_graph_diff(text_a=..., text_b=...) |
Structured diff between two career-graph snapshots over time |
client.job_match(job_description, candidate_text=...) |
Score a candidate against a job description |
client.job_match_delta(candidate_text_a=..., candidate_text_b=..., job_description_a=...) |
Why a match score moved between two calls |
client.talent_pool_search(target_skills) |
Search your own tracked candidates for a target skill set |
client.team_fit(team_members=..., candidate_skills=...) |
How a candidate complements an existing team |
client.embeddings(texts) |
Real embedding vectors |
client.org_chart_infer(postings) |
Infer likely reporting structure from job postings you provide |
client.trajectory_predict(candidate_text) |
Common next job titles for a role/seniority level |
client.negotiation_leverage(skills, target_role) |
Negotiation-leverage read plus a counter-offer script |
client.skills_freshness(skills) |
Per-skill current-vs-aging judgment |
client.attrition_risk(candidate_text=...) |
Observable tenure/role-change pattern signal (a weak heuristic, not a behavioral prediction) |
client.synthetic_profile(seed=42) |
Realistic-but-fake candidate profile for prototyping, no key required |
client.ontology() |
The published entity/relationship taxonomy, no key required |
client.chat(model, messages) |
Call an exact model by name |
client.chat_stream(on_event, model, messages) |
Same as chat(), streamed |
client.usage() |
Your key's real usage |
Example: career graph + webhook tracking
graph = client.career_graph(text=resume_text, candidate_id="cand-123")
print(graph["data"]["careerScore"])
Webhooks (account access token, not an API key)
hook = client.create_webhook(
"https://your-app.example.com/hooks/transformer",
access_token="your_account_access_token",
candidate_id="cand-123",
)
print(hook["secret"]) # shown once
Errors
from transformer import TransformerClient, TransformerError
try:
client.job_match("...", candidate_text="...")
except TransformerError as e:
print(e.status, str(e))
Context manager
with TransformerClient(api_key="...") as client:
...
Full endpoint reference: transformer.careerstudiomax.com/docs
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