The official Python client for the Vectros API — hybrid search, document ingestion, structured records, and grounded inference for your application.
pip install vectrosRequires Python 3.8+.
import os
from vectros import VectrosApi
client = VectrosApi(
base_url="https://api.vectros.ai",
token=os.environ["VECTROS_API_KEY"], # sk_live_... or sk_test_...
)
# Hybrid (keyword + semantic) search over your indexed content
results = client.search.content(
query="patient intake form diabetes",
)
# Ingest a document — extracted, chunked, and indexed for search + RAG
doc = client.documents.ingest_document(
title="Patient Intake Form — Jane Doe",
)
# Write a structured record against one of your schemas
record = client.records.create_record(
type_name="intake_form",
schema_id="6ba7b810-9dad-11d1-80b4-00c04fd430c8",
payload={"first_name": "Jane", "email": "jane@example.com"},
)An AsyncVectrosApi with the same surface is available for asyncio.
The SDK sends whatever credential you pass in the Authorization: Bearer <token>
header. Two credential types are accepted:
| Type | Prefix | Lifetime | Use from |
|---|---|---|---|
| API key | sk_live_* / sk_test_* |
Long-lived | Server only — full tenant access |
| Scoped token | st_* |
Short-lived | Server or browser — narrowed scope, auto-expiring |
Keep API keys server-side only. For untrusted runtimes, mint a short-lived
scoped token on your backend and pass it as token. See the
authentication guide for the full pattern.
- Hybrid search & RAG —
client.search,client.inference— vector + keyword search and grounded document Q&A over your indexed corpus. - Documents & folders —
client.documents,client.folders— ingest, organize, retrieve, and look documents up by field. - Structured records —
client.records— create, read, update (full and partial), delete, and look records up by indexed field. - Schemas —
client.schemas— define and evolve record/document schemas. - Identity & access —
client.identity,client.auth— manage clients, organizations, and users; mint and revoke scoped credentials.
Every method, parameter, and type is documented in
reference.md.
Requests are rate limited per account on a fixed one-minute window — writes, searches, and
inference count against it; reads do not. When you exceed the limit the API returns HTTP 429
with a Retry-After header (seconds until the window resets) plus X-RateLimit-Limit and
X-RateLimit-Remaining. Honor Retry-After (or back off exponentially with jitter), and pace
bulk work so your steady rate stays under your plan's per-minute budget. See the
rate limits guide for the
per-plan limits.
- Guides & reference: docs.vectros.ai
- Product: vectros.ai