interpretai 0.1.10

Official Python SDK for the InterpretAI public API.

interpretai

Official Python SDK for the InterpretAI public API.

This package wraps four feature surfaces — Match Reviewer, Spec Submittal Compliance Review, Agent Root-Cause Analysis (RCA), and Models — behind a resource-oriented client. Public Reviewer and SSC follow the canonical submit -> get -> wait shape; agent RCA is a four-call pipeline (agent_mdp.upload -> ontologies.register -> rca.submit_agent_job -> jobs.wait).

API keys

For an API key please email [e-mail] for early access.

Install

pip install interpretai

Quickstart

Hiring

import os
from interpretai import Client
from interpretai.types.public_reviewer import ReviewRequest, Subject

client = Client(api_key=os.environ["INTERPRETAI_API_KEY"])

job = client.public_reviewer.submit(ReviewRequest(
    subjects=[
        Subject(pdf="path/to/resume.pdf", label="candidate"),
        Subject(text=[
                "Company A: Working on Agentic AI for job matching", 
                "Looking for talented engineer with experience in the space",
            ], 
            pdf="path/to/relevant/job/description", 
        label="b"),
    ],
    prompt="Is the candidate a good fit, why or why not?",
    model_tier="tlarge",
))

result = client.public_reviewer.wait(job.job_id, timeout_s=1200)
print(result.score, result.reasoning)

Async

import asyncio
from interpretai import AsyncClient
from interpretai.types.public_reviewer import ReviewRequest, Subject

async def main() -> None:
    async with AsyncClient() as client:
        job = await client.public_reviewer.submit(ReviewRequest(
            subjects=[Subject(text="..."), Subject(text="...")],
            prompt="...",
            model_tier="tlarge",
        ))
        result = await client.public_reviewer.wait(job.job_id)
        print(result.score)

asyncio.run(main())

Per-request overrides

fast = client.with_options(timeout=10.0, max_retries=0)
fast.public_reviewer.submit(...)

Configuration

The SDK reads two environment variables when the constructor parameters are not supplied:

• INTERPRETAI_API_KEY — your API key (must start with iai_).
• INTERPRETAI_BASE_URL — base URL (defaults to https://stage-app.interpretai.tech).

Resources

Resource	Methods
client.auth	me()
client.public_reviewer	submit(), get(job_id), wait(job_id, ...)
client.ssc	submit(), get(job_id), wait(job_id, ...)
client.agent_mdp	upload(item) — durably persist an AgentTaskWithMetadata before RCA
client.ontologies	register(req), list(), get(id), delete(id) — per-org failure-mode catalogues
client.rca	submit_agent_job(req) — enqueue agent RCA, similar_failures(req) — kNN history search
client.jobs	get(job_id), list(), wait(job_id, ...) — unified poll for every async pipeline

Agent RCA quickstart

Agent RCA does not fit the canonical submit -> get -> wait shape. Trajectories must be persisted first so the worker can hydrate the full payload from storage; the flow is four calls:

import os
from interpretai import (
    AgentRcaResult,
    Client,
    CreateAgentRcaJobRequest,
    EvaluatorDefinition,
    RegisterOntologyRequest,
    load_agent_failures_ontologies,
)

client = Client(api_key=os.environ["INTERPRETAI_API_KEY"])

# 1) Upload one trajectory (see interpretai.types.agent_mdp for the dataclass shapes).
uploaded = client.agent_mdp.upload(item)  # build `item: AgentTaskWithMetadata`

# 2) Register each evaluator. The bundled `agent_failures` pack ships 5 baseline
# evaluators (goal interpretation, plan construction, hallucination control,
# runaway loops, post-action verification); layer per-org evaluators alongside.
bundled = load_agent_failures_ontologies()
custom = RegisterOntologyRequest(name="PII leakage", evaluators=[
    EvaluatorDefinition(
        name="pii_leakage",
        description="Did the agent emit PII anywhere it shouldn't?",
        scoring_type="global",
        inputs=["image", "axtree", "agent_action", "agent_output"],
    ),
])
ontology_ids = [client.ontologies.register(req).ontology_id for req in [*bundled, custom]]

# 3) Enqueue the RCA job.
accepted = client.rca.submit_agent_job(CreateAgentRcaJobRequest(
    dataset_id=uploaded.dataset_id or "default",
    task_id=uploaded.task_id,
    agent_model="ge-medium",
    ontology_ids=ontology_ids,
))

# 4) Poll the unified jobs endpoint and hydrate the verdict.
completed = client.jobs.wait(accepted.job_id, timeout_s=1800.0)
rca = AgentRcaResult.from_dict(completed.result or {})
for verdict in rca.ontology_results:
    print(verdict.ontology_id, verdict.success_or_failure, verdict.score)

Full worked example with progress logging, custom evaluator prompts, and post-run KNN search: interpret/experimental/rca_agent_e2e.py. See also the agent RCA docs page.

Architecture

A layered SDK in the shape of openai-python / anthropic-sdk-python, with a uniform submit -> get -> wait flow across every feature surface. Caller code reaches into a top-level Client (or AsyncClient), which composes one resource per feature on top of a single shared HTTP transport.

flowchart TD
    UserCode["User code"] --> Top["interpretai (top-level re-exports)"]
    Top --> Client["Client / AsyncClient (client.py)"]
    Client --> Resources["Resources (resources/*.py): auth, public_reviewer, ssc, rca, agent_mdp, jobs"]
    Resources --> Base["BaseResource / BaseAsyncResource (_base.py)"]
    Resources --> Polling["wait_for_terminal (_polling.py)"]
    Resources --> Types["Types (types/*.py) frozen dataclasses"]
    Base --> Transport["SyncTransport / AsyncTransport (_http.py)"]
    Transport --> Options["RequestOptions + merge_options (_options.py)"]
    Transport --> Errors["Errors (errors.py) + from_response"]
    Transport --> Constants["Protocol constants (_constants.py)"]
    Transport --> Httpx["httpx.Client / httpx.AsyncClient + tenacity"]

Layers

• Client (client.py) — Client / AsyncClient each compose a transport plus one resource per feature. with_options(...) derives a new client that shares the underlying httpx.Client but applies merged defaults (timeout, retries, headers, idempotency key).
• Resources (resources/) — one module per feature surface (auth, public_reviewer, ssc, rca, agent_mdp, jobs). Each subclasses BaseResource / BaseAsyncResource and exposes submit / get / wait against a /api/v1/<feature> route.
• Transport (_http.py) — SyncTransport / AsyncTransport wrap httpx.Client / httpx.AsyncClient. The transport injects Authorization: Bearer ..., User-Agent, the X-Interpretai-* analytics suite, and a per-POST Idempotency-Key. Retries are driven by tenacity and honor a server-supplied Retry-After (capped) plus the x-should-retry directive.
• Options (_options.py) — RequestOptions is a frozen dataclass; merge_options(...) overlays per-call overrides on top of client-level defaults so every call point goes through one merge rule.
• Polling (_polling.py) — every resource's wait() calls into a single wait_for_terminal (sync and async). Cadence is capped exponential growth (x1.5) from poll_interval_s to max_poll_interval_s, bounded by timeout_s.
• Types (types/) — request / response models are frozen @dataclass objects with explicit to_dict / from_dict. No pydantic; on-the-wire keys are snake_case.
• Errors (errors.py) — InterpretError root, with APIError / APIStatusError and one subclass per common HTTP status (400, 401, 403, 404, 409, 422, 429, 5xx), plus JobFailedError, JobTimeoutError, and ValidationError. from_response(...) maps an httpx.Response to the most specific subclass and carries the server request-id for support tickets.
• Constants (_constants.py) — protocol-level knobs: default base URL, default timeout, retry timing, RETRYABLE_STATUS = {408, 425, 429, 500, 502, 503, 504}, and the SDK's interpretai.* logger namespace.

Design principles

• Sync + async are parallel everywhere. Client vs AsyncClient, SyncTransport vs AsyncTransport, wait_for_terminal vs wait_for_terminal_async, BaseResource vs BaseAsyncResource. Same surface, two execution models.
• Uniform submit -> get -> wait shape across every job pipeline so adding a new feature is a copy of an existing resource module.
• Externally shipped, no monorepo imports. The SDK uses stdlib logging under interpretai.* namespaces — never interpret.tools.logger — and only __init__.py re-exports public symbols.
• Per-call overrides mirror openai-python. RequestOptions + client.with_options(...) gives the same ergonomics for tweaking timeout / retries / headers / idempotency on a single call or a derived client.

See the top-level SDK README for design notes and roadmap.

License

Apache-2.0