Snapshot date: August 4, 2026 Asia/Shanghai. The same-day server keyword report was missing. The latest available report showed high Tier 1 visibility for aiwave api, aiwave api documentation and aiwave pricing, with the United States as the largest Tier 1 country by impressions. This guide targets that docs-intent cluster.
Search intent
A developer searching for AIWave API documentation is already close to implementation. They do not need a broad introduction to Chinese AI models. They need to know whether an existing OpenAI Python integration can move to AIWave without SDK churn, whether model names can be governed, whether costs can be controlled in USD and whether production logs will be useful when a request fails.
The implementation should answer three questions quickly. What base URL should the client use? Which models are allowed for this project? What does the application do when the primary model fails validation, hits a rate limit or exceeds budget? The answer is a small client wrapper, not a rewrite of every call site. Keep the OpenAI SDK shape, route through a project policy layer and store enough metadata to debug cost and quality.
AIWave's internal path for this topic should connect the docs, the models endpoint documentation, pricing, the model catalog and the OpenAI-to-AIWave migration article. That cluster helps Tier 1 readers move from query to working code without sending them into low-intent price pages too early.
Production setup
Start by treating model access as configuration. A US SaaS project might allow DeepSeek V4 Flash for extraction, Qwen for general chat and GLM for tool-heavy reasoning. A German enterprise project might allow only a subset after legal review. A Japanese developer-tool project might test Kimi K3 for long code context but keep it disabled for customer data until retention policy is approved. The application should not decide this from a prompt string.
Next, separate three pricing concepts. The first is upstream provider pricing, such as DeepSeek's official cache-hit, cache-miss and output rates, Z.AI's GLM token and tool fees, QwenCloud's context-tiered prices and Kimi K3's long-context pricing. The second is AIWave's customer-facing USD pricing. The third is the application's internal budget, such as maximum cost per support reply or maximum cost per coding-agent step. Mixing those concepts creates confusing invoices and brittle code.
Finally, decide what gets logged. For most production teams, a safe starting point is metadata-first logging: project ID, customer account, model route, request type, prompt token count, cache-hit tokens when returned, cache-miss tokens, output tokens, latency, status code, validator result and fallback reason. Raw prompt storage should be controlled by customer policy, not by a developer convenience flag.
| Area | Minimum production control | Why it matters |
|---|---|---|
| Credentials | Environment variables or secret manager | Prevents keys from entering logs and source files |
| Model names | Project allowlist | Keeps unsupported or unreviewed models out of production |
| Budgets | Per-task maximum estimate | Stops long-context calls before they surprise finance |
| Retries | Idempotent-only fallback policy | Avoids duplicate side effects and hidden spend |
| Validation | JSON Schema or typed parser | Turns model output quality into a measurable signal |
| Privacy | Customer-specific retention flag | Supports GDPR and enterprise procurement review |
Runnable Python client
This wrapper uses the OpenAI Python SDK and keeps the application call surface small. It validates the route, caps output and supports JSON mode for extraction tasks. Set AIWAVE_API_KEY before running it.
# pip install openai
import json
import os
from dataclasses import dataclass
from typing import Literal
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AIWAVE_API_KEY"],
base_url=os.getenv("AIWAVE_BASE_URL", "https://api.aiwave.live/v1"),
)
ALLOWED_MODELS = {
"extract": "deepseek-v4-flash",
"chat": "qwen3.7-plus",
"review": "glm-5.2",
}
@dataclass
class CompletionJob:
kind: Literal["extract", "chat", "review"]
prompt: str
max_tokens: int = 800
require_json: bool = False
customer_region: str = "US"
def complete(job: CompletionJob) -> dict:
model = ALLOWED_MODELS[job.kind]
system = "Return valid compact JSON only." if job.require_json else "Return a concise production-ready answer."
response = client.chat.completions.create(
model=model,
messages=[{"role": "system", "content": system}, {"role": "user", "content": job.prompt}],
temperature=0.2,
max_tokens=job.max_tokens,
)
text = response.choices[0].message.content or ""
parsed = json.loads(text) if job.require_json else None
return {
"model": model,
"kind": job.kind,
"content": text,
"json": parsed,
"usage": getattr(response, "usage", None),
}
if __name__ == "__main__":
job = CompletionJob("extract", "Return JSON for vendor=Acme, amount=$42.10, due=2026-09-01.", require_json=True)
print(complete(job))
The wrapper is intentionally boring. That is a strength. It lets product code call one function while platform code owns route policy, observability and validation. As the project grows, move ALLOWED_MODELS into a database table with customer policy, source URL, checked date and model status. Do not scatter model names across controllers, background jobs and notebooks.
Governance checklist
Before routing real traffic, run a fixture suite. Each fixture should include prompt, task kind, expected schema, accepted model, maximum output tokens, expected maximum cost and whether fallback is allowed. Keep examples for easy, messy and adversarial cases. For JSON extraction, intentionally include malformed invoices, missing fields and unexpected currency symbols. For chat, include support questions that should be escalated instead of answered. For review, include patches that touch authentication, billing and personal data.
Retry policy should be explicit. Retry a network timeout with backoff when the request is idempotent. Retry malformed JSON once with a stricter system message. Do not retry side-effecting jobs unless the application uses idempotency keys and can prove the first attempt did not complete. A model router that silently tries three providers after a partial side effect is an incident waiting to happen.
For GDPR-aware deployments, document what AIWave stores, what your application stores and what each upstream route is allowed to receive. The model allowlist should be customer-specific. If a customer has approved DeepSeek for internal test data but not production customer content, the route policy must enforce that distinction. Sales copy should not promise unverified uptime, customer counts or legal conclusions; engineering controls should show what is actually enforced.
Docs SEO cluster
The latest available GSC report showed that brand and documentation queries are already visible but not converting strongly enough. That is a site architecture issue as much as a content issue. A docs-intent user should see a short path from search result to first API call, then to models, pricing, examples and troubleshooting. Blog articles can help by using exact internal anchors: AIWave API documentation, OpenAI-compatible Python, model list endpoint, usage logging and pricing governance.
This article should not compete with the docs page. It should support it. The docs should remain the canonical implementation reference, while the blog explains production choices and links readers back to the authoritative route. That distinction helps search engines understand the topic cluster and helps developers avoid stale code snippets.
Keep the title and metadata practical. "AIWave API Documentation: Python Production Setup" is close to the query and clear about the outcome. It does not overclaim performance or price advantage. The value proposition is a stable OpenAI-compatible workflow for overseas developers who want access to DeepSeek, Kimi, GLM and Qwen without rebuilding their application around every provider.