Gateway integration

Switching with the OpenAI SDK, Responses API and Chat Completions, streaming, mask-only usage, and virtual keys.

When you move your existing OpenAI integration to the Gateway, only two values change:

base_url → https://gw-tr.gurubase.io/v1
API key → the virtual key you generate in the panel

The rest is the standard OpenAI SDK; the model name, parameters, and response format stay the same. Masking runs automatically on every request: the input is masked before it is forwarded to the model.

Drop-in switch

In the examples below, only the base_url and key lines are specific to the Gateway; the rest of the code is identical to the version that goes directly to the provider.

Responses API

The default path is the Responses API. The name and the TCKN (Turkish national ID number) in the example are masked before reaching the model.

from openai import OpenAI

client = OpenAI(
    base_url="https://gw-tr.gurubase.io/v1",
    api_key="SANAL_ANAHTAR",
)

resp = client.responses.create(
    model="gpt-4o-mini",
    input="Ahmet Yılmaz, TCKN 10000000382, adres değişikliği istiyor.",
)
print(resp.output_text)

curl https://gw-tr.gurubase.io/v1/responses \
  -H "Authorization: Bearer $SANAL_ANAHTAR" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "input": "Ahmet Yılmaz, TCKN 10000000382, adres değişikliği istiyor."
  }'

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gw-tr.gurubase.io/v1",
  apiKey: process.env.SANAL_ANAHTAR,
});

const resp = await client.responses.create({
  model: "gpt-4o-mini",
  input: "Ahmet Yılmaz, TCKN 10000000382, adres değişikliği istiyor.",
});
console.log(resp.output_text);

Chat Completions

If your code uses chat.completions, you do not need to switch to the Responses API; the same two changes are enough here as well. The Python and TypeScript examples from this point on continue with the client defined above.

resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Ayşe Demir'in talebini özetle."},
    ],
)
print(resp.choices[0].message.content)

curl https://gw-tr.gurubase.io/v1/chat/completions \
  -H "Authorization: Bearer $SANAL_ANAHTAR" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      { "role": "user", "content": "Ayşe Demir'\''in talebini özetle." }
    ]
  }'

const resp = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "Ayşe Demir'in talebini özetle." }],
});
console.log(resp.choices[0].message.content);

Streaming

When you set stream: true, the response arrives chunk by chunk over SSE. The masking step does not move: the input is masked before it goes to the model, and the stream is generated from the masked text.

stream = client.chat.completions.create(
    model="gpt-4o-mini",
    stream=True,
    messages=[{"role": "user", "content": "Toplantı notlarını madde madde özetle."}],
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

const stream = await client.chat.completions.create({
  model: "gpt-4o-mini",
  stream: true,
  messages: [{ role: "user", content: "Toplantı notlarını madde madde özetle." }],
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

Mask-only (`gurubase-siper`)

You can get masking only, from the same endpoint, without sending the text to a language model. Set the model field to the reserved value gurubase-siper; the Gateway masks the text and returns the masked version directly.

curl https://gw-tr.gurubase.io/v1/responses \
  -H "Authorization: Bearer $SANAL_ANAHTAR" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gurubase-siper",
    "input": "Vatandaş Ahmet Yılmaz, TCKN 10000000382, başvuru durumu nedir?"
  }'

The masked text is returned in the output_text field:

Vatandaş <PERSON_1>, TCKN <TCKN_1>, başvuru durumu nedir?

The request never reaches any language model; gurubase-siper is not a language model but the reserved model name the Gateway sets aside for masking, and it also works with Chat Completions. If masking cannot be performed, the request stops with a 502 and the raw text is not returned either (fail-closed). If you also need the detection report (the list of categories and positions), use the /mask endpoint directly: Mask-only usage.

Virtual key

All requests are authenticated with the virtual key in the Authorization: Bearer header. You generate the virtual key in the panel; the real provider key stays inside the Gateway only, so you do not need to keep it in your application. You can revoke a key in the panel and generate a new one. See Panel for the steps.

Listing models

You get the models you can access from the /v1/models endpoint; the reserved gurubase-siper name also appears in the list.

curl https://gw-tr.gurubase.io/v1/models \
  -H "Authorization: Bearer $SANAL_ANAHTAR"

To use masking without the Gateway, as a step in your own pipeline, see Mask-only usage; to try it without writing code, see Playground.

If you are looking for a ready-made enterprise assistant instead of writing your own application, Gurubase offers agents that connect to your organization’s knowledge sources (Confluence, Zendesk, Google Drive, your website, and more), ground their answers in those sources, and cite them; it can be deployed in the cloud or on-premise.