Send a Prompt

Use this guide to validate AI/LLM integration through the Cloud and AI Gateway after configuring your repository.

Unlike storage tests, AI tests use the repository's OpenAI-compatible endpoint type and an AI LLM API key generated for that repository.

This is the AI equivalent of Store An Object: you confirm connectivity, authentication, routing, and response behavior before shipping production traffic.

Prerequisites

Before running examples, make sure all of the following are complete:

1. At least one AI provider is configured in AI → AI LLM.

2. The AI resource is attached to your repository.

3. The repository endpoint type is OPENAI.

4. You created a repository AI LLM API key.

Related setup guides:

• Configure an AI LLM

• Create a Repository

• Configure a Repository

Required endpoint and credentials

From your repository details, collect:

• OpenAI-compatible base URL (example: https://openai-us-east-1-aws.flashback.tech/v1)

• AI API key (Bearer token format)

• Model identifier available in your configured provider(s)

Then export them as environment variables:

export FB_OPENAI_BASE_URL="https://openai-us-east-1-aws.flashback.tech/v1"
export FB_OPENAI_API_KEY="YOUR_AI_API_KEY"
export FB_MODEL="gpt-4o-mini"

---

Quick connectivity check (cURL)

Run a simple chat completion request through Flashback:

curl -sS "$FB_OPENAI_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $FB_OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "'"$FB_MODEL"'",
    "messages": [
      {"role": "system", "content": "You are a concise assistant."},
      {"role": "user", "content": "Explain in one sentence what Flashback Gateway does."}
    ],
    "temperature": 0.2
  }'

If successful, you should receive a JSON response containing choices[0].message.content.

---

Python example

Install dependency:

pip install openai

# send_prompt.py
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["FB_OPENAI_API_KEY"],
    base_url=os.environ["FB_OPENAI_BASE_URL"],
)

response = client.chat.completions.create(
    model=os.environ.get("FB_MODEL", "gpt-4o-mini"),
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "List 3 benefits of using an API gateway for AI workloads."}
    ],
    temperature=0.3,
)

print(response.choices[0].message.content)

Run:

python send_prompt.py

JavaScript example

Install dependency:

npm install openai

// sendPrompt.mjs
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FB_OPENAI_API_KEY,
  baseURL: process.env.FB_OPENAI_BASE_URL,
});

const response = await client.chat.completions.create({
  model: process.env.FB_MODEL || "gpt-4o-mini",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Give me 3 best practices for AI key rotation." }
  ],
  temperature: 0.3,
});

console.log(response.choices[0].message.content);

Run:

node sendPrompt.mjs

Gateway integration validation checklist

When the call succeeds, validate these integration points:

• Authentication works with the repository-level AI LLM API key.

• The request is routed through your Flashback Gateway endpoint (not directly to provider).

• The selected model is authorized and available in your provider configuration.

• The same app code can keep a stable OpenAI-compatible contract while backend provider/policy evolves in Flashback.

Common errors and fixes

• 401 Unauthorized / 403 Forbidden

  • Verify you are using an AI key (not a storage key).

  • Check key status and access mode in repository API keys.

• 404 Not Found

  • Confirm FB_OPENAI_BASE_URL includes /v1.

  • Ensure repository endpoint type is OPENAI.

• 400 model not found / invalid model

  • Use a model exposed by your configured provider(s).

  • Recheck provider mapping/policies in AI LLM configuration.

• Timeout / network issues

  • Validate DNS/network access to your Flashback endpoint.

  • Confirm bridge node health and connectivity.

Next steps

After this test succeeds, you can:

• implement fallback and reliability patterns for multi-provider AI routing,

• apply governance/policy controls,

• move to production with key rotation and observability.

Explore practical patterns in AI LLM Use Cases.