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1. Create a workspace

Sign up at app.feder8d.ai/onboarding with an email and workspace name. You’ll land on the 14-day Business-tier trial. No card required.

2. Generate an API key

In the Tenant Console, navigate to Settings → API keys → Generate.

The key surfaces once at creation; we store it hashed (argon2id). Copy it now.

3. Send your first chat completion

The API is OpenAI-compatible, so the official OpenAI Python SDK works unchanged:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.feder8d.ai/v1",
    api_key="YOUR_FEDER8D_KEY",
)

resp = client.chat.completions.create(
    model="qwen2.5-7b-instruct",
    messages=[{"role": "user", "content": "hello"}],
)
print(resp.choices[0].message.content)

Streaming uses SSE:

stream = client.chat.completions.create(
    model="qwen2.5-7b-instruct",
    messages=[{"role": "user", "content": "tell me a joke"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

4. Ingest your first document

Documents live in collections. Create one and upload a document via the Tenant API:

curl -X POST https://api.feder8d.ai/v1/collections \
  -H "Authorization: Bearer YOUR_FEDER8D_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name": "handbook"}'

curl -X POST https://api.feder8d.ai/v1/collections/handbook/documents \
  -H "Authorization: Bearer YOUR_FEDER8D_KEY" \
  -F "file=@./handbook.pdf"

5. Ask a question with retrieval

resp = client.chat.completions.create(
    model="qwen2.5-7b-instruct",
    messages=[{"role": "user", "content": "what's our PTO policy?"}],
    extra_body={
        "x-feder8d-collections": ["handbook"],
    },
)
print(resp.choices[0].message.content)

feder8d retrieves the top-k chunks from the handbook collection, reranks, and feeds them into context. Citation metadata rides under x-feder8d-citations in the response.

Next steps