API Responses

POST https://api.shannon-ai.com/v1/responses

Minima petitio est id modelli et textus. Responsio non streamed est nisi eam petis.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1",
)

response = client.responses.create(
    model="shannon-3",
    input="Say hello in one sentence.",
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.shannon-ai.com/v1",
});

const response = await client.responses.create({
  model: "shannon-3",
  input: "Say hello in one sentence.",
});

console.log(response.output_text);

curl https://api.shannon-ai.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "shannon-3",
    "input": "Say hello in one sentence."
  }'

Responsio est objectum response. Eius output ratiocinationem, cum modellum ratiocinatum est, et nuntium tenet:

{
  "id": "resp_5f0c1e7a9b3d4c62a8e1f07d2b46c9a3",
  "object": "response",
  "created_at": 1791625200,
  "model": "shannon-3",
  "status": "completed",
  "error": null,
  "incomplete_details": null,
  "instructions": null,
  "metadata": {},
  "parallel_tool_calls": true,
  "temperature": null,
  "tool_choice": "auto",
  "tools": [],
  "top_p": null,
  "output": [
    {
      "id": "rs_8a2e6c1f4b7d4e03b9c5a1d70f3e2b64",
      "type": "reasoning",
      "summary": [
        {
          "type": "summary_text",
          "text": "The user wants a greeting in one sentence. Keep it short and friendly."
        }
      ]
    },
    {
      "id": "msg_1c7b9e3a5d2f4a86b0e4c8d19a6f7e25",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Hello, it is good to meet you.",
          "annotations": []
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 1184,
    "output_tokens": 46,
    "total_tokens": 1230
  }
}

Campi petitionis

model et input sunt quibus petitio eget. Columna Applied by modellos nominat in quibus campus responsionem mutat. Modelli open-weight hosted sunt duodecim ids indicis modellorum; familia Shannon 3 est shannon-3, shannon-3-pro, shannon-3.1 et shannon-3.1-pro. Modella et pretia

previous_response_id, store, metadata, parallel_tool_calls, truncation, include, user et prompt_cache_key accipiuntur ut codex clientis exstans sine mutatione currat. Responsionem non mutant. Quia nulla conversatio servatur, petitio sequens turnos priores in input fert.

Input items

Input item est aut nuntius, cum role et content, aut elementum typatum quod vocationem tool vel eventum eius fert. content est string aut array partium.

Pone instructiones in campo instructions. Cum id familiae Shannon 3, pone instructiones quae tenendae sunt in nuntio user.

Elementa output responsionis in input ut sunt redire possunt: elementa message et function_call leguntur, elementa reasoning praetereuntur.

Partes contenti

In gradibus Shannon imagines et fasciculi ex ultimo elemento user ipsius input leguntur. Magnitudines et limites paginam propriam habent. Imagines et fasciculi

Petitio cum instructionibus et optionibus

Haec petitio instructiones, conversationem ut input items, limitem outputi, temperaturam et effort ratiocinationis mittit. Modellum open-weight hosted utitur, quod omnia applicat.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1",
)

response = client.responses.create(
    model="DeepSeek-V4-Flash-0731-W4A16-AUTOROUND-REAP",
    instructions="You are a physics teacher. Answer in two sentences.",
    input=[
        {"role": "user", "content": "Why is the sky blue?"},
        {"role": "assistant", "content": "Air scatters blue light more than red light."},
        {"role": "user", "content": "And why are sunsets red?"},
    ],
    max_output_tokens=512,
    temperature=0.3,
    reasoning={"effort": "low"},
)

for item in response.output:
    if item.type == "reasoning":
        print("reasoning:", item.summary[0].text)
    elif item.type == "message":
        print("answer:", item.content[0].text)
print(response.usage)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.shannon-ai.com/v1",
});

const response = await client.responses.create({
  model: "DeepSeek-V4-Flash-0731-W4A16-AUTOROUND-REAP",
  instructions: "You are a physics teacher. Answer in two sentences.",
  input: [
    { role: "user", content: "Why is the sky blue?" },
    { role: "assistant", content: "Air scatters blue light more than red light." },
    { role: "user", content: "And why are sunsets red?" },
  ],
  max_output_tokens: 512,
  temperature: 0.3,
  reasoning: { effort: "low" },
});

for (const item of response.output) {
  if (item.type === "reasoning") console.log("reasoning:", item.summary[0].text);
  if (item.type === "message") console.log("answer:", item.content[0].text);
}
console.log(response.usage);

curl https://api.shannon-ai.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "DeepSeek-V4-Flash-0731-W4A16-AUTOROUND-REAP",
    "instructions": "You are a physics teacher. Answer in two sentences.",
    "input": [
      {"role": "user", "content": "Why is the sky blue?"},
      {"role": "assistant", "content": "Air scatters blue light more than red light."},
      {"role": "user", "content": "And why are sunsets red?"}
    ],
    "max_output_tokens": 512,
    "temperature": 0.3,
    "reasoning": {"effort": "low"}
  }'

Responsio modelli open-weight hosted habet id, object, model, created_at, status, output et usage. Eius usage tokens e cache lectos et tokens in ratiocinationem impensos addit:

{
  "usage": {
    "input_tokens": 58,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 67,
    "output_tokens_details": {
      "reasoning_tokens": 19
    },
    "total_tokens": 125
  }
}

Objectum response

id, object, status, output et usage in omni responsione adsunt. Codicem tuum in iis funda.

Output items

SDK OpenAI textum nuntii pro te in response.output_text iungunt.

Streaming

Pone stream ad true pro server-sent events. Quisque frame lineam event: cum nomine eventus et lineam data: cum objecto JSON habet; objectum nomen in type repetit. Coniunge deltas cum suo elemento per output_index.

Cum SDK OpenAI, lege eventus per type eorum:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.shannon-ai.com/v1",
)

stream = client.responses.create(
    model="shannon-3",
    input="Explain server-sent events in three sentences.",
    stream=True,
)

for event in stream:
    kind = getattr(event, "type", "error")     # an event without a type is an error event
    if kind == "response.reasoning_summary_text.delta":
        pass                                   # a piece of reasoning: event.delta
    elif kind == "response.output_text.delta":
        print(event.delta, end="", flush=True)
    elif kind == "response.output_item.done" and event.item.type == "function_call":
        print("\ntool call:", event.item.name, event.item.arguments)
    elif kind == "response.completed":
        print("\n", event.response.usage)
    elif kind in ("response.failed", "error"):
        raise RuntimeError("the stream failed, send the request again")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://api.shannon-ai.com/v1",
});

const stream = await client.responses.create({
  model: "shannon-3",
  input: "Explain server-sent events in three sentences.",
  stream: true,
});

for await (const event of stream) {
  if (event.type === "response.reasoning_summary_text.delta") {
    // a piece of reasoning: event.delta
  } else if (event.type === "response.output_text.delta") {
    process.stdout.write(event.delta);
  } else if (event.type === "response.output_item.done" && event.item.type === "function_call") {
    console.log("\ntool call:", event.item.name, event.item.arguments);
  } else if (event.type === "response.completed") {
    console.log("\n", event.response.usage);
  } else if (event.type === "response.failed" || event.type === "error") {
    throw new Error("the stream failed, send the request again");
  }
}

curl -N https://api.shannon-ai.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "shannon-3",
    "input": "Explain server-sent events in three sentences.",
    "stream": true
  }'

Eventus hoc ordine adveniunt. Greges pro ratiocinatione, nuntio et vocationibus tool apparent tantum cum responsio eos habet.

event: response.created
data: {"type":"response.created","response":{"id":"resp_5f0c…","object":"response","status":"in_progress","output":[], …}}

event: response.in_progress
data: {"type":"response.in_progress","response":{"id":"resp_5f0c…","object":"response","status":"in_progress","output":[], …}}

event: response.output_item.added
data: {"type":"response.output_item.added","output_index":0,"item":{"id":"msg_1c7b…","type":"message","role":"assistant","status":"in_progress","content":[]}}

event: response.content_part.added
data: {"type":"response.content_part.added","output_index":0,"content_index":0,"part":{"type":"output_text","text":""}}

event: response.output_text.delta
data: {"type":"response.output_text.delta","output_index":0,"content_index":0,"delta":"Server-sent"}

event: response.output_text.delta
data: {"type":"response.output_text.delta","output_index":0,"content_index":0,"delta":" events let a server push text over one HTTP reply."}

event: response.output_text.done
data: {"type":"response.output_text.done","output_index":0,"content_index":0,"text":"Server-sent events let a server push text over one HTTP reply."}

event: response.content_part.done
data: {"type":"response.content_part.done","output_index":0,"content_index":0,"part":{"type":"output_text","text":"Server-sent events let a server push text over one HTTP reply."}}

event: response.output_item.done
data: {"type":"response.output_item.done","output_index":0,"item":{"id":"msg_1c7b…","type":"message","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Server-sent events let a server push text over one HTTP reply.","annotations":[]}]}}

event: response.completed
data: {"type":"response.completed","response":{"id":"resp_5f0c…","object":"response","status":"completed","output":[…],"usage":{"input_tokens":1190,"output_tokens":18,"total_tokens":1208}, …}}

Stream qui deficit

In gradibus Shannon responsio defecta cum response.failed finit loco response.completed. Eius response.status est failed et response.error tenet code, rate_limit_exceeded vel server_error, et message.

In modellis open-weight hosted defectus est eventus error cum code api_error et message. response.completed tamen eum sequitur.

Stream ut defectum tracta cum response.failed vel eventum error tenet, et mitte petitionem iterum.

Tools

Tool est functio quam codex tuus exsequitur. Describe eam in tools; cum modellum eam vult, responsio elementum function_call loco nuntii, vel iuxta nuntium, tenet.

Definitio tool in tools:

[
  {
    "type": "function",
    "name": "get_weather",
    "description": "Current weather for a city.",
    "parameters": {
      "type": "object",
      "properties": {
        "city": {
          "type": "string"
        }
      },
      "required": [
        "city"
      ]
    }
  }
]

output responsionis quae eam vocat:

[
  {
    "id": "call_3d9a7c1e5b2f4a60c8e1d7f09b24a6c5",
    "type": "function_call",
    "name": "get_weather",
    "call_id": "call_3d9a7c1e5b2f4a60c8e1d7f09b24a6c5",
    "arguments": "{\"city\":\"Paris\"}"
  }
]

Exsequere functionem, deinde mitte petitionem iterum cum vocatione et eventu eius ad input additis:

[
  {
    "role": "user",
    "content": "What is the weather in Paris?"
  },
  {
    "type": "function_call",
    "call_id": "call_3d9a7c1e5b2f4a60c8e1d7f09b24a6c5",
    "name": "get_weather",
    "arguments": "{\"city\":\"Paris\"}"
  },
  {
    "type": "function_call_output",
    "call_id": "call_3d9a7c1e5b2f4a60c8e1d7f09b24a6c5",
    "output": "{\"temperature_c\": 18, \"sky\": \"cloudy\"}"
  }
]

Tantum tools typi function leguntur. In shannon-1.6-*, shannon-2-* et shannon-coder-1 schema parameters ad verba clavis communia JSON Schema reducitur: oneOf ut anyOf legitur, et verba clavis ut $ref, additionalProperties, const, default et title ad modellum non transmittuntur.

Valores tool_choice required et functio nominata a modellis open-weight hosted applicantur.

Circulus tool in pagina propria plene describitur. Vocatio functionum

Output structuratus

Haec forma output structuratum ut text.format scribit. Schema directe in format sedet, iuxta name et strict, non sub membro json_schema ut in Chat Completions.

• {"type": "json_object"} quodvis objectum JSON petit.
• {"type": "json_schema", "name", "schema", "strict"} JSON quod schema tuum sequitur petit. strict est true cum omittis.

{
  "model": "shannon-1.6-lite",
  "input": "Extract the city and the date: 'The conference opens in Lisbon on 12 May 2027.'",
  "text": {
    "format": {
      "type": "json_schema",
      "name": "event",
      "strict": true,
      "schema": {
        "type": "object",
        "properties": {
          "city": {
            "type": "string"
          },
          "date": {
            "type": "string",
            "description": "ISO 8601 date"
          }
        },
        "required": [
          "city",
          "date"
        ]
      }
    }
  }
}

JSON ut textus in parte output_text nuntii advenit; ipse analyse. Modellum open-weight hosted sine output structurato 400 reddit.

{"city": "Lisbon", "date": "2027-05-12"}

Quod modellum quam formam accipiat in pagina propria enumeratur. Exitus structi

Ratiocinatio

Modellum quod ratiocinatur ratiocinationem suam ut output item typi reasoning reddit, ante nuntium. In stream ut eventus response.reasoning_summary_text.delta advenit.

Modelli open-weight hosted reasoning.effort applicant: off, low, medium vel high, cum high si nihil mittis. off nullum elementum reasoning reddit. Mitte eum ut objectum; string nudus in hoc endpoint non legitur.

Gradus effort in pagina propria describuntur. Conatus ratiocinationis

Limes outputi

max_output_tokens est 4,096 cum eum non mittis. Est numerus tokenum de saldo tuo seposita cum petitio incipit; cum responsio completa est, illa quantitas eo quod petitio usa est substituitur. Valor qui in saldum tuum reliquum non capitur 429 Quota exceeded reddit.

Longitudinem responsionis in shannon-1.6-*, shannon-2-* et shannon-coder-1 limitat, et longitudinem textus responsionis in modellis open-weight hosted.

In hoc endpoint shannon-coder-1 in tokens de saldo tuo computatur, sicut omne aliud modellum.

Corpus petitionis ad 32 MiB esse potest. Limites et saldum

Errores

Errores corpus erroris OpenAI utuntur: objectum error cum type et message; quidam etiam code et param cum valore null ferunt. Tabula enumerat quae hoc endpoint saepissime reddit. Index plenus, cum eo quod iterandum sit, paginam propriam habet. Tractatio errorum