Documentación de la API

Cómo funciona

La API de Seven Assistant es totalmente compatible con OpenAI. Esto significa: cualquier herramienta, biblioteca y SDK que funcione con la API de OpenAI también funciona con Seven Assistant. Solo necesitas cambiar la URL base.

Primera solicitud con curl

curl YOUR_DOMAIN/v1/chat/completions \
  -H "Authorization: Bearer sk-educa-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "educa-ai",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is photosynthesis?"}
    ]
  }'

Obtener modelos a través de la API

curl YOUR_DOMAIN/v1/models \
  -H "Authorization: Bearer sk-educa-YOUR_API_KEY"

Cómo funciona el streaming

Establece "stream": true en la solicitud. La respuesta se transmite como Server-Sent Events (SSE). Cada fragmento contiene una parte del texto. El flujo termina con data: [DONE].

curl YOUR_DOMAIN/v1/chat/completions \
  -H "Authorization: Bearer sk-educa-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -N \
  -d '{
    "model": "educa-ai",
    "messages": [{"role": "user", "content": "Count to 5"}],
    "stream": true
  }'

Fragmentos del flujo

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1711900000,"model":"educa-ai","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1711900000,"model":"educa-ai","choices":[{"index":0,"delta":{"content":"1, "},"finish_reason":null}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1711900000,"model":"educa-ai","choices":[{"index":0,"delta":{"content":"2, "},"finish_reason":null}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1711900000,"model":"educa-ai","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

from openai import OpenAI

client = OpenAI(
    api_key="sk-educa-YOUR_API_KEY",
    base_url="YOUR_DOMAIN/v1/"
)

# Chat Completion
response = client.chat.completions.create(
    model="educa-ai",
    messages=[
        {"role": "system", "content": "You are a helpful teacher."},
        {"role": "user", "content": "Create 5 quiz questions about the Middle Ages."}
    ],
    temperature=0.7,
    max_tokens=1000
)

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

# Streaming
stream = client.chat.completions.create(
    model="educa-ai",
    messages=[{"role": "user", "content": "Explain democracy."}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

# Embeddings
embedding = client.embeddings.create(
    model="educa-ai",
    input="Photosynthesis explained for children"
)

print(embedding.data[0].embedding[:5])

Instalación: pip install openai

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-educa-YOUR_API_KEY",
  baseURL: "YOUR_DOMAIN/v1/"
});

// Chat Completion
const response = await client.chat.completions.create({
  model: "educa-ai",
  messages: [
    { role: "system", content: "You are a helpful teacher." },
    { role: "user", content: "What is the rule of three?" }
  ]
});

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

// Streaming
const stream = await client.chat.completions.create({
  model: "educa-ai",
  messages: [{ role: "user", content: "Explain the water cycle." }],
  stream: true
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}

Instalación: npm install openai

const response = await fetch("YOUR_DOMAIN/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": "Bearer sk-educa-YOUR_API_KEY",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "educa-ai",
    messages: [
      { role: "user", content: "Create a weekly plan for math, grade 5." }
    ]
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);

using Azure.AI.OpenAI;
using OpenAI.Chat;
using System.ClientModel;

var client = new ChatClient(
    model: "educa-ai",
    credential: new ApiKeyCredential("sk-educa-YOUR_API_KEY"),
    options: new OpenAIClientOptions
    {
        Endpoint = new Uri("YOUR_DOMAIN/v1/")
    }
);

var response = await client.CompleteChatAsync(
    new ChatMessage[]
    {
        new SystemChatMessage("You are a helpful teacher."),
        new UserChatMessage("What is a parabola in math?")
    }
);

Console.WriteLine(response.Value.Content[0].Text);

NuGet: Azure.AI.OpenAI

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="educa-ai",
    api_key="sk-educa-YOUR_API_KEY",
    base_url="YOUR_DOMAIN/v1/"
)

response = llm.invoke("Create a summary of the French Revolution.")
print(response.content)

Instalación: pip install langchain-openai

Puedes usar Seven Assistant como asistente de IA en tu IDE. Configura lo siguiente en el config.json de Continue.dev:

{
  "models": [
    {
      "title": "Seven Assistant",
      "provider": "openai",
      "model": "educa-ai",
      "apiKey": "sk-educa-YOUR_API_KEY",
      "apiBase": "YOUR_DOMAIN/v1/"
    }
  ]
}