{"id":"83ed9b00-0083-4a0f-88fc-8d357b980eab","object":"chat.completion","created":1782683642,"model":"deepseek-v4-flash","choices":[{"index":0,"message":{"role":"assistant","content":"Here's a Python function to calculate Fibonacci numbers with multiple approaches:\n\n## Method 1: Iterative (Most Efficient)\n\n```python\ndef fibonacci_iterative(n):\n \"\"\"\n Calculate the nth Fibonacci number using iteration.\n \n Args:\n n: Non-negative integer\n \n Returns:\n The nth Fibonacci number\n \"\"\"\n if n < 0:\n raise ValueError(\"n must be non-negative\")\n if n <= 1:\n return n\n \n a, b = 0, 1\n for _ in range(2, n + 1):\n a, b = b, a + b\n return b\n```\n\n## Method 2: Recursive (Simple but Inefficient)\n\n```python\ndef fibonacci_recursive(n):\n \"\"\"\n Calculate the nth Fibonacci number using recursion.\n \n Args:\n n: Non-negative integer\n \n Returns:\n The nth Fibonacci number\n \"\"\"\n if n < 0:\n raise ValueError(\"n must be non-negative\")\n if n <= 1:\n return n\n return fibonacci_recursive(n - 1) + fibonacci_recursive(n - 2)\n```\n\n## Method 3: Recursive with Memoization (Optimized)\n\n```python\nfrom functools import lru_cache\n\n@lru_cache(maxsize=None)\ndef fibonacci_memoized(n):\n \"\"\"\n Calculate the nth Fibonacci number using recursion with memoization.\n \n Args:\n n: Non-negative integer\n \n Returns:\n The nth Fibonacci number\n \"\"\"\n if n < 0:\n raise ValueError(\"n must be non-negative\")\n if n <= 1:\n return n\n return fibonacci_memoized(n - 1) + fibonacci_memoized(n - 2)\n```\n\n## Method 4: Generator for Fibonacci Sequence\n\n```python\ndef fibonacci_generator(n):\n \"\"\"\n Generate the first n Fibonacci numbers.\n \n Args:\n n: Number of Fibonacci numbers to generate\n \n Yields:\n Fibonacci numbers one by one\n \"\"\"\n a, b = 0, 1\n for _ in range(n):\n yield a\n a, b = b, a + b\n```\n\n## Usage Examples:\n\n```python\n# Test the functions\nprint(fibonacci_iterative(10)) # Output: 55\nprint(fibonacci_recursive(10)) # Output: 55\nprint(fibonacci_memoized(10)) # Output: 55\n\n# Using the generator\nprint(list(fibonacci_generator(10))) # Output: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\n\n# Generate first 10 Fibonacci numbers\nfor i in range(10):\n print(f\"F({i}) = {fibonacci_iterative(i)}\")\n```\n\n## Performance Comparison:\n\n- **Iterative**: O(n) time, O(1) space - Best for single calculations\n- **Recursive**: O(2^n) time, O(n) space - Very slow for large n\n- **Memoized**: O(n) time, O(n) space - Good for multiple calls\n- **Generator**: O(n) time, O(1) space - Best for generating sequences\n\nThe iterative approach is generally recommended for most use cases due to its efficiency and simplicity."},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":20,"completion_tokens":703,"total_tokens":723,"prompt_tokens_details":{"cached_tokens":0},"prompt_cache_hit_tokens":0,"prompt_cache_miss_tokens":20},"system_fingerprint":"fp_8b330d02d0_prod0820_fp8_kvcache_20260402"}
curl --location --request POST 'https://zylalabs.com/api/13017/deepseek+api/26129/chat+completions' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"model": "deepseek-chat",
"messages": [
{
"role": "system",
"content": "You are a helpful coding assistant."
},
{
"role": "user",
"content": "Write a Python function to calculate fibonacci numbers."
}
],
"max_tokens": 1024,
"temperature": 0.7,
"stream": false
}'
Após se cadastrar, cada desenvolvedor recebe uma chave de acesso à API pessoal, uma combinação única de letras e dígitos para acessar nosso endpoint de API. Para autenticar com a Deepseek API basta incluir seu token Bearer no cabeçalho Authorization.
| Cabeçalho | Descrição |
|---|---|
Authorization
|
Obrigatório
Deve ser Bearer access_key. Veja "Sua chave de acesso à API" acima quando você estiver inscrito.
|
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This API provides access to large language models supporting text generation, complex reasoning, code generation, and multi-turn conversational workflows.
Supported capabilities:
- Single and multi-turn chat completions
- System prompt and instruction following
- Structured reasoning with chain-of-thought output
- Function/tool calling for agentic workflows
- Vision input processing (image + text prompts)
- Streaming responses via server-sent events
- Context window up to 1,000,000 tokens
Available models:
- deepseek-v4-pro: Full-capability reasoning model with 1M token context
- deepseek-v4-flash: Lightweight model optimized for speed with 128K token context
Endpoint compatibility:
- OpenAI Chat Completions format (/v1/chat/completions)
- Anthropic Messages format (/v1/messages)
Request parameters include model selection, max_tokens, temperature, top_p, presence/frequency penalties, and stop sequences. Responses return token usage statistics including prompt tokens, completion tokens, and cached token counts.
Rate limits and pricing vary by model tier. API key authentication required via Bearer token or x-api-key header.
The Deepseek API's endpoints return generated text responses based on user input, including single-turn and multi-turn conversations. The output includes the assistant's message content and token usage statistics, such as prompt tokens and completion tokens.
Key fields in the response data include "id" (unique identifier), "object" (type of response), "created" (timestamp), "model" (used model), and "choices" (array containing the generated message and its role).
Users can customize requests with parameters such as "model" (to select the desired model), "max_tokens" (to limit response length), "temperature" (to control randomness), "top_p" (for nucleus sampling), and penalties for presence/frequency.
The response data is organized in a JSON format. It includes a main object with metadata (like "id" and "created") and a "choices" array, where each choice contains the assistant's message and its role, facilitating easy parsing and usage.
The endpoints provide information such as generated text responses, reasoning outputs, and code snippets. They support multi-turn conversations, structured reasoning, and function/tool calling, making them versatile for various applications.
Users can utilize the returned data by extracting the "content" field from the "choices" array for the generated response. Additionally, they can analyze token usage statistics to optimize their requests and manage costs effectively.
Casos de uso típicos incluem chatbots para suporte ao cliente ferramentas educacionais para tutoria geração de conteúdo para blogs e assistência em codificação para desenvolvedores As capacidades de múltiplas interações da API melhoram aplicações interativas
A precisão dos dados é mantida através do uso de modelos de linguagem avançados treinados em conjuntos de dados diversos Atualizações e melhorias contínuas nos modelos ajudam a garantir saídas de alta qualidade embora os usuários devam validar informações críticas de forma independente