{"id":"399db7f3-e2c8-43cf-b337-2eee2cb1e805","object":"chat.completion","created":1782912319,"model":"deepseek-v4-flash","choices":[{"index":0,"message":{"role":"assistant","content":"Here's a Python function to calculate Fibonacci numbers with multiple implementation approaches:\n\n## Method 1: Recursive (Simple but inefficient for large numbers)\n\n```python\ndef fibonacci_recursive(n):\n \"\"\"\n Calculate the nth Fibonacci number using recursion.\n Time complexity: O(2^n)\n Space complexity: O(n) due to call stack\n \"\"\"\n if n <= 0:\n return 0\n elif n == 1:\n return 1\n else:\n return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)\n```\n\n## Method 2: Iterative (Efficient)\n\n```python\ndef fibonacci_iterative(n):\n \"\"\"\n Calculate the nth Fibonacci number using iteration.\n Time complexity: O(n)\n Space complexity: O(1)\n \"\"\"\n if n <= 0:\n return 0\n elif n == 1:\n return 1\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 3: Memoization (Recursive with caching)\n\n```python\ndef fibonacci_memoization(n, memo={}):\n \"\"\"\n Calculate the nth Fibonacci number using memoization.\n Time complexity: O(n)\n Space complexity: O(n)\n \"\"\"\n if n in memo:\n return memo[n]\n \n if n <= 0:\n return 0\n elif n == 1:\n return 1\n \n memo[n] = fibonacci_memoization(n-1, memo) + fibonacci_memoization(n-2, memo)\n return memo[n]\n```\n\n## Method 4: Using functools.lru_cache\n\n```python\nfrom functools import lru_cache\n\n@lru_cache(maxsize=None)\ndef fibonacci_lru(n):\n \"\"\"\n Calculate the nth Fibonacci number using LRU cache.\n Time complexity: O(n)\n Space complexity: O(n)\n \"\"\"\n if n <= 0:\n return 0\n elif n == 1:\n return 1\n return fibonacci_lru(n-1) + fibonacci_lru(n-2)\n```\n\n## Method 5: Generator (for sequence)\n\n```python\ndef fibonacci_generator(n):\n \"\"\"\n Generate the first n Fibonacci numbers.\n Returns a list of Fibonacci numbers.\n \"\"\"\n if n <= 0:\n return []\n \n fib_sequence = [0, 1]\n if n == 1:\n return [0]\n elif n == 2:\n return fib_sequence\n \n for _ in range(2, n):\n fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n \n return fib_sequence[:n]\n```\n\n## Usage examples:\n\n```python\n# Test the functions\nprint(f\"Recursive (10): {fibonacci_recursive(10)}\") # Output: 55\nprint(f\"Iterative (10): {fibonacci_iterative(10)}\") # Output: 55\nprint(f\"Memoization (10): {fibonacci_memoization(10)}\") # Output: 55\nprint(f\"LRU Cache (10): {fibonacci_lru(10)}\") # Output: 55\nprint(f\"Generator (10): {fibonacci_generator(10)}\") # Output: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\n\n# For large numbers, use iterative or memoization\nprint(f\"Large number (100): {fibonacci_iterative(100)}\")\n# Output: 354224848179261915075\n```\n\n## Recommendation:\n- **For small n (n < 30)**: Any method works fine\n- **For large n**: Use `fibonacci_iterative()` - it's the most efficient with O(n) time and O(1) space\n- **For readability**: Use `fibonacci_lru()` with the `@lru_cache` decorator\n- **For generating sequences**: Use `fibonacci_generator()`"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":20,"completion_tokens":874,"total_tokens":894,"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
}'
Después de registrarte, a cada desarrollador se le asigna una clave de acceso a la API personal, una combinación única de letras y dígitos proporcionada para acceder a nuestro endpoint de la API. Para autenticarte con el Deepseek API simplemente incluye tu token de portador en el encabezado de Autorización.
| Encabezado | Descripción |
|---|---|
Autorización
|
Requerido
Debería ser Bearer access_key. Consulta "Tu Clave de Acceso a la API" arriba cuando estés suscrito.
|
Sin compromiso a largo plazo. Mejora, reduce o cancela en cualquier momento. La Prueba Gratuita incluye hasta 50 solicitudes.
(Ahorra 2 meses pagando anualmente 🎉)
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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 token.
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.
Los casos de uso típicos incluyen chatbots para soporte al cliente herramientas educativas para tutoría generación de contenido para blogs y asistencia en codificación para desarrolladores Las capacidades de múltiples turnos de la API mejoran las aplicaciones interactivas
La precisión de los datos se mantiene a través del uso de modelos de lenguaje avanzados entrenados en conjuntos de datos diversos Las actualizaciones continuas y las mejoras en los modelos ayudan a garantizar salidas de alta calidad aunque los usuarios deben validar la información crítica de manera independiente