{"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
}'
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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.