{"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
}'
साइन अप करने के बाद, प्रत्येक डेवलपर को एक पर्सनल API एक्सेस की असाइन की जाती है, जो अक्षरों और अंकों का एक यूनिक संयोजन होता है, जिसका उपयोग हमारे API एंडपॉइंट तक पहुंचने के लिए किया जाता है। प्रमाणीकरण के लिए Deepseek API के साथ बस अपने बेयरर टोकन को Authorization हेडर में शामिल करें।
| हेडर | विवरण |
|---|---|
Authorization
|
आवश्यक
होना चाहिए Bearer access_key. जब आप सब्सक्राइब हों तो ऊपर "Your API Access Key" देखें।
|
कोई लंबी अवधि की प्रतिबद्धता नहीं। कभी भी अपग्रेड, डाउनग्रेड या कैंसल करें। फ्री ट्रायल में 50 रिक्वेस्ट तक शामिल हैं।
(वार्षिक बिलिंग के साथ 2 महीने बचाएँ 🎉)
अग्रणी कंपनियों का भरोसा
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.
विशिष्ट उपयोग के मामलों में ग्राहक सहायता के लिए चैटबॉट्स, ट्यूशन के लिए शैक्षिक टूल, ब्लॉग के लिए सामग्री निर्माण, और डेवलपर्स के लिए कोडिंग सहायता शामिल हैं API की मल्टी-टर्न क्षमताएँ इंटरैक्टिव अनुप्रयोगों को बढ़ाती हैं
डेटा सटीकता को विविध डेटा सेट पर प्रशिक्षित उन्नत भाषा मॉडलों के उपयोग के माध्यम से बनाए रखा जाता है मॉडलों में सतत अद्यतन और सुधार उच्च गुणवत्ता वाले परिणाम सुनिश्चित करने में मदद करते हैं हालांकि उपयोगकर्ताओं को महत्वपूर्ण जानकारी को स्वतंत्र रूप से मान्य करना चाहिए