openai_api_completions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI APIs - Completions\n",
    "\n",
    "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n",
    "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/api-reference).\n",
    "\n",
    "This tutorial covers the following popular APIs:\n",
    "\n",
    "- `chat/completions`\n",
    "- `completions`\n",
    "\n",
    "Check out other tutorials to learn about [vision APIs](openai_api_vision.ipynb) for vision-language models and [embedding APIs](openai_api_embeddings.ipynb) for embedding models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Launch A Server\n",
    "\n",
    "Launch the server in your terminal and wait for it to initialize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sglang.test.doc_patch import launch_server_cmd\n",
    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
    "\n",
    "server_process, port = launch_server_cmd(\n",
    "    \"python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0 --log-level warning\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")\n",
    "print(f\"Server started on http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chat Completions\n",
    "\n",
    "### Usage\n",
    "\n",
    "The server fully implements the OpenAI API.\n",
    "It will automatically apply the chat template specified in the Hugging Face tokenizer, if one is available.\n",
    "You can also specify a custom chat template with `--chat-template` when launching the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=64,\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Thinking/Reasoning Support\n",
    "\n",
    "Some models support internal reasoning or thinking processes that can be exposed in the API response. SGLang provides unified support for various reasoning models through the `chat_template_kwargs` parameter and compatible reasoning parsers.\n",
    "\n",
    "#### Supported Models and Configuration\n",
    "\n",
    "| Model Family | Chat Template Parameter | Reasoning Parser | Notes |\n",
    "|--------------|------------------------|------------------|--------|\n",
    "| DeepSeek-R1 (R1, R1-0528, R1-Distill) | `enable_thinking` | `--reasoning-parser deepseek-r1` | Standard reasoning models |\n",
    "| DeepSeek-V3.1 | `thinking` | `--reasoning-parser deepseek-v3` | Hybrid model (thinking/non-thinking modes) |\n",
    "| Qwen3 (standard) | `enable_thinking` | `--reasoning-parser qwen3` | Hybrid model (thinking/non-thinking modes) |\n",
    "| Qwen3-Thinking | N/A (always enabled) | `--reasoning-parser qwen3-thinking` | Always generates reasoning |\n",
    "| Kimi | N/A (always enabled) | `--reasoning-parser kimi` | Kimi thinking models |\n",
    "| Gpt-Oss | N/A (always enabled) | `--reasoning-parser gpt-oss` | Gpt-Oss thinking models |\n",
    "\n",
    "#### Basic Usage\n",
    "\n",
    "To enable reasoning output, you need to:\n",
    "1. Launch the server with the appropriate reasoning parser\n",
    "2. Set the model-specific parameter in `chat_template_kwargs`\n",
    "3. Optionally use `separate_reasoning: False` to not get reasoning content separately (default to `True`)\n",
    "\n",
    "**Note for Qwen3-Thinking models:** These models always generate thinking content and do not support the `enable_thinking` parameter. Use `--reasoning-parser qwen3-thinking` or `--reasoning-parser qwen3` to parse the thinking content.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: Qwen3 Models\n",
    "\n",
    "```python\n",
    "# Launch server:\n",
    "# python3 -m sglang.launch_server --model Qwen/Qwen3-4B --reasoning-parser qwen3\n",
    "\n",
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=\"EMPTY\",\n",
    "    base_url=f\"http://127.0.0.1:30000/v1\",\n",
    ")\n",
    "\n",
    "model = \"Qwen/Qwen3-4B\"\n",
    "messages = [{\"role\": \"user\", \"content\": \"How many r's are in 'strawberry'?\"}]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=model,\n",
    "    messages=messages,\n",
    "    extra_body={\n",
    "        \"chat_template_kwargs\": {\"enable_thinking\": True},\n",
    "        \"separate_reasoning\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "print(\"Reasoning:\", response.choices[0].message.reasoning_content)\n",
    "print(\"-\"*100)\n",
    "print(\"Answer:\", response.choices[0].message.content)\n",
    "```\n",
    "\n",
    "**ExampleOutput:**\n",
    "```\n",
    "Reasoning: Okay, so the user is asking how many 'r's are in the word 'strawberry'. Let me think. First, I need to make sure I have the word spelled correctly. Strawberry... S-T-R-A-W-B-E-R-R-Y. Wait, is that right? Let me break it down.\n",
    "\n",
    "Starting with 'strawberry', let's write out the letters one by one. S, T, R, A, W, B, E, R, R, Y. Hmm, wait, that's 10 letters. Let me check again. S (1), T (2), R (3), A (4), W (5), B (6), E (7), R (8), R (9), Y (10). So the letters are S-T-R-A-W-B-E-R-R-Y. \n",
    "...\n",
    "Therefore, the answer should be three R's in 'strawberry'. But I need to make sure I'm not counting any other letters as R. Let me check again. S, T, R, A, W, B, E, R, R, Y. No other R's. So three in total. Yeah, that seems right.\n",
    "\n",
    "----------------------------------------------------------------------------------------------------\n",
    "Answer: The word \"strawberry\" contains **three** letters 'r'. Here's the breakdown:\n",
    "\n",
    "1. **S-T-R-A-W-B-E-R-R-Y**  \n",
    "   - The **third letter** is 'R'.  \n",
    "   - The **eighth and ninth letters** are also 'R's.  \n",
    "\n",
    "Thus, the total count is **3**.  \n",
    "\n",
    "**Answer:** 3.\n",
    "```\n",
    "\n",
    "**Note:** Setting `\"enable_thinking\": False` (or omitting it) will result in `reasoning_content` being `None`. Qwen3-Thinking models always generate reasoning content and don't support the `enable_thinking` parameter.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logit Bias Support\n",
    "\n",
    "SGLang supports the `logit_bias` parameter for both chat completions and completions APIs. This parameter allows you to modify the likelihood of specific tokens being generated by adding bias values to their logits. The bias values can range from -100 to 100, where:\n",
    "\n",
    "- **Positive values** (0 to 100) increase the likelihood of the token being selected\n",
    "- **Negative values** (-100 to 0) decrease the likelihood of the token being selected\n",
    "- **-100** effectively prevents the token from being generated\n",
    "\n",
    "The `logit_bias` parameter accepts a dictionary where keys are token IDs (as strings) and values are the bias amounts (as floats).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting Token IDs\n",
    "\n",
    "To use `logit_bias` effectively, you need to know the token IDs for the words you want to bias. Here's how to get token IDs:\n",
    "\n",
    "```python\n",
    "# Get tokenizer to find token IDs\n",
    "import tiktoken\n",
    "\n",
    "# For OpenAI models, use the appropriate encoding\n",
    "tokenizer = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")  # or your model\n",
    "\n",
    "# Get token IDs for specific words\n",
    "word = \"sunny\"\n",
    "token_ids = tokenizer.encode(word)\n",
    "print(f\"Token IDs for '{word}': {token_ids}\")\n",
    "\n",
    "# For SGLang models, you can access the tokenizer through the client\n",
    "# and get token IDs for bias\n",
    "```\n",
    "\n",
    "**Important:** The `logit_bias` parameter uses token IDs as string keys, not the actual words.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: DeepSeek-V3 Models\n",
    "\n",
    "DeepSeek-V3 models support thinking mode through the `thinking` parameter:\n",
    "\n",
    "```python\n",
    "# Launch server:\n",
    "# python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.1 --tp 8  --reasoning-parser deepseek-v3\n",
    "\n",
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=\"EMPTY\",\n",
    "    base_url=f\"http://127.0.0.1:30000/v1\",\n",
    ")\n",
    "\n",
    "model = \"deepseek-ai/DeepSeek-V3.1\"\n",
    "messages = [{\"role\": \"user\", \"content\": \"How many r's are in 'strawberry'?\"}]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=model,\n",
    "    messages=messages,\n",
    "    extra_body={\n",
    "        \"chat_template_kwargs\": {\"thinking\": True},\n",
    "        \"separate_reasoning\": True\n",
    "    }\n",
    ")\n",
    "\n",
    "print(\"Reasoning:\", response.choices[0].message.reasoning_content)\n",
    "print(\"-\"*100)\n",
    "print(\"Answer:\", response.choices[0].message.content)\n",
    "```\n",
    "\n",
    "**Example Output:**\n",
    "```\n",
    "Reasoning: First, the question is: \"How many r's are in 'strawberry'?\"\n",
    "\n",
    "I need to count the number of times the letter 'r' appears in the word \"strawberry\".\n",
    "\n",
    "Let me write out the word: S-T-R-A-W-B-E-R-R-Y.\n",
    "\n",
    "Now, I'll go through each letter and count the 'r's.\n",
    "...\n",
    "So, I have three 'r's in \"strawberry\".\n",
    "\n",
    "I should double-check. The word is spelled S-T-R-A-W-B-E-R-R-Y. The letters are at positions: 3, 8, and 9 are 'r's. Yes, that's correct.\n",
    "\n",
    "Therefore, the answer should be 3.\n",
    "----------------------------------------------------------------------------------------------------\n",
    "Answer: The word \"strawberry\" contains **3** instances of the letter \"r\". Here's a breakdown for clarity:\n",
    "\n",
    "- The word is spelled: S-T-R-A-W-B-E-R-R-Y\n",
    "- The \"r\" appears at the 3rd, 8th, and 9th positions.\n",
    "```\n",
    "\n",
    "**Note:** DeepSeek-V3 models use the `thinking` parameter (not `enable_thinking`) to control reasoning output.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example with logit_bias parameter\n",
    "# Note: You need to get the actual token IDs from your tokenizer\n",
    "# For demonstration, we'll use some example token IDs\n",
    "response = client.chat.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"Complete this sentence: The weather today is\"}\n",
    "    ],\n",
    "    temperature=0.7,\n",
    "    max_tokens=20,\n",
    "    logit_bias={\n",
    "        \"12345\": 50,  # Increase likelihood of token ID 12345\n",
    "        \"67890\": -50,  # Decrease likelihood of token ID 67890\n",
    "        \"11111\": 25,  # Slightly increase likelihood of token ID 11111\n",
    "    },\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response with logit bias: {response.choices[0].message.content}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters\n",
    "\n",
    "The chat completions API accepts OpenAI Chat Completions API's parameters. Refer to [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat/create) for more details.\n",
    "\n",
    "SGLang extends the standard API with the `extra_body` parameter, allowing for additional customization. One key option within `extra_body` is `chat_template_kwargs`, which can be used to pass arguments to the chat template processor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"system\",\n",
    "            \"content\": \"You are a knowledgeable historian who provides concise responses.\",\n",
    "        },\n",
    "        {\"role\": \"user\", \"content\": \"Tell me about ancient Rome\"},\n",
    "        {\n",
    "            \"role\": \"assistant\",\n",
    "            \"content\": \"Ancient Rome was a civilization centered in Italy.\",\n",
    "        },\n",
    "        {\"role\": \"user\", \"content\": \"What were their major achievements?\"},\n",
    "    ],\n",
    "    temperature=0.3,  # Lower temperature for more focused responses\n",
    "    max_tokens=128,  # Reasonable length for a concise response\n",
    "    top_p=0.95,  # Slightly higher for better fluency\n",
    "    presence_penalty=0.2,  # Mild penalty to avoid repetition\n",
    "    frequency_penalty=0.2,  # Mild penalty for more natural language\n",
    "    n=1,  # Single response is usually more stable\n",
    "    seed=42,  # Keep for reproducibility\n",
    ")\n",
    "\n",
    "print_highlight(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Streaming mode is also supported."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logit Bias Support\n",
    "\n",
    "The completions API also supports the `logit_bias` parameter with the same functionality as described in the chat completions section above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stream = client.chat.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    messages=[{\"role\": \"user\", \"content\": \"Say this is a test\"}],\n",
    "    stream=True,\n",
    ")\n",
    "for chunk in stream:\n",
    "    if chunk.choices[0].delta.content is not None:\n",
    "        print(chunk.choices[0].delta.content, end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example with logit_bias parameter for completions API\n",
    "# Note: You need to get the actual token IDs from your tokenizer\n",
    "# For demonstration, we'll use some example token IDs\n",
    "response = client.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    prompt=\"The best programming language for AI is\",\n",
    "    temperature=0.7,\n",
    "    max_tokens=20,\n",
    "    logit_bias={\n",
    "        \"12345\": 75,  # Strongly favor token ID 12345\n",
    "        \"67890\": -100,  # Completely avoid token ID 67890\n",
    "        \"11111\": -25,  # Slightly discourage token ID 11111\n",
    "    },\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response with logit bias: {response.choices[0].text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Completions\n",
    "\n",
    "### Usage\n",
    "Completions API is similar to Chat Completions API, but without the `messages` parameter or chat templates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    prompt=\"List 3 countries and their capitals.\",\n",
    "    temperature=0,\n",
    "    max_tokens=64,\n",
    "    n=1,\n",
    "    stop=None,\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters\n",
    "\n",
    "The completions API accepts OpenAI Completions API's parameters.  Refer to [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions/create) for more details.\n",
    "\n",
    "Here is an example of a detailed completions request:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    prompt=\"Write a short story about a space explorer.\",\n",
    "    temperature=0.7,  # Moderate temperature for creative writing\n",
    "    max_tokens=150,  # Longer response for a story\n",
    "    top_p=0.9,  # Balanced diversity in word choice\n",
    "    stop=[\"\\n\\n\", \"THE END\"],  # Multiple stop sequences\n",
    "    presence_penalty=0.3,  # Encourage novel elements\n",
    "    frequency_penalty=0.3,  # Reduce repetitive phrases\n",
    "    n=1,  # Generate one completion\n",
    "    seed=123,  # For reproducible results\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structured Outputs (JSON, Regex, EBNF)\n",
    "\n",
    "For OpenAI compatible structured outputs API, refer to [Structured Outputs](../advanced_features/structured_outputs.ipynb) for more details.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using LoRA Adapters\n",
    "\n",
    "SGLang supports LoRA (Low-Rank Adaptation) adapters with OpenAI-compatible APIs. You can specify which adapter to use directly in the `model` parameter using the `base-model:adapter-name` syntax.\n",
    "\n",
    "**Server Setup:**\n",
    "```bash\n",
    "python -m sglang.launch_server \\\n",
    "    --model-path qwen/qwen2.5-0.5b-instruct \\\n",
    "    --enable-lora \\\n",
    "    --lora-paths adapter_a=/path/to/adapter_a adapter_b=/path/to/adapter_b\n",
    "```\n",
    "\n",
    "For more details on LoRA serving configuration, see the [LoRA documentation](../advanced_features/lora.ipynb).\n",
    "\n",
    "**API Call:**\n",
    "\n",
    "(Recommended) Use the `model:adapter` syntax to specify which adapter to use:\n",
    "```python\n",
    "response = client.chat.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct:adapter_a\",  # ← base-model:adapter-name\n",
    "    messages=[{\"role\": \"user\", \"content\": \"Convert to SQL: show all users\"}],\n",
    "    max_tokens=50,\n",
    ")\n",
    "```\n",
    "\n",
    "**Backward Compatible: Using `extra_body`**\n",
    "\n",
    "The old `extra_body` method is still supported for backward compatibility:\n",
    "```python\n",
    "# Backward compatible method\n",
    "response = client.chat.completions.create(\n",
    "    model=\"qwen/qwen2.5-0.5b-instruct\",\n",
    "    messages=[{\"role\": \"user\", \"content\": \"Convert to SQL: show all users\"}],\n",
    "    extra_body={\"lora_path\": \"adapter_a\"},  # ← old method\n",
    "    max_tokens=50,\n",
    ")\n",
    "```\n",
    "**Note:** When both `model:adapter` and `extra_body[\"lora_path\"]` are specified, the `model:adapter` syntax takes precedence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  }
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