Add /v1/chat/completions/batch endpoint for batched chat completions (#38011)

Signed-off-by: Matej Rojec <64556640+MatejRojec@users.noreply.github.com>

examples/online_serving/batched_chat_completions.py

@@ -0,0 +1,194 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Examples of batched chat completions via the vLLM OpenAI-compatible API.
+
+The /v1/chat/completions/batch endpoint accepts ``messages`` as a list of
+conversations.  Each conversation is processed independently and the response
+contains one choice per conversation, indexed 0, 1, ..., N-1.
+
+Start a server first, e.g.:
+    vllm serve Qwen/Qwen2.5-1.5B-Instruct --port 8000
+
+Current limitations compared to /v1/chat/completions:
+    - Streaming is not supported.
+    - Tool use is not supported.
+    - Beam search is not supported.
+"""
+
+import json
+import os
+
+import httpx
+
+BASE_URL = os.environ.get("VLLM_BASE_URL", "http://localhost:8000")
+MODEL = os.environ.get("VLLM_MODEL", "Qwen/Qwen2.5-1.5B-Instruct")
+BATCH_URL = f"{BASE_URL}/v1/chat/completions/batch"
+
+
+def post_batch(payload: dict) -> dict:
+    response = httpx.post(BATCH_URL, json=payload, timeout=60)
+    response.raise_for_status()
+    return response.json()
+
+
+def main() -> None:
+    print("=== Example 1a: single conversation (standard endpoint) ===")
+    response = httpx.post(
+        f"{BASE_URL}/v1/chat/completions",
+        json={
+            "model": MODEL,
+            "messages": [{"role": "user", "content": "What is the capital of Japan?"}],
+        },
+        timeout=60,
+    )
+    response.raise_for_status()
+    data = response.json()
+    for choice in data["choices"]:
+        print(f"  [{choice['index']}] {choice['message']['content']}")
+
+    print("\n=== Example 1b: batched plain text (2 conversations) ===")
+    data = post_batch(
+        {
+            "model": MODEL,
+            "messages": [
+                [{"role": "user", "content": "What is the capital of France?"}],
+                [{"role": "user", "content": "What is the capital of Japan?"}],
+            ],
+        }
+    )
+    for choice in data["choices"]:
+        print(f"  [{choice['index']}] {choice['message']['content']}")
+
+    print("\n=== Example 2: batch with regex constraint (yes|no) ===")
+    data = post_batch(
+        {
+            "model": MODEL,
+            "messages": [
+                [{"role": "user", "content": "Is the sky blue? Answer yes or no."}],
+                [{"role": "user", "content": "Is fire cold? Answer yes or no."}],
+            ],
+            "structured_outputs": {"regex": "(yes|no)"},
+        }
+    )
+    for choice in data["choices"]:
+        print(f"  [{choice['index']}] {choice['message']['content']}")
+
+    print("\n=== Example 3: batch with json_schema ===")
+    person_schema = {
+        "type": "object",
+        "properties": {
+            "name": {"type": "string", "description": "Full name of the person"},
+            "age": {"type": "integer", "description": "Age in years"},
+        },
+        "required": ["name", "age"],
+    }
+    data = post_batch(
+        {
+            "model": MODEL,
+            "messages": [
+                [
+                    {
+                        "role": "user",
+                        "content": "Describe the person: name Alice, age 30.",
+                    }
+                ],
+                [{"role": "user", "content": "Describe the person: name Bob, age 25."}],
+            ],
+            "response_format": {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "person",
+                    "strict": True,
+                    "schema": person_schema,
+                },
+            },
+        }
+    )
+    for choice in data["choices"]:
+        person = json.loads(choice["message"]["content"])
+        print(f"  [{choice['index']}] {person}")
+
+    print("\n=== Example 4: batch book summaries ===")
+    book_schema = {
+        "type": "object",
+        "properties": {
+            "author": {
+                "type": "string",
+                "description": "Full name of the author",
+            },
+            "num_pages": {
+                "type": "integer",
+                "description": "Number of pages in the book",
+            },
+            "short_summary": {
+                "type": "string",
+                "description": "A one-sentence summary of the book",
+            },
+            "long_summary": {
+                "type": "string",
+                "description": (
+                    "A detailed two to three sentence summary covering "
+                    "the main themes and plot"
+                ),
+            },
+        },
+        "required": ["author", "num_pages", "short_summary", "long_summary"],
+    }
+    system_msg = {
+        "role": "system",
+        "content": (
+            "You are a literary analyst. Extract structured information "
+            "from book descriptions."
+        ),
+    }
+    data = post_batch(
+        {
+            "model": MODEL,
+            "messages": [
+                [
+                    system_msg,
+                    {
+                        "role": "user",
+                        "content": (
+                            "Extract information from this book: '1984' by George"
+                            " Orwell, published in 1949, 328 pages. A dystopian"
+                            " novel set in a totalitarian society ruled by Big"
+                            " Brother, following Winston Smith as he secretly"
+                            " rebels against the oppressive Party that surveils"
+                            " and controls every aspect of life."
+                        ),
+                    },
+                ],
+                [
+                    system_msg,
+                    {
+                        "role": "user",
+                        "content": (
+                            "Extract information from this book: 'The Hitchhiker's"
+                            " Guide to the Galaxy' by Douglas Adams, published in"
+                            " 1979, 193 pages. A comedic science fiction novel"
+                            " following Arthur Dent, an ordinary Englishman who is"
+                            " whisked off Earth moments before it is demolished to"
+                            " make way for a hyperspace bypass, and his subsequent"
+                            " absurd adventures across the universe."
+                        ),
+                    },
+                ],
+            ],
+            "response_format": {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "book_summary",
+                    "strict": True,
+                    "schema": book_schema,
+                },
+            },
+        }
+    )
+    for choice in data["choices"]:
+        book = json.loads(choice["message"]["content"])
+        print(f"  [{choice['index']}] {book}")
+
+
+if __name__ == "__main__":
+    main()

tests/entrypoints/openai/chat_completion/test_batched_chat_completions.py

@@ -0,0 +1,113 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+
+import httpx
+import pytest
+
+from tests.utils import RemoteOpenAIServer
+
+# any model with a chat template defined in tokenizer_config should work here
+MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
+
+
+@pytest.fixture(scope="module")
+def default_server_args():
+    return [
+        # use half precision for speed and memory savings in CI environment
+        "--max-model-len",
+        "2048",
+        "--max-num-seqs",
+        "128",
+        "--enforce-eager",
+    ]
+
+
+@pytest.fixture(scope="module")
+def server(default_server_args):
+    with RemoteOpenAIServer(MODEL_NAME, default_server_args) as remote_server:
+        yield remote_server
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME],
+)
+async def test_batched_chat_completions(
+    server: RemoteOpenAIServer, model_name: str
+) -> None:
+    conversations = [
+        [{"role": "user", "content": "Reply with exactly the word: alpha"}],
+        [{"role": "user", "content": "Reply with exactly the word: beta"}],
+    ]
+
+    async with httpx.AsyncClient() as http_client:
+        response = await http_client.post(
+            f"{server.url_for('v1/chat/completions/batch')}",
+            json={
+                "model": model_name,
+                "messages": conversations,
+            },
+            timeout=60,
+        )
+
+    assert response.status_code == 200, response.text
+    data = response.json()
+
+    choices = data["choices"]
+    assert len(choices) == 2
+
+    indices = {choice["index"] for choice in choices}
+    assert indices == {0, 1}
+
+    # Each conversation should produce a non-empty text response.
+    for choice in choices:
+        assert choice["message"]["content"]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME],
+)
+async def test_batched_chat_completions_with_json_schema(
+    server: RemoteOpenAIServer, model_name: str
+) -> None:
+    schema = {
+        "type": "object",
+        "properties": {
+            "answer": {"type": "string", "enum": ["yes", "no"]},
+        },
+        "required": ["answer"],
+    }
+    conversations = [
+        [{"role": "user", "content": "Is the sky blue? Answer in JSON."}],
+        [{"role": "user", "content": "Is fire cold? Answer in JSON."}],
+    ]
+
+    async with httpx.AsyncClient() as http_client:
+        response = await http_client.post(
+            f"{server.url_for('v1/chat/completions/batch')}",
+            json={
+                "model": model_name,
+                "messages": conversations,
+                "response_format": {
+                    "type": "json_schema",
+                    "json_schema": {"name": "answer", "schema": schema, "strict": True},
+                },
+            },
+            timeout=60,
+        )
+
+    assert response.status_code == 200, response.text
+    data = response.json()
+
+    choices = data["choices"]
+    assert len(choices) == 2
+
+    for choice in choices:
+        parsed = json.loads(choice["message"]["content"])
+        assert "answer" in parsed
+        assert parsed["answer"] in ("yes", "no")

tests/entrypoints/openai/test_openai_schema.py

@@ -174,6 +174,7 @@ def test_openapi_stateless(case: Case):
     timeout = {
         # requires a longer timeout
         ("POST", "/v1/chat/completions"): LONG_TIMEOUT_SECONDS,
+        ("POST", "/v1/chat/completions/batch"): LONG_TIMEOUT_SECONDS,
         ("POST", "/v1/completions"): LONG_TIMEOUT_SECONDS,
         ("POST", "/v1/messages"): LONG_TIMEOUT_SECONDS,
     }.get(key, DEFAULT_TIMEOUT_SECONDS)

vllm/entrypoints/openai/chat_completion/api_router.py

@@ -7,7 +7,9 @@ from http import HTTPStatus
 from fastapi import APIRouter, Depends, FastAPI, Request
 from fastapi.responses import JSONResponse, StreamingResponse
 
+from vllm.entrypoints.openai.chat_completion.batch_serving import OpenAIServingChatBatch
 from vllm.entrypoints.openai.chat_completion.protocol import (
+    BatchChatCompletionRequest,
     ChatCompletionRequest,
     ChatCompletionResponse,
 )
@@ -31,6 +33,10 @@ def chat(request: Request) -> OpenAIServingChat | None:
     return request.app.state.openai_serving_chat
 
 
+def batch_chat(request: Request) -> OpenAIServingChatBatch | None:
+    return request.app.state.openai_serving_chat_batch
+
+
 @router.post(
     "/v1/chat/completions",
     dependencies=[Depends(validate_json_request)],
@@ -68,5 +74,33 @@ async def create_chat_completion(request: ChatCompletionRequest, raw_request: Re
     return StreamingResponse(content=generator, media_type="text/event-stream")
 
 
+@router.post(
+    "/v1/chat/completions/batch",
+    dependencies=[Depends(validate_json_request)],
+    responses={
+        HTTPStatus.OK.value: {},
+        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
+        HTTPStatus.NOT_FOUND.value: {"model": ErrorResponse},
+        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
+        HTTPStatus.NOT_IMPLEMENTED.value: {"model": ErrorResponse},
+    },
+)
+@with_cancellation
+@load_aware_call
+async def create_batch_chat_completion(
+    request: BatchChatCompletionRequest, raw_request: Request
+):
+    handler = batch_chat(raw_request)
+    if handler is None:
+        raise NotImplementedError("The model does not support Chat Completions API")
+
+    result = await handler.create_batch_chat_completion(request, raw_request)
+
+    if isinstance(result, ErrorResponse):
+        return JSONResponse(content=result.model_dump(), status_code=result.error.code)
+
+    return JSONResponse(content=result.model_dump())
+
+
 def attach_router(app: FastAPI):
     app.include_router(router)

vllm/entrypoints/openai/chat_completion/batch_serving.py

@@ -0,0 +1,317 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import time
+from collections.abc import AsyncGenerator
+from http import HTTPStatus
+
+from fastapi import Request
+
+from vllm.entrypoints.chat_utils import ConversationMessage
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    BatchChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionResponseChoice,
+    ChatMessage,
+)
+from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
+from vllm.entrypoints.openai.engine.protocol import (
+    ErrorResponse,
+    RequestResponseMetadata,
+    UsageInfo,
+)
+from vllm.entrypoints.utils import get_max_tokens
+from vllm.inputs import EngineInput
+from vllm.logger import init_logger
+from vllm.outputs import RequestOutput
+from vllm.reasoning import ReasoningParser
+from vllm.tokenizers import TokenizerLike
+from vllm.utils.async_utils import merge_async_iterators
+from vllm.utils.collection_utils import as_list
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingChatBatch(OpenAIServingChat):
+    """Extends OpenAIServingChat with the /v1/chat/completions/batch endpoint.
+
+    Processes N conversations from a single request concurrently and returns
+    one choice per conversation indexed 0, 1, ..., N-1.
+    """
+
+    async def render_batch_chat_request(
+        self,
+        request: BatchChatCompletionRequest,
+    ) -> tuple[list[list[ConversationMessage]], list[EngineInput]] | ErrorResponse:
+        """Validate the model and preprocess a batched chat completion request.
+
+        Performs engine-aware checks then delegates per-conversation
+        preprocessing to OpenAIServingRender, validating the chat template
+        once for the whole batch.
+
+        Returns:
+            A tuple of (all_conversations, engine_prompts) on success — one
+            entry per conversation — or an ErrorResponse on failure.
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            logger.error("Error with model %s", error_check_ret)
+            return error_check_ret
+
+        if self.engine_client.errored:
+            raise self.engine_client.dead_error
+
+        render = self.openai_serving_render
+
+        if not render.use_harmony:
+            # Common case: validate the chat template once for the whole batch.
+            error_check_ret = render.validate_chat_template(
+                request_chat_template=request.chat_template,
+                chat_template_kwargs=request.chat_template_kwargs,
+                trust_request_chat_template=render.trust_request_chat_template,
+            )
+            if error_check_ret is not None:
+                return error_check_ret
+
+        tool_parser = render.tool_parser
+        tool_dicts: list[dict] | None = None
+
+        all_conversations: list[list[ConversationMessage]] = []
+        all_engine_prompts: list[EngineInput] = []
+
+        for messages in request.messages:
+            single_request = request.to_chat_completion_request(messages)
+            if render.use_harmony:
+                conversation, engine_prompts = render._make_request_with_harmony(
+                    single_request, should_include_tools=tool_dicts is not None
+                )
+            else:
+                conversation, engine_prompts = await render.preprocess_chat(
+                    single_request,
+                    messages,
+                    default_template=render.chat_template,
+                    default_template_content_format=render.chat_template_content_format,
+                    default_template_kwargs=render.default_chat_template_kwargs,
+                    tool_dicts=tool_dicts,
+                    tool_parser=tool_parser,
+                )
+            all_conversations.append(conversation)
+            all_engine_prompts.append(engine_prompts[0])
+
+        return all_conversations, all_engine_prompts
+
+    async def create_batch_chat_completion(
+        self,
+        request: BatchChatCompletionRequest,
+        raw_request: Request | None = None,
+    ) -> ChatCompletionResponse | ErrorResponse:
+        """Batch Chat Completion endpoint (/v1/chat/completions/batch).
+
+        Processes N conversations from a single request concurrently and
+        returns one choice per conversation indexed 0, 1, ..., N-1.
+        Streaming, tool use, and beam search are not supported.
+        """
+        tokenizer = self.renderer.tokenizer
+        assert tokenizer is not None
+
+        reasoning_parser: ReasoningParser | None = None
+        if self.reasoning_parser_cls:
+            chat_template_kwargs = self._prepare_extra_chat_template_kwargs(
+                request.chat_template_kwargs,
+                self.default_chat_template_kwargs,
+            )
+            reasoning_parser = self.reasoning_parser_cls(
+                tokenizer,
+                chat_template_kwargs=chat_template_kwargs,  # type: ignore[call-arg]
+            )
+
+        render_result = await self.render_batch_chat_request(request)
+        if isinstance(render_result, ErrorResponse):
+            return render_result
+        all_conversations, engine_prompts = render_result
+
+        request_id = (
+            f"chatcmpl-{self._base_request_id(raw_request, request.request_id)}"
+        )
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
+        lora_request = self._maybe_get_adapters(request, supports_default_mm_loras=True)
+        model_name = self.models.model_name(lora_request)
+        data_parallel_rank = self._get_data_parallel_rank(raw_request)
+        max_model_len = self.model_config.max_model_len
+
+        generators: list[AsyncGenerator[RequestOutput, None]] = []
+        for i, engine_prompt in enumerate(engine_prompts):
+            sub_request_id = f"{request_id}_{i}"
+            max_tokens = get_max_tokens(
+                max_model_len,
+                request.max_completion_tokens
+                if request.max_completion_tokens is not None
+                else request.max_tokens,
+                self._extract_prompt_len(engine_prompt),
+                self.default_sampling_params,
+                self.override_max_tokens,
+            )
+            single_request = request.to_chat_completion_request(request.messages[i])
+            sampling_params = single_request.to_sampling_params(
+                max_tokens, self.default_sampling_params
+            )
+            self._log_inputs(
+                sub_request_id,
+                engine_prompt,
+                params=sampling_params,
+                lora_request=lora_request,
+            )
+            trace_headers = (
+                None
+                if raw_request is None
+                else await self._get_trace_headers(raw_request.headers)
+            )
+            generators.append(
+                self.engine_client.generate(
+                    engine_prompt,
+                    sampling_params,
+                    sub_request_id,
+                    lora_request=lora_request,
+                    trace_headers=trace_headers,
+                    priority=request.priority if hasattr(request, "priority") else 0,
+                    data_parallel_rank=data_parallel_rank,
+                    reasoning_ended=None,
+                )
+            )
+
+        return await self.chat_completion_full_generator_batch(
+            request,  # type: ignore[arg-type]
+            generators,
+            request_id,
+            model_name,
+            all_conversations,
+            tokenizer,
+            request_metadata,
+            reasoning_parser,
+        )
+
+    async def chat_completion_full_generator_batch(
+        self,
+        request: BatchChatCompletionRequest,  # type: ignore[override]
+        generators: list[AsyncGenerator[RequestOutput, None]],
+        request_id: str,
+        model_name: str,
+        all_conversations: list[list[ConversationMessage]],
+        tokenizer: TokenizerLike,
+        request_metadata: RequestResponseMetadata,
+        reasoning_parser: ReasoningParser | None = None,
+    ) -> ErrorResponse | ChatCompletionResponse:
+        """Handle batched (non-streaming) chat completions.
+
+        Fans out N generators (one per conversation in the batch), collects
+        the final output for each, and assembles a single
+        ``ChatCompletionResponse`` whose ``choices`` are indexed 0,...,N-1.
+
+        Tool-use and streaming are rejected upstream by the
+        ``check_batch_mode`` validator, so neither needs to be handled here.
+        """
+        created_time = int(time.time())
+        role = self.get_chat_request_role(request)  # type: ignore[arg-type]
+
+        final_results: dict[int, RequestOutput] = {}
+        try:
+            async for prompt_idx, res in merge_async_iterators(*generators):
+                final_results[prompt_idx] = res
+        except asyncio.CancelledError:
+            return self.create_error_response("Client disconnected")
+
+        choices: list[ChatCompletionResponseChoice] = []
+        total_prompt_tokens = 0
+        total_completion_tokens = 0
+
+        for prompt_idx in range(len(generators)):
+            final_res = final_results.get(prompt_idx)
+            if final_res is None:
+                return self.create_error_response(
+                    f"No output received from the engine for prompt {prompt_idx}.",
+                    err_type="InternalServerError",
+                    status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
+                )
+
+            assert final_res.prompt_token_ids is not None
+            num_prompt_tokens = len(final_res.prompt_token_ids)
+            if final_res.encoder_prompt_token_ids is not None:
+                num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
+            total_prompt_tokens += num_prompt_tokens
+            total_completion_tokens += sum(
+                len(output.token_ids) for output in final_res.outputs
+            )
+
+            for output in final_res.outputs:
+                self._raise_if_error(output.finish_reason, request_id)
+
+                if request.logprobs and request.top_logprobs is not None:
+                    assert output.logprobs is not None, "Did not output logprobs"
+                    logprobs = self._create_chat_logprobs(
+                        token_ids=output.token_ids,
+                        top_logprobs=output.logprobs,
+                        num_output_top_logprobs=request.top_logprobs,
+                        tokenizer=tokenizer,
+                        return_as_token_id=request.return_token_ids,
+                    )
+                else:
+                    logprobs = None
+
+                if reasoning_parser:
+                    reasoning, content = reasoning_parser.extract_reasoning(
+                        output.text,
+                        request=request,  # type: ignore[arg-type]
+                    )
+                    if not getattr(request, "include_reasoning", True):
+                        reasoning = None
+                else:
+                    reasoning = None
+                    content = output.text
+
+                message = ChatMessage(role=role, reasoning=reasoning, content=content)
+
+                if request.echo:
+                    conversation = all_conversations[prompt_idx]
+                    last_msg_content: str | list[dict[str, str]] = ""
+                    if conversation and "content" in conversation[-1]:
+                        last_msg_content = conversation[-1]["content"] or ""
+                    if isinstance(last_msg_content, list):
+                        last_msg_content = "\n".join(
+                            msg["text"] for msg in last_msg_content
+                        )
+                    message.content = last_msg_content + (message.content or "")
+
+                choice_data = ChatCompletionResponseChoice(
+                    index=prompt_idx,
+                    message=message,
+                    logprobs=logprobs,
+                    finish_reason=output.finish_reason
+                    if output.finish_reason
+                    else "stop",
+                    stop_reason=output.stop_reason,
+                    token_ids=(
+                        as_list(output.token_ids) if request.return_token_ids else None
+                    ),
+                )
+                choices.append(choice_data)
+
+        usage = UsageInfo(
+            prompt_tokens=total_prompt_tokens,
+            completion_tokens=total_completion_tokens,
+            total_tokens=total_prompt_tokens + total_completion_tokens,
+        )
+        request_metadata.final_usage_info = usage
+
+        choices.sort(key=lambda c: c.index)
+
+        return ChatCompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+        )

vllm/entrypoints/openai/chat_completion/protocol.py

@@ -787,3 +787,83 @@ class ChatCompletionRequest(OpenAIBaseModel):
         if data.get("reasoning_effort") == "none":
             data["include_reasoning"] = False
         return data
+
+
+class BatchChatCompletionRequest(OpenAIBaseModel):
+    """Request model for the /v1/chat/completions/batch endpoint.
+
+    Accepts the same fields as ChatCompletionRequest except that ``messages``
+    is a list of conversations (each conversation is a
+    ``list[ChatCompletionMessageParam]``).  Each conversation is processed
+    independently and the response contains one choice per conversation,
+    indexed 0, 1, ..., N-1.
+
+    Current limitations compared to the single-conversation endpoint:
+    - Streaming is not supported (``stream`` must be False or omitted).
+    - Tool use is not supported (``tools`` must be omitted).
+    - Beam search is not supported (``use_beam_search`` must be False or omitted).
+    - The ``n`` parameter must be 1 (or omitted).
+    """
+
+    messages: list[list[ChatCompletionMessageParam]] = Field(..., min_length=1)
+    model: str | None = None
+
+    # Shared sampling / generation fields — mirror ChatCompletionRequest.
+    frequency_penalty: float | None = 0.0
+    logit_bias: dict[str, float] | None = None
+    logprobs: bool | None = False
+    top_logprobs: int | None = 0
+    max_tokens: int | None = None
+    max_completion_tokens: int | None = None
+    n: int | None = 1
+    presence_penalty: float | None = 0.0
+    response_format: Any | None = None
+    seed: int | None = Field(None, ge=_INT64_MIN, le=_INT64_MAX)
+    stop: str | list[str] | None = Field(default_factory=list)
+    temperature: float | None = 0.7
+    top_p: float | None = 1.0
+    user: str | None = None
+
+    # vLLM extensions
+    best_of: int | None = None
+    use_beam_search: bool = False
+    top_k: int | None = None
+    min_p: float | None = 0.0
+    repetition_penalty: float | None = 1.0
+    length_penalty: float | None = 1.0
+    early_stopping: bool = False
+    structured_outputs: StructuredOutputsParams | None = None
+    request_id: str | None = None
+    add_generation_prompt: bool = True
+    continue_final_message: bool = False
+    chat_template: str | None = None
+    chat_template_kwargs: dict[str, Any] | None = None
+    include_stop_str_in_output: bool = False
+    guided_decoding_backend: str | None = None
+    echo: bool = False
+    return_token_ids: bool = False
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_batch_mode(cls, data: Any) -> Any:
+        if isinstance(data, BatchChatCompletionRequest):
+            data = data.model_dump(exclude_unset=True)
+        if data.get("use_beam_search"):
+            raise ValueError(
+                "Batch chat completions do not support beam search. "
+                "Please set `use_beam_search` to False."
+            )
+        n = data.get("n", 1)
+        if n is not None and n != 1:
+            raise ValueError(
+                "Batch chat completions do not support `n > 1`. Please set `n` to 1."
+            )
+        return data
+
+    def to_chat_completion_request(
+        self, messages: list[ChatCompletionMessageParam]
+    ) -> ChatCompletionRequest:
+        """Build a single-conversation ChatCompletionRequest from one conversation."""
+        data = self.model_dump(exclude={"messages"}, exclude_none=True)
+        data["messages"] = messages
+        return ChatCompletionRequest.model_validate(data)

vllm/entrypoints/openai/engine/serving.py

@@ -26,6 +26,7 @@ from vllm.entrypoints.chat_utils import (
 )
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.chat_completion.protocol import (
+    BatchChatCompletionRequest,
     ChatCompletionNamedToolChoiceParam,
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -124,7 +125,10 @@ CompletionLikeRequest: TypeAlias = (
 )
 
 ChatLikeRequest: TypeAlias = (
-    ChatCompletionRequest | TokenizeChatRequest | PoolingChatRequest
+    ChatCompletionRequest
+    | BatchChatCompletionRequest
+    | TokenizeChatRequest
+    | PoolingChatRequest
 )
 
 SpeechToTextRequest: TypeAlias = TranscriptionRequest | TranslationRequest

vllm/entrypoints/openai/generate/api_router.py

@@ -56,6 +56,9 @@ async def init_generate_state(
         MCPToolServer,
         ToolServer,
     )
+    from vllm.entrypoints.openai.chat_completion.batch_serving import (
+        OpenAIServingChatBatch,
+    )
     from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
     from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion
     from vllm.entrypoints.openai.responses.serving import OpenAIServingResponses
@@ -96,27 +99,31 @@ async def init_generate_state(
         if "generate" in supported_tasks
         else None
     )
+    _chat_kwargs = dict(
+        engine_client=engine_client,
+        models=state.openai_serving_models,
+        response_role=args.response_role,
+        openai_serving_render=state.openai_serving_render,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+        default_chat_template_kwargs=args.default_chat_template_kwargs,
+        trust_request_chat_template=args.trust_request_chat_template,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+        enable_auto_tools=args.enable_auto_tool_choice,
+        exclude_tools_when_tool_choice_none=args.exclude_tools_when_tool_choice_none,
+        tool_parser=args.tool_call_parser,
+        reasoning_parser=args.structured_outputs_config.reasoning_parser,
+        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
+        enable_force_include_usage=args.enable_force_include_usage,
+        enable_log_outputs=args.enable_log_outputs,
+        enable_log_deltas=args.enable_log_deltas,
+    )
     state.openai_serving_chat = (
-        OpenAIServingChat(
-            engine_client,
-            state.openai_serving_models,
-            args.response_role,
-            openai_serving_render=state.openai_serving_render,
-            request_logger=request_logger,
-            chat_template=resolved_chat_template,
-            chat_template_content_format=args.chat_template_content_format,
-            default_chat_template_kwargs=args.default_chat_template_kwargs,
-            trust_request_chat_template=args.trust_request_chat_template,
-            return_tokens_as_token_ids=args.return_tokens_as_token_ids,
-            enable_auto_tools=args.enable_auto_tool_choice,
-            exclude_tools_when_tool_choice_none=args.exclude_tools_when_tool_choice_none,
-            tool_parser=args.tool_call_parser,
-            reasoning_parser=args.structured_outputs_config.reasoning_parser,
-            enable_prompt_tokens_details=args.enable_prompt_tokens_details,
-            enable_force_include_usage=args.enable_force_include_usage,
-            enable_log_outputs=args.enable_log_outputs,
-            enable_log_deltas=args.enable_log_deltas,
-        )
+        OpenAIServingChat(**_chat_kwargs) if "generate" in supported_tasks else None
+    )
+    state.openai_serving_chat_batch = (
+        OpenAIServingChatBatch(**_chat_kwargs)
         if "generate" in supported_tasks
         else None
     )