support image input in api #3971 #4061

README.md

@@ -456,6 +456,9 @@ docker compose -f ./docker-compose.yml up -d
 CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 ```
 
+> [!TIP]
+> Visit https://platform.openai.com/docs/api-reference/chat/create for API document.
+
 ### Download from ModelScope Hub
 
 If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.

README_zh.md

@@ -454,6 +454,9 @@ docker compose -f ./docker-compose.yml up -d
 CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 ```
 
+> [!TIP]
+> API 文档请查阅 https://platform.openai.com/docs/api-reference/chat/create。
+
 ### 从魔搭社区下载
 
 如果您在 Hugging Face 模型和数据集的下载中遇到了问题，可以通过下述方法使用魔搭社区。

src/llamafactory/api/chat.py

@@ -1,10 +1,11 @@
 import json
+import os
 import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
 
 from ..data import Role as DataRole
 from ..extras.logging import get_logger
-from ..extras.packages import is_fastapi_available
+from ..extras.packages import is_fastapi_available, is_pillow_available
 from .common import dictify, jsonify
 from .protocol import (
     ChatCompletionMessage,
@@ -25,7 +26,14 @@ if is_fastapi_available():
     from fastapi import HTTPException, status
 
 
+if is_pillow_available():
+    import requests
+    from PIL import Image
+
+
 if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
     from ..chat import ChatModel
     from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
 
@@ -40,7 +48,9 @@ ROLE_MAPPING = {
 }
 
 
-def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, str]], str, str]:
+def _process_request(
+    request: "ChatCompletionRequest",
+) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["NDArray"]]:
     logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
 
     if len(request.messages) == 0:
@@ -49,12 +59,13 @@ def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, s
     if request.messages[0].role == Role.SYSTEM:
         system = request.messages.pop(0).content
     else:
-        system = ""
+        system = None
 
     if len(request.messages) % 2 == 0:
         raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
 
     input_messages = []
+    image = None
     for i, message in enumerate(request.messages):
         if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
             raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
@@ -66,6 +77,18 @@ def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, s
             arguments = message.tool_calls[0].function.arguments
             content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
             input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
+        elif isinstance(message.content, list):
+            for input_item in message.content:
+                if input_item.type == "text":
+                    input_messages.append({"role": ROLE_MAPPING[message.role], "content": input_item.text})
+                else:
+                    image_url = input_item.image_url.url
+                    if os.path.isfile(image_url):
+                        image_path = open(image_url, "rb")
+                    else:
+                        image_path = requests.get(image_url, stream=True).raw
+
+                    image = Image.open(image_path).convert("RGB")
         else:
             input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
 
@@ -76,9 +99,9 @@ def _process_request(request: "ChatCompletionRequest") -> Tuple[List[Dict[str, s
         except Exception:
             raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
     else:
-        tools = ""
+        tools = None
 
-    return input_messages, system, tools
+    return input_messages, system, tools, image
 
 
 def _create_stream_chat_completion_chunk(
@@ -97,11 +120,12 @@ async def create_chat_completion_response(
     request: "ChatCompletionRequest", chat_model: "ChatModel"
 ) -> "ChatCompletionResponse":
     completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
-    input_messages, system, tools = _process_request(request)
+    input_messages, system, tools, image = _process_request(request)
     responses = await chat_model.achat(
         input_messages,
         system,
         tools,
+        image,
         do_sample=request.do_sample,
         temperature=request.temperature,
         top_p=request.top_p,
@@ -145,7 +169,7 @@ async def create_stream_chat_completion_response(
     request: "ChatCompletionRequest", chat_model: "ChatModel"
 ) -> AsyncGenerator[str, None]:
     completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
-    input_messages, system, tools = _process_request(request)
+    input_messages, system, tools, image = _process_request(request)
     if tools:
         raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
 
@@ -159,6 +183,7 @@ async def create_stream_chat_completion_response(
         input_messages,
         system,
         tools,
+        image,
         do_sample=request.do_sample,
         temperature=request.temperature,
         top_p=request.top_p,

src/llamafactory/api/protocol.py

@@ -56,9 +56,19 @@ class FunctionCall(BaseModel):
     function: Function
 
 
+class ImageURL(BaseModel):
+    url: str
+
+
+class MultimodalInputItem(BaseModel):
+    type: Literal["text", "image_url"]
+    text: Optional[str] = None
+    image_url: Optional[ImageURL] = None
+
+
 class ChatMessage(BaseModel):
     role: Role
-    content: Optional[str] = None
+    content: Optional[Union[str, List[MultimodalInputItem]]] = None
     tool_calls: Optional[List[FunctionCall]] = None