"""Utilities for processing multi-modal data (images/videos) for specific vision-language models.
Supported models:
- Qwen2.5-VL, Qwen3-VL series
- Kimi VL series
- GLM VL series
Provides functions to:
1. Parse prompts with media tags (<image>/<video>)
2. Validate multi-modal content in conversations
3. Preprocess media inputs for inference/training
4. Construct model-compatible message formats
Note:
Only processors with class names containing both ("Qwen", "Kimi" OR "Glm") AND "Processor" are supported.
Relies on `qwen_vl_utils.process_vision_info` for media extraction.
"""
import re
from typing import Any, Dict, List, Union
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def is_qwen_like_processor(processor: Any) -> bool:
return re.search(r"(Qwen|Kimi|Glm).*Processor", processor.__class__.__name__) is not None
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def build_multi_modal_data(
processor: Any,
messages: List[Dict],
) -> Dict[str, Any]:
"""Extract and preprocess vision inputs from multi-modal messages for vLLM inference.
Processes messages containing image/video placeholders using model-specific vision utilities.
Returns structured media inputs compatible with vLLM's multi-modal API.
Args:
processor: Vision-language processor instance (must have class name containing
("Qwen", "Kimi" OR "Glm") AND "Processor").
messages: List of conversation messages in model-expected format. Each message's "content"
may be a string or list of content items (text/image/video dictionaries).
Returns:
Dictionary containing processed media inputs with keys:
- "image": List of processed image objects (if images exist)
- "video": List of processed video objects (if videos exist)
Keys are omitted when no corresponding media is present.
Raises:
NotImplementedError: If processor class name doesn't match supported patterns.
ImportError: If required `qwen_vl_utils` module is unavailable.
Example:
>>> messages = [{"role": "user", "content": [{"type": "image", "image": "img.jpg"}, {"type": "text", "text": "Describe this"}]}]
>>> build_multi_modal_data(processor, messages)
{"image": [processed_image]}
"""
processor_class_name = processor.__class__.__name__
if is_qwen_like_processor(processor):
from qwen_vl_utils import process_vision_info
image_inputs, video_inputs = process_vision_info(messages)
multi_modal_data = {}
if image_inputs:
multi_modal_data["image"] = image_inputs
if video_inputs:
multi_modal_data["video"] = video_inputs
return multi_modal_data
raise NotImplementedError(
f"Processor '{processor_class_name}' not supported. Only Qwen/Kimi/Glm VL processors are supported."
)
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def build_mm_message(
prompt: str, images: List[Union[str, Any]], videos: List[Union[str, Any]]
) -> Dict[str, Any]:
"""Construct multi-modal message by injecting media references at tag positions in prompt.
Parses prompt for <image>/<video> tags, replaces them with corresponding media references,
and handles surplus media items. Extra media (beyond tag count) is prepended to content.
Args:
prompt: Text containing optional <image> and <video> tags as media placeholders.
Example: "First <image> then <video> and finally <image>"
images: List of image references (file paths, URLs, or PIL images) in order of appearance.
videos: List of video references (file paths, URLs) in order of appearance.
Returns:
Message dictionary formatted for VL models:
{
"role": "user",
"content": [
{"type": "image", "image": ...}, # Surplus media first
{"type": "video", "video": ...},
{"type": "text", "text": "First "},
{"type": "image", "image": ...}, # Tag-replaced media
...
]
}
Raises:
ValueError: If prompt contains more <image> tags than provided images,
or more <video> tags than provided videos.
Behavior details:
- Tags are case-sensitive and must be exact: "<image>", "<video>"
- Empty text segments between tags are omitted
- Surplus media (images/videos beyond tag count) appears at START of content list
- Text segments preserve original prompt ordering around tags
"""
content_list = []
segments = re.split(r"(<image>|<video>)", prompt)
img_idx, vid_idx = 0, 0
for segment in segments:
if segment == "<image>":
if img_idx >= len(images):
raise ValueError("More <image> tags in prompt than images provided.")
content_list.append({"type": "image", "image": images[img_idx]})
img_idx += 1
elif segment == "<video>":
if vid_idx >= len(videos):
raise ValueError("More <video> tags in prompt than videos provided.")
content_list.append({"type": "video", "video": videos[vid_idx]})
vid_idx += 1
elif len(segment) == 0:
continue
else:
content_list.append({"type": "text", "text": segment})
# Prepend surplus media items (not referenced by tags)
surplus_content = []
while img_idx < len(images):
surplus_content.append({"type": "image", "image": images[img_idx]})
img_idx += 1
while vid_idx < len(videos):
surplus_content.append({"type": "video", "video": videos[vid_idx]})
vid_idx += 1
content_list = surplus_content + content_list
if len(content_list) == 1 and content_list[0]["type"] == "text":
return {"role": "user", "content": content_list[0]["text"]}
return {"role": "user", "content": content_list}
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def has_multi_modal_content(messages: List[Dict]) -> bool:
"""Check if any message contains non-text (image/video) content.
Inspects message content structure to detect multi-modal elements. Handles both:
- String content (text-only, returns False)
- List content (multi-modal candidates)
Args:
messages: List of conversation messages. Each message must contain a "content" field.
Content may be:
- str: Plain text message
- List[Dict]: Multi-modal content items (each with "type" key)
Returns:
True if any message contains at least one non-text content item (type != "text"),
False otherwise.
Example:
>>> msg = [{"role": "user", "content": [{"type": "text", "text": "Hi"}, {"type": "image", "image": "..."}]}]
>>> has_multi_modal_content(msg)
True
"""
for message in messages:
content = message.get("content", [])
if isinstance(content, list):
for item in content:
if item.get("type", "text") != "text":
return True
return False
