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from langchain_core.messages import HumanMessage
from helpers import (
chat_media,
ephemeral_images,
files,
history,
images,
parallel_tools,
runtime,
)
from helpers.tool import Response, Tool
from plugins._model_config.helpers.model_config import (
build_vision_model,
get_chat_model_config,
get_vision_model_config,
)
# image token estimation for context window
TOKENS_ESTIMATE = 1500
class VisionLoad(Tool):
async def execute(
self,
paths: list[str] | str = [],
query: str = "",
**kwargs,
) -> Response:
self.images_dict = {}
self.loaded_paths: list[str] = []
self.skipped_paths: list[str] = []
self.vision_config = get_vision_model_config(self.agent)
if isinstance(paths, str):
paths = [paths]
if not isinstance(paths, list):
return Response(
message="vision_load error: `paths` must be a string or an array.",
break_loop=False,
)
max_embeds = self._get_max_embeds()
requested = [
(str(path or "").strip(), self._display_input_path(str(path or "").strip(), idx + 1))
for idx, path in enumerate(paths)
]
limited_paths = requested if max_embeds <= 0 else requested[-max_embeds:]
self.skipped_paths = (
[display for _, display in requested[:-max_embeds]]
if max_embeds > 0 and len(requested) > max_embeds
else []
)
for idx, (path, display_path) in enumerate(limited_paths):
if not path:
continue
if ephemeral_images.is_ref(path):
image = ephemeral_images.consume_image(
path,
context_id=self._context_id(),
)
if image is None:
continue
display = image.display_name or display_path
stored_ref = self._store_ephemeral_image(image)
if stored_ref:
self.images_dict[display] = stored_ref
self.loaded_paths.append(display)
continue
if self._is_data_image_url(path):
stored_ref = self._store_data_url(path, preferred_name=f"vision-load-{idx + 1}.png")
if stored_ref:
self.images_dict[display_path] = stored_ref
self.loaded_paths.append(display_path)
continue
if not await runtime.call_development_function(files.exists, str(path)):
continue
if path not in self.images_dict:
mime_type, _ = guess_type(str(path))
if mime_type and mime_type.startswith("image/"):
try:
stored_ref = self._store_local_image(path, preferred_name=files.basename(path))
self.images_dict[display_path] = stored_ref
self.loaded_paths.append(display_path)
except (FileNotFoundError, OSError, ValueError):
continue
message = self._summary() if self.images_dict or self.skipped_paths else "No images processed"
if self.vision_config and self.images_dict:
try:
capsule = await self._call_vision_model(
list(self.images_dict.values()),
query,
)
message = (
f"Analyzed {len(self.images_dict)} image(s)"
f"; {len(self.skipped_paths)} skipped.\n\n{capsule.strip()}"
)
except Exception as exc:
message = f"Image analysis error: {str(exc)[:1000]}"
return Response(message=message, break_loop=False)
def _get_max_embeds(self) -> int:
cfg = self.vision_config or get_chat_model_config(self.agent)
return int(cfg.get("max_embeds", 10) or 0)
def _context_id(self) -> str:
context = getattr(self.agent, "context", None)
get_data = getattr(context, "get_data", None)
parent_id = (
get_data(parallel_tools.PARALLEL_WORKER_PARENT_CONTEXT_KEY)
if get_data
else ""
)
return str(parent_id or getattr(context, "id", "") or "").strip()
async def _call_vision_model(self, image_paths: list[str], query: str) -> str:
user_message = getattr(self.agent, "last_user_message", None)
output_text = getattr(user_message, "output_text", None)
request = str(output_text() if callable(output_text) else "").strip()
content = [
{
"type": "text",
"text": self.agent.read_prompt(
"fw.vision_load.md",
request=request,
query=str(query or "").strip(),
),
}
]
content.extend(
{"type": "image_url", "image_url": {"url": path}}
for path in image_paths
)
response, _ = await build_vision_model(self.agent).unified_call(
messages=[HumanMessage(content=content)],
)
if not str(response or "").strip():
raise RuntimeError("Vision Model returned an empty response.")
return str(response)
def _store_ephemeral_image(self, image: ephemeral_images.EphemeralImage) -> str:
context_id = self._context_id()
if not context_id:
return image.data_url
source = chat_media.infer_source(image.ref, image.display_name)
category = chat_media.category_for_source(source)
saved = chat_media.save_image_base64(
context_id=context_id,
data=image.data,
mime_type=image.mime,
category=category,
source=source,
preferred_name=image.display_name,
)
return saved.a0_path
def _store_data_url(self, data_url: str, *, preferred_name: str = "") -> str:
context_id = self._context_id()
if not context_id:
return data_url
source = chat_media.infer_source(data_url, preferred_name)
category = chat_media.category_for_source(source)
saved = chat_media.save_image_data_url(
context_id=context_id,
data_url=data_url,
category=category,
source=source,
preferred_name=preferred_name,
)
return saved.a0_path
def _store_local_image(self, path: str, *, preferred_name: str = "") -> str:
context_id = self._context_id()
if not context_id:
return images.to_data_url(path)
return chat_media.materialize_image_ref(
context_id=context_id,
url=path,
source=chat_media.infer_source(path, preferred_name),
preferred_name=preferred_name,
)
def _summary(self) -> str:
loaded = "\n".join(self.loaded_paths) if self.loaded_paths else "none"
summary = f"Loaded images ({len(self.loaded_paths)}):\n{loaded}"
if self.skipped_paths:
summary += (
f"\n\nSkipped images ({len(self.skipped_paths)}, max {self._get_max_embeds()}):\n"
+ "\n".join(self.skipped_paths)
)
return summary
@staticmethod
def _is_data_image_url(value: str) -> bool:
normalized = str(value or "").strip().lower()
return normalized.startswith("data:image/") and ";base64," in normalized
@classmethod
def _display_input_path(cls, value: str, index: int) -> str:
if ephemeral_images.is_ref(value):
return ephemeral_images.display_ref(value)
if cls._is_data_image_url(value):
prefix = value.split(",", 1)[0]
return f"{prefix},<ephemeral-image-{index}>"
return value
async def after_execution(self, response: Response, **kwargs):
await super().after_execution(response, **kwargs)
if self.images_dict and not self.vision_config:
content = [
{"type": "image_url", "image_url": {"url": image_path}}
for image_path in self.images_dict.values()
]
raw_message = history.RawMessage(
raw_content=content,
preview="<Image attachments loaded by path>",
)
tokens = TOKENS_ESTIMATE * len(content)
if not parallel_tools.queue_parallel_parent_history(
self.agent,
content=raw_message,
tokens=tokens,
):
self.agent.hist_add_message(
False,
content=raw_message,
tokens=tokens,
)
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