feat(chat): add workspace chat, attachments and agent delegation

backend/app/providers/anthropic_messages.py

@@ -39,6 +39,9 @@ class AnthropicMessagesProvider(OpenAICompatibleProvider):
             else:
                 role = message.role.value
                 content = [{"type": "text", "text": message.content}] if message.content else []
+                for uri in message.images:
+                    header, data = uri.split(",", 1)
+                    content.append({"type":"image", "source":{"type":"base64", "media_type":header[5:].split(";")[0], "data":data}})
                 content += [{"type": "tool_use", "id": call.tool_call_id, "name": call.name,
                              "input": call.arguments} for call in message.tool_calls]
             if not content:

backend/app/providers/context_budget.py

@@ -34,7 +34,7 @@ async def prepare_context(request, config, complete, *, stream=False):
     budget = policy.context_window - reserve
     if budget <= 0:
         raise ProviderError("CONTEXT_CONFIG_CONFLICT", "输出及思考预算已占满上下文窗口，请调整模型上下文配置。")
-    if request.attachments:
+    if request.attachments or any(m.images for m in request.messages):
         raise ProviderError("CONTEXT_ESTIMATE_UNSUPPORTED", "当前上下文检测只支持文本；附件 Token 无法可靠估算，请关闭该模型的检测或移除附件。")
     before = estimate(request)
     if before < budget * policy.threshold:

backend/app/providers/ollama.py

@@ -80,6 +80,7 @@ class OllamaProvider(EventStreamingMixin, HTTPProviderMixin):
             messages.append({"role": "system", "content": request.system})
         for message in request.messages:
             item: dict[str, object] = {"role": message.role.value, "content": message.content}
+            if message.images: item["images"] = [uri.split(",",1)[1] for uri in message.images]
             if message.tool_calls:
                 item["tool_calls"] = [
                     {"function": {"name": call.name, "arguments": call.arguments}}

backend/app/providers/openai_compatible.py

@@ -156,6 +156,8 @@ class OpenAICompatibleProvider(EventStreamingMixin, HTTPProviderMixin):
             result.append({"role": "system", "content": request.system})
         for message in request.messages:
             item: dict[str, object] = {"role": message.role.value, "content": message.content}
+            if message.images and message.role == MessageRole.user:
+                item['content'] = [{'type':'text','text':message.content}] + [{'type':'image_url','image_url':{'url':uri}} for uri in message.images]
             if message.role == MessageRole.assistant and message.reasoning_content is not None:
                 item['reasoning_content'] = message.reasoning_content
             if message.name:

backend/app/providers/openai_responses.py

@@ -26,7 +26,7 @@ class OpenAIResponsesProvider(OpenAICompatibleProvider):
                                "output": message.content})
                 continue
             if message.content or not message.tool_calls:
-                inputs.append({"role": message.role.value, "content": message.content})
+                inputs.append({"role": message.role.value, "content": ([{"type":"input_text","text":message.content}] + [{"type":"input_image","image_url":uri} for uri in message.images]) if message.images else message.content})
             for call in message.tool_calls:
                 inputs.append({"type": "function_call", "call_id": call.tool_call_id,
                                "name": call.name, "arguments": json.dumps(call.arguments)})