fix: input Pydantic bug

src/memos/mem_feedback/feedback.py

@@ -17,7 +17,7 @@
 from memos.mem_feedback.base import BaseMemFeedback
 from memos.mem_feedback.utils import should_keep_update, split_into_chunks
 from memos.mem_reader.factory import MemReaderFactory
-from memos.mem_reader.simple_struct import detect_lang
+from memos.mem_reader.read_multi_modal import detect_lang
 from memos.memories.textual.item import TextualMemoryItem, TreeNodeTextualMemoryMetadata
 from memos.memories.textual.tree_text_memory.organize.manager import (
     MemoryManager,

src/memos/mem_reader/multi_modal_struct.py

@@ -7,8 +7,8 @@
 from memos import log
 from memos.configs.mem_reader import MultiModalStructMemReaderConfig
 from memos.context.context import ContextThreadPoolExecutor
-from memos.mem_reader.read_multi_modal import MultiModalParser
-from memos.mem_reader.simple_struct import SimpleStructMemReader, detect_lang
+from memos.mem_reader.read_multi_modal import MultiModalParser, detect_lang
+from memos.mem_reader.simple_struct import SimpleStructMemReader
 from memos.memories.textual.item import TextualMemoryItem
 from memos.templates.tool_mem_prompts import TOOL_TRAJECTORY_PROMPT_EN, TOOL_TRAJECTORY_PROMPT_ZH
 from memos.types import MessagesType

src/memos/mem_reader/read_multi_modal/__init__.py

@@ -23,7 +23,7 @@
 from .text_content_parser import TextContentParser
 from .tool_parser import ToolParser
 from .user_parser import UserParser
-from .utils import coerce_scene_data, extract_role
+from .utils import coerce_scene_data, detect_lang, extract_role
 
 
 __all__ = [
@@ -38,5 +38,6 @@
     "ToolParser",
     "UserParser",
     "coerce_scene_data",
+    "detect_lang",
     "extract_role",
 ]

src/memos/mem_reader/read_multi_modal/image_parser.py

@@ -1,14 +1,23 @@
 """Parser for image_url content parts."""
 
+import json
+import re
+
 from typing import Any
 
 from memos.embedders.base import BaseEmbedder
 from memos.llms.base import BaseLLM
 from memos.log import get_logger
-from memos.memories.textual.item import SourceMessage, TextualMemoryItem
+from memos.memories.textual.item import (
+    SourceMessage,
+    TextualMemoryItem,
+    TreeNodeTextualMemoryMetadata,
+)
+from memos.templates.mem_reader_prompts import IMAGE_ANALYSIS_PROMPT_EN, IMAGE_ANALYSIS_PROMPT_ZH
 from memos.types.openai_chat_completion_types import ChatCompletionContentPartImageParam
 
-from .base import BaseMessageParser
+from .base import BaseMessageParser, _derive_key
+from .utils import detect_lang
 
 
 logger = get_logger(__name__)
@@ -43,7 +52,7 @@ def create_source(
                 detail = "auto"
             return SourceMessage(
                 type="image",
-                content=f"[image_url]: {url}",
+                content=url,
                 original_part=message,
                 url=url,
                 detail=detail,
@@ -87,7 +96,262 @@ def parse_fine(
         info: dict[str, Any],
         **kwargs,
     ) -> list[TextualMemoryItem]:
-        """Parse image_url in fine mode - placeholder for future vision model integration."""
-        # Fine mode processing would use vision models to extract text from images
-        # For now, return empty list
-        return []
+        """
+        Parse image_url in fine mode using vision models to extract information from images.
+
+        Args:
+            message: Image message to parse
+            info: Dictionary containing user_id and session_id
+            **kwargs: Additional parameters (e.g., context_items, custom_tags)
+
+        Returns:
+            List of TextualMemoryItem objects extracted from the image
+        """
+        if not self.llm:
+            logger.warning("[ImageParser] LLM not available for fine mode processing")
+            return []
+
+        # Extract image information
+        if not isinstance(message, dict):
+            logger.warning(f"[ImageParser] Expected dict, got {type(message)}")
+            return []
+
+        image_url = message.get("image_url", {})
+        if isinstance(image_url, dict):
+            url = image_url.get("url", "")
+            detail = image_url.get("detail", "auto")
+        else:
+            url = str(image_url)
+            detail = "auto"
+
+        if not url:
+            logger.warning("[ImageParser] No image URL found in message")
+            return []
+
+        # Create source for this image
+        source = self.create_source(message, info)
+
+        # Get context items if available
+        context_items = kwargs.get("context_items")
+
+        # Determine language from context if available
+        lang = "en"
+        if context_items:
+            for item in context_items:
+                if hasattr(item, "memory") and item.memory:
+                    lang = detect_lang(item.memory)
+                    break
+
+        # Select prompt based on language
+        image_analysis_prompt = (
+            IMAGE_ANALYSIS_PROMPT_ZH if lang == "zh" else IMAGE_ANALYSIS_PROMPT_EN
+        )
+
+        # Build messages with image content
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": image_analysis_prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": url,
+                            "detail": detail,
+                        },
+                    },
+                ],
+            }
+        ]
+
+        # Add context if available
+        if context_items:
+            context_text = ""
+            for item in context_items:
+                if hasattr(item, "memory") and item.memory:
+                    context_text += f"{item.memory}\n"
+            if context_text:
+                messages.insert(
+                    0,
+                    {
+                        "role": "system",
+                        "content": f"Context from previous conversation:\n{context_text}",
+                    },
+                )
+
+        try:
+            # Call LLM with vision model
+            response_text = self.llm.generate(messages)
+            if not response_text:
+                logger.warning("[ImageParser] Empty response from LLM")
+                return []
+
+            # Parse JSON response
+            response_json = self._parse_json_result(response_text)
+
+            # Extract memory items from response
+            memory_items = []
+            memory_list = response_json.get("memory list", [])
+
+            if not memory_list:
+                logger.warning("[ImageParser] No memory items extracted from image")
+                # Fallback: create a simple memory item with the summary
+                summary = response_json.get(
+                    "summary", "Image analyzed but no specific memories extracted."
+                )
+                if summary:
+                    memory_items.append(
+                        self._create_memory_item(
+                            value=summary,
+                            info=info,
+                            memory_type="LongTermMemory",
+                            tags=["image", "visual"],
+                            key=_derive_key(summary),
+                            sources=[source],
+                            background=summary,
+                        )
+                    )
+                return memory_items
+
+            # Create memory items from parsed response
+            for mem_data in memory_list:
+                try:
+                    # Normalize memory_type
+                    memory_type = (
+                        mem_data.get("memory_type", "LongTermMemory")
+                        .replace("长期记忆", "LongTermMemory")
+                        .replace("用户记忆", "UserMemory")
+                    )
+                    if memory_type not in ["LongTermMemory", "UserMemory"]:
+                        memory_type = "LongTermMemory"
+
+                    value = mem_data.get("value", "").strip()
+                    if not value:
+                        continue
+
+                    tags = mem_data.get("tags", [])
+                    if not isinstance(tags, list):
+                        tags = []
+                    # Add image-related tags
+                    if "image" not in [t.lower() for t in tags]:
+                        tags.append("image")
+                    if "visual" not in [t.lower() for t in tags]:
+                        tags.append("visual")
+
+                    key = mem_data.get("key", "")
+                    background = response_json.get("summary", "")
+
+                    memory_item = self._create_memory_item(
+                        value=value,
+                        info=info,
+                        memory_type=memory_type,
+                        tags=tags,
+                        key=key if key else _derive_key(value),
+                        sources=[source],
+                        background=background,
+                    )
+                    memory_items.append(memory_item)
+                except Exception as e:
+                    logger.error(f"[ImageParser] Error creating memory item: {e}")
+                    continue
+
+            return memory_items
+
+        except Exception as e:
+            logger.error(f"[ImageParser] Error processing image in fine mode: {e}")
+            # Fallback: create a simple memory item
+            fallback_value = f"Image analyzed: {url}"
+            return [
+                self._create_memory_item(
+                    value=fallback_value,
+                    info=info,
+                    memory_type="LongTermMemory",
+                    tags=["image", "visual"],
+                    key=_derive_key(fallback_value),
+                    sources=[source],
+                    background="Image processing encountered an error.",
+                )
+            ]
+
+    def _parse_json_result(self, response_text: str) -> dict:
+        """
+        Parse JSON result from LLM response.
+        Similar to SimpleStructMemReader.parse_json_result.
+        """
+        s = (response_text or "").strip()
+
+        # Try to extract JSON from code blocks
+        m = re.search(r"```(?:json)?\s*([\s\S]*?)```", s, flags=re.I)
+        s = (m.group(1) if m else s.replace("```", "")).strip()
+
+        # Find first {
+        i = s.find("{")
+        if i == -1:
+            return {}
+        s = s[i:].strip()
+
+        try:
+            return json.loads(s)
+        except json.JSONDecodeError:
+            pass
+
+        # Try to find the last } or ]
+        j = max(s.rfind("}"), s.rfind("]"))
+        if j != -1:
+            try:
+                return json.loads(s[: j + 1])
+            except json.JSONDecodeError:
+                pass
+
+        # Try to close brackets
+        def _cheap_close(t: str) -> str:
+            t += "}" * max(0, t.count("{") - t.count("}"))
+            t += "]" * max(0, t.count("[") - t.count("]"))
+            return t
+
+        t = _cheap_close(s)
+        try:
+            return json.loads(t)
+        except json.JSONDecodeError as e:
+            if "Invalid \\escape" in str(e):
+                s = s.replace("\\", "\\\\")
+                try:
+                    return json.loads(s)
+                except json.JSONDecodeError:
+                    pass
+            logger.error(f"[ImageParser] Failed to parse JSON: {e}\nResponse: {response_text}")
+            return {}
+
+    def _create_memory_item(
+        self,
+        value: str,
+        info: dict[str, Any],
+        memory_type: str,
+        tags: list[str],
+        key: str,
+        sources: list[SourceMessage],
+        background: str = "",
+    ) -> TextualMemoryItem:
+        """Create a TextualMemoryItem with the given parameters."""
+        info_ = info.copy()
+        user_id = info_.pop("user_id", "")
+        session_id = info_.pop("session_id", "")
+
+        return TextualMemoryItem(
+            memory=value,
+            metadata=TreeNodeTextualMemoryMetadata(
+                user_id=user_id,
+                session_id=session_id,
+                memory_type=memory_type,
+                status="activated",
+                tags=tags,
+                key=key,
+                embedding=self.embedder.embed([value])[0],
+                usage=[],
+                sources=sources,
+                background=background,
+                confidence=0.99,
+                type="fact",
+                info=info_,
+            ),
+        )

src/memos/mem_reader/read_multi_modal/multi_modal_parser.py

@@ -226,6 +226,8 @@ def process_transfer(
             parser = self.file_content_parser
         elif source.type == "text":
             parser = self.text_content_parser
+        elif source.type in ["image", "image_url"]:
+            parser = self.image_parser
         elif source.role:
             # Chat message, use role parser
             parser = self.role_parsers.get(source.role)

src/memos/mem_reader/read_multi_modal/user_parser.py

@@ -85,8 +85,20 @@ def create_source(
                                 original_part=part,
                             )
                         )
+                    elif part_type == "image_url":
+                        image_info = part.get("image_url", {})
+                        sources.append(
+                            SourceMessage(
+                                type="image",
+                                role=role,
+                                chat_time=chat_time,
+                                message_id=message_id,
+                                image_path=image_info.get("url"),
+                                original_part=part,
+                            )
+                        )
                     else:
-                        # image_url, input_audio, etc.
+                        # input_audio, etc.
                         sources.append(
                             SourceMessage(
                                 type=part_type,