feat: image parse in file

src/memos/mem_reader/read_multi_modal/file_content_parser.py

@@ -2,6 +2,7 @@
 
 import concurrent.futures
 import os
+import re
 import tempfile
 
 from typing import Any
@@ -13,6 +14,7 @@
 from memos.llms.base import BaseLLM
 from memos.log import get_logger
 from memos.mem_reader.read_multi_modal.base import BaseMessageParser, _derive_key
+from memos.mem_reader.read_multi_modal.image_parser import ImageParser
 from memos.mem_reader.read_multi_modal.utils import (
     detect_lang,
     get_parser,
@@ -129,6 +131,137 @@ def _handle_local(self, data: str) -> str:
         logger.info("[FileContentParser] Local file paths are not supported in fine mode.")
         return ""
 
+    def _process_single_image(
+        self, image_url: str, original_ref: str, info: dict[str, Any], **kwargs
+    ) -> tuple[str, str]:
+        """
+        Process a single image and return (original_ref, replacement_text).
+
+        Args:
+            image_url: URL of the image to process
+            original_ref: Original markdown image reference to replace
+            info: Dictionary containing user_id and session_id
+            **kwargs: Additional parameters for ImageParser
+
+        Returns:
+            Tuple of (original_ref, replacement_text)
+        """
+        try:
+            # Construct image message format for ImageParser
+            image_message = {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url,
+                    "detail": "auto",
+                },
+            }
+
+            # Process image using ImageParser
+            logger.debug(f"[FileContentParser] Processing image: {image_url}")
+            memory_items = self.image_parser.parse_fine(image_message, info, **kwargs)
+
+            # Extract text content from memory items (only strings as requested)
+            extracted_texts = []
+            for item in memory_items:
+                if hasattr(item, "memory") and item.memory:
+                    extracted_texts.append(str(item.memory))
+
+            if extracted_texts:
+                # Combine all extracted texts
+                extracted_content = "\n".join(extracted_texts)
+                # Replace image with extracted content
+                return (
+                    original_ref,
+                    f"\n[Image Content from {image_url}]:\n{extracted_content}\n",
+                )
+            else:
+                # If no content extracted, keep original with a note
+                logger.warning(f"[FileContentParser] No content extracted from image: {image_url}")
+                return (
+                    original_ref,
+                    f"\n[Image: {image_url} - No content extracted]\n",
+                )
+
+        except Exception as e:
+            logger.error(f"[FileContentParser] Error processing image {image_url}: {e}")
+            # On error, keep original image reference
+            return (original_ref, original_ref)
+
+    def _extract_and_process_images(self, text: str, info: dict[str, Any], **kwargs) -> str:
+        """
+        Extract all images from markdown text and process them using ImageParser in parallel.
+        Replaces image references with extracted text content.
+
+        Args:
+            text: Markdown text containing image references
+            info: Dictionary containing user_id and session_id
+            **kwargs: Additional parameters for ImageParser
+
+        Returns:
+            Text with image references replaced by extracted content
+        """
+        if not text or not self.image_parser:
+            return text
+
+        # Pattern to match markdown images: ![](url) or ![alt](url)
+        image_pattern = r"!\[([^\]]*)\]\(([^)]+)\)"
+
+        # Find all image matches first
+        image_matches = list(re.finditer(image_pattern, text))
+        if not image_matches:
+            return text
+
+        logger.info(f"[FileContentParser] Found {len(image_matches)} images to process in parallel")
+
+        # Prepare tasks for parallel processing
+        tasks = []
+        for match in image_matches:
+            image_url = match.group(2)
+            original_ref = match.group(0)
+            tasks.append((image_url, original_ref))
+
+        # Process images in parallel
+        replacements = {}
+        max_workers = min(len(tasks), 10)  # Limit concurrent image processing
+
+        with ContextThreadPoolExecutor(max_workers=max_workers) as executor:
+            futures = {
+                executor.submit(
+                    self._process_single_image, image_url, original_ref, info, **kwargs
+                ): (image_url, original_ref)
+                for image_url, original_ref in tasks
+            }
+
+            # Collect results with progress tracking
+            for future in tqdm(
+                concurrent.futures.as_completed(futures),
+                total=len(futures),
+                desc="[FileContentParser] Processing images",
+            ):
+                try:
+                    original_ref, replacement = future.result()
+                    replacements[original_ref] = replacement
+                except Exception as e:
+                    image_url, original_ref = futures[future]
+                    logger.error(f"[FileContentParser] Future failed for image {image_url}: {e}")
+                    # On error, keep original image reference
+                    replacements[original_ref] = original_ref
+
+        # Replace all images in the text
+        processed_text = text
+        for original, replacement in replacements.items():
+            processed_text = processed_text.replace(original, replacement, 1)
+
+        # Count successfully extracted images
+        success_count = sum(
+            1 for replacement in replacements.values() if "Image Content from" in replacement
+        )
+        logger.info(
+            f"[FileContentParser] Processed {len(image_matches)} images in parallel, "
+            f"extracted content for {success_count} images"
+        )
+        return processed_text
+
     def __init__(
         self,
         embedder: BaseEmbedder,
@@ -149,6 +282,8 @@ def __init__(
         """
         super().__init__(embedder, llm)
         self.parser = parser
+        # Initialize ImageParser for processing images in markdown
+        self.image_parser = ImageParser(embedder, llm) if llm else None
 
         # Get inner markdown hostnames from config or environment
         if direct_markdown_hostnames is not None:
@@ -521,6 +656,10 @@ def parse_fine(
                         f"[FileContentParser] Failed to delete temp file {temp_file_path}: {e}"
                     )
 
+        # Extract and process images from parsed_text
+        if is_markdown and parsed_text and self.image_parser:
+            parsed_text = self._extract_and_process_images(parsed_text, info, **kwargs)
+
         # Extract info fields
         if not info:
             info = {}

src/memos/memories/textual/tree_text_memory/organize/manager.py

@@ -92,7 +92,7 @@ def add(
         """
         added_ids: list[str] = []
 
-        with ContextThreadPoolExecutor(max_workers=50) as executor:
+        with ContextThreadPoolExecutor(max_workers=10) as executor:
             futures = {executor.submit(self._process_memory, m, user_name): m for m in memories}
             for future in as_completed(futures, timeout=500):
                 try: