♻️ (memory_system.py): reformat code for consistency, readability, and maintainability

- Reorder and group imports for clarity and PEP8 compliance. - Standardize string quoting and whitespace for consistency. - Refactor long function signatures and dictionary constructions for better readability. - Use double quotes for all string literals and dictionary keys. - Improve formatting of multiline statements and function calls. - Add or adjust line breaks to keep lines within recommended length. - Reformat class and method docstrings for clarity. - Use consistent indentation and spacing throughout the file. These changes improve code readability, maintainability, and consistency, making it easier for future contributors to understand and modify the codebase. No functional logic is changed.
2026-01-22 06:51:01 +01:00 · 2025-10-27 00:20:35 +03:00
parent 189c6d4226
commit bb1bd01222
1 changed files with 205 additions and 218 deletions
--- a/memory_system.py
+++ b/memory_system.py
@@ -15,18 +15,19 @@ from enum import Enum
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union

 import numpy as np
-from pydantic import BaseModel, ConfigDict, Field, ValidationError as PydanticValidationError
-
-from open_webui.utils.chat import generate_chat_completion
+from fastapi import Request
 from open_webui.models.users import Users
 from open_webui.routers.memories import Memories
-from fastapi import Request
+from open_webui.utils.chat import generate_chat_completion
+from pydantic import BaseModel, ConfigDict, Field
+from pydantic import ValidationError as PydanticValidationError

 logger = logging.getLogger(__name__)

 _SHARED_SKIP_DETECTOR_CACHE = {}
 _SHARED_SKIP_DETECTOR_CACHE_LOCK = asyncio.Lock()

+
 class Constants:
    """Centralized configuration constants for the memory system."""

@@ -44,10 +45,10 @@ class Constants:
    CACHE_KEY_HASH_PREFIX_LENGTH = 10  # Hash prefix length for cache keys

    # Retrieval & Similarity
-    SEMANTIC_RETRIEVAL_THRESHOLD = 0.25 # Semantic similarity threshold for retrieval
-    RELAXED_SEMANTIC_THRESHOLD_MULTIPLIER = 0.8 # Multiplier for relaxed similarity threshold in secondary operations
-    EXTENDED_MAX_MEMORY_MULTIPLIER = 1.6 # Multiplier for expanding memory candidates in advanced operations
-    LLM_RERANKING_TRIGGER_MULTIPLIER = 0.8 # Multiplier for LLM reranking trigger threshold
+    SEMANTIC_RETRIEVAL_THRESHOLD = 0.25  # Semantic similarity threshold for retrieval
+    RELAXED_SEMANTIC_THRESHOLD_MULTIPLIER = 0.8  # Multiplier for relaxed similarity threshold in secondary operations
+    EXTENDED_MAX_MEMORY_MULTIPLIER = 1.6  # Multiplier for expanding memory candidates in advanced operations
+    LLM_RERANKING_TRIGGER_MULTIPLIER = 0.8  # Multiplier for LLM reranking trigger threshold

    # Skip Detection
    SKIP_CATEGORY_MARGIN = 0.5  # Margin above conversational similarity for skip category classification
@@ -62,6 +63,7 @@ class Constants:
    # Default Models
    DEFAULT_LLM_MODEL = "google/gemini-2.5-flash-lite"

+
 class Prompts:
    """Container for all LLM prompts used in the memory system."""

@@ -181,6 +183,7 @@ Return: {{"ids": []}}
 Explanation: Query seeks general technical explanation without personal context. Job and family information don't affect how quantum computing concepts should be explained.
 """

+
 class Models:
    """Container for all Pydantic models used in the memory system."""

@@ -203,7 +206,7 @@ class Models:
    class MemoryOperation(StrictModel):
        """Pydantic model for memory operations with validation."""

-        operation: 'Models.MemoryOperationType' = Field(description="Type of memory operation to perform")
+        operation: "Models.MemoryOperationType" = Field(description="Type of memory operation to perform")
        content: str = Field(description="Memory content (required for CREATE/UPDATE, empty for DELETE)")
        id: str = Field(description="Memory ID (empty for CREATE, required for UPDATE/DELETE)")

@@ -221,7 +224,7 @@ class Models:
    class ConsolidationResponse(BaseModel):
        """Pydantic model for memory consolidation LLM response - object containing array of memory operations."""

-        ops: List['Models.MemoryOperation'] = Field(default_factory=list, description="List of memory operations to execute")
+        ops: List["Models.MemoryOperation"] = Field(default_factory=list, description="List of memory operations to execute")

    class MemoryRerankingResponse(BaseModel):
        """Pydantic model for memory reranking LLM response - object containing array of memory IDs."""
@@ -446,48 +449,38 @@ class SkipDetector:
    def _initialize_reference_embeddings(self) -> None:
        """Compute and cache embeddings for category descriptions."""
        try:
-            technical_embeddings = self.embedding_function(
-                self.TECHNICAL_CATEGORY_DESCRIPTIONS
-            )
+            technical_embeddings = self.embedding_function(self.TECHNICAL_CATEGORY_DESCRIPTIONS)

-            instruction_embeddings = self.embedding_function(
-                self.INSTRUCTION_CATEGORY_DESCRIPTIONS
-            )
+            instruction_embeddings = self.embedding_function(self.INSTRUCTION_CATEGORY_DESCRIPTIONS)

-            pure_math_embeddings = self.embedding_function(
-                self.PURE_MATH_CALCULATION_CATEGORY_DESCRIPTIONS
-            )
+            pure_math_embeddings = self.embedding_function(self.PURE_MATH_CALCULATION_CATEGORY_DESCRIPTIONS)

-            translation_embeddings = self.embedding_function(
-                self.EXPLICIT_TRANSLATION_CATEGORY_DESCRIPTIONS
-            )
+            translation_embeddings = self.embedding_function(self.EXPLICIT_TRANSLATION_CATEGORY_DESCRIPTIONS)

-            grammar_embeddings = self.embedding_function(
-                self.GRAMMAR_PROOFREADING_CATEGORY_DESCRIPTIONS
-            )
+            grammar_embeddings = self.embedding_function(self.GRAMMAR_PROOFREADING_CATEGORY_DESCRIPTIONS)

-            conversational_embeddings = self.embedding_function(
-                self.CONVERSATIONAL_CATEGORY_DESCRIPTIONS
-            )
+            conversational_embeddings = self.embedding_function(self.CONVERSATIONAL_CATEGORY_DESCRIPTIONS)

            self._reference_embeddings = {
-                'technical': np.array(technical_embeddings),
-                'instruction': np.array(instruction_embeddings),
-                'pure_math': np.array(pure_math_embeddings),
-                'translation': np.array(translation_embeddings),
-                'grammar': np.array(grammar_embeddings),
-                'conversational': np.array(conversational_embeddings),
+                "technical": np.array(technical_embeddings),
+                "instruction": np.array(instruction_embeddings),
+                "pure_math": np.array(pure_math_embeddings),
+                "translation": np.array(translation_embeddings),
+                "grammar": np.array(grammar_embeddings),
+                "conversational": np.array(conversational_embeddings),
            }

            total_skip_categories = (
-                len(self.TECHNICAL_CATEGORY_DESCRIPTIONS) + 
-                len(self.INSTRUCTION_CATEGORY_DESCRIPTIONS) +
-                len(self.PURE_MATH_CALCULATION_CATEGORY_DESCRIPTIONS) +
-                len(self.EXPLICIT_TRANSLATION_CATEGORY_DESCRIPTIONS) +
-                len(self.GRAMMAR_PROOFREADING_CATEGORY_DESCRIPTIONS)
+                len(self.TECHNICAL_CATEGORY_DESCRIPTIONS)
+                + len(self.INSTRUCTION_CATEGORY_DESCRIPTIONS)
+                + len(self.PURE_MATH_CALCULATION_CATEGORY_DESCRIPTIONS)
+                + len(self.EXPLICIT_TRANSLATION_CATEGORY_DESCRIPTIONS)
+                + len(self.GRAMMAR_PROOFREADING_CATEGORY_DESCRIPTIONS)
            )

-            logger.info(f"SkipDetector initialized with {total_skip_categories} skip categories and {len(self.CONVERSATIONAL_CATEGORY_DESCRIPTIONS)} personal categories")
+            logger.info(
+                f"SkipDetector initialized with {total_skip_categories} skip categories and {len(self.CONVERSATIONAL_CATEGORY_DESCRIPTIONS)} personal categories"
+            )
        except Exception as e:
            logger.error(f"Failed to initialize SkipDetector reference embeddings: {e}")
            self._reference_embeddings = None
@@ -506,7 +499,7 @@ class SkipDetector:
        msg_len = len(message)

        # Pattern 1: Multiple URLs (5+ full URLs indicates link lists or technical references)
-        url_pattern_count = message.count('http://') + message.count('https://')
+        url_pattern_count = message.count("http://") + message.count("https://")
        if url_pattern_count >= 5:
            return self.SkipReason.SKIP_TECHNICAL.value

@@ -514,95 +507,94 @@ class SkipDetector:
        words = message.split()
        for word in words:
            cleaned = word.strip('.,;:!?()[]{}"\'"')
-            if len(cleaned) > 80 and cleaned.replace('-', '').replace('_', '').isalnum():
+            if len(cleaned) > 80 and cleaned.replace("-", "").replace("_", "").isalnum():
                return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 3: Markdown/text separators (repeated ---, ===, ___, ***)
-        separator_patterns = ['---', '===', '___', '***']
+        separator_patterns = ["---", "===", "___", "***"]
        for pattern in separator_patterns:
            if message.count(pattern) >= 2:
                return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 4: Command-line patterns with context-aware detection
-        lines_stripped = [line.strip() for line in message.split('\n') if line.strip()]
+        lines_stripped = [line.strip() for line in message.split("\n") if line.strip()]
        if lines_stripped:
            actual_command_lines = 0
            for line in lines_stripped:
-                if line.startswith('$ ') and len(line) > 2:
+                if line.startswith("$ ") and len(line) > 2:
                    parts = line[2:].split()
                    if parts and parts[0].isalnum():
                        actual_command_lines += 1
-                elif '$ ' in line:
-                    dollar_index = line.find('$ ')
-                    if dollar_index > 0 and line[dollar_index-1] in (' ', ':', '\t'):
-                        parts = line[dollar_index+2:].split()
-                        if parts and len(parts[0]) > 0 and (parts[0].isalnum() or parts[0] in ['curl', 'wget', 'git', 'npm', 'pip', 'docker']):
+                elif "$ " in line:
+                    dollar_index = line.find("$ ")
+                    if dollar_index > 0 and line[dollar_index - 1] in (" ", ":", "\t"):
+                        parts = line[dollar_index + 2 :].split()
+                        if parts and len(parts[0]) > 0 and (parts[0].isalnum() or parts[0] in ["curl", "wget", "git", "npm", "pip", "docker"]):
                            actual_command_lines += 1
-                elif line.startswith('# ') and len(line) > 2:
+                elif line.startswith("# ") and len(line) > 2:
                    rest = line[2:].strip()
-                    if rest and not rest[0].isupper() and ' ' in rest:
+                    if rest and not rest[0].isupper() and " " in rest:
                        actual_command_lines += 1
-                elif line.startswith('> ') and len(line) > 2:
+                elif line.startswith("> ") and len(line) > 2:
                    pass

-            if actual_command_lines >= 1 and any(c in message for c in ['http://', 'https://', ' | ']):
+            if actual_command_lines >= 1 and any(c in message for c in ["http://", "https://", " | "]):
                return self.SkipReason.SKIP_TECHNICAL.value
            if actual_command_lines >= 3:
                return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 5: High path/URL density (dots and slashes suggesting file paths or URLs)
        if msg_len > 30:
-            slash_count = message.count('/') + message.count('\\')
-            dot_count = message.count('.')
+            slash_count = message.count("/") + message.count("\\")
+            dot_count = message.count(".")
            path_chars = slash_count + dot_count
            if path_chars > 10 and (path_chars / msg_len) > 0.15:
                return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 6: Markup character density (structured data)
-        markup_chars = sum(message.count(c) for c in '{}[]<>')
+        markup_chars = sum(message.count(c) for c in "{}[]<>")
        if markup_chars >= 6:
            if markup_chars / msg_len > 0.10:
                return self.SkipReason.SKIP_TECHNICAL.value
-            curly_count = message.count('{') + message.count('}')
+            curly_count = message.count("{") + message.count("}")
            if curly_count >= 10:
                return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 7: Structured nested content with colons (key: value patterns)
-        line_count = message.count('\n')
+        line_count = message.count("\n")
        if line_count >= 8:
-            lines = message.split('\n')
+            lines = message.split("\n")
            non_empty_lines = [line for line in lines if line.strip()]
            if non_empty_lines:
-                colon_lines = sum(1 for line in non_empty_lines if ':' in line and not line.strip().startswith('#'))
-                indented_lines = sum(1 for line in non_empty_lines if line.startswith((' ', '\t')))
+                colon_lines = sum(1 for line in non_empty_lines if ":" in line and not line.strip().startswith("#"))
+                indented_lines = sum(1 for line in non_empty_lines if line.startswith((" ", "\t")))

-                if (colon_lines / len(non_empty_lines) > 0.4 and 
-                    indented_lines / len(non_empty_lines) > 0.5):
+                if colon_lines / len(non_empty_lines) > 0.4 and indented_lines / len(non_empty_lines) > 0.5:
                    return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 8: Highly structured multi-line content (require markup chars for technical confidence)
        if line_count > 15:
-            lines = message.split('\n')
+            lines = message.split("\n")
            non_empty_lines = [line for line in lines if line.strip()]
            if non_empty_lines:
-                markup_in_lines = sum(1 for line in non_empty_lines if any(c in line for c in '{}[]<>'))
-                structured_lines = sum(1 for line in non_empty_lines if line.startswith((' ', '\t')))
+                markup_in_lines = sum(1 for line in non_empty_lines if any(c in line for c in "{}[]<>"))
+                structured_lines = sum(1 for line in non_empty_lines if line.startswith((" ", "\t")))

                if markup_in_lines / len(non_empty_lines) > 0.3:
                    return self.SkipReason.SKIP_TECHNICAL.value
                elif structured_lines / len(non_empty_lines) > 0.6:
-                    technical_keywords = ['function', 'class', 'import', 'return', 'const', 'var', 'let', 'def']
+                    technical_keywords = ["function", "class", "import", "return", "const", "var", "let", "def"]
                    if any(keyword in message.lower() for keyword in technical_keywords):
                        return self.SkipReason.SKIP_TECHNICAL.value

        # Pattern 9: Code-like indentation pattern (require code indicators to avoid false positives from bullet lists)
        if line_count >= 3:
-            lines = message.split('\n')
+            lines = message.split("\n")
            non_empty_lines = [line for line in lines if line.strip()]
            if non_empty_lines:
-                indented_lines = sum(1 for line in non_empty_lines if line[0] in (' ', '\t'))
+                indented_lines = sum(1 for line in non_empty_lines if line[0] in (" ", "\t"))
                if indented_lines / len(non_empty_lines) > 0.5:
-                    code_indicators = ['def ', 'class ', 'function ', 'return ', 'import ', 'const ', 'let ', 'var ', 'public ', 'private ']
+                    code_indicators = ["def ", "class ", "function ", "return ", "import ", "const ", "let ", "var ", "public ", "private "]
                    if any(indicator in message.lower() for indicator in code_indicators):
                        return self.SkipReason.SKIP_TECHNICAL.value

@@ -617,7 +609,7 @@ class SkipDetector:

        return None

-    def detect_skip_reason(self, message: str, max_message_chars: int, memory_system: 'Filter') -> Optional[str]:
+    def detect_skip_reason(self, message: str, max_message_chars: int, memory_system: "Filter") -> Optional[str]:
        """
        Detect if a message should be skipped using two-stage detection:
        1. Fast-path structural patterns (~95% confidence)
@@ -641,28 +633,22 @@ class SkipDetector:
        try:
            message_embedding = np.array(self.embedding_function([message.strip()])[0])

-            conversational_similarities = np.dot(
-                message_embedding, 
-                self._reference_embeddings['conversational'].T
-            )
+            conversational_similarities = np.dot(message_embedding, self._reference_embeddings["conversational"].T)
            max_conversational_similarity = float(conversational_similarities.max())

            skip_categories = [
-                ('instruction', self.SkipReason.SKIP_INSTRUCTION, self.INSTRUCTION_CATEGORY_DESCRIPTIONS),
-                ('translation', self.SkipReason.SKIP_TRANSLATION, self.EXPLICIT_TRANSLATION_CATEGORY_DESCRIPTIONS),
-                ('grammar', self.SkipReason.SKIP_GRAMMAR_PROOFREAD, self.GRAMMAR_PROOFREADING_CATEGORY_DESCRIPTIONS),
-                ('technical', self.SkipReason.SKIP_TECHNICAL, self.TECHNICAL_CATEGORY_DESCRIPTIONS),
-                ('pure_math', self.SkipReason.SKIP_PURE_MATH, self.PURE_MATH_CALCULATION_CATEGORY_DESCRIPTIONS),
+                ("instruction", self.SkipReason.SKIP_INSTRUCTION, self.INSTRUCTION_CATEGORY_DESCRIPTIONS),
+                ("translation", self.SkipReason.SKIP_TRANSLATION, self.EXPLICIT_TRANSLATION_CATEGORY_DESCRIPTIONS),
+                ("grammar", self.SkipReason.SKIP_GRAMMAR_PROOFREAD, self.GRAMMAR_PROOFREADING_CATEGORY_DESCRIPTIONS),
+                ("technical", self.SkipReason.SKIP_TECHNICAL, self.TECHNICAL_CATEGORY_DESCRIPTIONS),
+                ("pure_math", self.SkipReason.SKIP_PURE_MATH, self.PURE_MATH_CALCULATION_CATEGORY_DESCRIPTIONS),
            ]

            qualifying_categories = []
            margin_threshold = max_conversational_similarity + Constants.SKIP_CATEGORY_MARGIN

            for cat_key, skip_reason, descriptions in skip_categories:
-                similarities = np.dot(
-                    message_embedding, 
-                    self._reference_embeddings[cat_key].T
-                )
+                similarities = np.dot(message_embedding, self._reference_embeddings[cat_key].T)
                max_similarity = float(similarities.max())

                if max_similarity > margin_threshold:
@@ -670,7 +656,9 @@ class SkipDetector:

            if qualifying_categories:
                highest_similarity, highest_cat_key, highest_skip_reason = max(qualifying_categories, key=lambda x: x[0])
-                logger.info(f"🚫 Skipping message: {highest_skip_reason.value} (sim {highest_similarity:.3f} > conv {max_conversational_similarity:.3f} + {Constants.SKIP_CATEGORY_MARGIN:.3f})")
+                logger.info(
+                    f"🚫 Skipping message: {highest_skip_reason.value} (sim {highest_similarity:.3f} > conv {max_conversational_similarity:.3f} + {Constants.SKIP_CATEGORY_MARGIN:.3f})"
+                )
                return highest_skip_reason.value

            return None
@@ -724,9 +712,7 @@ CANDIDATE MEMORIES:
            logger.warning(f"🤖 LLM reranking failed during memory relevance analysis: {str(e)}")
            return candidate_memories

-    async def rerank_memories(
-        self, user_message: str, candidate_memories: List[Dict], emitter: Optional[Callable] = None
-    ) -> Tuple[List[Dict], Dict[str, Any]]:
+    async def rerank_memories(self, user_message: str, candidate_memories: List[Dict], emitter: Optional[Callable] = None) -> Tuple[List[Dict], Dict[str, Any]]:
        start_time = time.time()
        max_injection = self.memory_system.valves.max_memories_returned

@@ -737,9 +723,7 @@ CANDIDATE MEMORIES:
        if should_use_llm:
            extended_count = int(self.memory_system.valves.max_memories_returned * Constants.EXTENDED_MAX_MEMORY_MULTIPLIER)
            llm_candidates = candidate_memories[:extended_count]
-            await self.memory_system._emit_status(
-                emitter, f"🤖 LLM Analyzing {len(llm_candidates)} Memories for Relevance", done=False
-            )
+            await self.memory_system._emit_status(emitter, f"🤖 LLM Analyzing {len(llm_candidates)} Memories for Relevance", done=False)
            logger.info(f"Using LLM reranking: {decision_reason}")

            selected_memories = await self._llm_select_memories(user_message, llm_candidates, max_injection, emitter)
@@ -812,7 +796,7 @@ class LLMConsolidationService:
            candidates, threshold_info = self._filter_consolidation_candidates(all_similarities)
        else:
            candidates = []
-            threshold_info = 'N/A'
+            threshold_info = "N/A"

        logger.info(f"🎯 Found {len(candidates)} candidate memories for consolidation (threshold: {threshold_info})")

@@ -820,7 +804,9 @@ class LLMConsolidationService:

        return candidates

-    async def generate_consolidation_plan(self, user_message: str, candidate_memories: List[Dict[str, Any]], emitter: Optional[Callable] = None) -> List[Dict[str, Any]]:
+    async def generate_consolidation_plan(
+        self, user_message: str, candidate_memories: List[Dict[str, Any]], emitter: Optional[Callable] = None
+    ) -> List[Dict[str, Any]]:
        """Generate consolidation plan using LLM with clear system/user prompt separation."""
        if candidate_memories:
            memory_lines = self.memory_system._format_memories_for_llm(candidate_memories)
@@ -1020,11 +1006,18 @@ class Filter:
        max_message_chars: int = Field(default=Constants.MAX_MESSAGE_CHARS, description="Maximum user message length before skipping memory operations")
        max_memories_returned: int = Field(default=Constants.MAX_MEMORIES_PER_RETRIEVAL, description="Maximum number of memories to return in context")

-        semantic_retrieval_threshold: float = Field(default=Constants.SEMANTIC_RETRIEVAL_THRESHOLD, description="Minimum similarity threshold for memory retrieval")
-        relaxed_semantic_threshold_multiplier: float = Field(default=Constants.RELAXED_SEMANTIC_THRESHOLD_MULTIPLIER, description="Adjusts similarity threshold for memory consolidation (lower = more candidates)")        
+        semantic_retrieval_threshold: float = Field(
+            default=Constants.SEMANTIC_RETRIEVAL_THRESHOLD, description="Minimum similarity threshold for memory retrieval"
+        )
+        relaxed_semantic_threshold_multiplier: float = Field(
+            default=Constants.RELAXED_SEMANTIC_THRESHOLD_MULTIPLIER,
+            description="Adjusts similarity threshold for memory consolidation (lower = more candidates)",
+        )

        enable_llm_reranking: bool = Field(default=True, description="Enable LLM-based memory reranking for improved contextual selection")
-        llm_reranking_trigger_multiplier: float = Field(default=Constants.LLM_RERANKING_TRIGGER_MULTIPLIER, description="Controls when LLM reranking activates (lower = more aggressive)")
+        llm_reranking_trigger_multiplier: float = Field(
+            default=Constants.LLM_RERANKING_TRIGGER_MULTIPLIER, description="Controls when LLM reranking activates (lower = more aggressive)"
+        )

    def __init__(self):
        """Initialize the Memory System filter with production validation."""
@@ -1043,8 +1036,13 @@ class Filter:
        self._llm_reranking_service = LLMRerankingService(self)
        self._llm_consolidation_service = LLMConsolidationService(self)

-    async def _set_pipeline_context(self, __event_emitter__: Optional[Callable] = None, __user__: Optional[Dict[str, Any]] = None, 
-                             __model__: Optional[str] = None, __request__: Optional[Request] = None) -> None:
+    async def _set_pipeline_context(
+        self,
+        __event_emitter__: Optional[Callable] = None,
+        __user__: Optional[Dict[str, Any]] = None,
+        __model__: Optional[str] = None,
+        __request__: Optional[Request] = None,
+    ) -> None:
        """Set pipeline context parameters to avoid duplication in inlet/outlet methods."""
        if __event_emitter__:
            self.__current_event_emitter__ = __event_emitter__
@@ -1055,14 +1053,14 @@ class Filter:
        if __request__:
            self.__request__ = __request__

-            if self._embedding_function is None and hasattr(__request__.app.state, 'EMBEDDING_FUNCTION'):
+            if self._embedding_function is None and hasattr(__request__.app.state, "EMBEDDING_FUNCTION"):
                self._embedding_function = __request__.app.state.EMBEDDING_FUNCTION
                logger.info(f"✅ Using OpenWebUI's embedding function")

                if self._skip_detector is None:
                    global _SHARED_SKIP_DETECTOR_CACHE, _SHARED_SKIP_DETECTOR_CACHE_LOCK
-                    embedding_engine = getattr(__request__.app.state.config, 'RAG_EMBEDDING_ENGINE', '')
-                    embedding_model = getattr(__request__.app.state.config, 'RAG_EMBEDDING_MODEL', '')
+                    embedding_engine = getattr(__request__.app.state.config, "RAG_EMBEDDING_ENGINE", "")
+                    embedding_model = getattr(__request__.app.state.config, "RAG_EMBEDDING_MODEL", "")
                    cache_key = f"{embedding_engine}:{embedding_model}"

                    async with _SHARED_SKIP_DETECTOR_CACHE_LOCK:
@@ -1072,6 +1070,7 @@ class Filter:
                        else:
                            logger.info(f"🤖 Initializing skip detector with OpenWebUI embeddings: {cache_key}")
                            embedding_fn = self._embedding_function
+
                            def embedding_wrapper(texts: Union[str, List[str]]) -> Union[np.ndarray, List[np.ndarray]]:
                                result = embedding_fn(texts, prefix=None, user=None)
                                if isinstance(result, list):
@@ -1084,7 +1083,6 @@ class Filter:
                            _SHARED_SKIP_DETECTOR_CACHE[cache_key] = self._skip_detector
                            logger.info(f"✅ Skip detector initialized and cached")

-
    def _truncate_content(self, content: str, max_length: Optional[int] = None) -> str:
        """Truncate content with ellipsis if needed."""
        if max_length is None:
@@ -1181,16 +1179,10 @@ class Filter:
                uncached_hashes.append(text_hash)

        if uncached_texts:
-            user = await asyncio.to_thread(Users.get_user_by_id, user_id) if hasattr(self, '__user__') else None
+            user = await asyncio.to_thread(Users.get_user_by_id, user_id) if hasattr(self, "__user__") else None

            loop = asyncio.get_event_loop()
-            raw_embeddings = await loop.run_in_executor(
-                None, 
-                self._embedding_function, 
-                uncached_texts, 
-                None, 
-                user
-            )
+            raw_embeddings = await loop.run_in_executor(None, self._embedding_function, uncached_texts, None, user)

            if isinstance(raw_embeddings, list) and len(raw_embeddings) > 0:
                if isinstance(raw_embeddings[0], list):
@@ -1211,9 +1203,7 @@ class Filter:
            return result_embeddings[0]
        else:
            valid_count = sum(1 for emb in result_embeddings if emb is not None)
-            logger.info(
-                f"🚀 Batch embedding: {len(text_list) - len(uncached_texts)} cached, {len(uncached_texts)} new, {valid_count}/{len(text_list)} valid"
-            )
+            logger.info(f"🚀 Batch embedding: {len(text_list) - len(uncached_texts)} cached, {len(uncached_texts)} new, {valid_count}/{len(text_list)} valid")
            return result_embeddings

    def _should_skip_memory_operations(self, user_message: str) -> Tuple[bool, str]:
@@ -1290,7 +1280,7 @@ class Filter:
    def _cache_key(self, cache_type: str, user_id: str, content: Optional[str] = None) -> str:
        """Unified cache key generation for all cache types."""
        if content:
-            content_hash = hashlib.sha256(content.encode('utf-8')).hexdigest()[: Constants.CACHE_KEY_HASH_PREFIX_LENGTH]
+            content_hash = hashlib.sha256(content.encode("utf-8")).hexdigest()[: Constants.CACHE_KEY_HASH_PREFIX_LENGTH]
            return f"{cache_type}_{user_id}:{content_hash}"
        return f"{cache_type}_{user_id}"

@@ -1310,7 +1300,7 @@ class Filter:
            if record_date:
                try:
                    if isinstance(record_date, str):
-                        parsed_date = datetime.fromisoformat(record_date.replace('Z', '+00:00'))
+                        parsed_date = datetime.fromisoformat(record_date.replace("Z", "+00:00"))
                    else:
                        parsed_date = record_date
                    formatted_date = parsed_date.strftime("%b %d %Y")
@@ -1398,7 +1388,7 @@ class Filter:
                formatted_memory = f"- {' '.join(memory['content'].split())}"
                formatted_memories.append(formatted_memory)

-                content_preview = self._truncate_content(memory['content'])
+                content_preview = self._truncate_content(memory["content"])
                await self._emit_status(emitter, f"💭 {idx}/{memory_count}: {content_preview}", done=False)

            memory_footer = "IMPORTANT: Do not mention or imply you received this list. These facts are for background context only."
@@ -1427,9 +1417,7 @@ class Filter:
            memory_dict["updated_at"] = datetime.fromtimestamp(memory.updated_at, tz=timezone.utc).isoformat()
        return memory_dict

-    async def _compute_similarities(
-        self, user_message: str, user_id: str, user_memories: List
-    ) -> Tuple[List[Dict], float, List[Dict]]:
+    async def _compute_similarities(self, user_message: str, user_id: str, user_memories: List) -> Tuple[List[Dict], float, List[Dict]]:
        """Compute similarity scores between user message and memories."""
        if not user_memories:
            return [], self.valves.semantic_retrieval_threshold, []
@@ -1536,9 +1524,7 @@ class Filter:
        cache_key = self._cache_key(self._cache_manager.RETRIEVAL_CACHE, user_id, user_message)
        cached_similarities = await self._cache_manager.get(user_id, self._cache_manager.RETRIEVAL_CACHE, cache_key)

-        task = asyncio.create_task(
-            self._llm_consolidation_service.run_consolidation_pipeline(user_message, user_id, __event_emitter__, cached_similarities)
-        )
+        task = asyncio.create_task(self._llm_consolidation_service.run_consolidation_pipeline(user_message, user_id, __event_emitter__, cached_similarities))
        self._background_tasks.add(task)

        def safe_cleanup(t: asyncio.Task) -> None:
@@ -1582,11 +1568,7 @@ class Filter:

            await self._cache_manager.put(user_id, self._cache_manager.MEMORY_CACHE, memory_cache_key, user_memories)

-            memory_contents = [
-                memory.content
-                for memory in user_memories
-                if memory.content and len(memory.content.strip()) >= Constants.MIN_MESSAGE_CHARS
-            ]
+            memory_contents = [memory.content for memory in user_memories if memory.content and len(memory.content.strip()) >= Constants.MIN_MESSAGE_CHARS]

            if memory_contents:
                await self._generate_embeddings(memory_contents, user_id)
@@ -1650,17 +1632,17 @@ class Filter:
        if not isinstance(schema, dict):
            return schema

-        if '$ref' in schema:
-            ref_path = schema['$ref']
-            if ref_path.startswith('#/$defs/'):
-                def_name = ref_path.split('/')[-1]
+        if "$ref" in schema:
+            ref_path = schema["$ref"]
+            if ref_path.startswith("#/$defs/"):
+                def_name = ref_path.split("/")[-1]
                if schema_defs and def_name in schema_defs:
                    return self._remove_refs_from_schema(schema_defs[def_name].copy(), schema_defs)
-            return {'type': 'object'}
+            return {"type": "object"}

        result = {}
        for key, value in schema.items():
-            if key == '$defs':
+            if key == "$defs":
                continue
            elif isinstance(value, dict):
                result[key] = self._remove_refs_from_schema(value, schema_defs)
@@ -1669,8 +1651,8 @@ class Filter:
            else:
                result[key] = value

-        if result.get('type') == 'object' and 'properties' in result:
-            result['required'] = list(result['properties'].keys())
+        if result.get("type") == "object" and "properties" in result:
+            result["required"] = list(result["properties"].keys())

        return result

@@ -1692,9 +1674,9 @@ class Filter:

        if response_model:
            raw_schema = response_model.model_json_schema()
-            schema_defs = raw_schema.get('$defs', {})
+            schema_defs = raw_schema.get("$defs", {})
            schema = self._remove_refs_from_schema(raw_schema, schema_defs)
-            schema['type'] = 'object'
+            schema["type"] = "object"
            form_data["response_format"] = {"type": "json_schema", "json_schema": {"name": response_model.__name__, "strict": True, "schema": schema}}

        try:
@@ -1718,7 +1700,12 @@ class Filter:

        if isinstance(response_data, dict) and "choices" in response_data and isinstance(response_data["choices"], list) and len(response_data["choices"]) > 0:
            first_choice = response_data["choices"][0]
-            if isinstance(first_choice, dict) and "message" in first_choice and isinstance(first_choice["message"], dict) and "content" in first_choice["message"]:
+            if (
+                isinstance(first_choice, dict)
+                and "message" in first_choice
+                and isinstance(first_choice["message"], dict)
+                and "content" in first_choice["message"]
+            ):
                content = first_choice["message"]["content"]
            else:
                raise ValueError("🤖 Invalid response structure: missing content in message")