""" title: Memory System description: A semantic memory management system for Open WebUI that consolidates, deduplicates, and retrieves personalized user memories using LLM operations. version: 1.1.0 authors: https://github.com/mtayfur license: Apache-2.0 """ import asyncio import hashlib import json import logging import statistics import time from collections import OrderedDict from datetime import datetime, timezone from enum import Enum from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np from fastapi import Request from open_webui.models.users import Users from open_webui.routers.memories import Memories 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.""" # Core System Limits MAX_MEMORY_CONTENT_CHARS = 500 # Character limit for LLM prompt memory content MAX_MEMORIES_PER_RETRIEVAL = 10 # Maximum memories returned per query MAX_MESSAGE_CHARS = 2500 # Maximum message length for validation MIN_MESSAGE_CHARS = 10 # Minimum message length for validation DATABASE_OPERATION_TIMEOUT_SEC = 10 # Timeout for DB operations like user lookup LLM_CONSOLIDATION_TIMEOUT_SEC = 60.0 # Timeout for LLM consolidation operations # Cache System MAX_CACHE_ENTRIES_PER_TYPE = 500 # Maximum cache entries per cache type MAX_CONCURRENT_USER_CACHES = 50 # Maximum concurrent user cache instances 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 ) # Skip Detection SKIP_CATEGORY_MARGIN = ( 0.20 # Margin above personal similarity for skip category classification ) DEDUPLICATION_SIMILARITY_THRESHOLD = ( 0.90 # Similarity threshold for deduplication checks ) # Safety & Operations MAX_DELETE_OPERATIONS_RATIO = 0.6 # Maximum delete operations ratio for safety MIN_OPS_FOR_DELETE_RATIO_CHECK = 6 # Minimum operations to apply ratio check # Content Display CONTENT_PREVIEW_LENGTH = 100 # Maximum length for content preview display class Prompts: """Container for all LLM prompts used in the memory system.""" MEMORY_CONSOLIDATION = f"""You are the Memory System Consolidator, a specialist in creating precise user memories. ## OBJECTIVE Your goal is to build precise memories of the user's personal narrative with factual, temporal statements. ## AVAILABLE OPERATIONS - CREATE: For new, personal facts. Must be semantically and temporally enhanced. - UPDATE: To modify existing memories, including making facts historical with a date range. - DELETE: For explicit user requests or to resolve contradictions. - SKIP: When no new, personal information is provided. ## PROCESSING GUIDELINES - Personal Facts Only: Store only significant facts with lasting relevance to the user's life and identity. Exclude transient situations, questions, general knowledge, casual mentions, or momentary states. - **Filter for Intent:** You MUST SKIP if the user's primary intent is instructional, technical, or analytical, even if the message contains personal details. This includes requests to: - Rewrite, revise, translate, or proofread a block of text (e.g., "revise this review for me"). - Answer a general knowledge, math, or technical question. - Explain a concept, perform a calculation, or act as a persona. **Only store facts when the user is *directly stating* them as part of a personal narrative, not when providing them as content for a task.** - Maintain Temporal Accuracy: - Capture Dates: Record temporal information when explicitly stated or clearly derivable. Convert relative references (last month, yesterday) to specific dates. - Preserve History: Transform superseded facts into past-tense statements with defined time boundaries. - Avoid Assumptions: Do not assign current dates to ongoing states, habits, or conditions lacking explicit temporal context. - Build Rich Entities: - Fuse Identifiers: Combine nouns/pronouns with specific names into a single entity. - Capture Relationships: Always store relationships in first-person format with complete relationship context. Never store incomplete relationships, always specify with whom. - Retroactive Enrichment: If a name is provided for prior entity, UPDATE only if substantially valuable. - Ensure Memory Quality: - High Bar for Creation: Only CREATE memories for significant life facts, relationships, events, or core personal attributes. Skip trivial details or passing interests. - Mandatory Semantic Enhancement: Enhance entities with descriptive categorical nouns for better retrieval. - Verify Nouns/Pronouns: Link pronouns (he, she, they) and nouns to specific entities. - First-Person Format: Write all memories in English from the user's perspective. ## DECISION FRAMEWORK - Selectivity: Verify the user's *primary intent* is to state a direct, personally significant fact with lasting importance. If the intent is instructional, analytical, or a general question, SKIP. Never create duplicate memories. Skip momentary events or casual mentions. Be conservative with CREATE and UPDATE operations. - Strategy: Strongly prioritize enriching existing memories over creating new ones. Analyze the message holistically to identify naturally connected facts (same person, event, or timeframe) and combine them into a unified, cohesive memory rather than fragmenting them. Each memory must be self-contained and **never** merge unrelated information. - Execution: For new significant facts, use CREATE. For simple attribute changes, use UPDATE only if it meaningfully improves the memory. For significant changes, use UPDATE to make the old memory historical, then CREATE the new one. For contradictions, use DELETE. ## EXAMPLES (Assumes Current Date: September 15, 2025) ### Example 1 Message: "My wife Sarah loves hiking and outdoor activities. She has an active lifestyle and enjoys rock climbing. I started this new hobby last month and it's been great." Memories: [] Return: {{"ops": [{{"operation": "CREATE", "id": "", "content": "My wife Sarah has an active lifestyle and enjoys hiking, outdoor activities, and rock climbing"}}, {{"operation": "CREATE", "id": "", "content": "I started rock climbing in August 2025 as a new hobby and have been enjoying it"}}]}} Explanation: Multiple facts about the same person (Sarah's active lifestyle, love for hiking, outdoor activities, and rock climbing) are combined into a single cohesive memory. The user's separate rock climbing hobby is kept as a distinct memory since it's about a different person. ### Example 2 Message: "My daughter Emma just turned 12. We adopted a dog named Max for her 11th birthday. What should I give her for her 12th birthday?" Memories: [id:mem-002] My daughter Emma is 10 years old [noted at March 20 2024] [id:mem-101] I have a golden retriever [noted at September 20 2024] Return: {{"ops": [{{"operation": "UPDATE", "id": "mem-002", "content": "My daughter Emma turned 12 years old in September 2025"}}, {{"operation": "UPDATE", "id": "mem-101", "content": "I have a golden retriever named Max that was adopted in September 2024 as a birthday gift for my daughter Emma when she turned 11"}}]}} Explanation: Dog memory enriched with related context (Emma, birthday gift, age 11) and temporal anchoring (September 2024). The instructional question ("What should I give her...?") is ignored as per the 'Filter for Intent' rule. ### Example 3 Message: "Can you recommend some good tapas restaurants in Barcelona? I moved here from Madrid last month." Memories: [id:mem-005] I live in Madrid Spain [noted at June 12 2025] Return: {{"ops": [{{"operation": "UPDATE", "id": "mem-005", "content": "I lived in Madrid Spain until August 2025"}}, {{"operation": "CREATE", "id": "", "content": "I moved to Barcelona Spain in August 2025"}}]}} Explanation: Relocation is a significant life event. The request for recommendations is instructional and is ignored. ### Example 4 Message: "My wife Sofia and I just got married in August. What are some good honeymoon destinations?" Memories: [id:mem-008] I am single [noted at January 5 2025] Return: {{"ops": [{{"operation": "DELETE", "id": "mem-008", "content": ""}}, {{"operation": "CREATE", "id": "", "content": "I married Sofia in August 2025 and she is now my wife"}}]}} Explanation: Marriage is an enduring life event. The instructional question ("What are some good honeymoon destinations?") is ignored. ### Example 5 Message: "¡Hola! Me mudé de Madrid a Barcelona el mes pasado y me casé con mi novia Sofía en agosto. ¿Me puedes recomendar un buen restaurante para celebrar?" Memories: [id:mem-005] I live in Madrid Spain [noted at June 12 2025] [id:mem-006] I am dating Sofia [noted at February 10 2025] [id:mem-008] I am single [noted at January 5 2025] Return: {{"ops": [{{"operation": "UPDATE", "id": "mem-005", "content": "I lived in Madrid Spain until August 2025"}}, {{"operation": "DELETE", "id": "mem-008", "content": ""}}, {{"operation": "UPDATE", "id": "mem-006", "content": "I moved to Barcelona Spain and married my girlfriend Sofia in August 2025, who is now my wife"}}]}} Explanation: The user's move and marriage are significant, related life events. They are consolidated into a single memory. The request for a recommendation is ignored. ### Example 6 Message: "I'm feeling stressed about work this week and looking for some relaxation tips. I have a big presentation coming up on Friday." Memories: [] Return: {{"ops": []}} Explanation: Transient state (stress) and a request for information (relaxation tips). The primary intent is instructional/analytical, and the facts (presentation) are not significant, lasting personal narrative. Nothing to store. """ MEMORY_RERANKING = f"""You are the Memory Relevance Analyzer. ## OBJECTIVE Your goal is to analyze the user's message and select the most relevant memories to personalize the AI's response. Prioritize direct connections and supporting context. ## RELEVANCE CATEGORIES - Direct: Memories explicitly about the query topic, people, or domain. - Contextual: Personal info that affects response recommendations or understanding. - Background: Situational context that provides useful personalization. ## SELECTION FRAMEWORK - Prioritize Current Info: Give current facts higher relevance than historical ones unless the query is about the past or historical context directly informs the current situation. - Hierarchy: Prioritize topic matches first (Direct), then context that enhances the response (Contextual), and finally general background (Background). - Ordering: Order IDs by relevance, most relevant first. - Maximum Limit: Return up to {Constants.MAX_MEMORIES_PER_RETRIEVAL} memory IDs. ## EXAMPLES (Assumes Current Date: September 15, 2025) ### Example 1 Message: "I'm struggling with imposter syndrome at my new job. Any advice?" Memories: [id:mem-001] I work as a senior software engineer at Tesla [noted at September 10 2025] [id:mem-002] I started my current job 3 months ago [noted at June 15 2025] [id:mem-003] I used to work in marketing [noted at March 5 2025] [id:mem-004] I graduated with a computer science degree [noted at May 15 2020] Return: {{"ids": ["mem-001", "mem-002", "mem-003", "mem-004"]}} Explanation: Career transition history (marketing → software engineering) directly informs current imposter syndrome at new job, making historical context relevant. ### Example 2 Message: "Necesito ideas para una cena saludable y con muchas verduras esta noche." Memories: [id:mem-030] I am trying a vegetarian diet [noted at September 20 2025] [id:mem-031] My favorite cuisine is Italian [noted at August 15 2025] [id:mem-032] I dislike spicy food [noted at August 5 2025] Return: {{"ids": ["mem-030", "mem-031", "mem-032"]}} Explanation: Vegetarian diet is directly relevant to healthy vegetable-focused dinner. Italian cuisine and spice preference provide contextual personalization for recipe recommendations. ### Example 3 Message: "What are some good anniversary gift ideas for my wife, Sarah?" Memories: [id:mem-101] My wife is named Sarah. [id:mem-102] My wife Sarah loves hiking and mystery novels. [id:mem-103] My wedding anniversary with Sarah is in October. [id:mem-104] I am on a tight budget this month. [id:mem-105] I live in Denver. [id:mem-106] I have a golden retriever named Max. Return: {{"ids": ["mem-102", "mem-103", "mem-101", "mem-104"]}} Explanation: Wife's interests (hiking, mystery novels) are direct matches for gift suggestions. Anniversary timing and budget constraints are contextual factors. Location and pet are background details not relevant to gift selection. ### Example 4 Message: "I've been reading about quantum computing and I'm confused. Can you break down how quantum bits work differently from regular computer bits?" Memories: [id:mem-026] I work as a senior software engineer at Tesla [noted at September 15 2025] [id:mem-027] My wife is named Sarah [noted at August 5 2025] 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.""" class MemoryOperationType(Enum): CREATE = "CREATE" UPDATE = "UPDATE" DELETE = "DELETE" class OperationResult(Enum): SKIPPED_EMPTY_CONTENT = "SKIPPED_EMPTY_CONTENT" SKIPPED_EMPTY_ID = "SKIPPED_EMPTY_ID" UNSUPPORTED = "UNSUPPORTED" FAILED = "FAILED" class StrictModel(BaseModel): """Base model with strict JSON schema for LLM structured output.""" model_config = ConfigDict(extra="forbid") class MemoryOperation(StrictModel): """Pydantic model for memory operations with validation.""" 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)" ) def validate_operation(self, existing_memory_ids: Optional[set] = None) -> bool: """Validate the memory operation against existing memory IDs.""" if existing_memory_ids is None: existing_memory_ids = set() if self.operation == Models.MemoryOperationType.CREATE: return True elif self.operation in [ Models.MemoryOperationType.UPDATE, Models.MemoryOperationType.DELETE, ]: return self.id in existing_memory_ids return False 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" ) class MemoryRerankingResponse(BaseModel): """Pydantic model for memory reranking LLM response - object containing array of memory IDs.""" ids: List[str] = Field( default_factory=list, description="List of memory IDs selected as most relevant for the user query", ) class UnifiedCacheManager: """Unified cache manager handling all cache types with user isolation and LRU eviction.""" def __init__(self, max_cache_size_per_type: int, max_users: int): self.max_cache_size_per_type = max_cache_size_per_type self.max_users = max_users self.caches: OrderedDict[str, Dict[str, OrderedDict[str, Any]]] = OrderedDict() self._lock = asyncio.Lock() self.EMBEDDING_CACHE = "embedding" self.RETRIEVAL_CACHE = "retrieval" self.MEMORY_CACHE = "memory" async def get(self, user_id: str, cache_type: str, key: str) -> Optional[Any]: """Get value from cache with LRU updates.""" async with self._lock: if user_id not in self.caches: return None user_cache = self.caches[user_id] if cache_type not in user_cache: return None type_cache = user_cache[cache_type] if key in type_cache: type_cache.move_to_end(key) self.caches.move_to_end(user_id) return type_cache[key] return None async def put(self, user_id: str, cache_type: str, key: str, value: Any) -> None: """Store value in cache with size limits and LRU eviction.""" async with self._lock: if user_id not in self.caches: if len(self.caches) >= self.max_users: evicted_user, _ = self.caches.popitem(last=False) self.caches[user_id] = {} user_cache = self.caches[user_id] if cache_type not in user_cache: user_cache[cache_type] = OrderedDict() type_cache = user_cache[cache_type] if ( key not in type_cache and len(type_cache) >= self.max_cache_size_per_type ): evicted_key, _ = type_cache.popitem(last=False) if key in type_cache: type_cache[key] = value type_cache.move_to_end(key) else: type_cache[key] = value self.caches.move_to_end(user_id) async def clear_user_cache( self, user_id: str, cache_type: Optional[str] = None ) -> int: """Clear specific cache type for user, or all caches for user if cache_type is None.""" async with self._lock: if user_id not in self.caches: return 0 user_cache = self.caches[user_id] if cache_type is None: total_cleared = sum( len(type_cache) for type_cache in user_cache.values() ) del self.caches[user_id] return total_cleared else: if cache_type in user_cache: cleared_count = len(user_cache[cache_type]) del user_cache[cache_type] if not user_cache: del self.caches[user_id] return cleared_count return 0 async def clear_all_caches(self) -> None: """Clear all caches for all users.""" async with self._lock: self.caches.clear() class SkipDetector: """Binary content classifier: personal vs non-personal using semantic analysis.""" NON_PERSONAL_CATEGORY_DESCRIPTIONS = [ "programming language syntax, data types like string or integer, algorithm logic, function, method, programming class, object-oriented paradigm, variable scope, control flow, import, module, package, library, framework, recursion, iteration", "software design patterns, creational: singleton, factory, builder; structural: adapter, decorator, facade, proxy; behavioral: observer, strategy, command, mediator, chain of responsibility; abstract interface, polymorphism, composition", "error handling, exception, stack trace, TypeError, NullPointerException, IndexError, segmentation fault, core dump, stack overflow, runtime vs compile-time error, assertion failed, syntax error, null pointer dereference, memory leak, bug", "HTTP status codes: 404 Not Found, 500 Internal Server Error, 403 Forbidden, 401 Unauthorized, 200 OK, 201 Created. API response, 502 Bad Gateway, 503 Service Unavailable, 400 Bad Request, 429 Too Many Requests, timeout, CORS", "terminal command line shell prompt, bash, zsh, powershell, cmd. Filesystem navigation: cd, ls, pwd. File management: mkdir, rm, cp, mv, chmod, chown. Text processing with grep, sed, awk, cat. User permissions: sudo, root access", "developer CLI tools, package manager, install, update. Network requests with curl, wget. Secure shell access with SSH. Version control with git: clone, commit, push, pull. Containerization with docker: run, build, compose; npm, pip", "data interchange formats, serialization, deserialization, parsing. JSON object, array, key-value pair. XML tags, attributes. YAML indentation, TOML, CSV, .ini properties. Config file, env variables, dictionary, map, protocol buffers", "WebSocket real-time bidirectional communication, server-client connection on a port, binary message protocol, handshake, HTTP upgrade, socket programming, TCP, UDP, listening, binding, accepting, streaming, pub-sub, broadcast channel", "API design, endpoint, REST, GraphQL, SOAP, RPC. HTTP methods: GET, POST, PUT, DELETE, PATCH. Request-response cycle, payload, authentication token, bearer, JWT, OAuth, API key, query parameters, path variables, request body", "file system path, directory structure, config log bin, absolute vs relative path, operating system, filesystem, mount point, home, /tmp, /var, shared library, symbolic link, inode, file permissions, owner, group, read write execute", "algorithm analysis, O(log n) time complexity, space complexity, data structures, hash table, array, linked list, queue, stack, heap, priority queue, graph, adjacency matrix, depth-first search (DFS), breadth-first search (BFS)", "sorting algorithms performance and implementation, including merge sort, quicksort, insertion sort, selection sort. Understanding stable vs unstable sorts, in-place operations, comparison-based sorting, and computational complexity", "markdown syntax for text formatting, horizontal rule, separator using dashes, headings, fenced code block with triple backticks, inline code, emphasis with bold and italic, strikethrough, blockquote, nested list, task list, markdown table", "code formatting and style, indentation with whitespace, tabs vs spaces, nested function body, class method, structured code, syntax highlighting for languages like Python, JavaScript, Java, C++, Go, Rust, TypeScript, Prettier, ESLint", "container orchestration, cluster management, service scaling, replication, load balancing, namespace, pod, deployment, infrastructure, Kubernetes (K8s), Docker Swarm, container runtime (CRI-O, containerd), image registry, Dockerfile", "querying a database, SQL statement, database table, column, row, index, primary key, foreign key relationship, join, filter, select, insert, update, delete, relational vs NoSQL, MongoDB, PostgreSQL, MySQL, Redis, schema, transaction", "application logging, log output, stack trace levels like INFO, WARN, ERROR, DEBUG, FATAL. Log message components: timestamp, module, line number. Diagnostic telemetry, monitoring, and observability for system health and debugging", "regex pattern, regular expression matching, groups, capturing, backslash escapes, metacharacters, wildcards, quantifiers, character classes, lookaheads, lookbehinds, alternation, anchors, word boundary, multiline flag, global search", "software testing, unit test, assertion, mock, stub, fixture, test suite, test case, verification, automated QA, validation framework, JUnit, pytest, Jest. Integration, end-to-end (E2E), functional, regression, acceptance testing", "cloud computing platforms, infrastructure as a service (IaaS), PaaS, AWS, Azure, GCP, compute instance, region, availability zone, elasticity, distributed system, virtual machine, container, serverless, Lambda, edge computing, CDN", "format the output as structured data. Return the answer as JSON with specific keys and values, or as YAML. Organize information into a CSV file or a database-style table with columns and rows. Present as a list of objects or an array.", "style the text presentation. Use markdown formatting like bullet points, a numbered list, or a task list. Organize content into a grid or tabular layout with proper alignment. Create a hierarchical structure with nested elements for clarity.", "adjust the response length. Make the answer shorter, more concise, brief, or condensed. Summarize the key points. Trim down the text to reduce the overall word count or meet a specific character limit. Be less verbose and more direct.", "change the explanation depth. Make the response more detailed, comprehensive, and elaborate. Expand on previous points and go into more depth. Provide a thorough, in-depth analysis. Explain the topic with more complexity and nuance.", "rewrite the previous response. Rephrase, paraphrase, or reformulate the answer using different wording. Restate the information in another way to offer an alternative perspective. Express the same meaning but with a new structure or vocabulary.", "alter the response tone. Change the writing style to be more formal, academic, or professional. Alternatively, make it more casual, friendly, and conversational. Adapt the register and voice to suit a specific audience or context level.", "explain the concept in simpler terms. Break down the topic step-by-step for a beginner. Clarify a confusing point. Explain it like I'm five years old (ELI5). Use an analogy or a concrete example to help me understand the idea clearly.", "continue the generated response. Keep going with the explanation or list. Provide more information and finish your thought. Complete the rest of the content or story. Proceed with the next steps. Do not stop until you have concluded.", "act as a specific persona or role. Respond as if you were a pirate, a scientist, or a travel guide. Adopt the character's voice, style, and knowledge base in your answer. Maintain the persona throughout the entire response.", "compare and contrast two or more topics. Explain the similarities and differences between A and B. Provide a detailed analysis of what they have in common and how they diverge. Create a table to highlight the key distinctions.", "perform a pure arithmetic calculation with explicit numbers. Solve, multiply, add, subtract, and divide. Compute a numeric expression following the order of operations (PEMDAS/BODMAS). What is 23 plus 456 minus 78 times 9 divided by 3?", "evaluate a mathematical expression containing numbers and operators, such as 2 plus 3 times 4 divided by 5. Solve this numerical problem and compute the final result. Simplify the arithmetic and show the final answer. Calculate 123 * 456.", "convert units between measurement systems with numeric values. Convert 100 kilometers to miles, 72 fahrenheit to celsius, or 5 feet 9 inches to centimeters. Change between metric and imperial for distance, weight, volume, or temperature.", "calculate a percentage of a number. What is 25 percent of 800? Determine the price after a 30% discount. Compute a 15% tip on a $65.40 bill. Find the value corresponding to a specific proportion or calculate sales tax or interest.", "solve an algebraic equation for a variable like x. For the equation 2x + 5 = 15, find the value of x. Use the quadratic formula for numeric values. Solve simultaneous linear equations to find the value of the unknown variables. Isolate x.", "perform a geometry calculation with numeric measurements. Find the area of a circle with a radius of 5, or the volume of a cube with a side of 10. Calculate the circumference, perimeter, or diameter. What is the square root of 144?", "calculate compound interest on an investment or savings. With a principal of $5000 at an annual rate of 4% for 10 years, what is the future value? Compute a monthly mortgage payment for a $300,000 loan. Financial calculation, ROI, APR.", "compute descriptive statistics for a dataset of numbers like 12, 15, 18, 20, 22. Calculate the mean, median, mode, average, and standard deviation. Find the variance, range, quartiles, and percentiles for a given sample distribution.", "calculate health and fitness metrics using a numeric formula. Compute the Body Mass Index (BMI) given a weight in pounds or kilograms and height in feet, inches, or meters. Find my basal metabolic rate (BMR) or target heart rate.", "calculate the time difference between two dates. How many days, hours, or minutes are between two points in time? Find the duration or elapsed time. Act as an age calculator for a birthday or find the time until a future anniversary.", "translate the explicitly quoted text 'Hello, how are you?' to a foreign language like Spanish, French, or German. This is a translation instruction that includes the word 'translate' and the source text in quotes for direct conversion.", "how do you say a specific word or phrase in another language? For example, how do you say 'thank you', 'computer', or 'goodbye' in Japanese, Chinese, or Korean? This is a request for a direct translation of a common expression or term.", "convert a block of text or a paragraph from a source language to a target language. Translate the following content to Italian, Arabic, Portuguese, or Russian. This is a language conversion request for a larger piece of text provided.", "provide the translation for the sentence 'Where is the train station?' into a specific foreign language like Turkish, Hindi, or Polish. This is a translation request for a complete sentence, often enclosed in quotes or brackets for clarity.", "what is the translation of the source text 'The quick brown fox jumps over the lazy dog' into a target language? Give me the resulting translated output in German, French, or Dutch. This is a query for the translated equivalent of a text.", "translate the following passage to Spanish. This is an instruction to convert the provided text content into a specified foreign language. The request uses a direct command format, indicating a clear source and a clear target language.", "what is the foreign language word for 'house', 'beautiful', or 'water'? Provide the translation for these common vocabulary words in Italian, Swedish, or another language. This is a request for single-word vocabulary translation.", "how do I say 'I am learning to code' in German? Convert this specific English phrase into its equivalent in another language. This is a request for a practical, conversational phrase translation for personal or professional use.", "translate this informal or slang expression to its colloquial equivalent in Spanish. How would you say 'What's up?' in Japanese in a casual context? This request focuses on capturing the correct tone and nuance of informal language.", "provide the formal and professional translation for 'Please find the attached document for your review' in French. Translate this business email phrase to German, ensuring the terminology and register are appropriate for a corporate context.", "proofread the following text for errors. Here is my draft, please check it for typos and mistakes: 'Teh quick brown fox jumpped'. Review, revise, and correct any misspellings or grammatical issues you find in the provided passage.", "correct the grammar in this sentence: 'She don't like it'. Resolve grammatical issues like subject-verb agreement, incorrect verb tense, pronoun reference errors, or misplaced modifiers in the provided text. Address faulty sentence structure.", "check the spelling and punctuation in this passage. Please review the following text and correct any textual errors: 'its a beautiful day, isnt it'. Amend mistakes with commas, periods, apostrophes, quotation marks, colons, or capitalization.", "review this sentence and tell me if it is grammatically correct. Is the sentence 'There going to they're house' proper? Validate the grammar, check word usage (like their/there/they're), and verify that the sentence is well-formed.", "proofread my email before I send it. Here is the draft. Please check for clarity, flow, coherence, and readability. Improve my writing, make it better, and polish the text to ensure it sounds professional and is free of textual errors.", "fix the punctuation in this run-on sentence or comma splice. Correct sentence fragments and ensure proper use of capitalization. Address errors with apostrophes, quotation marks, periods, semicolons, dashes, and other punctuation marks.", "suggest a better word choice or alternative phrasing. Can you help me improve my vocabulary and diction in this sentence? Replace words with more precise or impactful synonyms. Refine the expression for better clarity, tone, or style.", "rewrite this sentence from passive voice to active voice. Help me make my writing more direct and concise by eliminating passive constructions. Restructure the sentence to be more engaging and clear. Identify and fix faulty parallelism.", "improve the clarity and flow of this paragraph. Make the writing smoother and more readable. Restructure the sentences for better coherence and logical progression. Ensure the ideas connect seamlessly and eliminate any awkward phrasing.", "check my essay for conciseness and remove any redundancy. Help me edit this text to be more direct and to the point. Identify and eliminate wordiness, filler words, and repetitive phrases to strengthen the overall quality of the writing.", ] PERSONAL_CATEGORY_DESCRIPTIONS = [ "discussing my family members, like my spouse, children, parents, or siblings. Mentioning relatives by name or role, such as my husband, wife, son, daughter, mother, or father. Sharing stories or asking questions about my family.", "expressing lasting personal feelings, core values, beliefs, or principles. My worldview, deeply held opinions, philosophy, or moral standards. Things I love, hate, or feel strongly about in life, such as my passion for animal welfare.", "describing my established personal hobbies, regular activities, or consistent interests. My passions and what I do in my leisure time, such as creative outlets like painting, sports like hiking, or other recreational pursuits I enjoy.", "sharing information about my career or current job. My position, workplace, company name, or professional role. My responsibilities at work, my occupation, or the industry I work in. My employment situation, job title, and employer.", "talking about my major life plans, long-term aspirations, or personal goals. My dreams for the future, important intentions, and what I want to achieve. Milestones, ambitions, or a bucket list. My personal vision or mission in life.", "reflecting on a meaningful personal story, memory, or significant past life experience. A transformative event or milestone that shaped me. A defining moment, a lesson learned from my childhood, or a memory from growing up that I cherish.", "sharing my personal background, like my hometown, childhood upbringing, or education. My cultural heritage, ethnicity, or where I grew up. Information about the university I graduated from or formative life experiences that define my identity.", "asking for personal advice about a specific life situation, relationship, family decision, or career choice. Seeking guidance on a personal challenge, problem, or dilemma I'm facing. Needing help or counsel on a difficult issue or conflict.", "requesting personalized recommendations based on my stated context, preferences, or needs. For example, suggesting a movie based on genres I like, or a restaurant that fits my dietary restrictions, budget, and location requirements.", "talking about my personal learning journey or educational pursuits. A course or class I'm taking, a certification I'm working on, or a degree program. My efforts in personal development, skill acquisition, or knowledge building.", "discussing my child, spouse, or other family member's interests or needs. Helping my son with a school project, finding a hobby for my daughter, or supporting my partner's career goals. Questions related to supporting my loved ones.", "describing my personal challenges with a work task, learning a new skill, or a technology problem. Feeling confused, stressed, or overwhelmed. Dealing with imposter syndrome, self-doubt, or needing assistance with a difficult project.", "planning a personal event like a party, celebration, or family gathering. Organizing my daughter's birthday, my son's graduation, or a wedding anniversary. Discussing arrangements for a special occasion or festive milestone commemoration.", "mentioning my pet, such as my dog, cat, or another animal companion. I adopted a puppy, or I have a cat named Luna. Discussing my pet's breed, age, behavior, or my general feelings about animals, pet care, and pet ownership.", "discussing moving or relocating to a new city, state, or country. I just moved into a new apartment or house. The personal reasons for my move, like a job or family. The process of settling into a new home, neighborhood, or location.", "stating my long-term dietary preference or restriction, such as being vegetarian, vegan, pescatarian, gluten-free, or having a food allergy. My eating habits and favorite cuisines, based on health, ethical, or personal reasons.", "talking about my religious or cultural practices. I celebrate Christmas, observe Ramadan, or follow Buddhist traditions. My faith, beliefs, spirituality, or cultural background. Religious identity, worship, prayers, rituals, or holidays.", "describing my living situation. I live with roommates, alone, with my parents, or with a partner. I bought or rented a house or apartment. My home environment, housing arrangements, and household composition in my current residence.", "talking about my personal finances, such as saving for a down payment on a house, managing a tight budget, or planning for retirement. My investment goals, strategies for handling debt, or my general approach to financial security.", "working on a personal creative project. I am writing a novel, composing music, painting a picture, or developing a side project. A meaningful creative pursuit or hobby that involves a personal, emotional investment and artistic expression.", "describing my fitness routine or exercise habits. I go to the gym, run, do yoga, or swim regularly. My consistent activities for health and wellness, my workout regimen, or my training schedule and fitness goals for an active lifestyle.", "sharing my personal values and what I care about deeply. I believe strongly in environmental sustainability, social justice, or equality. Causes I support, my principles, ethics, morals, and convictions that shape my worldview and priorities.", "discussing a personal achievement or milestone. I got promoted, received an award, won a competition, or completed a marathon. A significant accomplishment I am proud of, a goal I reached, or a success that marked a personal triumph.", "referencing my social preferences. I am an introvert, an extrovert, or an ambivert. I prefer small groups over large crowds. My personality trait regarding socializing, my interaction style, and where I get my energy in social settings.", "discussing everyday problems or logistics. Dealing with a car repair, a household issue like a broken appliance, losing my keys, managing appointments, or troubleshooting a personal device. Life's daily challenges and practical solutions.", ] class SkipReason(Enum): SKIP_SIZE = "SKIP_SIZE" SKIP_NON_PERSONAL = "SKIP_NON_PERSONAL" STATUS_MESSAGES = { SkipReason.SKIP_SIZE: "📏 Message Length Out of Limits, skipping memory operations", SkipReason.SKIP_NON_PERSONAL: "🚫 Non-Personal Content Detected, skipping memory operations", } def __init__( self, embedding_function: Callable[ [Union[str, List[str]]], Union[np.ndarray, List[np.ndarray]] ], ): """Initialize the skip detector with an embedding function and compute reference embeddings.""" self.embedding_function = embedding_function self._reference_embeddings = None self._initialize_reference_embeddings() def _initialize_reference_embeddings(self) -> None: """Compute and cache embeddings for category descriptions.""" try: non_personal_embeddings = self.embedding_function( self.NON_PERSONAL_CATEGORY_DESCRIPTIONS ) personal_embeddings = self.embedding_function( self.PERSONAL_CATEGORY_DESCRIPTIONS ) self._reference_embeddings = { "non_personal": np.array(non_personal_embeddings), "personal": np.array(personal_embeddings), } logger.info( f"SkipDetector initialized with {len(self.NON_PERSONAL_CATEGORY_DESCRIPTIONS)} non-personal categories and {len(self.PERSONAL_CATEGORY_DESCRIPTIONS)} personal categories" ) except Exception as e: logger.error(f"Failed to initialize SkipDetector reference embeddings: {e}") self._reference_embeddings = None def validate_message_size( self, message: str, max_message_chars: int ) -> Optional[str]: """Validate message size constraints.""" if not message or not message.strip(): return SkipDetector.SkipReason.SKIP_SIZE.value trimmed = message.strip() if ( len(trimmed) < Constants.MIN_MESSAGE_CHARS or len(trimmed) > max_message_chars ): return SkipDetector.SkipReason.SKIP_SIZE.value return None def _fast_path_skip_detection(self, message: str) -> Optional[bool]: """Language-agnostic structural pattern detection with high confidence and low false positive rate.""" 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://") if url_pattern_count >= 5: return True # Pattern 2: Long unbroken alphanumeric strings (tokens, hashes, base64) words = message.split() for word in words: cleaned = word.strip('.,;:!?()[]{}"\'"') if ( len(cleaned) > 80 and cleaned.replace("-", "").replace("_", "").isalnum() ): return True # Pattern 3: Markdown/text separators (repeated ---, ===, ___, ***) separator_patterns = ["---", "===", "___", "***"] for pattern in separator_patterns: if message.count(pattern) >= 2: return True # Pattern 4: Command-line patterns with context-aware detection 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: 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"] ) ): actual_command_lines += 1 elif line.startswith("# ") and len(line) > 2: rest = line[2:].strip() if rest and not rest[0].isupper() and " " in rest: actual_command_lines += 1 elif line.startswith("> ") and len(line) > 2: pass if actual_command_lines >= 1 and any( c in message for c in ["http://", "https://", " | "] ): return True if actual_command_lines >= 3: return True # 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(".") path_chars = slash_count + dot_count if path_chars > 10 and (path_chars / msg_len) > 0.15: return True # Pattern 6: Markup character density (structured data) markup_chars = sum(message.count(c) for c in "{}[]<>") if markup_chars >= 6: if markup_chars / msg_len > 0.10: return True curly_count = message.count("{") + message.count("}") if curly_count >= 10: return True # Pattern 7: Structured nested content with colons (key: value patterns) line_count = message.count("\n") if line_count >= 8: 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")) ) if ( colon_lines / len(non_empty_lines) > 0.4 and indented_lines / len(non_empty_lines) > 0.5 ): words_outside_kv = 0 for line in non_empty_lines: if ":" not in line: words_outside_kv += len(line.split()) if words_outside_kv < 5: return True # Pattern 8: Highly structured multi-line content (require markup chars for technical confidence) if line_count > 15: 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")) ) if markup_in_lines / len(non_empty_lines) > 0.3: return True elif structured_lines / len(non_empty_lines) > 0.6: operators = [ "=", "+", "-", "*", "/", "<", ">", "&", "|", "!", ":", "?", ] operator_count = sum(message.count(op) for op in operators) if (operator_count / msg_len) > 0.05: return True # Pattern 9: Code-like indentation pattern (require code indicators to avoid false positives from bullet lists) if line_count >= 3: 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") ) if indented_lines / len(non_empty_lines) > 0.5: code_ending_chars = ["{", "}", "(", ")", ";"] lines_with_code_endings = sum( 1 for line in non_empty_lines if line.strip().endswith(tuple(code_ending_chars)) ) if lines_with_code_endings / len(non_empty_lines) > 0.2: return True # Pattern 10: Very high special character ratio (encoded data, technical output) if msg_len > 50: special_chars = sum( 1 for c in message if not c.isalnum() and not c.isspace() ) special_ratio = special_chars / msg_len if special_ratio > 0.35: alphanumeric = sum(1 for c in message if c.isalnum()) if alphanumeric / msg_len < 0.50: return True return None 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) 2. Binary semantic classification (personal vs non-personal) Returns: Skip reason string if content should be skipped, None otherwise """ size_issue = self.validate_message_size(message, max_message_chars) if size_issue: return size_issue fast_skip = self._fast_path_skip_detection(message) if fast_skip: logger.info(f"Fast-path skip: {self.SkipReason.SKIP_NON_PERSONAL.value}") return self.SkipReason.SKIP_NON_PERSONAL.value if self._reference_embeddings is None: logger.warning( "SkipDetector reference embeddings not initialized, allowing message through" ) return None try: message_embedding = np.array(self.embedding_function([message.strip()])[0]) personal_similarities = np.dot( message_embedding, self._reference_embeddings["personal"].T ) max_personal_similarity = float(personal_similarities.max()) non_personal_similarities = np.dot( message_embedding, self._reference_embeddings["non_personal"].T ) max_non_personal_similarity = float(non_personal_similarities.max()) if ( max_non_personal_similarity - max_personal_similarity ) > Constants.SKIP_CATEGORY_MARGIN: logger.info( f"🚫 Skipping message: non-personal content detected " f"(non-personal sim {max_non_personal_similarity:.3f} > " f"personal sim {max_personal_similarity:.3f} + {Constants.SKIP_CATEGORY_MARGIN:.3f})" ) return self.SkipReason.SKIP_NON_PERSONAL.value return None except Exception as e: logger.error(f"Error in semantic skip detection: {e}") return None class LLMRerankingService: """Language-agnostic LLM-based memory reranking service.""" def __init__(self, memory_system): self.memory_system = memory_system def _should_use_llm_reranking(self, memories: List[Dict]) -> Tuple[bool, str]: if not self.memory_system.valves.enable_llm_reranking: return False, "LLM reranking disabled" llm_trigger_threshold = int( self.memory_system.valves.max_memories_returned * self.memory_system.valves.llm_reranking_trigger_multiplier ) if len(memories) > llm_trigger_threshold: return ( True, f"{len(memories)} candidate memories exceed {llm_trigger_threshold} threshold", ) return ( False, f"{len(memories)} candidate memories within threshold of {llm_trigger_threshold}", ) async def _llm_select_memories( self, user_message: str, candidate_memories: List[Dict], max_count: int, emitter: Optional[Callable] = None, ) -> List[Dict]: """Use LLM to select most relevant memories.""" memory_lines = self.memory_system._format_memories_for_llm(candidate_memories) memory_context = "\n".join(memory_lines) user_prompt = f"""CURRENT DATE/TIME: {self.memory_system.format_current_datetime()} USER MESSAGE: {user_message} CANDIDATE MEMORIES: {memory_context}""" try: response = await self.memory_system._query_llm( Prompts.MEMORY_RERANKING, user_prompt, response_model=Models.MemoryRerankingResponse, ) selected_memories = [] for memory in candidate_memories: if memory["id"] in response.ids and len(selected_memories) < max_count: selected_memories.append(memory) logger.info( f"🧠 LLM selected {len(selected_memories)} out of {len(candidate_memories)} candidates" ) return selected_memories except Exception as e: 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]]: start_time = time.time() max_injection = self.memory_system.valves.max_memories_returned should_use_llm, decision_reason = self._should_use_llm_reranking( candidate_memories ) analysis_info = { "llm_decision": should_use_llm, "decision_reason": decision_reason, "candidate_count": len(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, ) logger.info(f"Using LLM reranking: {decision_reason}") selected_memories = await self._llm_select_memories( user_message, llm_candidates, max_injection, emitter ) if not selected_memories: logger.info("📭 No relevant memories after LLM analysis") await self.memory_system._emit_status( emitter, f"📭 No Relevant Memories After LLM Analysis", done=True ) return selected_memories, analysis_info else: logger.info(f"Skipping LLM reranking: {decision_reason}") selected_memories = candidate_memories[:max_injection] duration = time.time() - start_time duration_text = f" in {duration:.2f}s" if duration >= 0.01 else "" retrieval_method = "LLM" if should_use_llm else "Semantic" await self.memory_system._emit_status( emitter, f"🎯 {retrieval_method} Memory Retrieval Complete{duration_text}", done=True, ) return selected_memories, analysis_info class LLMConsolidationService: """Language-agnostic LLM-based memory consolidation service.""" def __init__(self, memory_system): self.memory_system = memory_system async def _check_semantic_duplicate( self, content: str, existing_memories: List, user_id: str ) -> Optional[str]: """ Check if content is semantically duplicate of existing memories using embeddings. Returns the ID of duplicate memory if found, None otherwise. """ if not existing_memories: return None try: content_embedding = await self.memory_system._generate_embeddings( content, user_id ) for memory in existing_memories: if ( not memory.content or len(memory.content.strip()) < Constants.MIN_MESSAGE_CHARS ): continue memory_embedding = await self.memory_system._generate_embeddings( memory.content, user_id ) similarity = float(np.dot(content_embedding, memory_embedding)) if similarity >= Constants.DEDUPLICATION_SIMILARITY_THRESHOLD: logger.info( f"🔍 Semantic duplicate detected: similarity={similarity:.3f} with memory {memory.id}" ) return str(memory.id) return None except Exception as e: logger.warning(f"⚠️ Semantic duplicate check failed: {str(e)}") return None def _filter_consolidation_candidates( self, similarities: List[Dict[str, Any]] ) -> Tuple[List[Dict[str, Any]], str]: """Filter consolidation candidates by threshold and return candidates with threshold info.""" consolidation_threshold = self.memory_system._get_retrieval_threshold( is_consolidation=True ) candidates = [ mem for mem in similarities if mem["relevance"] >= consolidation_threshold ] max_consolidation_memories = int( self.memory_system.valves.max_memories_returned * Constants.EXTENDED_MAX_MEMORY_MULTIPLIER ) candidates = candidates[:max_consolidation_memories] threshold_info = ( f"{consolidation_threshold:.3f} (max: {max_consolidation_memories})" ) return candidates, threshold_info async def collect_consolidation_candidates( self, user_message: str, user_id: str, cached_similarities: Optional[List[Dict[str, Any]]] = None, ) -> List[Dict[str, Any]]: """Collect candidate memories for consolidation analysis using cached or computed similarities.""" if cached_similarities: candidates, threshold_info = self._filter_consolidation_candidates( cached_similarities ) logger.info( f"🎯 Found {len(candidates)} candidate memories for consolidation (threshold: {threshold_info})" ) self.memory_system._log_retrieved_memories(candidates, "consolidation") return candidates try: user_memories = await self.memory_system._get_user_memories(user_id) except asyncio.TimeoutError: raise TimeoutError( f"⏱️ Memory retrieval timed out after {Constants.DATABASE_OPERATION_TIMEOUT_SEC}s" ) except Exception as e: logger.error(f"💾 Failed to retrieve user memories from database: {str(e)}") return [] if not user_memories: logger.info("💭 No existing memories found for consolidation") return [] logger.info( f"🚀 Reusing cached user memories for consolidation: {len(user_memories)} memories" ) try: all_similarities, _, _ = await self.memory_system._compute_similarities( user_message, user_id, user_memories ) except Exception as e: logger.error( f"🔍 Failed to compute memory similarities for retrieval: {str(e)}" ) return [] if all_similarities: candidates, threshold_info = self._filter_consolidation_candidates( all_similarities ) else: candidates = [] threshold_info = "N/A" logger.info( f"🎯 Found {len(candidates)} candidate memories for consolidation (threshold: {threshold_info})" ) self.memory_system._log_retrieved_memories(candidates, "consolidation") 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]]: """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 ) memory_context = f"EXISTING MEMORIES FOR CONSOLIDATION:\n{chr(10).join(memory_lines)}\n\n" else: memory_context = "EXISTING MEMORIES FOR CONSOLIDATION:\n[]\n\nNote: No existing memories found - Focus on extracting new memories from the user message below.\n\n" user_prompt = f"""CURRENT DATE/TIME: {self.memory_system.format_current_datetime()} {memory_context}USER MESSAGE: {user_message}""" try: response = await asyncio.wait_for( self.memory_system._query_llm( Prompts.MEMORY_CONSOLIDATION, user_prompt, response_model=Models.ConsolidationResponse, ), timeout=Constants.LLM_CONSOLIDATION_TIMEOUT_SEC, ) except Exception as e: logger.warning( f"🤖 LLM consolidation failed during memory processing: {str(e)}" ) await self.memory_system._emit_status( emitter, f"⚠️ Memory Consolidation Failed", done=True ) return [] operations = response.ops existing_memory_ids = {memory["id"] for memory in candidate_memories} total_operations = len(operations) delete_operations = [ op for op in operations if op.operation == Models.MemoryOperationType.DELETE ] delete_ratio = ( len(delete_operations) / total_operations if total_operations > 0 else 0 ) if ( delete_ratio > Constants.MAX_DELETE_OPERATIONS_RATIO and total_operations >= Constants.MIN_OPS_FOR_DELETE_RATIO_CHECK ): logger.warning( f"⚠️ Consolidation safety: {len(delete_operations)}/{total_operations} operations are deletions ({delete_ratio*100:.1f}%) - rejecting plan" ) return [] deduplicated_operations = [] seen_contents = set() seen_update_ids = set() for op in operations: if not op.validate_operation(existing_memory_ids): continue if ( op.operation == Models.MemoryOperationType.UPDATE and op.id in seen_update_ids ): logger.info( f"⏭️ Skipping duplicate UPDATE for memory {op.id} in LLM response" ) continue if op.operation in [ Models.MemoryOperationType.CREATE, Models.MemoryOperationType.UPDATE, ]: normalized_content = op.content.strip().lower() if normalized_content in seen_contents: op_type = ( "CREATE" if op.operation == Models.MemoryOperationType.CREATE else f"UPDATE {op.id}" ) logger.info( f"⏭️ Skipping duplicate {op_type} in LLM response: {self.memory_system._truncate_content(op.content)}" ) continue seen_contents.add(normalized_content) if op.operation == Models.MemoryOperationType.UPDATE: seen_update_ids.add(op.id) deduplicated_operations.append(op.model_dump()) valid_operations = deduplicated_operations if valid_operations: create_count = sum( 1 for op in valid_operations if op.get("operation") == Models.MemoryOperationType.CREATE.value ) update_count = sum( 1 for op in valid_operations if op.get("operation") == Models.MemoryOperationType.UPDATE.value ) delete_count = sum( 1 for op in valid_operations if op.get("operation") == Models.MemoryOperationType.DELETE.value ) operation_details = self.memory_system._build_operation_details( create_count, update_count, delete_count ) logger.info( f"🎯 Planned {len(valid_operations)} memory operations: {', '.join(operation_details)}" ) else: logger.info("🎯 No valid memory operations planned") return valid_operations async def _deduplicate_operations( self, operations: List, current_memories: List, user_id: str, operation_type: str, delete_operations: Optional[List] = None, ) -> List: """ Deduplicate operations against existing memories using semantic similarity. For UPDATE operations, preserves enriched content and deletes the duplicate. """ deduplicated = [] for operation in operations: memories_to_check = current_memories if operation_type == "UPDATE": memories_to_check = [ m for m in current_memories if str(m.id) != operation.id ] duplicate_id = await self._check_semantic_duplicate( operation.content, memories_to_check, user_id ) if duplicate_id: if operation_type == "UPDATE" and delete_operations is not None: logger.info( f"🔄 UPDATE creates duplicate: keeping enriched content from memory {operation.id}, " f"deleting duplicate memory {duplicate_id}" ) deduplicated.append(operation) delete_operations.append( Models.MemoryOperation( operation=Models.MemoryOperationType.DELETE, content="", id=duplicate_id, ) ) else: logger.info( f"⏭️ Skipping duplicate {operation_type}: " f"{self.memory_system._truncate_content(operation.content)} " f"(matches memory {duplicate_id})" ) continue deduplicated.append(operation) return deduplicated async def execute_memory_operations( self, operations: List[Dict[str, Any]], user_id: str, emitter: Optional[Callable] = None, ) -> Tuple[int, int, int, int]: """Execute consolidation operations with simplified tracking.""" if not operations or not user_id: return 0, 0, 0, 0 try: user = await asyncio.wait_for( asyncio.to_thread(Users.get_user_by_id, user_id), timeout=Constants.DATABASE_OPERATION_TIMEOUT_SEC, ) except asyncio.TimeoutError: raise TimeoutError( f"⏱️ User lookup timed out after {Constants.DATABASE_OPERATION_TIMEOUT_SEC}s" ) except Exception as e: raise RuntimeError(f"👤 User lookup failed: {str(e)}") if not user: raise ValueError(f"👤 User not found for consolidation: {user_id}") created_count = updated_count = deleted_count = failed_count = 0 operations_by_type = {"CREATE": [], "UPDATE": [], "DELETE": []} for operation_data in operations: try: operation = Models.MemoryOperation(**operation_data) operations_by_type[operation.operation.value].append(operation) except Exception as e: failed_count += 1 operation_type = operation_data.get( "operation", Models.OperationResult.UNSUPPORTED.value ) content_preview = "" if "content" in operation_data: content = operation_data.get("content", "") content_preview = f" - Content: {self.memory_system._truncate_content(content, Constants.CONTENT_PREVIEW_LENGTH)}" elif "id" in operation_data: content_preview = f" - ID: {operation_data['id']}" error_message = ( f"Failed {operation_type} operation{content_preview}: {str(e)}" ) logger.error(error_message) memory_contents_for_deletion = {} if operations_by_type["DELETE"]: try: user_memories = await self.memory_system._get_user_memories(user_id) memory_contents_for_deletion = { str(mem.id): mem.content for mem in user_memories } except Exception as e: logger.warning( f"⚠️ Failed to fetch memories for DELETE preview: {str(e)}" ) if operations_by_type["CREATE"] or operations_by_type["UPDATE"]: try: current_memories = await self.memory_system._get_user_memories(user_id) if operations_by_type["CREATE"]: operations_by_type["CREATE"] = await self._deduplicate_operations( operations_by_type["CREATE"], current_memories, user_id, operation_type="CREATE", ) if operations_by_type["UPDATE"]: operations_by_type["UPDATE"] = await self._deduplicate_operations( operations_by_type["UPDATE"], current_memories, user_id, operation_type="UPDATE", delete_operations=operations_by_type["DELETE"], ) except Exception as e: logger.warning( f"⚠️ Semantic deduplication check failed, proceeding with original operations: {str(e)}" ) for operation_type, ops in operations_by_type.items(): if not ops: continue batch_tasks = [] for operation in ops: task = self.memory_system._execute_single_operation(operation, user) batch_tasks.append(task) try: results = await asyncio.gather(*batch_tasks, return_exceptions=True) for idx, result in enumerate(results): operation = ops[idx] if isinstance(result, Exception): failed_count += 1 await self.memory_system._emit_status( emitter, f"❌ Failed {operation_type}", done=False ) elif result == Models.MemoryOperationType.CREATE.value: created_count += 1 content_preview = self.memory_system._truncate_content( operation.content ) await self.memory_system._emit_status( emitter, f"📝 Created: {content_preview}", done=False ) elif result == Models.MemoryOperationType.UPDATE.value: updated_count += 1 content_preview = self.memory_system._truncate_content( operation.content ) await self.memory_system._emit_status( emitter, f"✏️ Updated: {content_preview}", done=False ) elif result == Models.MemoryOperationType.DELETE.value: deleted_count += 1 content_preview = memory_contents_for_deletion.get( operation.id, operation.id ) if content_preview and content_preview != operation.id: content_preview = self.memory_system._truncate_content( content_preview ) await self.memory_system._emit_status( emitter, f"🗑️ Deleted: {content_preview}", done=False ) elif result in [ Models.OperationResult.FAILED.value, Models.OperationResult.UNSUPPORTED.value, ]: failed_count += 1 await self.memory_system._emit_status( emitter, f"❌ Failed {operation_type}", done=False ) except Exception as e: failed_count += len(ops) logger.error( f"❌ Batch {operation_type} operations failed during memory consolidation: {str(e)}" ) await self.memory_system._emit_status( emitter, f"❌ Batch {operation_type} Failed", done=False ) total_executed = created_count + updated_count + deleted_count logger.info( f"✅ Memory processing completed {total_executed}/{len(operations)} operations (Created {created_count}, Updated {updated_count}, Deleted {deleted_count}, Failed {failed_count})" ) if total_executed > 0: operation_details = self.memory_system._build_operation_details( created_count, updated_count, deleted_count ) logger.info(f"🔄 Memory Operations: {', '.join(operation_details)}") await self.memory_system._refresh_user_cache(user_id) return created_count, updated_count, deleted_count, failed_count async def run_consolidation_pipeline( self, user_message: str, user_id: str, emitter: Optional[Callable] = None, cached_similarities: Optional[List[Dict[str, Any]]] = None, ) -> None: """Complete consolidation pipeline with simplified flow.""" start_time = time.time() try: if self.memory_system._shutdown_event.is_set(): return candidates = await self.collect_consolidation_candidates( user_message, user_id, cached_similarities ) if self.memory_system._shutdown_event.is_set(): return operations = await self.generate_consolidation_plan( user_message, candidates, emitter ) if self.memory_system._shutdown_event.is_set(): return if operations: created_count, updated_count, deleted_count, failed_count = ( await self.execute_memory_operations(operations, user_id, emitter) ) duration = time.time() - start_time logger.info(f"💾 Memory Consolidation Complete In {duration:.2f}s") total_operations = created_count + updated_count + deleted_count if total_operations > 0 or failed_count > 0: await self.memory_system._emit_status( emitter, f"💾 Memory Consolidation Complete in {duration:.2f}s", done=False, ) operation_details = self.memory_system._build_operation_details( created_count, updated_count, deleted_count ) memory_word = "Memory" if total_operations == 1 else "Memories" operations_summary = f"{', '.join(operation_details)} {memory_word}" if failed_count > 0: operations_summary += f" (❌ {failed_count} Failed)" await self.memory_system._emit_status( emitter, operations_summary, done=True ) except Exception as e: duration = time.time() - start_time raise RuntimeError( f"❌ Memory consolidation failed after {duration:.2f}s: {str(e)}" ) class Filter: """Enhanced multi-model embedding and memory filter with LRU caching.""" __current_event_emitter__: Callable[[dict], Any] __user__: Dict[str, Any] __model__: str __request__: Request class Valves(BaseModel): """Configuration valves for the Memory System.""" memory_model: Optional[str] = Field( default=None, description="Custom model to use for memory operations. If Default, uses the current chat model", ) max_memories_returned: int = Field( default=Constants.MAX_MEMORIES_PER_RETRIEVAL, description="Maximum number of memories to return in context", ) max_message_chars: int = Field( default=Constants.MAX_MESSAGE_CHARS, description="Maximum user message length before skipping memory operations", ) 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)", ) def __init__(self): """Initialize the Memory System filter with production validation.""" global _SHARED_SKIP_DETECTOR_CACHE self.valves = self.Valves() self._validate_system_configuration() self._cache_manager = UnifiedCacheManager( Constants.MAX_CACHE_ENTRIES_PER_TYPE, Constants.MAX_CONCURRENT_USER_CACHES ) self._background_tasks: set = set() self._shutdown_event = asyncio.Event() self._embedding_function = None self._embedding_dimension = None self._skip_detector = None 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: """Set pipeline context parameters to avoid duplication in inlet/outlet methods.""" if __event_emitter__: self.__current_event_emitter__ = __event_emitter__ if __user__: self.__user__ = __user__ if __model__: self.__model__ = __model__ if __request__: self.__request__ = __request__ 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") self._detect_embedding_dimension() 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", "" ) cache_key = f"{embedding_engine}:{embedding_model}" async with _SHARED_SKIP_DETECTOR_CACHE_LOCK: if cache_key in _SHARED_SKIP_DETECTOR_CACHE: logger.info(f"♻️ Reusing cached skip detector: {cache_key}") self._skip_detector = _SHARED_SKIP_DETECTOR_CACHE[cache_key] 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): if isinstance(result[0], list): return [ np.array(emb, dtype=np.float16) for emb in result ] return np.array(result, dtype=np.float16) return np.array(result, dtype=np.float16) self._skip_detector = SkipDetector(embedding_wrapper) _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: max_length = Constants.CONTENT_PREVIEW_LENGTH return content[:max_length] + "..." if len(content) > max_length else content def _get_retrieval_threshold(self, is_consolidation: bool = False) -> float: """Calculate retrieval threshold for semantic similarity filtering.""" if is_consolidation: return ( self.valves.semantic_retrieval_threshold * self.valves.relaxed_semantic_threshold_multiplier ) return self.valves.semantic_retrieval_threshold def _extract_text_from_content(self, content) -> str: if isinstance(content, str): return content elif isinstance(content, list): text_parts = [] for item in content: if isinstance(item, dict) and item.get("type") == "text": text_parts.append(item.get("text", "")) return " ".join(text_parts) elif isinstance(content, dict) and "text" in content: return content["text"] return "" def _validate_system_configuration(self) -> None: """Validate configuration and fail if invalid.""" if self.valves.max_memories_returned <= 0: raise ValueError( f"📊 Invalid max memories returned: {self.valves.max_memories_returned}" ) if not (0.0 <= self.valves.semantic_retrieval_threshold <= 1.0): raise ValueError( f"🎯 Invalid semantic retrieval threshold: {self.valves.semantic_retrieval_threshold} (must be 0.0-1.0)" ) logger.info("✅ Configuration validated") async def _get_embedding_cache(self, user_id: str, key: str) -> Optional[Any]: """Get embedding from cache.""" return await self._cache_manager.get( user_id, self._cache_manager.EMBEDDING_CACHE, key ) async def _put_embedding_cache(self, user_id: str, key: str, value: Any) -> None: """Store embedding in cache.""" await self._cache_manager.put( user_id, self._cache_manager.EMBEDDING_CACHE, key, value ) def _compute_text_hash(self, text: str) -> str: """Compute SHA256 hash for text caching.""" return hashlib.sha256(text.encode()).hexdigest() def _detect_embedding_dimension(self) -> None: """Detect embedding dimension by generating a test embedding.""" try: test_embedding = self._embedding_function(["dummy"], prefix=None, user=None) if isinstance(test_embedding, list): test_embedding = test_embedding[0] self._embedding_dimension = np.squeeze(test_embedding).shape[0] logger.info(f"🎯 Detected embedding dimension: {self._embedding_dimension}") except Exception as e: raise RuntimeError(f"Failed to detect embedding dimension: {str(e)}") def _normalize_embedding( self, embedding: Union[List[float], np.ndarray] ) -> np.ndarray: """Normalize embedding vector and ensure 1D shape.""" if isinstance(embedding, list): embedding = np.array(embedding, dtype=np.float16) else: embedding = embedding.astype(np.float16) embedding = np.squeeze(embedding) if embedding.ndim != 1: raise ValueError( f"Embedding must be 1D after squeeze, got shape {embedding.shape}" ) if ( self._embedding_dimension and embedding.shape[0] != self._embedding_dimension ): raise ValueError( f"Embedding must have {self._embedding_dimension} dimensions, got {embedding.shape[0]}" ) norm = np.linalg.norm(embedding) return embedding / norm if norm > 0 else embedding async def _generate_embeddings( self, texts: Union[str, List[str]], user_id: str ) -> Union[np.ndarray, List[np.ndarray]]: """Unified embedding generation for single text or batch with optimized caching using OpenWebUI's embedding function.""" if self._embedding_function is None: raise RuntimeError( "🤖 Embedding function not initialized. Ensure pipeline context is set." ) is_single = isinstance(texts, str) text_list = [texts] if is_single else texts if not text_list: if is_single: raise ValueError("📏 Empty text provided for embedding generation") return [] result_embeddings = [] uncached_texts = [] uncached_indices = [] uncached_hashes = [] for i, text in enumerate(text_list): if not text or len(str(text).strip()) < Constants.MIN_MESSAGE_CHARS: if is_single: raise ValueError("📏 Text too short for embedding generation") result_embeddings.append(None) continue text_hash = self._compute_text_hash(str(text)) cached = await self._get_embedding_cache(user_id, text_hash) if cached is not None: result_embeddings.append(cached) else: result_embeddings.append(None) uncached_texts.append(text) uncached_indices.append(i) 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 ) loop = asyncio.get_event_loop() 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, np.ndarray)): new_embeddings = [ self._normalize_embedding(emb) for emb in raw_embeddings ] else: new_embeddings = [self._normalize_embedding(raw_embeddings)] else: new_embeddings = [self._normalize_embedding(raw_embeddings)] for j, embedding in enumerate(new_embeddings): original_idx = uncached_indices[j] text_hash = uncached_hashes[j] await self._put_embedding_cache(user_id, text_hash, embedding) result_embeddings[original_idx] = embedding if is_single: if uncached_texts: logger.info("💾 User message embedding: generated and cached") 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" ) return result_embeddings def _should_skip_memory_operations(self, user_message: str) -> Tuple[bool, str]: if self._skip_detector is None: raise RuntimeError("🤖 Skip detector not initialized") skip_reason = self._skip_detector.detect_skip_reason( user_message, self.valves.max_message_chars, memory_system=self ) if skip_reason: status_key = SkipDetector.SkipReason(skip_reason) return True, SkipDetector.STATUS_MESSAGES[status_key] return False, "" def _process_user_message( self, body: Dict[str, Any] ) -> Tuple[Optional[str], bool, str]: """Extract user message and determine if memory operations should be skipped.""" if not body or "messages" not in body or not isinstance(body["messages"], list): return ( None, True, SkipDetector.STATUS_MESSAGES[SkipDetector.SkipReason.SKIP_SIZE], ) messages = body["messages"] user_message = None for message in reversed(messages): if not isinstance(message, dict) or message.get("role") != "user": continue content = message.get("content", "") user_message = self._extract_text_from_content(content) if user_message: break if not user_message or not user_message.strip(): return ( None, True, SkipDetector.STATUS_MESSAGES[SkipDetector.SkipReason.SKIP_SIZE], ) should_skip, skip_reason = self._should_skip_memory_operations(user_message) return user_message, should_skip, skip_reason async def _get_user_memories( self, user_id: str, timeout: Optional[float] = None ) -> List: """Get user memories with timeout handling.""" if timeout is None: timeout = Constants.DATABASE_OPERATION_TIMEOUT_SEC try: return await asyncio.wait_for( asyncio.to_thread(Memories.get_memories_by_user_id, user_id), timeout=timeout, ) except asyncio.TimeoutError: raise TimeoutError(f"⏱️ Memory retrieval timed out after {timeout}s") except Exception as e: raise RuntimeError(f"💾 Memory retrieval failed: {str(e)}") def _log_retrieved_memories( self, memories: List[Dict[str, Any]], context_type: str = "semantic" ) -> None: """Log retrieved memories with concise formatting showing key statistics and semantic values.""" if not memories: return scores = [memory["relevance"] for memory in memories] if not scores: return top_score = max(scores) lowest_score = min(scores) median_score = statistics.median(scores) context_label = ( "📊 Consolidation candidate memories" if context_type == "consolidation" else "📊 Retrieved memories" ) max_scores_to_show = int( self.valves.max_memories_returned * Constants.EXTENDED_MAX_MEMORY_MULTIPLIER ) scores_str = ", ".join( [f"{score:.3f}" for score in scores[:max_scores_to_show]] ) suffix = "..." if len(scores) > max_scores_to_show else "" logger.info( f"{context_label}: {len(memories)} memories | Top: {top_score:.3f} | Median: {median_score:.3f} | Lowest: {lowest_score:.3f}" ) logger.info(f"Scores: [{scores_str}{suffix}]") def _build_operation_details( self, created_count: int, updated_count: int, deleted_count: int ) -> List[str]: operations = [ (created_count, "📝 Created"), (updated_count, "✏️ Updated"), (deleted_count, "🗑️ Deleted"), ] return [f"{label} {count}" for count, label in operations if count > 0] 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 ] return f"{cache_type}_{user_id}:{content_hash}" return f"{cache_type}_{user_id}" def format_current_datetime(self) -> str: try: now = datetime.now(timezone.utc) return now.strftime("%A %B %d %Y at %H:%M:%S UTC") except Exception as e: raise RuntimeError(f"📅 Failed to format datetime: {str(e)}") def _format_memories_for_llm(self, memories: List[Dict[str, Any]]) -> List[str]: """Format memories for LLM consumption with hybrid format and human-readable timestamps.""" memory_lines = [] for memory in memories: line = f"[{memory['id']}] {memory['content']}" record_date = memory.get("updated_at") or memory.get("created_at") if record_date: try: if isinstance(record_date, str): parsed_date = datetime.fromisoformat( record_date.replace("Z", "+00:00") ) else: parsed_date = record_date formatted_date = parsed_date.strftime("%b %d %Y") line += f" [noted at {formatted_date}]" except Exception as e: logger.warning(f"Failed to format date {record_date}: {str(e)}") line += f" [noted at {record_date}]" memory_lines.append(line) return memory_lines async def _emit_status( self, emitter: Optional[Callable], description: str, done: bool = True ) -> None: """Emit status messages for memory operations.""" if not emitter: return payload = {"type": "status", "data": {"description": description, "done": done}} try: result = emitter(payload) if asyncio.iscoroutine(result): await result except Exception: pass async def _retrieve_relevant_memories( self, user_message: str, user_id: str, user_memories: Optional[List] = None, emitter: Optional[Callable] = None, cached_similarities: Optional[List[Dict[str, Any]]] = None, ) -> Dict[str, Any]: """Retrieve memories for injection using similarity computation with optional LLM reranking.""" if cached_similarities is not None: memories = [ m for m in cached_similarities if m.get("relevance", 0) >= self.valves.semantic_retrieval_threshold ] logger.info( f"🔍 Using cached similarities for {len(memories)} candidate memories" ) final_memories, reranking_info = ( await self._llm_reranking_service.rerank_memories( user_message, memories, emitter ) ) self._log_retrieved_memories(final_memories, "semantic") return { "memories": final_memories, "threshold": self.valves.semantic_retrieval_threshold, "all_similarities": cached_similarities, "reranking_info": reranking_info, } if user_memories is None: user_memories = await self._get_user_memories(user_id) if not user_memories: logger.info("📭 No memories found for user") await self._emit_status(emitter, "📭 No Memories Found", done=True) return {"memories": [], "threshold": None} memories, threshold, all_similarities = await self._compute_similarities( user_message, user_id, user_memories ) if memories: final_memories, reranking_info = ( await self._llm_reranking_service.rerank_memories( user_message, memories, emitter ) ) else: logger.info("📭 No relevant memories found above similarity threshold") await self._emit_status(emitter, "📭 No Relevant Memories Found", done=True) final_memories = memories reranking_info = {"llm_decision": False, "decision_reason": "no_candidates"} self._log_retrieved_memories(final_memories, "semantic") return { "memories": final_memories, "threshold": threshold, "all_similarities": all_similarities, "reranking_info": reranking_info, } async def _add_memory_context( self, body: Dict[str, Any], memories: Optional[List[Dict[str, Any]]] = None, user_id: Optional[str] = None, emitter: Optional[Callable] = None, ) -> None: """Add memory context to request body with simplified logic.""" if not body or "messages" not in body or not body["messages"]: logger.warning("⚠️ Invalid request body or no messages found") return content_parts = [f"Current Date/Time: {self.format_current_datetime()}"] memory_count = 0 if memories and user_id: memory_count = len(memories) memory_header = f"CONTEXT: The following {'fact' if memory_count == 1 else 'facts'} about the user are provided for background only. Not all facts may be relevant to the current request." formatted_memories = [] for idx, memory in enumerate(memories, 1): formatted_memory = f"- {' '.join(memory['content'].split())}" formatted_memories.append(formatted_memory) 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." memory_context_block = f"{memory_header}\n{chr(10).join(formatted_memories)}\n\n{memory_footer}" content_parts.append(memory_context_block) memory_context = "\n\n".join(content_parts) system_index = next( ( i for i, msg in enumerate(body["messages"]) if msg.get("role") == "system" ), None, ) if system_index is not None: body["messages"][system_index][ "content" ] = f"{body['messages'][system_index].get('content', '')}\n\n{memory_context}" else: body["messages"].insert(0, {"role": "system", "content": memory_context}) if memories and user_id: description = f"🧠 Injected {memory_count} {'Memory' if memory_count == 1 else 'Memories'} to Context" await self._emit_status(emitter, description, done=True) def _build_memory_dict(self, memory, similarity: float) -> Dict[str, Any]: """Build memory dictionary with standardized timestamp conversion.""" memory_dict = { "id": str(memory.id), "content": memory.content, "relevance": similarity, } if hasattr(memory, "created_at") and memory.created_at: memory_dict["created_at"] = datetime.fromtimestamp( memory.created_at, tz=timezone.utc ).isoformat() if hasattr(memory, "updated_at") and memory.updated_at: 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]]: """Compute similarity scores between user message and memories.""" if not user_memories: return [], self.valves.semantic_retrieval_threshold, [] query_embedding = await self._generate_embeddings(user_message, user_id) memory_contents = [memory.content for memory in user_memories] memory_embeddings = await self._generate_embeddings(memory_contents, user_id) if len(memory_embeddings) != len(user_memories): logger.error( f"🔢 Embedding generation failed: generated {len(memory_embeddings)} embeddings but expected {len(user_memories)} for user memories" ) return [], self.valves.semantic_retrieval_threshold, [] similarity_scores = [] memory_data = [] for memory_index, memory in enumerate(user_memories): memory_embedding = memory_embeddings[memory_index] if memory_embedding is None: continue similarity = float(np.dot(query_embedding, memory_embedding)) similarity_scores.append(similarity) memory_dict = self._build_memory_dict(memory, similarity) memory_data.append(memory_dict) if not similarity_scores: return [], self.valves.semantic_retrieval_threshold, [] memory_data.sort(key=lambda x: x["relevance"], reverse=True) threshold = self.valves.semantic_retrieval_threshold filtered_memories = [m for m in memory_data if m["relevance"] >= threshold] return filtered_memories, threshold, memory_data async def inlet( self, body: Dict[str, Any], __event_emitter__: Optional[Callable] = None, __user__: Optional[Dict[str, Any]] = None, __request__: Optional[Request] = None, ) -> Dict[str, Any]: """Simplified inlet processing for memory retrieval and injection.""" model_to_use = self.valves.memory_model or ( body.get("model") if isinstance(body, dict) else None ) if self.valves.memory_model: logger.info(f"🤖 Using the custom model for memory : {model_to_use}") await self._set_pipeline_context( __event_emitter__, __user__, model_to_use, __request__ ) user_id = __user__.get("id") if body and __user__ else None if not user_id: return body user_message, should_skip, skip_reason = self._process_user_message(body) if not user_message or should_skip: if __event_emitter__ and skip_reason: await self._emit_status(__event_emitter__, skip_reason, done=True) await self._add_memory_context(body, [], user_id, __event_emitter__) return body try: memory_cache_key = self._cache_key( self._cache_manager.MEMORY_CACHE, user_id ) user_memories = await self._cache_manager.get( user_id, self._cache_manager.MEMORY_CACHE, memory_cache_key ) if user_memories is None: user_memories = await self._get_user_memories(user_id) await self._cache_manager.put( user_id, self._cache_manager.MEMORY_CACHE, memory_cache_key, user_memories, ) retrieval_result = await self._retrieve_relevant_memories( user_message, user_id, user_memories, __event_emitter__ ) memories = retrieval_result.get("memories", []) threshold = retrieval_result.get("threshold") all_similarities = retrieval_result.get("all_similarities", []) if all_similarities: cache_key = self._cache_key( self._cache_manager.RETRIEVAL_CACHE, user_id, user_message ) await self._cache_manager.put( user_id, self._cache_manager.RETRIEVAL_CACHE, cache_key, all_similarities, ) await self._add_memory_context(body, memories, user_id, __event_emitter__) except Exception as e: raise RuntimeError(f"💾 Memory retrieval failed: {str(e)}") return body async def outlet( self, body: dict, __event_emitter__: Optional[Callable] = None, __user__: Optional[dict] = None, __request__: Optional[Request] = None, ) -> dict: """Simplified outlet processing for background memory consolidation.""" model_to_use = self.valves.memory_model or ( body.get("model") if isinstance(body, dict) else None ) if self.valves.memory_model: logger.info(f"🤖 Using the custom model for memory : {model_to_use}") await self._set_pipeline_context( __event_emitter__, __user__, model_to_use, __request__ ) user_id = __user__.get("id") if body and __user__ else None if not user_id: return body user_message, should_skip, skip_reason = self._process_user_message(body) if not user_message or should_skip: return body 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 ) ) self._background_tasks.add(task) def safe_cleanup(t: asyncio.Task) -> None: try: self._background_tasks.discard(t) if t.exception() and not t.cancelled(): exception = t.exception() logger.error( f"❌ Background memory consolidation task failed: {str(exception)}" ) except Exception as e: logger.error(f"❌ Failed to cleanup background memory task: {str(e)}") task.add_done_callback(safe_cleanup) return body async def shutdown(self) -> None: """Cleanup method to properly shutdown background tasks.""" self._shutdown_event.set() if self._background_tasks: await asyncio.gather(*self._background_tasks, return_exceptions=True) self._background_tasks.clear() await self._cache_manager.clear_all_caches() async def _refresh_user_cache(self, user_id: str) -> None: """Refresh user cache - clear stale caches and update with fresh embeddings.""" start_time = time.time() try: retrieval_cleared = await self._cache_manager.clear_user_cache( user_id, self._cache_manager.RETRIEVAL_CACHE ) embedding_cleared = await self._cache_manager.clear_user_cache( user_id, self._cache_manager.EMBEDDING_CACHE ) logger.info( f"🔄 Cleared {retrieval_cleared} retrieval + {embedding_cleared} embedding cache entries for user {user_id}" ) user_memories = await self._get_user_memories(user_id) memory_cache_key = self._cache_key( self._cache_manager.MEMORY_CACHE, user_id ) if not user_memories: await self._cache_manager.put( user_id, self._cache_manager.MEMORY_CACHE, memory_cache_key, [] ) logger.info("📭 No memories found for user") return 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 ] if memory_contents: await self._generate_embeddings(memory_contents, user_id) duration = time.time() - start_time logger.info( f"🔄 Cache updated with {len(memory_contents)} embeddings for user {user_id} in {duration:.2f}s" ) except Exception as e: raise RuntimeError( f"🧹 Failed to refresh cache for user {user_id} after {(time.time() - start_time):.2f}s: {str(e)}" ) async def _execute_single_operation( self, operation: Models.MemoryOperation, user: Any ) -> str: """Execute a single memory operation.""" try: if operation.operation == Models.MemoryOperationType.CREATE: content_stripped = operation.content.strip() if not content_stripped: logger.warning(f"⚠️ Skipping CREATE operation: empty content") return Models.OperationResult.SKIPPED_EMPTY_CONTENT.value await asyncio.wait_for( asyncio.to_thread( Memories.insert_new_memory, user.id, content_stripped ), timeout=Constants.DATABASE_OPERATION_TIMEOUT_SEC, ) return Models.MemoryOperationType.CREATE.value elif operation.operation == Models.MemoryOperationType.UPDATE: id_stripped = operation.id.strip() if not id_stripped: logger.warning(f"⚠️ Skipping UPDATE operation: empty ID") return Models.OperationResult.SKIPPED_EMPTY_ID.value content_stripped = operation.content.strip() if not content_stripped: logger.warning( f"⚠️ Skipping UPDATE operation for {id_stripped}: empty content" ) return Models.OperationResult.SKIPPED_EMPTY_CONTENT.value await asyncio.wait_for( asyncio.to_thread( Memories.update_memory_by_id_and_user_id, id_stripped, user.id, content_stripped, ), timeout=Constants.DATABASE_OPERATION_TIMEOUT_SEC, ) return Models.MemoryOperationType.UPDATE.value elif operation.operation == Models.MemoryOperationType.DELETE: id_stripped = operation.id.strip() if not id_stripped: logger.warning(f"⚠️ Skipping DELETE operation: empty ID") return Models.OperationResult.SKIPPED_EMPTY_ID.value await asyncio.wait_for( asyncio.to_thread( Memories.delete_memory_by_id_and_user_id, id_stripped, user.id ), timeout=Constants.DATABASE_OPERATION_TIMEOUT_SEC, ) return Models.MemoryOperationType.DELETE.value else: logger.error(f"❓ Unsupported operation: {operation}") return Models.OperationResult.UNSUPPORTED.value except Exception as e: logger.error( f"💾 Database operation failed for {operation.operation.value}: {str(e)}" ) return Models.OperationResult.FAILED.value def _remove_refs_from_schema( self, schema: Dict[str, Any], schema_defs: Optional[Dict[str, Any]] = None ) -> Dict[str, Any]: """Remove $ref references and ensure required fields for Azure OpenAI.""" if not isinstance(schema, dict): return schema if "$ref" in schema: ref_path = schema["$ref"] if ref_path.startswith("#/$defs/"): def_name = ref_path.split("/")[-1] if schema_defs and def_name in schema_defs: return self._remove_refs_from_schema( schema_defs[def_name].copy(), schema_defs ) return {"type": "object"} result = {} for key, value in schema.items(): if key == "$defs": continue elif isinstance(value, dict): result[key] = self._remove_refs_from_schema(value, schema_defs) elif isinstance(value, list): result[key] = [ ( self._remove_refs_from_schema(item, schema_defs) if isinstance(item, dict) else item ) for item in value ] else: result[key] = value if result.get("type") == "object" and "properties" in result: result["required"] = list(result["properties"].keys()) return result async def _query_llm( self, system_prompt: str, user_prompt: str, response_model: Optional[BaseModel] = None, ) -> Union[str, BaseModel]: """Query OpenWebUI's internal model system with Pydantic model parsing.""" if not hasattr(self, "__request__") or not hasattr(self, "__user__"): raise RuntimeError( "🔧 Pipeline interface not properly initialized. __request__ and __user__ required." ) model_to_use = self.__model__ if not model_to_use: raise ValueError("🤖 No model specified for LLM operations") form_data = { "model": model_to_use, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], "max_tokens": 4096, "stream": False, } if response_model: raw_schema = response_model.model_json_schema() schema_defs = raw_schema.get("$defs", {}) schema = self._remove_refs_from_schema(raw_schema, schema_defs) schema["type"] = "object" form_data["response_format"] = { "type": "json_schema", "json_schema": { "name": response_model.__name__, "strict": True, "schema": schema, }, } try: response = await asyncio.wait_for( generate_chat_completion( self.__request__, form_data, user=await asyncio.to_thread( Users.get_user_by_id, self.__user__["id"] ), ), timeout=Constants.LLM_CONSOLIDATION_TIMEOUT_SEC, ) except asyncio.TimeoutError: raise TimeoutError( f"⏱️ LLM query timed out after {Constants.LLM_CONSOLIDATION_TIMEOUT_SEC}s" ) except Exception as e: raise RuntimeError(f"🤖 LLM query failed: {str(e)}") try: if hasattr(response, "body") and hasattr( getattr(response, "body", None), "decode" ): body = getattr(response, "body") response_data = json.loads(body.decode("utf-8")) else: response_data = response except (json.JSONDecodeError, AttributeError) as e: raise RuntimeError(f"🔍 Failed to decode response body: {str(e)}") 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"] ): content = first_choice["message"]["content"] else: raise ValueError( "🤖 Invalid response structure: missing content in message" ) else: raise ValueError(f"🤖 Unexpected LLM response format: {response_data}") if response_model: try: parsed_data = json.loads(content) return response_model.model_validate(parsed_data) except json.JSONDecodeError as e: raise ValueError( f"🔍 Invalid JSON from LLM: {str(e)}\nContent: {content}" ) except PydanticValidationError as e: raise ValueError( f"🔍 LLM response validation failed: {str(e)}\nContent: {content}" ) if not content or content.strip() == "": raise ValueError("🤖 Empty response from LLM") return content