[split-required] Split final 43 files (500-668 LOC) to complete refactoring
klausur-service (11 files): - cv_gutter_repair, ocr_pipeline_regression, upload_api - ocr_pipeline_sessions, smart_spell, nru_worksheet_generator - ocr_pipeline_overlays, mail/aggregator, zeugnis_api - cv_syllable_detect, self_rag backend-lehrer (17 files): - classroom_engine/suggestions, generators/quiz_generator - worksheets_api, llm_gateway/comparison, state_engine_api - classroom/models (→ 4 submodules), services/file_processor - alerts_agent/api/wizard+digests+routes, content_generators/pdf - classroom/routes/sessions, llm_gateway/inference - classroom_engine/analytics, auth/keycloak_auth - alerts_agent/processing/rule_engine, ai_processor/print_versions agent-core (5 files): - brain/memory_store, brain/knowledge_graph, brain/context_manager - orchestrator/supervisor, sessions/session_manager admin-lehrer (5 components): - GridOverlay, StepGridReview, DevOpsPipelineSidebar - DataFlowDiagram, sbom/wizard/page website (2 files): - DependencyMap, lehrer/abitur-archiv Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Benjamin Admin
307
agent-core/brain/context_models.py
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307
agent-core/brain/context_models.py
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"""
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Context Models for Breakpilot Agents
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Data classes for conversation messages and context management.
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"""
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from typing import Dict, Any, List, Optional
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from enum import Enum
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import logging
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logger = logging.getLogger(__name__)
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class MessageRole(Enum):
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"""Message roles in a conversation"""
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SYSTEM = "system"
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USER = "user"
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ASSISTANT = "assistant"
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TOOL = "tool"
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@dataclass
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class Message:
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"""Represents a message in a conversation"""
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role: MessageRole
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content: str
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timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
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metadata: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> Dict[str, Any]:
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return {
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"role": self.role.value,
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"content": self.content,
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"timestamp": self.timestamp.isoformat(),
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"metadata": self.metadata
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}
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "Message":
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return cls(
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role=MessageRole(data["role"]),
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content=data["content"],
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timestamp=datetime.fromisoformat(data["timestamp"]) if "timestamp" in data else datetime.now(timezone.utc),
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metadata=data.get("metadata", {})
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)
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@dataclass
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class ConversationContext:
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"""
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Context for a running conversation.
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Maintains:
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- Message history with automatic compression
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- Extracted entities
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- Intent history
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- Conversation summary
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"""
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messages: List[Message] = field(default_factory=list)
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entities: Dict[str, Any] = field(default_factory=dict)
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intent_history: List[str] = field(default_factory=list)
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summary: Optional[str] = None
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max_messages: int = 50
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system_prompt: Optional[str] = None
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metadata: Dict[str, Any] = field(default_factory=dict)
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def add_message(
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self,
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role: MessageRole,
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content: str,
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metadata: Optional[Dict[str, Any]] = None
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) -> Message:
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"""
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Adds a message to the conversation.
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Args:
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role: Message role
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content: Message content
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metadata: Optional message metadata
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Returns:
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The created Message
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"""
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message = Message(
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role=role,
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content=content,
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metadata=metadata or {}
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)
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self.messages.append(message)
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# Compress if needed
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if len(self.messages) > self.max_messages:
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self._compress_history()
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return message
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def add_user_message(
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self,
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content: str,
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metadata: Optional[Dict[str, Any]] = None
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) -> Message:
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"""Convenience method to add a user message"""
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return self.add_message(MessageRole.USER, content, metadata)
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def add_assistant_message(
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self,
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content: str,
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metadata: Optional[Dict[str, Any]] = None
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) -> Message:
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"""Convenience method to add an assistant message"""
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return self.add_message(MessageRole.ASSISTANT, content, metadata)
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def add_system_message(
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self,
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content: str,
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metadata: Optional[Dict[str, Any]] = None
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) -> Message:
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"""Convenience method to add a system message"""
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return self.add_message(MessageRole.SYSTEM, content, metadata)
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def add_intent(self, intent: str) -> None:
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"""
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Records an intent in the history.
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Args:
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intent: The detected intent
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"""
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self.intent_history.append(intent)
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# Keep last 20 intents
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if len(self.intent_history) > 20:
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self.intent_history = self.intent_history[-20:]
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def set_entity(self, name: str, value: Any) -> None:
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"""
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Sets an entity value.
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Args:
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name: Entity name
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value: Entity value
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"""
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self.entities[name] = value
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def get_entity(self, name: str, default: Any = None) -> Any:
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"""
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Gets an entity value.
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Args:
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name: Entity name
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default: Default value if not found
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Returns:
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Entity value or default
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"""
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return self.entities.get(name, default)
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def get_last_message(self, role: Optional[MessageRole] = None) -> Optional[Message]:
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"""
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Gets the last message, optionally filtered by role.
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Args:
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role: Optional role filter
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Returns:
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The last matching message or None
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"""
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if not self.messages:
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return None
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if role is None:
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return self.messages[-1]
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for msg in reversed(self.messages):
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if msg.role == role:
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return msg
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return None
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def get_messages_for_llm(self) -> List[Dict[str, str]]:
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"""
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Gets messages formatted for LLM API calls.
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Returns:
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List of message dicts with role and content
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"""
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result = []
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# Add system prompt first
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if self.system_prompt:
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result.append({
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"role": "system",
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"content": self.system_prompt
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})
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# Add summary if we have one and history was compressed
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if self.summary:
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result.append({
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"role": "system",
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"content": f"Previous conversation summary: {self.summary}"
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})
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# Add recent messages
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for msg in self.messages:
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result.append({
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"role": msg.role.value,
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"content": msg.content
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})
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return result
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def _compress_history(self) -> None:
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"""
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Compresses older messages to save context window space.
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Keeps:
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- System messages
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- Last 20 messages
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- Creates summary of compressed middle messages
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"""
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# Keep system messages
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system_msgs = [m for m in self.messages if m.role == MessageRole.SYSTEM]
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# Keep last 20 messages
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recent_msgs = self.messages[-20:]
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# Middle messages to summarize
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middle_start = len(system_msgs)
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middle_end = len(self.messages) - 20
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middle_msgs = self.messages[middle_start:middle_end]
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if middle_msgs:
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# Create a basic summary (can be enhanced with LLM-based summarization)
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self.summary = self._create_summary(middle_msgs)
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# Combine
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self.messages = system_msgs + recent_msgs
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logger.debug(
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f"Compressed conversation: {middle_end - middle_start} messages summarized"
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)
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def _create_summary(self, messages: List[Message]) -> str:
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"""
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Creates a summary of messages.
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This is a basic implementation - can be enhanced with LLM-based summarization.
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Args:
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messages: Messages to summarize
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Returns:
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Summary string
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"""
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# Count message types
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user_count = sum(1 for m in messages if m.role == MessageRole.USER)
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assistant_count = sum(1 for m in messages if m.role == MessageRole.ASSISTANT)
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# Extract key topics (simplified - could use NLP)
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topics = set()
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for msg in messages:
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# Simple keyword extraction
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words = msg.content.lower().split()
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# Filter common words
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keywords = [w for w in words if len(w) > 5][:3]
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topics.update(keywords)
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topics_str = ", ".join(list(topics)[:5])
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return (
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f"Earlier conversation: {user_count} user messages, "
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f"{assistant_count} assistant responses. "
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f"Topics discussed: {topics_str}"
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)
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def clear(self) -> None:
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"""Clears all context"""
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self.messages.clear()
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self.entities.clear()
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self.intent_history.clear()
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self.summary = None
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def to_dict(self) -> Dict[str, Any]:
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"""Serializes context to dict"""
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return {
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"messages": [m.to_dict() for m in self.messages],
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"entities": self.entities,
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"intent_history": self.intent_history,
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"summary": self.summary,
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"max_messages": self.max_messages,
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"system_prompt": self.system_prompt,
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"metadata": self.metadata
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}
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "ConversationContext":
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"""Deserializes context from dict"""
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ctx = cls(
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messages=[Message.from_dict(m) for m in data.get("messages", [])],
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entities=data.get("entities", {}),
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intent_history=data.get("intent_history", []),
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summary=data.get("summary"),
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max_messages=data.get("max_messages", 50),
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system_prompt=data.get("system_prompt"),
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metadata=data.get("metadata", {})
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)
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return ctx
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