feat(async-context-compression): upgrade summary prompt to Working Memory architecture
- Redefine summary task as 'Working Memory' generation for higher density - Add explicit instructions to extract facts from raw JSON tool outputs - Implement 'Incremental Integration' rule to prevent recursive summary degradation - Enforce strict Markdown state structure (Goal, Facts, Code, Pending)
This commit is contained in:
fujie
@@ -1516,27 +1516,31 @@ class Filter:
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"index": index,
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"role": message.get("role", "unknown"),
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"has_tool_calls": bool(isinstance(tool_calls, list) and tool_calls),
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"tool_call_count": len(tool_calls)
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if isinstance(tool_calls, list)
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else 0,
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"tool_call_id_lengths": [
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len(str(tc.get("id", "")))
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for tc in tool_calls[:3]
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if isinstance(tc, dict)
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]
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if isinstance(tool_calls, list)
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else [],
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"tool_call_count": (
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len(tool_calls) if isinstance(tool_calls, list) else 0
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),
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"tool_call_id_lengths": (
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[
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len(str(tc.get("id", "")))
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for tc in tool_calls[:3]
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if isinstance(tc, dict)
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]
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if isinstance(tool_calls, list)
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else []
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),
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"has_tool_call_id": isinstance(message.get("tool_call_id"), str),
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"tool_call_id_length": len(str(message.get("tool_call_id", "")))
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if isinstance(message.get("tool_call_id"), str)
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else 0,
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"tool_call_id_length": (
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len(str(message.get("tool_call_id", "")))
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if isinstance(message.get("tool_call_id"), str)
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else 0
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),
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"content_type": type(content).__name__,
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"content_length": len(content) if isinstance(content, str) else 0,
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"has_tool_details_block": isinstance(content, str)
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and '<details type="tool_calls"' in content,
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"metadata_keys": sorted(metadata.keys())[:8]
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if isinstance(metadata, dict)
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else [],
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"metadata_keys": (
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sorted(metadata.keys())[:8] if isinstance(metadata, dict) else []
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),
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}
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if isinstance(content, list):
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@@ -1585,14 +1589,16 @@ class Filter:
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return {
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"body_keys": sorted(body.keys()),
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"metadata_keys": sorted(metadata.keys()) if isinstance(metadata, dict) else [],
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"metadata_keys": (
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sorted(metadata.keys()) if isinstance(metadata, dict) else []
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),
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"params_keys": sorted(params.keys()) if isinstance(params, dict) else [],
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"metadata_function_calling": metadata.get("function_calling")
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if isinstance(metadata, dict)
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else None,
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"params_function_calling": params.get("function_calling")
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if isinstance(params, dict)
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else None,
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"metadata_function_calling": (
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metadata.get("function_calling") if isinstance(metadata, dict) else None
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),
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"params_function_calling": (
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params.get("function_calling") if isinstance(params, dict) else None
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),
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"message_count": len(messages) if isinstance(messages, list) else 0,
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"role_counts": role_counts,
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"assistant_tool_call_indices": assistant_tool_call_indices[:8],
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@@ -1624,9 +1630,11 @@ class Filter:
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"id": message.get("id", ""),
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"parentId": message.get("parentId") or message.get("parent_id"),
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"tool_call_id": message.get("tool_call_id", ""),
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"tool_call_count": len(message.get("tool_calls", []))
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if isinstance(message.get("tool_calls"), list)
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else 0,
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"tool_call_count": (
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len(message.get("tool_calls", []))
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if isinstance(message.get("tool_calls"), list)
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else 0
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),
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"is_summary": self._is_summary_message(message),
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"content_length": len(content) if isinstance(content, str) else 0,
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}
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@@ -1647,9 +1655,11 @@ class Filter:
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"id": message.get("id", ""),
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"parentId": message.get("parentId") or message.get("parent_id"),
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"tool_call_id": message.get("tool_call_id", ""),
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"tool_call_count": len(message.get("tool_calls", []))
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if isinstance(message.get("tool_calls"), list)
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else 0,
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"tool_call_count": (
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len(message.get("tool_calls", []))
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if isinstance(message.get("tool_calls"), list)
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else 0
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),
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"is_summary": self._is_summary_message(message),
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"content_length": len(content) if isinstance(content, str) else 0,
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}
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@@ -1659,7 +1669,9 @@ class Filter:
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"message_count": len(messages),
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"summary_state": summary_state,
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"original_history_count": self._get_original_history_count(messages),
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"target_compressed_count": self._calculate_target_compressed_count(messages),
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"target_compressed_count": self._calculate_target_compressed_count(
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messages
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),
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"effective_keep_first": self._get_effective_keep_first(messages),
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"head_sample": sample,
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"tail_sample": tail_sample,
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@@ -1681,20 +1693,25 @@ class Filter:
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continue
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# If it's an assistant message with the hidden 'output' field, unfold it
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if msg.get("role") == "assistant" and isinstance(msg.get("output"), list) and msg.get("output"):
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if (
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msg.get("role") == "assistant"
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and isinstance(msg.get("output"), list)
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and msg.get("output")
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):
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try:
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from open_webui.utils.misc import convert_output_to_messages
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expanded = convert_output_to_messages(msg["output"], raw=True)
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if expanded:
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unfolded.extend(expanded)
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continue
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except ImportError:
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pass # Fallback if for some reason the internal import fails
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pass # Fallback if for some reason the internal import fails
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# Clean message (strip 'output' field just like inlet does)
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clean_msg = {k: v for k, v in msg.items() if k != "output"}
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unfolded.append(clean_msg)
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return unfolded
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def _get_function_calling_mode(self, body: dict) -> str:
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@@ -1831,7 +1848,9 @@ class Filter:
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)
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except ValueError as ve:
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if "broadcast" in str(ve).lower():
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logger.debug("Cannot broadcast to frontend without explicit room; suppressing further frontend logs in this session.")
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logger.debug(
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"Cannot broadcast to frontend without explicit room; suppressing further frontend logs in this session."
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)
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self.valves.show_debug_log = False
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else:
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logger.error(f"Failed to process log to frontend: ValueError: {ve}")
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@@ -2545,10 +2564,22 @@ class Filter:
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# In the outlet phase, the frontend payload often lacks the hidden 'output' field.
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# We try to load the full, raw history from the database first.
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db_messages = self._load_full_chat_messages(chat_id)
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messages_to_unfold = db_messages if (db_messages and len(db_messages) >= len(messages)) else messages
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messages_to_unfold = (
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db_messages
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if (db_messages and len(db_messages) >= len(messages))
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else messages
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)
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summary_messages = self._unfold_messages(messages_to_unfold)
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message_source = "outlet-db-unfolded" if db_messages and len(summary_messages) != len(messages) else "outlet-body-unfolded" if len(summary_messages) != len(messages) else "outlet-body"
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message_source = (
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"outlet-db-unfolded"
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if db_messages and len(summary_messages) != len(messages)
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else (
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"outlet-body-unfolded"
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if len(summary_messages) != len(messages)
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else "outlet-body"
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)
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)
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if self.valves.show_debug_log and __event_call__:
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source_progress = self._build_summary_progress_snapshot(summary_messages)
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@@ -3179,43 +3210,37 @@ class Filter:
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event_call=__event_call__,
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)
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# Build summary prompt (Optimized)
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# Build summary prompt (Optimized for State/Working Memory and Tool Calling)
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summary_prompt = f"""
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You are a professional conversation context compression expert. Your task is to create a high-fidelity summary of the following conversation content.
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This conversation may contain previous summaries (as system messages or text) and subsequent conversation content.
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You are an expert Context Compression Engine. Your goal is to create a high-fidelity, highly dense "Working Memory" from the provided conversation.
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This conversation may contain previous Working Memories and raw native tool-calling sequences (JSON arguments and results).
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### Core Objectives
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1. **Comprehensive Summary**: Concisely summarize key information, user intent, and assistant responses from the conversation.
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2. **De-noising**: Remove greetings, repetitions, confirmations, and other non-essential information.
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3. **Key Retention**:
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* **Code snippets, commands, and technical parameters must be preserved verbatim. Do not modify or generalize them.**
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* User intent, core requirements, decisions, and action items must be clearly preserved.
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4. **Coherence**: The generated summary should be a cohesive whole that can replace the original conversation as context.
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5. **Detailed Record**: Since length is permitted, please preserve details, reasoning processes, and nuances of multi-turn interactions as much as possible, rather than just high-level generalizations.
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### Rules of Engagement
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1. **Incremental Integration**: If the conversation begins with an existing Working Memory/Summary, you must PRESERVE its core facts and MERGE the new conversation events into it. Do not discard older facts.
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2. **Tool-Call Decompression**: Raw JSON/Text outputs from tools are noisy. Extract ONLY the definitive facts, actionable data, or root causes of errors. Ignore the structural payload.
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3. **Ruthless Denoising**: Completely eliminate greetings, apologies ("I'm sorry for the error"), acknowledgments ("Sure, I can do that"), and redundant confirmations.
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4. **Verbatim Retention**: ANY code snippets, shell commands, file paths, specific parameters, and Message IDs (e.g., [ID: ...]) MUST be kept exactly as they appear to maintain traceability.
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5. **Logic Preservation**: Clearly link "what the user asked" -> "what the tool found" -> "how the system reacted".
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### Output Requirements
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* **Format**: Structured text, logically clear.
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* **Language**: Consistent with the conversation language (usually English).
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* **Length**: Strictly control within {self.valves.max_summary_tokens} Tokens.
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* **Strictly Forbidden**: Do not output "According to the conversation...", "The summary is as follows..." or similar filler. Output the summary content directly.
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### Output Constraints
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* **Format**: Strictly follow the Markdown structure below.
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* **Length**: Maximum {self.valves.max_summary_tokens} Tokens.
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* **Tone**: Robotic, objective, dense.
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* **Language**: Consistent with the conversation language.
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* **Forbidden**: NO conversational openings/closings (e.g., "Here is the summary", "Hope this helps"). Output the data directly.
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### Suggested Summary Structure
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* **Current Goal/Topic**: A one-sentence summary of the problem currently being solved.
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* **Key Information & Context**:
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* Confirmed facts/parameters.
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* **Code/Technical Details** (Wrap in code blocks).
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* **Progress & Conclusions**: Completed steps and reached consensus.
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* **Action Items/Next Steps**: Clear follow-up actions.
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### Identity Traceability
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The input dialogue contains message IDs (e.g., [ID: ...]) and optional names.
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If a specific message contributes a critical decision, a unique code snippet, or a tool-calling result, please reference its ID or Name in your summary to maintain traceability.
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* **Current Goal**: What is the user ultimately trying to achieve?
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* **Working Memory & Facts**: (Bullet points of established facts, parsed tool results, and constraints. Cite Message IDs if critical).
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* **Code & Artifacts**: (Only if applicable. Include exact code blocks).
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* **Recent Actions**: (e.g., "Attempted to run script, failed with SyntaxError, applied fix").
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* **Pending/Next Steps**: What is waiting to be done.
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---
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{new_conversation_text}
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---
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Based on the content above, generate the summary (including key message identities where relevant):
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Generate the Working Memory:
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"""
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# Determine the model to use
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model = self._clean_model_id(self.valves.summary_model) or self._clean_model_id(
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