"""Prompt templates for memory update and injection."""
from typing import Any
# Prompt template for updating memory based on conversation
MEMORY_UPDATE_PROMPT = """You are a memory management system. Your task is to analyze a conversation and update the user's memory profile.
Current Memory State:
{current_memory}
New Conversation to Process:
{conversation}
Instructions:
1. Analyze the conversation for important information about the user
2. Extract relevant facts, preferences, and context
3. Update the memory sections as needed:
- workContext: User's work-related information (job, projects, tools, technologies)
- personalContext: Personal preferences, communication style, background
- topOfMind: Current focus areas, ongoing tasks, immediate priorities
4. For facts extraction:
- Extract specific, verifiable facts about the user
- Assign appropriate categories: preference, knowledge, context, behavior, goal
- Estimate confidence (0.0-1.0) based on how explicit the information is
- Avoid duplicating existing facts
5. Update history sections:
- recentMonths: Summary of recent activities and discussions
- earlierContext: Important historical context
- longTermBackground: Persistent background information
Output Format (JSON):
{{
"user": {{
"workContext": {{ "summary": "...", "shouldUpdate": true/false }},
"personalContext": {{ "summary": "...", "shouldUpdate": true/false }},
"topOfMind": {{ "summary": "...", "shouldUpdate": true/false }}
}},
"history": {{
"recentMonths": {{ "summary": "...", "shouldUpdate": true/false }},
"earlierContext": {{ "summary": "...", "shouldUpdate": true/false }},
"longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
}},
"newFacts": [
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
],
"factsToRemove": ["fact_id_1", "fact_id_2"]
}}
Important Rules:
- Only set shouldUpdate=true if there's meaningful new information
- Keep summaries concise (1-3 sentences each)
- Only add facts that are clearly stated or strongly implied
- Remove facts that are contradicted by new information
- Preserve existing information that isn't contradicted
- Focus on information useful for future interactions
Return ONLY valid JSON, no explanation or markdown."""
# Prompt template for extracting facts from a single message
FACT_EXTRACTION_PROMPT = """Extract factual information about the user from this message.
Message:
{message}
Extract facts in this JSON format:
{{
"facts": [
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
]
}}
Categories:
- preference: User preferences (likes/dislikes, styles, tools)
- knowledge: User's expertise or knowledge areas
- context: Background context (location, job, projects)
- behavior: Behavioral patterns
- goal: User's goals or objectives
Rules:
- Only extract clear, specific facts
- Confidence should reflect certainty (explicit statement = 0.9+, implied = 0.6-0.8)
- Skip vague or temporary information
Return ONLY valid JSON."""
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
"""Format memory data for injection into system prompt.
Args:
memory_data: The memory data dictionary.
max_tokens: Maximum tokens to use (approximate via character count).
Returns:
Formatted memory string for system prompt injection.
"""
if not memory_data:
return ""
sections = []
# Format user context
user_data = memory_data.get("user", {})
if user_data:
user_sections = []
work_ctx = user_data.get("workContext", {})
if work_ctx.get("summary"):
user_sections.append(f"Work: {work_ctx['summary']}")
personal_ctx = user_data.get("personalContext", {})
if personal_ctx.get("summary"):
user_sections.append(f"Personal: {personal_ctx['summary']}")
top_of_mind = user_data.get("topOfMind", {})
if top_of_mind.get("summary"):
user_sections.append(f"Current Focus: {top_of_mind['summary']}")
if user_sections:
sections.append("User Context:\n" + "\n".join(f"- {s}" for s in user_sections))
# Format history
history_data = memory_data.get("history", {})
if history_data:
history_sections = []
recent = history_data.get("recentMonths", {})
if recent.get("summary"):
history_sections.append(f"Recent: {recent['summary']}")
earlier = history_data.get("earlierContext", {})
if earlier.get("summary"):
history_sections.append(f"Earlier: {earlier['summary']}")
if history_sections:
sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
# Format facts (most relevant ones)
facts = memory_data.get("facts", [])
if facts:
# Sort by confidence and take top facts
sorted_facts = sorted(facts, key=lambda f: f.get("confidence", 0), reverse=True)
# Limit to avoid too much content
top_facts = sorted_facts[:15]
fact_lines = []
for fact in top_facts:
content = fact.get("content", "")
category = fact.get("category", "")
if content:
fact_lines.append(f"- [{category}] {content}")
if fact_lines:
sections.append("Known Facts:\n" + "\n".join(fact_lines))
if not sections:
return ""
result = "\n\n".join(sections)
# Rough token limit (approximate 4 chars per token)
max_chars = max_tokens * 4
if len(result) > max_chars:
result = result[:max_chars] + "\n..."
return result
def format_conversation_for_update(messages: list[Any]) -> str:
"""Format conversation messages for memory update prompt.
Args:
messages: List of conversation messages.
Returns:
Formatted conversation string.
"""
lines = []
for msg in messages:
role = getattr(msg, "type", "unknown")
content = getattr(msg, "content", str(msg))
# Handle content that might be a list (multimodal)
if isinstance(content, list):
text_parts = [p.get("text", "") for p in content if isinstance(p, dict) and "text" in p]
content = " ".join(text_parts) if text_parts else str(content)
# Truncate very long messages
if len(str(content)) > 1000:
content = str(content)[:1000] + "..."
if role == "human":
lines.append(f"User: {content}")
elif role == "ai":
lines.append(f"Assistant: {content}")
return "\n\n".join(lines)