feat: add memory extraction LLM tool and prompt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
18
prompts/memory_extraction.txt
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18
prompts/memory_extraction.txt
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You are a memory extraction system for a Discord bot. Given a conversation between a user and the bot, extract any noteworthy information worth remembering for future interactions.
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RULES:
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- Only extract genuinely NEW information not already in the user's profile.
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- Be concise — each memory should be one sentence max.
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- Assign appropriate expiration based on how long the information stays relevant:
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- "permanent": Stable facts — name, job, hobbies, games they play, personality traits, pets, relationships
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- "30d": Semi-stable preferences, ongoing situations — "trying to quit Warzone", "grinding for rank 500"
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- "7d": Temporary situations — "excited about upcoming DLC", "on vacation this week"
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- "3d": Short-term context — "had a bad day", "playing with friends tonight"
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- "1d": Momentary state — "drunk right now", "tilted from losses", "in a good mood"
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- Assign topic tags that would help retrieve this memory later (game names, "personal", "work", "mood", etc.)
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- Assign importance: "high" for things they'd expect you to remember, "medium" for useful context, "low" for minor color
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- If you learn a permanent fact about the user, provide a profile_update that incorporates the new fact into their existing profile. Rewrite the ENTIRE profile summary — don't just append. Keep it under 500 characters.
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- If nothing worth remembering was said, return an empty memories array and null profile_update.
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- Do NOT store things the bot said — only facts about or from the user.
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Use the extract_memories tool to report your findings.
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@@ -203,6 +203,55 @@ CONVERSATION_TOOL = {
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},
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}
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MEMORY_EXTRACTION_TOOL = {
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"type": "function",
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"function": {
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"name": "extract_memories",
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"description": "Extract noteworthy memories from a conversation for future reference.",
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"parameters": {
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"type": "object",
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"properties": {
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"memories": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"memory": {
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"type": "string",
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"description": "A concise fact or observation worth remembering.",
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},
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"topics": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Topic tags for retrieval (e.g., 'gta', 'personal', 'warzone').",
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},
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"expiration": {
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"type": "string",
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"enum": ["1d", "3d", "7d", "30d", "permanent"],
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"description": "How long this memory stays relevant.",
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},
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"importance": {
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"type": "string",
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"enum": ["low", "medium", "high"],
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"description": "How important this memory is for future interactions.",
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},
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},
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"required": ["memory", "topics", "expiration", "importance"],
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},
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"description": "Memories to store. Only include genuinely new or noteworthy information.",
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},
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"profile_update": {
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"type": ["string", "null"],
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"description": "Full updated profile summary incorporating new permanent facts, or null if no profile changes.",
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},
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},
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"required": ["memories"],
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},
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},
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}
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MEMORY_EXTRACTION_PROMPT = (_PROMPTS_DIR / "memory_extraction.txt").read_text(encoding="utf-8")
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_NO_TEMPERATURE_MODELS = {"gpt-5-nano", "o1", "o1-mini", "o1-preview", "o3", "o3-mini", "o4-mini"}
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@@ -626,6 +675,122 @@ class LLMClient:
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self._log_llm("chat", elapsed, False, req_json, error=str(e))
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return None
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async def extract_memories(
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self,
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conversation: list[dict[str, str]],
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username: str,
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current_profile: str = "",
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) -> dict | None:
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"""Extract memories from a conversation for a specific user.
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Returns dict with "memories" list and optional "profile_update", or None on failure.
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"""
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# Format conversation as readable lines
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convo_lines = []
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for msg in conversation:
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role = msg.get("role", "")
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content = msg.get("content", "")
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if role == "assistant":
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convo_lines.append(f"Bot: {content}")
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else:
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convo_lines.append(f"{username}: {content}")
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convo_text = "\n".join(convo_lines)
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user_content = ""
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if current_profile:
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user_content += f"=== CURRENT PROFILE FOR {username} ===\n{current_profile}\n\n"
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else:
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user_content += f"=== CURRENT PROFILE FOR {username} ===\n(no profile yet)\n\n"
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user_content += f"=== CONVERSATION ===\n{convo_text}\n\n"
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user_content += f"Extract any noteworthy memories from this conversation with {username}."
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user_content = self._append_no_think(user_content)
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req_json = json.dumps([
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{"role": "system", "content": MEMORY_EXTRACTION_PROMPT[:500]},
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{"role": "user", "content": user_content[:500]},
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], default=str)
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t0 = time.monotonic()
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async with self._semaphore:
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try:
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temp_kwargs = {"temperature": 0.3} if self._supports_temperature else {}
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response = await self._client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": MEMORY_EXTRACTION_PROMPT},
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{"role": "user", "content": user_content},
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],
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tools=[MEMORY_EXTRACTION_TOOL],
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tool_choice={"type": "function", "function": {"name": "extract_memories"}},
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**temp_kwargs,
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max_completion_tokens=1024,
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)
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elapsed = int((time.monotonic() - t0) * 1000)
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choice = response.choices[0]
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usage = response.usage
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if choice.message.tool_calls:
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tool_call = choice.message.tool_calls[0]
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resp_text = tool_call.function.arguments
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args = json.loads(resp_text)
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self._log_llm("memory_extraction", elapsed, True, req_json, resp_text,
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input_tokens=usage.prompt_tokens if usage else None,
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output_tokens=usage.completion_tokens if usage else None)
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return self._validate_memory_result(args)
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logger.warning("No tool call in memory extraction response.")
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self._log_llm("memory_extraction", elapsed, False, req_json, error="No tool call")
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return None
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except Exception as e:
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elapsed = int((time.monotonic() - t0) * 1000)
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logger.error("LLM memory extraction error: %s", e)
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self._log_llm("memory_extraction", elapsed, False, req_json, error=str(e))
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return None
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@staticmethod
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def _validate_memory_result(result: dict) -> dict:
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"""Validate and normalize memory extraction result."""
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valid_expirations = {"1d", "3d", "7d", "30d", "permanent"}
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valid_importances = {"low", "medium", "high"}
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memories = []
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for mem in result.get("memories", []):
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if not isinstance(mem, dict):
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continue
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memory_text = str(mem.get("memory", ""))[:500]
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if not memory_text:
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continue
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topics = mem.get("topics", [])
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if not isinstance(topics, list):
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topics = []
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topics = [str(t).lower() for t in topics]
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expiration = str(mem.get("expiration", "7d"))
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if expiration not in valid_expirations:
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expiration = "7d"
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importance = str(mem.get("importance", "medium"))
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if importance not in valid_importances:
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importance = "medium"
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memories.append({
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"memory": memory_text,
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"topics": topics,
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"expiration": expiration,
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"importance": importance,
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})
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profile_update = result.get("profile_update")
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if profile_update is not None:
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profile_update = str(profile_update)[:500]
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return {
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"memories": memories,
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"profile_update": profile_update,
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}
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async def analyze_image(
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self,
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image_bytes: bytes,
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