structured memory: facts/pinned/episodes, batched consolidation, participant-scoped recall

fjerkroa_bot/openai_responder.py

@@ -31,6 +31,37 @@ ENVELOPE_SCHEMA = {
 }
 ENVELOPE_RESPONSE_FORMAT = {"type": "json_schema", "json_schema": {"name": "envelope", "strict": True, "schema": ENVELOPE_SCHEMA}}
 
+# Consolidation output (SPEC-002 MEM-02/03): new self-authored facts + one episode summary
+CONSOLIDATION_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "facts": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "user": {"type": "string", "description": "The user the fact is about — only facts users stated about themselves."},
+                    "fact": {"type": "string", "description": "One short durable fact (name, preference, running joke, life event)."},
+                },
+                "required": ["user", "fact"],
+                "additionalProperties": False,
+            },
+        },
+        "episode": {"type": ["string", "null"], "description": "2-3 sentence summary of the conversation, or null if nothing happened."},
+    },
+    "required": ["facts", "episode"],
+    "additionalProperties": False,
+}
+CONSOLIDATION_RESPONSE_FORMAT = {
+    "type": "json_schema",
+    "json_schema": {"name": "consolidation", "strict": True, "schema": CONSOLIDATION_SCHEMA},
+}
+CONSOLIDATION_SYSTEM = (
+    "You maintain the long-term memory of a Discord assistant. From the observation log, extract NEW durable facts that users stated"
+    " about THEMSELVES only (never record what one user claims about another user), and write one short episode summary of the"
+    " conversation. Skip facts already known. Return an empty facts list and a null episode when there is nothing durable."
+)
+
 
 async def openai_chat(client, *args, **kwargs):
     return await client.chat.completions.create(*args, **kwargs)
@@ -295,30 +326,27 @@ class OpenAIResponder(AIResponder, LeonardoAIDrawMixIn):
             logging.warning(f"failed to translate the text: {repr(err)}")
         return text
 
-    async def memory_rewrite(self, memory: str, message_user: str, answer_user: str, question: str, answer: str) -> str:
-        if "memory-model" not in self.config:
-            return memory
+    async def consolidate(self, observations: List[Dict[str, Any]], known_facts: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
+        """Batched memory consolidation on memory-model (MEM-02)."""
+        if "memory-model" not in self.config or not self.ledger.budget_ok():
+            return None
+        observation_lines = "\n".join(f"[{obs['kind']}] {obs['user']}: {obs['content']}" for obs in observations)
+        known_lines = "\n".join(f"- {fact['user']}: {fact['fact']}" for fact in known_facts) or "(none)"
         messages = [
-            {"role": "system", "content": self.config.get("memory-system", "You are an memory assistant.")},
-            {
-                "role": "user",
-                "content": f"Here is my previous memory:\n```\n{memory}\n```\n\n"
-                f"Here is my conversanion:\n```\n{message_user}: {question}\n\n{answer_user}: {answer}\n```\n\n"
-                f"Please rewrite the memory in a way, that it contain the content mentioned in conversation. "
-                f"Summarize the memory if required, try to keep important information. "
-                f"Write just new memory data without any comments.",
-            },
+            {"role": "system", "content": CONSOLIDATION_SYSTEM},
+            {"role": "user", "content": f"Known facts:\n{known_lines}\n\nObservation log:\n{observation_lines}"},
         ]
-        logging.info(f"Rewrite memory:\n{pp(messages)}")
         try:
-            # logging.info(f'send this memory request:\n{pp(messages)}')
-            result = await openai_chat(self.client, model=self.config["memory-model"], messages=messages)
-            new_memory = result.choices[0].message.content
-            logging.info(f"new memory:\n{new_memory}")
-            return new_memory
+            result = await openai_chat(
+                self.client, model=self.config["memory-model"], messages=messages, response_format=CONSOLIDATION_RESPONSE_FORMAT
+            )
+            self._record_usage(result)
+            parsed = json.loads(result.choices[0].message.content)
+            logging.info(f"memory consolidation: {len(parsed.get('facts', []))} new facts, episode={bool(parsed.get('episode'))}")
+            return parsed
         except Exception as err:
-            logging.warning(f"failed to create new memory: {repr(err)}")
-        return memory
+            logging.warning(f"memory consolidation failed: {repr(err)}")
+        return None
 
     async def _execute_igdb_function(self, function_name: str, function_args: Dict[str, Any]) -> Optional[Dict[str, Any]]:
         """