Add support for procedural memory (#2460)

mem0/memory/main.py

@@ -11,17 +11,15 @@ import pytz
 from pydantic import ValidationError
 
 from mem0.configs.base import MemoryConfig, MemoryItem
-from mem0.configs.prompts import get_update_memory_messages
+from mem0.configs.enums import MemoryType
+from mem0.configs.prompts import (PROCEDURAL_MEMORY_SYSTEM_PROMPT,
+                                  get_update_memory_messages)
 from mem0.memory.base import MemoryBase
 from mem0.memory.setup import setup_config
 from mem0.memory.storage import SQLiteManager
 from mem0.memory.telemetry import capture_event
-from mem0.memory.utils import (
-    get_fact_retrieval_messages,
-    parse_messages,
-    parse_vision_messages,
-    remove_code_blocks,
-)
+from mem0.memory.utils import (get_fact_retrieval_messages, parse_messages,
+                               parse_vision_messages, remove_code_blocks)
 from mem0.utils.factory import EmbedderFactory, LlmFactory, VectorStoreFactory
 
 # Setup user config
@@ -89,6 +87,7 @@ class Memory(MemoryBase):
         metadata=None,
         filters=None,
         infer=True,
+        memory_type=None,
         prompt=None,
     ):
         """
@@ -102,8 +101,8 @@ class Memory(MemoryBase):
             metadata (dict, optional): Metadata to store with the memory. Defaults to None.
             filters (dict, optional): Filters to apply to the search. Defaults to None.
             infer (bool, optional): Whether to infer the memories. Defaults to True.
-            prompt (str, optional): Prompt to use for memory deduction. Defaults to None.
-
+            memory_type (str, optional): Type of memory to create. Defaults to None. By default, it creates the short term memories and long term (semantic and episodic) memories. Pass "procedural_memory" to create procedural memories.
+            prompt (str, optional): Prompt to use for the memory creation. Defaults to None.
         Returns:
             dict: A dictionary containing the result of the memory addition operation.
             result: dict of affected events with each dict has the following key:
@@ -131,9 +130,18 @@ class Memory(MemoryBase):
         if not any(key in filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError("One of the filters: user_id, agent_id or run_id is required!")
 
+        if memory_type is not None and memory_type != MemoryType.PROCEDURAL.value:
+            raise ValueError(
+                f"Invalid 'memory_type'. Please pass {MemoryType.PROCEDURAL.value} to create procedural memories."
+            )
+
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
+        if agent_id is not None and memory_type == MemoryType.PROCEDURAL.value:
+            results = self._create_procedural_memory(messages, metadata, prompt)
+            return results
+
         if self.config.llm.config.get("enable_vision"):
             messages = parse_vision_messages(messages, self.llm, self.config.llm.config.get("vision_details"))
         else:
@@ -595,11 +603,11 @@ class Memory(MemoryBase):
         return self.db.get_history(memory_id)
 
     def _create_memory(self, data, existing_embeddings, metadata=None):
-        logging.info(f"Creating memory with {data=}")
+        logging.debug(f"Creating memory with {data=}")
         if data in existing_embeddings:
             embeddings = existing_embeddings[data]
         else:
-            embeddings = self.embedding_model.embed(data, "add")
+            embeddings = self.embedding_model.embed(data, memory_action="add")
         memory_id = str(uuid.uuid4())
         metadata = metadata or {}
         metadata["data"] = data
@@ -615,6 +623,50 @@ class Memory(MemoryBase):
         capture_event("mem0._create_memory", self, {"memory_id": memory_id})
         return memory_id
 
+    def _create_procedural_memory(self, messages, metadata, llm=None, prompt=None):
+        """
+        Create a procedural memory
+        """
+        try:
+            from langchain_core.messages.utils import convert_to_messages  # type: ignore
+        except Exception:
+            logger.error("Import error while loading langchain-core. Please install 'langchain-core' to use procedural memory.")
+            raise
+
+        logger.info("Creating procedural memory")
+
+        parsed_messages = [
+            {"role": "system", "content": prompt or PROCEDURAL_MEMORY_SYSTEM_PROMPT},
+            *messages,
+            {"role": "user", "content": "Create procedural memory of the above conversation."},
+        ]
+
+        try:
+            if llm is not None:
+                parsed_messages = convert_to_messages(parsed_messages)
+                response = llm.invoke(messages=parsed_messages)
+                procedural_memory = response.content
+            else:
+                procedural_memory = self.llm.generate_response(messages=parsed_messages)
+        except Exception as e:
+            logger.error(f"Error generating procedural memory summary: {e}")
+            raise
+
+        if metadata is None:
+            raise ValueError("Metadata cannot be done for procedural memory.")
+
+        metadata["memory_type"] = MemoryType.PROCEDURAL.value
+        # Generate embeddings for the summary
+        embeddings = self.embedding_model.embed(procedural_memory, memory_action="add")
+        # Create the memory
+        memory_id = self._create_memory(procedural_memory, {procedural_memory: embeddings}, metadata=metadata)
+        capture_event("mem0._create_procedural_memory", self, {"memory_id": memory_id})
+
+        # Return results in the same format as add()
+        result = {"results": [{"id": memory_id, "memory": procedural_memory, "event": "ADD"}]}
+
+        return result
+
     def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
         logger.info(f"Updating memory with {data=}")