Add infer param and version bump (#2389)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

mem0/memory/main.py

@@ -16,8 +16,12 @@ 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
@@ -82,6 +86,7 @@ class Memory(MemoryBase):
         run_id=None,
         metadata=None,
         filters=None,
+        infer=True,
         prompt=None,
     ):
         """
@@ -94,6 +99,7 @@ class Memory(MemoryBase):
             run_id (str, optional): ID of the run creating the memory. Defaults to None.
             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.
 
         Returns:
@@ -132,7 +138,7 @@ class Memory(MemoryBase):
             messages = parse_vision_messages(messages)
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
-            future1 = executor.submit(self._add_to_vector_store, messages, metadata, filters)
+            future1 = executor.submit(self._add_to_vector_store, messages, metadata, filters, infer)
             future2 = executor.submit(self._add_to_graph, messages, filters)
 
             concurrent.futures.wait([future1, future2])
@@ -158,7 +164,16 @@ class Memory(MemoryBase):
 
         return {"results": vector_store_result}
 
-    def _add_to_vector_store(self, messages, metadata, filters):
+    def _add_to_vector_store(self, messages, metadata, filters, infer):
+        if not infer:
+            returned_memories = []
+            for message in messages:
+                if message["role"] != "system":
+                    message_embeddings = self.embedding_model.embed(message["content"], "add")
+                    memory_id = self._create_memory(message["content"], message_embeddings, metadata)
+                    returned_memories.append({"id": memory_id, "memory": message["content"], "event": "ADD"})
+            return returned_memories
+
         parsed_messages = parse_messages(messages)
 
         if self.custom_prompt: