Code formatting

cookbooks/helper/mem0_teachability.py

@@ -12,7 +12,7 @@
 from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
 from termcolor import colored
 from mem0 import Memory
-from mem0.configs.base import MemoryConfig
+
 
 class Mem0Teachability(AgentCapability):
     def __init__(
@@ -60,7 +60,6 @@ def process_last_received_message(self, text: Union[Dict, str]):
         return expanded_text
 
     def _consider_memo_storage(self, comment: Union[Dict, str]):
-        memo_added = False
         response = self._analyze(
             comment,
             "Does any part of the TEXT ask the agent to perform a task or solve a problem? Answer with just one word, yes or no.",
@@ -85,8 +84,9 @@ def _consider_memo_storage(self, comment: Union[Dict, str]):
 
                 if self.verbosity >= 1:
                     print(colored("\nREMEMBER THIS TASK-ADVICE PAIR", "light_yellow"))
-                self.memory.add([{"role": "user", "content": f"Task: {general_task}\nAdvice: {advice}"}], agent_id=self.agent_id)
-                memo_added = True
+                self.memory.add(
+                    [{"role": "user", "content": f"Task: {general_task}\nAdvice: {advice}"}], agent_id=self.agent_id
+                )
 
         response = self._analyze(
             comment,
@@ -105,8 +105,9 @@ def _consider_memo_storage(self, comment: Union[Dict, str]):
 
             if self.verbosity >= 1:
                 print(colored("\nREMEMBER THIS QUESTION-ANSWER PAIR", "light_yellow"))
-            self.memory.add([{"role": "user", "content": f"Question: {question}\nAnswer: {answer}"}], agent_id=self.agent_id)
-            memo_added = True
+            self.memory.add(
+                [{"role": "user", "content": f"Question: {question}\nAnswer: {answer}"}], agent_id=self.agent_id
+            )
 
     def _consider_memo_retrieval(self, comment: Union[Dict, str]):
         if self.verbosity >= 1:

mem0/client/main.py

@@ -117,7 +117,7 @@ def add(self, messages: Union[str, List[Dict[str, str]]], **kwargs) -> Dict[str,
         payload = self._prepare_payload(messages, kwargs)
         response = self.client.post("/v1/memories/", json=payload)
         response.raise_for_status()
-        capture_client_event("client.add", self)
+        capture_client_event("client.add", self, {"keys": list(kwargs.keys())})
         return response.json()
 
     @api_error_handler
@@ -162,7 +162,7 @@ def get_all(self, version: str = "v1", **kwargs) -> List[Dict[str, Any]]:
         capture_client_event(
             "client.get_all",
             self,
-            {"filters": len(params), "limit": kwargs.get("limit", 100)},
+            {"version": version, "keys": list(kwargs.keys())},
         )
         return response.json()
 
@@ -186,7 +186,7 @@ def search(self, query: str, version: str = "v1", **kwargs) -> List[Dict[str, An
         payload.update({k: v for k, v in kwargs.items() if v is not None})
         response = self.client.post(f"/{version}/memories/search/", json=payload)
         response.raise_for_status()
-        capture_client_event("client.search", self, {"limit": kwargs.get("limit", 100)})
+        capture_client_event("client.search", self, {"version": version, "keys": list(kwargs.keys())})
         return response.json()
 
     @api_error_handler
@@ -239,7 +239,7 @@ def delete_all(self, **kwargs) -> Dict[str, str]:
         params = self._prepare_params(kwargs)
         response = self.client.delete("/v1/memories/", params=params)
         response.raise_for_status()
-        capture_client_event("client.delete_all", self, {"params": len(params)})
+        capture_client_event("client.delete_all", self, {"keys": list(kwargs.keys())})
         return response.json()
 
     @api_error_handler
@@ -390,7 +390,7 @@ async def add(self, messages: Union[str, List[Dict[str, str]]], **kwargs) -> Dic
         payload = self.sync_client._prepare_payload(messages, kwargs)
         response = await self.async_client.post("/v1/memories/", json=payload)
         response.raise_for_status()
-        capture_client_event("async_client.add", self.sync_client)
+        capture_client_event("async_client.add", self.sync_client, {"keys": list(kwargs.keys())})
         return response.json()
 
     @api_error_handler
@@ -409,7 +409,7 @@ async def get_all(self, version: str = "v1", **kwargs) -> List[Dict[str, Any]]:
             response = await self.async_client.post(f"/{version}/memories/", json=params)
         response.raise_for_status()
         capture_client_event(
-            "async_client.get_all", self.sync_client, {"filters": len(params), "limit": kwargs.get("limit", 100)}
+            "async_client.get_all", self.sync_client, {"version": version, "keys": list(kwargs.keys())}
         )
         return response.json()
 
@@ -419,7 +419,7 @@ async def search(self, query: str, version: str = "v1", **kwargs) -> List[Dict[s
         payload.update(self.sync_client._prepare_params(kwargs))
         response = await self.async_client.post(f"/{version}/memories/search/", json=payload)
         response.raise_for_status()
-        capture_client_event("async_client.search", self.sync_client, {"limit": kwargs.get("limit", 100)})
+        capture_client_event("async_client.search", self.sync_client, {"version": version, "keys": list(kwargs.keys())})
         return response.json()
 
     @api_error_handler
@@ -441,7 +441,7 @@ async def delete_all(self, **kwargs) -> Dict[str, str]:
         params = self.sync_client._prepare_params(kwargs)
         response = await self.async_client.delete("/v1/memories/", params=params)
         response.raise_for_status()
-        capture_client_event("async_client.delete_all", self.sync_client, {"params": len(params)})
+        capture_client_event("async_client.delete_all", self.sync_client, {"keys": list(kwargs.keys())})
         return response.json()
 
     @api_error_handler

mem0/configs/vector_stores/chroma.py

@@ -1,5 +1,3 @@
-import subprocess
-import sys
 from typing import Any, ClassVar, Dict, Optional
 
 from pydantic import BaseModel, Field, model_validator

mem0/memory/main.py

@@ -37,11 +37,11 @@ def __init__(self, config: MemoryConfig = MemoryConfig()):
         self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config)
         self.db = SQLiteManager(self.config.history_db_path)
         self.collection_name = self.config.vector_store.config.collection_name
-        self.version = self.config.version
+        self.api_version = self.config.version
 
         self.enable_graph = False
 
-        if self.version == "v1.1" and self.config.graph_store.config:
+        if self.api_version == "v1.1" and self.config.graph_store.config:
             from mem0.memory.graph_memory import MemoryGraph
 
             self.graph = MemoryGraph(self.config)
@@ -119,7 +119,7 @@ def add(
             vector_store_result = future1.result()
             graph_result = future2.result()
 
-        if self.version == "v1.1":
+        if self.api_version == "v1.1":
             return {
                 "results": vector_store_result,
                 "relations": graph_result,
@@ -226,13 +226,13 @@ def _add_to_vector_store(self, messages, metadata, filters):
         except Exception as e:
             logging.error(f"Error in new_memories_with_actions: {e}")
 
-        capture_event("mem0.add", self)
+        capture_event("mem0.add", self, {"version": self.api_version, "keys": list(filters.keys())})
 
         return returned_memories
 
     def _add_to_graph(self, messages, filters):
         added_entities = []
-        if self.version == "v1.1" and self.enable_graph:
+        if self.api_version == "v1.1" and self.enable_graph:
             if filters["user_id"]:
                 self.graph.user_id = filters["user_id"]
             elif filters["agent_id"]:
@@ -305,13 +305,13 @@ def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100):
         if run_id:
             filters["run_id"] = run_id
 
-        capture_event("mem0.get_all", self, {"filters": len(filters), "limit": limit})
+        capture_event("mem0.get_all", self, {"limit": limit, "keys": list(filters.keys())})
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future_memories = executor.submit(self._get_all_from_vector_store, filters, limit)
             future_graph_entities = (
                 executor.submit(self.graph.get_all, filters, limit)
-                if self.version == "v1.1" and self.enable_graph
+                if self.api_version == "v1.1" and self.enable_graph
                 else None
             )
 
@@ -322,7 +322,7 @@ def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100):
             all_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None
 
-        if self.version == "v1.1":
+        if self.api_version == "v1.1":
             if self.enable_graph:
                 return {"results": all_memories, "relations": graph_entities}
             else:
@@ -398,14 +398,14 @@ def search(self, query, user_id=None, agent_id=None, run_id=None, limit=100, fil
         capture_event(
             "mem0.search",
             self,
-            {"filters": len(filters), "limit": limit, "version": self.version},
+            {"limit": limit, "version": self.api_version, "keys": list(filters.keys())},
         )
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future_memories = executor.submit(self._search_vector_store, query, filters, limit)
             future_graph_entities = (
                 executor.submit(self.graph.search, query, filters, limit)
-                if self.version == "v1.1" and self.enable_graph
+                if self.api_version == "v1.1" and self.enable_graph
                 else None
             )
 
@@ -416,7 +416,7 @@ def search(self, query, user_id=None, agent_id=None, run_id=None, limit=100, fil
             original_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None
 
-        if self.version == "v1.1":
+        if self.api_version == "v1.1":
             if self.enable_graph:
                 return {"results": original_memories, "relations": graph_entities}
             else:
@@ -518,14 +518,14 @@ def delete_all(self, user_id=None, agent_id=None, run_id=None):
                 "At least one filter is required to delete all memories. If you want to delete all memories, use the `reset()` method."
             )
 
-        capture_event("mem0.delete_all", self, {"filters": len(filters)})
+        capture_event("mem0.delete_all", self, {"keys": list(filters.keys())})
         memories = self.vector_store.list(filters=filters)[0]
         for memory in memories:
             self._delete_memory(memory.id)
 
         logger.info(f"Deleted {len(memories)} memories")
 
-        if self.version == "v1.1" and self.enable_graph:
+        if self.api_version == "v1.1" and self.enable_graph:
             self.graph.delete_all(filters)
 
         return {"message": "Memories deleted successfully!"}
@@ -603,6 +603,7 @@ def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
             created_at=new_metadata["created_at"],
             updated_at=new_metadata["updated_at"],
         )
+        capture_event("mem0._update_memory", self, {"memory_id": memory_id})
         return memory_id
 
     def _delete_memory(self, memory_id):
@@ -611,6 +612,7 @@ def _delete_memory(self, memory_id):
         prev_value = existing_memory.payload["data"]
         self.vector_store.delete(vector_id=memory_id)
         self.db.add_history(memory_id, prev_value, None, "DELETE", is_deleted=1)
+        capture_event("mem0._delete_memory", self, {"memory_id": memory_id})
         return memory_id
 
     def reset(self):

tests/embeddings/test_azure_openai_embeddings.py

@@ -33,11 +33,7 @@ def test_embed_text(mock_openai_client):
 
 @pytest.mark.parametrize(
     "default_headers, expected_header",
-    [
-        (None, None),
-        ({"Test": "test_value"}, "test_value"),
-        ({}, None)
-    ],
+    [(None, None), ({"Test": "test_value"}, "test_value"), ({}, None)],
 )
 def test_embed_text_with_default_headers(default_headers, expected_header):
     config = BaseEmbedderConfig(
@@ -47,8 +43,8 @@ def test_embed_text_with_default_headers(default_headers, expected_header):
             "api_version": "test_version",
             "azure_endpoint": "test_endpoint",
             "azuer_deployment": "test_deployment",
-            "default_headers": default_headers
-        }
+            "default_headers": default_headers,
+        },
     )
     embedder = AzureOpenAIEmbedding(config)
     assert embedder.client.api_key == "test"

tests/embeddings/test_gemini.py

@@ -12,17 +12,11 @@ def mock_genai():
 
 @pytest.fixture
 def config():
-    return BaseEmbedderConfig(
-        api_key="dummy_api_key",
-        model="test_model"
-    )
+    return BaseEmbedderConfig(api_key="dummy_api_key", model="test_model")
 
 
 def test_embed_query(mock_genai, config):
-
-    mock_embedding_response = {
-        'embedding': [0.1, 0.2, 0.3, 0.4]
-    }
+    mock_embedding_response = {"embedding": [0.1, 0.2, 0.3, 0.4]}
     mock_genai.return_value = mock_embedding_response
 
     embedder = GoogleGenAIEmbedding(config)
@@ -31,7 +25,4 @@ def test_embed_query(mock_genai, config):
     embedding = embedder.embed(text)
 
     assert embedding == [0.1, 0.2, 0.3, 0.4]
-    mock_genai.assert_called_once_with(
-        model="test_model",
-        content="Hello, world!"
-    )
+    mock_genai.assert_called_once_with(model="test_model", content="Hello, world!")

tests/embeddings/test_vertexai_embeddings.py

@@ -1,7 +1,6 @@
 import pytest
 from unittest.mock import Mock, patch
 from mem0.embeddings.vertexai import VertexAIEmbedding
-from mem0.configs.embeddings.base import BaseEmbedderConfig
 
 
 @pytest.fixture
@@ -35,15 +34,11 @@ def test_embed_default_model(mock_text_embedding_model, mock_os_environ, mock_co
     embedder = VertexAIEmbedding(config)
 
     mock_embedding = Mock(values=[0.1, 0.2, 0.3])
-    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.return_value = [
-        mock_embedding
-    ]
+    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.return_value = [mock_embedding]
 
-    result = embedder.embed("Hello world")
+    embedder.embed("Hello world")
 
-    mock_text_embedding_model.from_pretrained.assert_called_once_with(
-        "text-embedding-004"
-    )
+    mock_text_embedding_model.from_pretrained.assert_called_once_with("text-embedding-004")
     mock_text_embedding_model.from_pretrained.return_value.get_embeddings.assert_called_once_with(
         texts=["Hello world"], output_dimensionality=256
     )
@@ -60,15 +55,11 @@ def test_embed_custom_model(mock_text_embedding_model, mock_os_environ, mock_con
     embedder = VertexAIEmbedding(config)
 
     mock_embedding = Mock(values=[0.4, 0.5, 0.6])
-    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.return_value = [
-        mock_embedding
-    ]
+    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.return_value = [mock_embedding]
 
     result = embedder.embed("Test embedding")
 
-    mock_text_embedding_model.from_pretrained.assert_called_with(
-        "custom-embedding-model"
-    )
+    mock_text_embedding_model.from_pretrained.assert_called_with("custom-embedding-model")
     mock_text_embedding_model.from_pretrained.return_value.get_embeddings.assert_called_once_with(
         texts=["Test embedding"], output_dimensionality=512
     )
@@ -93,26 +84,20 @@ def test_missing_credentials(mock_os, mock_text_embedding_model, mock_config):
 
     config = mock_config()
 
-    with pytest.raises(
-        ValueError, match="Google application credentials JSON is not provided"
-    ):
+    with pytest.raises(ValueError, match="Google application credentials JSON is not provided"):
         VertexAIEmbedding(config)
 
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
-def test_embed_with_different_dimensions(
-    mock_text_embedding_model, mock_os_environ, mock_config
-):
+def test_embed_with_different_dimensions(mock_text_embedding_model, mock_os_environ, mock_config):
     mock_config.vertex_credentials_json = "/path/to/credentials.json"
     mock_config.return_value.embedding_dims = 1024
 
     config = mock_config()
     embedder = VertexAIEmbedding(config)
 
     mock_embedding = Mock(values=[0.1] * 1024)
-    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.return_value = [
-        mock_embedding
-    ]
+    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.return_value = [mock_embedding]
 
     result = embedder.embed("Large embedding test")