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Merge branch 'main' into gl/refactor/agent-adapter-typing-and-docs

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This commit is contained in:
Greyson LaLonde
2025-09-12 15:57:27 -04:00
committed by GitHub
parent e9dc590fc6 1b00cc71ef
commit b379e3d012
2 changed files with 55 additions and 25 deletions
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50
src/crewai/memory/storage/mem0_storage.py
View File

@@ -1,10 +1,12 @@
import os
from typing import Any, Dict, List
import re
from collections import defaultdict
from mem0 import Memory, MemoryClient
from crewai.utilities.chromadb import sanitize_collection_name
from typing import Any, Iterable
from mem0 import Memory, MemoryClient # type: ignore[import-untyped]
from crewai.memory.storage.interface import Storage
from crewai.utilities.chromadb import sanitize_collection_name
MAX_AGENT_ID_LENGTH_MEM0 = 255
@@ -86,9 +88,28 @@ class Mem0Storage(Storage):
return filter
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
def save(self, value: Any, metadata: dict[str, Any]) -> None:
def _last_content(messages: Iterable[dict[str, Any]], role: str) -> str:
return next(
(m.get("content", "") for m in reversed(list(messages)) if m.get("role") == role),
""
)
conversations = []
messages = metadata.pop("messages", None)
if messages:
last_user = _last_content(messages, "user")
last_assistant = _last_content(messages, "assistant")
if user_msg := self._get_user_message(last_user):
conversations.append({"role": "user", "content": user_msg})
if assistant_msg := self._get_assistant_message(last_assistant):
conversations.append({"role": "assistant", "content": assistant_msg})
else:
conversations.append({"role": "assistant", "content": value})
user_id = self.config.get("user_id", "")
assistant_message = [{"role" : "assistant","content" : value}]
base_metadata = {
"short_term": "short_term",
@@ -119,9 +140,9 @@ class Mem0Storage(Storage):
if agent_id := self.config.get("agent_id", self._get_agent_name()):
params["agent_id"] = agent_id
self.memory.add(assistant_message, **params)
self.memory.add(conversations, **params)
def search(self,query: str,limit: int = 3,score_threshold: float = 0.35) -> List[Any]:
def search(self,query: str,limit: int = 3,score_threshold: float = 0.35) -> list[Any]:
params = {
"query": query,
"limit": limit,
@@ -160,7 +181,7 @@ class Mem0Storage(Storage):
# This makes it compatible for Contextual Memory to retrieve
for result in results["results"]:
result["context"] = result["memory"]
return [r for r in results["results"]]
def reset(self):
@@ -181,3 +202,16 @@ class Mem0Storage(Storage):
agents = [self._sanitize_role(agent.role) for agent in agents]
agents = "_".join(agents)
return sanitize_collection_name(name=agents, max_collection_length=MAX_AGENT_ID_LENGTH_MEM0)
def _get_assistant_message(self, text: str) -> str:
marker = "Final Answer:"
if marker in text:
return text.split(marker, 1)[1].strip()
return text
def _get_user_message(self, text: str) -> str:
pattern = r"User message:\s*(.*)"
match = re.search(pattern, text)
if match:
return match.group(1).strip()
return text

30
tests/storage/test_mem0_storage.py
View File

@@ -16,8 +16,7 @@ class MockCrew:
@pytest.fixture
def mock_mem0_memory():
"""Fixture to create a mock Memory instance"""
mock_memory = MagicMock(spec=Memory)
return mock_memory
return MagicMock(spec=Memory)
@pytest.fixture
@@ -73,8 +72,7 @@ def test_mem0_storage_initialization(mem0_storage_with_mocked_config, mock_mem0_
@pytest.fixture
def mock_mem0_memory_client():
"""Fixture to create a mock MemoryClient instance"""
mock_memory = MagicMock(spec=MemoryClient)
return mock_memory
return MagicMock(spec=MemoryClient)
@pytest.fixture
@@ -96,8 +94,7 @@ def mem0_storage_with_memory_client_using_config_from_crew(mock_mem0_memory_clie
"infer": True
}
mem0_storage = Mem0Storage(type="short_term", crew=crew, config=embedder_config)
return mem0_storage
return Mem0Storage(type="short_term", crew=crew, config=embedder_config)
@pytest.fixture
@@ -111,8 +108,7 @@ def mem0_storage_with_memory_client_using_explictly_config(mock_mem0_memory_clie
crew = MockCrew()
new_config = {"provider": "mem0", "config": {"api_key": "new-api-key"}}
mem0_storage = Mem0Storage(type="short_term", crew=crew, config=new_config)
return mem0_storage
return Mem0Storage(type="short_term", crew=crew, config=new_config)
def test_mem0_storage_with_memory_client_initialization(
@@ -172,14 +168,14 @@ def test_save_method_with_memory_oss(mem0_storage_with_mocked_config):
# Test short_term memory type (already set in fixture)
test_value = "This is a test memory"
test_metadata = {"key": "value"}
test_metadata = {'description': 'Respond to user conversation. User message: What do you know about me?', 'messages': [{'role': 'system', 'content': 'You are Friendly chatbot assistant. You are a kind and knowledgeable chatbot assistant. You excel at understanding user needs, providing helpful responses, and maintaining engaging conversations. You remember previous interactions to provide a personalized experience.\nYour personal goal is: Engage in useful and interesting conversations with users while remembering context.\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!'}, {'role': 'user', 'content': '\nCurrent Task: Respond to user conversation. User message: What do you know about me?\n\nThis is the expected criteria for your final answer: Contextually appropriate, helpful, and friendly response.\nyou MUST return the actual complete content as the final answer, not a summary.\n\n# Useful context: \nExternal memories:\n- User is from India\n- User is interested in the solar system\n- User name is Vidit Ostwal\n- User is interested in French cuisine\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:'}, {'role': 'assistant', 'content': "I now can give a great answer \nFinal Answer: Hi Vidit! From our previous conversations, I know you're from India and have a great interest in the solar system. It's fascinating to explore the wonders of space, isn't it? Also, I remember you have a passion for French cuisine, which has so many delightful dishes to explore. If there's anything specific you'd like to discuss or learn about—whether it's about the solar system or some great French recipes—feel free to let me know! I'm here to help."}], 'agent': 'Friendly chatbot assistant'}
mem0_storage.save(test_value, test_metadata)
mem0_storage.memory.add.assert_called_once_with(
[{"role": "assistant" , "content": test_value}],
[{'role': 'user', 'content': 'What do you know about me?'}, {'role': 'assistant', 'content': "Hi Vidit! From our previous conversations, I know you're from India and have a great interest in the solar system. It's fascinating to explore the wonders of space, isn't it? Also, I remember you have a passion for French cuisine, which has so many delightful dishes to explore. If there's anything specific you'd like to discuss or learn about—whether it's about the solar system or some great French recipes—feel free to let me know! I'm here to help."}],
infer=True,
metadata={"type": "short_term", "key": "value"},
metadata={'type': 'short_term', 'description': 'Respond to user conversation. User message: What do you know about me?', 'agent': 'Friendly chatbot assistant'},
run_id="my_run_id",
user_id="test_user",
agent_id='Test_Agent'
@@ -191,14 +187,14 @@ def test_save_method_with_multiple_agents(mem0_storage_with_mocked_config):
mem0_storage.memory.add = MagicMock()
test_value = "This is a test memory"
test_metadata = {"key": "value"}
test_metadata = {'description': 'Respond to user conversation. User message: What do you know about me?', 'messages': [{'role': 'system', 'content': 'You are Friendly chatbot assistant. You are a kind and knowledgeable chatbot assistant. You excel at understanding user needs, providing helpful responses, and maintaining engaging conversations. You remember previous interactions to provide a personalized experience.\nYour personal goal is: Engage in useful and interesting conversations with users while remembering context.\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!'}, {'role': 'user', 'content': '\nCurrent Task: Respond to user conversation. User message: What do you know about me?\n\nThis is the expected criteria for your final answer: Contextually appropriate, helpful, and friendly response.\nyou MUST return the actual complete content as the final answer, not a summary.\n\n# Useful context: \nExternal memories:\n- User is from India\n- User is interested in the solar system\n- User name is Vidit Ostwal\n- User is interested in French cuisine\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:'}, {'role': 'assistant', 'content': "I now can give a great answer \nFinal Answer: Hi Vidit! From our previous conversations, I know you're from India and have a great interest in the solar system. It's fascinating to explore the wonders of space, isn't it? Also, I remember you have a passion for French cuisine, which has so many delightful dishes to explore. If there's anything specific you'd like to discuss or learn about—whether it's about the solar system or some great French recipes—feel free to let me know! I'm here to help."}], 'agent': 'Friendly chatbot assistant'}
mem0_storage.save(test_value, test_metadata)
mem0_storage.memory.add.assert_called_once_with(
[{"role": "assistant" , "content": test_value}],
[{'role': 'user', 'content': 'What do you know about me?'}, {'role': 'assistant', 'content': "Hi Vidit! From our previous conversations, I know you're from India and have a great interest in the solar system. It's fascinating to explore the wonders of space, isn't it? Also, I remember you have a passion for French cuisine, which has so many delightful dishes to explore. If there's anything specific you'd like to discuss or learn about—whether it's about the solar system or some great French recipes—feel free to let me know! I'm here to help."}],
infer=True,
metadata={"type": "short_term", "key": "value"},
metadata={'type': 'short_term', 'description': 'Respond to user conversation. User message: What do you know about me?', 'agent': 'Friendly chatbot assistant'},
run_id="my_run_id",
user_id="test_user",
agent_id='Test_Agent_Test_Agent_2_Test_Agent_3'
@@ -212,14 +208,14 @@ def test_save_method_with_memory_client(mem0_storage_with_memory_client_using_co
# Test short_term memory type (already set in fixture)
test_value = "This is a test memory"
test_metadata = {"key": "value"}
test_metadata = {'description': 'Respond to user conversation. User message: What do you know about me?', 'messages': [{'role': 'system', 'content': 'You are Friendly chatbot assistant. You are a kind and knowledgeable chatbot assistant. You excel at understanding user needs, providing helpful responses, and maintaining engaging conversations. You remember previous interactions to provide a personalized experience.\nYour personal goal is: Engage in useful and interesting conversations with users while remembering context.\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!'}, {'role': 'user', 'content': '\nCurrent Task: Respond to user conversation. User message: What do you know about me?\n\nThis is the expected criteria for your final answer: Contextually appropriate, helpful, and friendly response.\nyou MUST return the actual complete content as the final answer, not a summary.\n\n# Useful context: \nExternal memories:\n- User is from India\n- User is interested in the solar system\n- User name is Vidit Ostwal\n- User is interested in French cuisine\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:'}, {'role': 'assistant', 'content': "I now can give a great answer \nFinal Answer: Hi Vidit! From our previous conversations, I know you're from India and have a great interest in the solar system. It's fascinating to explore the wonders of space, isn't it? Also, I remember you have a passion for French cuisine, which has so many delightful dishes to explore. If there's anything specific you'd like to discuss or learn about—whether it's about the solar system or some great French recipes—feel free to let me know! I'm here to help."}], 'agent': 'Friendly chatbot assistant'}
mem0_storage.save(test_value, test_metadata)
mem0_storage.memory.add.assert_called_once_with(
[{'role': 'assistant' , 'content': test_value}],
[{'role': 'user', 'content': 'What do you know about me?'}, {'role': 'assistant', 'content': "Hi Vidit! From our previous conversations, I know you're from India and have a great interest in the solar system. It's fascinating to explore the wonders of space, isn't it? Also, I remember you have a passion for French cuisine, which has so many delightful dishes to explore. If there's anything specific you'd like to discuss or learn about—whether it's about the solar system or some great French recipes—feel free to let me know! I'm here to help."}],
infer=True,
metadata={"type": "short_term", "key": "value"},
metadata={'type': 'short_term', 'description': 'Respond to user conversation. User message: What do you know about me?', 'agent': 'Friendly chatbot assistant'},
version="v2",
run_id="my_run_id",
includes="include1",