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1.2.0
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devin/1740
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6827aef5e6 | ||
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3343c1f827 |
@@ -1,3 +1,4 @@
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from datetime import datetime
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from typing import Any, Dict, Optional
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from pydantic import PrivateAttr
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@@ -52,10 +53,34 @@ class ShortTermMemory(Memory):
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metadata: Optional[Dict[str, Any]] = None,
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agent: Optional[str] = None,
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) -> None:
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"""
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Save a memory item to the storage.
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Args:
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value: The data to save.
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metadata: Optional metadata to associate with the memory.
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agent: Optional agent identifier.
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Raises:
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ValueError: If the item's timestamp is in the future.
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"""
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import logging
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item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
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if item.timestamp > datetime.now():
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raise ValueError("Cannot save memory item with future timestamp")
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logging.debug(f"Saving memory item with timestamp: {item.timestamp}")
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if self._memory_provider == "mem0":
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item.data = f"Remember the following insights from Agent run: {item.data}"
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# Include timestamp in metadata
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if item.metadata is None:
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item.metadata = {}
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item.metadata["timestamp"] = item.timestamp.isoformat()
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super().save(value=item.data, metadata=item.metadata, agent=item.agent)
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def search(
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@@ -1,3 +1,4 @@
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from datetime import datetime
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from typing import Any, Dict, Optional
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@@ -7,7 +8,11 @@ class ShortTermMemoryItem:
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data: Any,
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agent: Optional[str] = None,
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metadata: Optional[Dict[str, Any]] = None,
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timestamp: Optional[datetime] = None,
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):
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if timestamp is not None and timestamp > datetime.now():
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raise ValueError("Timestamp cannot be in the future")
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self.data = data
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self.agent = agent
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self.metadata = metadata if metadata is not None else {}
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self.timestamp = timestamp if timestamp is not None else datetime.now()
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@@ -114,13 +114,32 @@ class RAGStorage(BaseRAGStorage):
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limit: int = 3,
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filter: Optional[dict] = None,
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score_threshold: float = 0.35,
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recency_weight: float = 0.3,
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time_decay_days: float = 1.0,
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) -> List[Any]:
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"""
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Search for entries in the storage based on semantic similarity and recency.
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Args:
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query: The search query string.
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limit: Maximum number of results to return.
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filter: Optional filter to apply to the search.
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score_threshold: Minimum score threshold for results.
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recency_weight: Weight given to recency vs. semantic similarity (0.0-1.0).
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Higher values prioritize recent memories more strongly.
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time_decay_days: Number of days over which recency factor decays to zero.
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Smaller values make older memories lose relevance faster.
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Returns:
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List of search results, each containing id, metadata, context, and score.
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Results are sorted by combined semantic similarity and recency score.
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"""
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if not hasattr(self, "app"):
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self._initialize_app()
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try:
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with suppress_logging():
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response = self.collection.query(query_texts=query, n_results=limit)
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response = self.collection.query(query_texts=query, n_results=limit * 2) # Get more results to allow for recency filtering
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results = []
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for i in range(len(response["ids"][0])):
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@@ -130,10 +149,27 @@ class RAGStorage(BaseRAGStorage):
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"context": response["documents"][0][i],
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"score": response["distances"][0][i],
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}
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# Apply recency boost if timestamp exists in metadata
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if "timestamp" in result["metadata"]:
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try:
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from datetime import datetime
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timestamp = datetime.fromisoformat(result["metadata"]["timestamp"])
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now = datetime.now()
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# Calculate recency factor (newer = higher score)
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time_diff_seconds = (now - timestamp).total_seconds()
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recency_factor = max(0, 1 - (time_diff_seconds / (time_decay_days * 24 * 60 * 60)))
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# Adjust score with recency factor
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result["score"] = result["score"] * (1 - recency_weight) + recency_factor * recency_weight
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except (ValueError, TypeError):
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pass # If timestamp parsing fails, use original score
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if result["score"] >= score_threshold:
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results.append(result)
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return results
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# Sort by adjusted score (higher is better)
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results.sort(key=lambda x: x["score"], reverse=True)
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return results[:limit] # Return only the requested number of results
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except Exception as e:
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logging.error(f"Error during {self.type} search: {str(e)}")
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return []
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130
tests/memory/test_memory_topic_changes.py
Normal file
130
tests/memory/test_memory_topic_changes.py
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@@ -0,0 +1,130 @@
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import time
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from datetime import datetime, timedelta
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from unittest.mock import patch
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import pytest
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from crewai.agent import Agent
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from crewai.crew import Crew
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from crewai.memory.short_term.short_term_memory import ShortTermMemory
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from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
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from crewai.memory.storage.rag_storage import RAGStorage
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from crewai.task import Task
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@pytest.fixture
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def short_term_memory():
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"""Fixture to create a ShortTermMemory instance"""
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agent = Agent(
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role="Tutor",
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goal="Teach programming concepts",
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backstory="You are a programming tutor helping students learn.",
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tools=[],
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verbose=True,
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)
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task = Task(
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description="Explain programming concepts to students.",
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expected_output="Clear explanations of programming concepts.",
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agent=agent,
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)
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return ShortTermMemory(crew=Crew(agents=[agent], tasks=[task]))
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def test_memory_prioritizes_recent_topic(short_term_memory):
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"""Test that memory retrieval prioritizes the most recent topic in a conversation."""
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# First topic: Python variables
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topic1_data = "Variables in Python are dynamically typed. You can assign any value to a variable without declaring its type."
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topic1_timestamp = datetime.now() - timedelta(minutes=10) # Older memory
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# Second topic: Python abstract classes
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topic2_data = "Abstract classes in Python are created using the ABC module. They cannot be instantiated and are used as a blueprint for other classes."
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topic2_timestamp = datetime.now() # More recent memory
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# Mock search results to simulate what would be returned by RAGStorage
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mock_results = [
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{
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"id": "2",
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"metadata": {
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"agent": "Tutor",
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"topic": "python_abstract_classes",
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"timestamp": topic2_timestamp.isoformat()
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},
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"context": topic2_data,
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"score": 0.85, # Higher score due to recency boost
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},
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{
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"id": "1",
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"metadata": {
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"agent": "Tutor",
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"topic": "python_variables",
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"timestamp": topic1_timestamp.isoformat()
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},
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"context": topic1_data,
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"score": 0.75, # Lower score due to being older
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}
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]
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# Mock the search method to return our predefined results
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with patch.object(RAGStorage, 'search', return_value=mock_results):
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# Query that could match both topics but should prioritize the more recent one
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query = "Can you give me another example of that?"
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# Search with recency consideration
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results = short_term_memory.search(query)
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# Verify that the most recent topic (abstract classes) is prioritized
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assert len(results) > 0, "No search results returned"
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# The first result should be about abstract classes (the more recent topic)
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assert "abstract classes" in results[0]["context"].lower(), "Recent topic (abstract classes) not prioritized"
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# If there are multiple results, check if the older topic is also returned but with lower priority
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if len(results) > 1:
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assert "variables" in results[1]["context"].lower(), "Older topic should be second"
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# Verify that the scores reflect the recency prioritization
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assert results[0]["score"] > results[1]["score"], "Recent topic should have higher score"
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def test_future_timestamp_validation():
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"""Test that ShortTermMemoryItem raises ValueError for future timestamps."""
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# Setup agent and task for memory
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agent = Agent(
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role="Tutor",
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goal="Teach programming concepts",
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backstory="You are a programming tutor helping students learn.",
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tools=[],
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verbose=True,
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)
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task = Task(
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description="Explain programming concepts to students.",
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expected_output="Clear explanations of programming concepts.",
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agent=agent,
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)
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# Create a future timestamp
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future_timestamp = datetime.now() + timedelta(days=1)
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# Test constructor validation
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with pytest.raises(ValueError, match="Timestamp cannot be in the future"):
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ShortTermMemoryItem(data="Test data", timestamp=future_timestamp)
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# Test save method validation
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memory = ShortTermMemory(crew=Crew(agents=[agent], tasks=[task]))
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# Create a memory item with a future timestamp
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future_data = "Test data with future timestamp"
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# We need to pass the data directly to the save method
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# The save method will create a ShortTermMemoryItem internally
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# and then we'll modify its timestamp before it's saved
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# Mock datetime.now to return a fixed time
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with patch('crewai.memory.short_term.short_term_memory_item.datetime') as mock_datetime:
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# Set up the mock to return our future timestamp when now() is called
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mock_datetime.now.return_value = future_timestamp
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with pytest.raises(ValueError, match="Cannot save memory item with future timestamp"):
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memory.save(value=future_data)
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