95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
import pytest
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import letta.functions.function_sets.base as base_functions
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from letta import create_client
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.llm_config import LLMConfig
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from .utils import wipe_config
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# test_agent_id = "test_agent"
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client = None
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@pytest.fixture(scope="module")
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def agent_obj():
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"""Create a test agent that we can call functions on"""
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wipe_config()
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global client
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client = create_client()
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client.set_default_llm_config(LLMConfig.default_config("gpt-4o-mini"))
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client.set_default_embedding_config(EmbeddingConfig.default_config(provider="openai"))
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agent_state = client.create_agent()
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global agent_obj
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agent_obj = client.server.load_agent(agent_id=agent_state.id, actor=client.user)
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yield agent_obj
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client.delete_agent(agent_obj.agent_state.id)
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def query_in_search_results(search_results, query):
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for result in search_results:
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if query.lower() in result["content"].lower():
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return True
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return False
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def test_archival(agent_obj):
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"""Test archival memory functions comprehensively."""
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# Test 1: Basic insertion and retrieval
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base_functions.archival_memory_insert(agent_obj, "The cat sleeps on the mat")
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base_functions.archival_memory_insert(agent_obj, "The dog plays in the park")
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base_functions.archival_memory_insert(agent_obj, "Python is a programming language")
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# Test exact text search
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results, _ = base_functions.archival_memory_search(agent_obj, "cat")
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assert query_in_search_results(results, "cat")
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# Test semantic search (should return animal-related content)
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results, _ = base_functions.archival_memory_search(agent_obj, "animal pets")
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assert query_in_search_results(results, "cat") or query_in_search_results(results, "dog")
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# Test unrelated search (should not return animal content)
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results, _ = base_functions.archival_memory_search(agent_obj, "programming computers")
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assert query_in_search_results(results, "python")
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# Test 2: Test pagination
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# Insert more items to test pagination
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for i in range(10):
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base_functions.archival_memory_insert(agent_obj, f"Test passage number {i}")
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# Get first page
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page0_results, next_page = base_functions.archival_memory_search(agent_obj, "Test passage", page=0)
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# Get second page
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page1_results, _ = base_functions.archival_memory_search(agent_obj, "Test passage", page=1, start=next_page)
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assert page0_results != page1_results
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assert query_in_search_results(page0_results, "Test passage")
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assert query_in_search_results(page1_results, "Test passage")
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# Test 3: Test complex text patterns
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base_functions.archival_memory_insert(agent_obj, "Important meeting on 2024-01-15 with John")
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base_functions.archival_memory_insert(agent_obj, "Follow-up meeting scheduled for next week")
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base_functions.archival_memory_insert(agent_obj, "Project deadline is approaching")
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# Search for meeting-related content
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results, _ = base_functions.archival_memory_search(agent_obj, "meeting schedule")
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assert query_in_search_results(results, "meeting")
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assert query_in_search_results(results, "2024-01-15") or query_in_search_results(results, "next week")
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# Test 4: Test error handling
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# Test invalid page number
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try:
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base_functions.archival_memory_search(agent_obj, "test", page="invalid")
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assert False, "Should have raised ValueError"
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except ValueError:
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pass
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def test_recall(agent_obj):
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base_functions.conversation_search(agent_obj, "banana")
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base_functions.conversation_search(agent_obj, "banana", page=0)
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base_functions.conversation_search_date(agent_obj, start_date="2022-01-01", end_date="2022-01-02")
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