290 lines
9.5 KiB
Python
290 lines
9.5 KiB
Python
import uuid
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import os
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import time
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import threading
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from dotenv import load_dotenv
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from memgpt.server.rest_api.server import start_server
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from memgpt import Admin, create_client
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from memgpt.constants import DEFAULT_PRESET
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from dotenv import load_dotenv
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from tests.config import TestMGPTConfig
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from memgpt.credentials import MemGPTCredentials
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from memgpt.data_types import EmbeddingConfig, LLMConfig
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from .utils import wipe_config, wipe_memgpt_home
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import pytest
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import uuid
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test_agent_name = f"test_client_{str(uuid.uuid4())}"
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# test_preset_name = "test_preset"
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test_preset_name = DEFAULT_PRESET
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test_agent_state = None
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client = None
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test_agent_state_post_message = None
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test_user_id = uuid.uuid4()
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test_base_url = "http://localhost:8283"
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# admin credentials
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test_server_token = "test_server_token"
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def run_server():
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import uvicorn
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from memgpt.server.rest_api.server import app
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load_dotenv()
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# Use os.getenv with a fallback to os.environ.get
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db_url = os.getenv("PGVECTOR_TEST_DB_URL") or os.environ.get("PGVECTOR_TEST_DB_URL")
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assert db_url, "Missing PGVECTOR_TEST_DB_URL"
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if os.getenv("OPENAI_API_KEY"):
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config = TestMGPTConfig(
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archival_storage_uri=db_url,
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recall_storage_uri=db_url,
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metadata_storage_uri=db_url,
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archival_storage_type="postgres",
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recall_storage_type="postgres",
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metadata_storage_type="postgres",
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# embeddings
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default_embedding_config=EmbeddingConfig(
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embedding_endpoint_type="openai",
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embedding_endpoint="https://api.openai.com/v1",
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embedding_dim=1536,
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),
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# llms
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default_llm_config=LLMConfig(
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model_endpoint_type="openai",
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model_endpoint="https://api.openai.com/v1",
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model="gpt-4",
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),
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)
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credentials = MemGPTCredentials(
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openai_key=os.getenv("OPENAI_API_KEY"),
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)
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else: # hosted
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config = TestMGPTConfig(
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archival_storage_uri=db_url,
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recall_storage_uri=db_url,
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metadata_storage_uri=db_url,
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archival_storage_type="postgres",
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recall_storage_type="postgres",
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metadata_storage_type="postgres",
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# embeddings
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default_embedding_config=EmbeddingConfig(
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embedding_endpoint_type="hugging-face",
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embedding_endpoint="https://embeddings.memgpt.ai",
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embedding_model="BAAI/bge-large-en-v1.5",
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embedding_dim=1024,
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),
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# llms
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default_llm_config=LLMConfig(
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model_endpoint_type="vllm",
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model_endpoint="https://api.memgpt.ai",
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model="ehartford/dolphin-2.5-mixtral-8x7b",
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),
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)
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credentials = MemGPTCredentials()
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config.save()
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credentials.save()
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start_server(debug=True)
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@pytest.fixture(scope="session", autouse=True)
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def start_uvicorn_server():
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"""Starts Uvicorn server in a background thread."""
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thread = threading.Thread(target=run_server, daemon=True)
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thread.start()
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print("Starting server...")
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time.sleep(5)
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yield
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@pytest.fixture(scope="module")
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def user_token():
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# Setup: Create a user via the client before the tests
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admin = Admin(test_base_url, test_server_token)
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response = admin.create_user(test_user_id) # Adjust as per your client's method
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user_id = response.user_id
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token = response.api_key
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yield token
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# Teardown: Delete the user after the test (or after all tests if fixture scope is module/class)
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admin.delete_user(test_user_id) # Adjust as per your client's method
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# Fixture to create clients with different configurations
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# @pytest.fixture(params=[{"base_url": test_base_url}, {"base_url": None}], scope="module")
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@pytest.fixture(params=[{"base_url": test_base_url}], scope="module")
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def client(request, user_token):
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# use token or not
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if request.param["base_url"]:
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token = user_token
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else:
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token = None
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client = create_client(**request.param, token=token) # This yields control back to the test function
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yield client
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# Fixture for test agent
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@pytest.fixture(scope="module")
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def agent(client):
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agent_state = client.create_agent(name=test_agent_name, preset=test_preset_name)
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print("AGENT ID", agent_state.id)
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yield agent_state
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# delete agent
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client.delete_agent(agent_state.id)
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def test_agent(client, agent):
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# test client.rename_agent
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new_name = "RenamedTestAgent"
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client.rename_agent(agent_id=agent.id, new_name=new_name)
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renamed_agent = client.get_agent(agent_id=str(agent.id))
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assert renamed_agent.name == new_name, "Agent renaming failed"
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# test client.delete_agent and client.agent_exists
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delete_agent = client.create_agent(name="DeleteTestAgent", preset=test_preset_name)
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assert client.agent_exists(agent_id=delete_agent.id), "Agent creation failed"
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client.delete_agent(agent_id=delete_agent.id)
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assert client.agent_exists(agent_id=delete_agent.id) == False, "Agent deletion failed"
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def test_memory(client, agent):
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memory_response = client.get_agent_memory(agent_id=agent.id)
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print("MEMORY", memory_response)
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updated_memory = {"human": "Updated human memory", "persona": "Updated persona memory"}
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client.update_agent_core_memory(agent_id=str(agent.id), new_memory_contents=updated_memory)
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updated_memory_response = client.get_agent_memory(agent_id=agent.id)
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assert (
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updated_memory_response.core_memory.human == updated_memory["human"]
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and updated_memory_response.core_memory.persona == updated_memory["persona"]
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), "Memory update failed"
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def test_agent_interactions(client, agent):
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message = "Hello, agent!"
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message_response = client.user_message(agent_id=str(agent.id), message=message)
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command = "/memory"
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command_response = client.run_command(agent_id=str(agent.id), command=command)
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print("command", command_response)
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def test_archival_memory(client, agent):
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memory_content = "Archival memory content"
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insert_response = client.insert_archival_memory(agent_id=agent.id, memory=memory_content)
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assert insert_response, "Inserting archival memory failed"
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archival_memory_response = client.get_agent_archival_memory(agent_id=agent.id, limit=1)
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archival_memories = [memory.contents for memory in archival_memory_response.archival_memory]
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assert memory_content in archival_memories, f"Retrieving archival memory failed: {archival_memories}"
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memory_id_to_delete = archival_memory_response.archival_memory[0].id
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client.delete_archival_memory(agent_id=agent.id, memory_id=memory_id_to_delete)
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# TODO: check deletion
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def test_messages(client, agent):
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send_message_response = client.send_message(agent_id=agent.id, message="Test message", role="user")
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assert send_message_response, "Sending message failed"
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messages_response = client.get_messages(agent_id=agent.id, limit=1)
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assert len(messages_response.messages) > 0, "Retrieving messages failed"
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def test_humans_personas(client, agent):
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humans_response = client.list_humans()
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print("HUMANS", humans_response)
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personas_response = client.list_personas()
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print("PERSONAS", personas_response)
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persona_name = "TestPersona"
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persona = client.create_persona(name=persona_name, persona="Persona text")
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assert persona.name == persona_name
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assert persona.text == "Persona text", "Creating persona failed"
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human_name = "TestHuman"
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human = client.create_human(name=human_name, human="Human text")
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assert human.name == human_name
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assert human.text == "Human text", "Creating human failed"
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def test_tools(client, agent):
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tools_response = client.list_tools()
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print("TOOLS", tools_response)
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tool_name = "TestTool"
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tool_response = client.create_tool(name=tool_name, source_code="print('Hello World')", source_type="python")
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assert tool_response, "Creating tool failed"
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def test_config(client, agent):
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models_response = client.list_models()
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print("MODELS", models_response)
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config_response = client.get_config()
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# TODO: ensure config is the same as the one in the server
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print("CONFIG", config_response)
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def test_sources(client, agent):
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if not hasattr(client, "base_url"):
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pytest.skip("Skipping test_sources because base_url is None")
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# list sources
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sources = client.list_sources()
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print("listed sources", sources)
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# create a source
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source = client.create_source(name="test_source")
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# list sources
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sources = client.list_sources()
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print("listed sources", sources)
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assert len(sources) == 1
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# check agent archival memory size
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archival_memories = client.get_agent_archival_memory(agent_id=agent.id).archival_memory
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print(archival_memories)
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assert len(archival_memories) == 0
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# load a file into a source
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filename = "CONTRIBUTING.md"
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num_passages = 20
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response = client.load_file_into_source(filename, source.id)
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print(response)
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# attach a source
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# TODO: make sure things run in the right order
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client.attach_source_to_agent(source_name="test_source", agent_id=agent.id)
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# list archival memory
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archival_memories = client.get_agent_archival_memory(agent_id=agent.id).archival_memory
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print(archival_memories)
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assert len(archival_memories) == num_passages
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# detach the source
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# TODO: add when implemented
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# client.detach_source(source.name, agent.id)
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# delete the source
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client.delete_source(source.id)
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