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200 lines
8.2 KiB
Python
200 lines
8.2 KiB
Python
import os
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import uuid
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import subprocess
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import sys
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import pytest
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# subprocess.check_call(
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# [sys.executable, "-m", "pip", "install", "pgvector", "psycopg", "psycopg2-binary"]
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# ) # , "psycopg_binary"]) # "psycopg", "libpq-dev"])
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#
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# subprocess.check_call([sys.executable, "-m", "pip", "install", "lancedb"])
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from memgpt.connectors.storage import StorageConnector, TableType
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from memgpt.embeddings import embedding_model
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from memgpt.data_types import Message, Passage
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from memgpt.config import MemGPTConfig, AgentConfig
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from memgpt.utils import get_local_time
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from memgpt.connectors.storage import StorageConnector, TableType
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from memgpt.constants import DEFAULT_MEMGPT_MODEL, DEFAULT_PERSONA, DEFAULT_HUMAN
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import argparse
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from datetime import datetime, timedelta
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# Note: the database will filter out rows that do not correspond to agent1 and test_user by default.
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texts = ["This is a test passage", "This is another test passage", "Cinderella wept"]
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start_date = datetime(2009, 10, 5, 18, 00)
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dates = [start_date, start_date - timedelta(weeks=1), start_date + timedelta(weeks=1)]
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roles = ["user", "agent", "agent"]
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agent_ids = ["agent1", "agent2", "agent1"]
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ids = [uuid.uuid4(), uuid.uuid4(), uuid.uuid4()]
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user_id = "test_user"
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# Data generation functions: Passages
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def generate_passages(embed_model):
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"""Generate list of 3 Passage objects"""
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# embeddings: use openai if env is set, otherwise local
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passages = []
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for text, _, _, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
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embedding = None
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if embed_model:
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embedding = embed_model.get_text_embedding(text)
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passages.append(Passage(user_id=user_id, text=text, agent_id=agent_id, embedding=embedding, data_source="test_source", id=id))
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return passages
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# Data generation functions: Messages
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def generate_messages():
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"""Generate list of 3 Message objects"""
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messages = []
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for text, date, role, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
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messages.append(Message(user_id=user_id, text=text, agent_id=agent_id, role=role, created_at=date, id=id, model="gpt4"))
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print(messages[-1].text)
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return messages
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@pytest.mark.parametrize("storage_connector", ["postgres", "chroma", "sqlite"])
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@pytest.mark.parametrize("table_type", [TableType.RECALL_MEMORY, TableType.ARCHIVAL_MEMORY])
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def test_storage(storage_connector, table_type):
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# setup memgpt config
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# TODO: set env for different config path
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config = MemGPTConfig()
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if storage_connector == "postgres":
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if not os.getenv("PGVECTOR_TEST_DB_URL"):
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print("Skipping test, missing PG URI")
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return
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config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
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config.recall_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
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config.archival_storage_type = "postgres"
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config.recall_storage_type = "postgres"
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if storage_connector == "lancedb":
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# TODO: complete lancedb implementation
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if not os.getenv("LANCEDB_TEST_URL"):
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print("Skipping test, missing LanceDB URI")
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return
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config.archival_storage_uri = os.getenv("LANCEDB_TEST_URL")
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config.recall_storage_uri = os.getenv("LANCEDB_TEST_URL")
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config.archival_storage_type = "lancedb"
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config.recall_storage_type = "lancedb"
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if storage_connector == "chroma":
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if table_type == TableType.RECALL_MEMORY:
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print("Skipping test, chroma only supported for archival memory")
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return
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config.archival_storage_type = "chroma"
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config.archival_storage_path = "./test_chroma"
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if storage_connector == "sqlite":
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if table_type == TableType.ARCHIVAL_MEMORY:
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print("Skipping test, sqlite only supported for recall memory")
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return
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config.recall_storage_type = "local"
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# get embedding model
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embed_model = None
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if os.getenv("OPENAI_API_KEY"):
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config.embedding_endpoint_type = "openai"
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config.embedding_endpoint = "https://api.openai.com/v1"
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config.embedding_dim = 1536
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config.openai_key = os.getenv("OPENAI_API_KEY")
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else:
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config.embedding_endpoint_type = "local"
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config.embedding_endpoint = None
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config.embedding_dim = 384
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config.save()
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embed_model = embedding_model()
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# create agent
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agent_config = AgentConfig(
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name="agent1",
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persona=DEFAULT_PERSONA,
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human=DEFAULT_HUMAN,
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model=DEFAULT_MEMGPT_MODEL,
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)
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# create storage connector
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conn = StorageConnector.get_storage_connector(storage_type=storage_connector, table_type=table_type, agent_config=agent_config)
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# conn.client.delete_collection(conn.collection.name) # clear out data
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conn.delete_table()
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conn = StorageConnector.get_storage_connector(storage_type=storage_connector, table_type=table_type, agent_config=agent_config)
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# override filters
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conn.user_id = user_id
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conn.filters = {"user_id": user_id, "agent_id": "agent1"}
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# generate data
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if table_type == TableType.ARCHIVAL_MEMORY:
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records = generate_passages(embed_model)
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elif table_type == TableType.RECALL_MEMORY:
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records = generate_messages()
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else:
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raise NotImplementedError(f"Table type {table_type} not implemented")
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# test: insert
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conn.insert(records[0])
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assert conn.size() == 1, f"Expected 1 record, got {conn.size()}: {conn.get_all()}"
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# test: insert_many
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conn.insert_many(records[1:])
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assert (
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conn.size() == 2
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), f"Expected 1 record, got {conn.size()}: {conn.get_all()}" # expect 2, since storage connector filters for agent1
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# test: list_loaded_data
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# TODO: add back
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# if table_type == TableType.ARCHIVAL_MEMORY:
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# sources = StorageConnector.list_loaded_data(storage_type=storage_connector)
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# assert len(sources) == 1, f"Expected 1 source, got {len(sources)}"
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# assert sources[0] == "test_source", f"Expected 'test_source', got {sources[0]}"
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# test: get_all_paginated
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paginated_total = 0
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for page in conn.get_all_paginated(page_size=1):
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paginated_total += len(page)
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assert paginated_total == 2, f"Expected 2 records, got {paginated_total}"
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# test: get_all
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all_records = conn.get_all()
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assert len(all_records) == 2, f"Expected 2 records, got {len(all_records)}"
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all_records = conn.get_all(limit=1)
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assert len(all_records) == 1, f"Expected 1 records, got {len(all_records)}"
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# test: get
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print("GET ID", ids[0], records)
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res = conn.get(id=ids[0])
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assert res.text == texts[0], f"Expected {texts[0]}, got {res.text}"
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# test: size
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assert conn.size() == 2, f"Expected 2 records, got {conn.size()}"
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assert conn.size(filters={"agent_id": "agent1"}) == 2, f"Expected 2 records, got {conn.size(filters={'agent_id', 'agent1'})}"
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if table_type == TableType.RECALL_MEMORY:
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assert conn.size(filters={"role": "user"}) == 1, f"Expected 1 record, got {conn.size(filters={'role': 'user'})}"
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# test: query (vector)
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if table_type == TableType.ARCHIVAL_MEMORY:
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query = "why was she crying"
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query_vec = embed_model.get_text_embedding(query)
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res = conn.query(None, query_vec, top_k=2)
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assert len(res) == 2, f"Expected 2 results, got {len(res)}"
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print("Archival memory results", res)
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assert "wept" in res[0].text, f"Expected 'wept' in results, but got {res[0].text}"
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# test optional query functions for recall memory
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if table_type == TableType.RECALL_MEMORY:
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# test: query_text
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query = "CindereLLa"
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res = conn.query_text(query)
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assert len(res) == 1, f"Expected 1 result, got {len(res)}"
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assert "Cinderella" in res[0].text, f"Expected 'Cinderella' in results, but got {res[0].text}"
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# test: query_date (recall memory only)
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print("Testing recall memory date search")
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start_date = datetime(2009, 10, 5, 18, 00)
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start_date = start_date - timedelta(days=1)
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end_date = start_date + timedelta(days=1)
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res = conn.query_date(start_date=start_date, end_date=end_date)
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print("DATE", res)
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assert len(res) == 1, f"Expected 1 result, got {len(res)}: {res}"
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# test: delete
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conn.delete({"id": ids[0]})
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assert conn.size() == 1, f"Expected 2 records, got {conn.size()}"
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