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langchain/libs/community/tests/unit_tests/chat_models/test_kinetica.py

68 lines
2.0 KiB
Python

"""Test `Kinetica` chat models"""
import logging
from typing import Any
from langchain_core.messages import AIMessage
from langchain_community.chat_models.kinetica import ChatKinetica, KineticaUtil
LOG = logging.getLogger(__name__)
class TestChatKinetica:
test_ctx_json = """
{
"payload":{
"context":[
{
"table":"demo.test_profiles",
"columns":[
"username VARCHAR (32) NOT NULL",
"name VARCHAR (32) NOT NULL",
"sex VARCHAR (1) NOT NULL",
"address VARCHAR (64) NOT NULL",
"mail VARCHAR (32) NOT NULL",
"birthdate TIMESTAMP NOT NULL"
],
"description":"Contains user profiles.",
"rules":[
]
},
{
"samples":{
"How many male users are there?":
"select count(1) as num_users from demo.test_profiles where sex = ''M'';"
}
}
]
}
}
"""
def test_convert_messages(self, monkeypatch: Any) -> None:
"""Test convert messages from context."""
def patch_kdbc() -> None:
return None
monkeypatch.setattr(KineticaUtil, "create_kdbc", patch_kdbc)
def patch_execute_sql(*args: Any, **kwargs: Any) -> dict:
return dict(Prompt=self.test_ctx_json)
monkeypatch.setattr(ChatKinetica, "_execute_sql", patch_execute_sql)
kinetica_llm = ChatKinetica()
test_messages = kinetica_llm.load_messages_from_context("test")
LOG.info(f"test_messages: {test_messages}")
ai_message = test_messages[-1]
assert isinstance(ai_message, AIMessage)
assert (
ai_message.content
== "select count(1) as num_users from demo.test_profiles where sex = 'M';"
)