92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
from pathlib import Path
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import hellocomputer
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import polars as pl
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import pytest
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from hellocomputer.config import Settings, StorageEngines
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from hellocomputer.db.sessions import SessionDB
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from hellocomputer.extraction import extract_code_block
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from hellocomputer.models import AvailableModels
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_community.agent_toolkits import SQLDatabaseToolkit
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from langchain_openai import ChatOpenAI
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settings = Settings(
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storage_engine=StorageEngines.local,
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path=Path(hellocomputer.__file__).parents[2] / "test" / "output",
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)
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TEST_XLS_PATH = (
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Path(hellocomputer.__file__).parents[2]
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/ "test"
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/ "data"
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/ "TestExcelHelloComputer.xlsx"
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)
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SID = "test"
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@pytest.mark.asyncio
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@pytest.mark.skipif(settings.llm_api_key == "Awesome API", reason="API Key not set")
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async def test_chat_simple():
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llm = ChatOpenAI(
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base_url=settings.llm_base_url,
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api_key=settings.llm_api_key,
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model=AvailableModels.mixtral_8x7b,
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temperature=0.5,
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)
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prompt = ChatPromptTemplate.from_template(
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"""Say literally {word}, a single word. Don't be verbose,
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I'll be disappointed if you say more than a single word"""
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)
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chain = prompt | llm
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response = await chain.ainvoke({"word": "Hello"})
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assert response.content.lower().startswith("hello")
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@pytest.mark.asyncio
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@pytest.mark.skipif(settings.llm_api_key == "Awesome API", reason="API Key not set")
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async def test_query_context():
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db = SessionDB(settings, sid=SID).load_xls(TEST_XLS_PATH).llmsql
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llm = ChatOpenAI(
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base_url=settings.llm_base_url,
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api_key=settings.llm_api_key,
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model=AvailableModels.mixtral_8x7b,
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temperature=0.5,
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)
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toolkit = SQLDatabaseToolkit(db=db, llm=llm)
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context = toolkit.get_context()
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assert "table_info" in context
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assert "table_names" in context
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#
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# chat = await chat.sql_eval(db.query_prompt(query))
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# query = extract_code_block(chat.last_response_content())
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# assert query.startswith("SELECT")
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#
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#
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# @pytest.mark.asyncio
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# @pytest.mark.skipif(settings.llm_api_key == "Awesome API", reason="API Key not set")
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# async def test_data_query():
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# q = "Find the average score of all the sudents"
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#
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# llm = Chat(
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# api_key=settings.llm_api_key,
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# temperature=0.5,
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# )
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# db = SessionDB(
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# storage_engine=TEST_STORAGE, path=TEST_OUTPUT_FOLDER, sid="test"
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# ).load_folder()
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#
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# chat = await llm.sql_eval(db.query_prompt(q))
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# query = extract_code_block(chat.last_response_content())
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# result: pl.DataFrame = db.query(query).pl()
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#
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# assert result.shape[0] == 1
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# assert result.select([pl.col("avg(Score)")]).to_series()[0] == 0.5
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#
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