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* Update caching llm to use history inputs * formatting * linting * update glean sections to have continuous history
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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"""Caching LLM Tests."""
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import asyncio
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from typing import Any, cast
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from graphrag.llm import CompletionLLM, LLMOutput
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from graphrag.llm.base.caching_llm import CachingLLM
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from graphrag.llm.openai.openai_history_tracking_llm import OpenAIHistoryTrackingLLM
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from graphrag.llm.types import LLMCache
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class TestCache(LLMCache):
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def __init__(self):
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self.cache = {}
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async def has(self, key: str) -> bool:
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return key in self.cache
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async def get(self, key: str) -> dict | None:
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entry = self.cache.get(key)
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return entry["result"] if entry else None
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async def set(
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self, key: str, value: str, debug_data: dict[str, Any] | None = None
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) -> None:
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self.cache[key] = {"result": value, **(debug_data or {})}
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async def mock_responder(input: str, **kwargs: dict) -> LLMOutput:
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await asyncio.sleep(0.0001)
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return LLMOutput(output=f"response to [{input}]")
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def throwing_responder(input: str, **kwargs: dict) -> LLMOutput:
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raise ValueError
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mock_responder_llm = cast(CompletionLLM, mock_responder)
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throwing_llm = cast(CompletionLLM, throwing_responder)
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async def test_caching_llm() -> None:
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"""Test a composite LLM."""
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llm = CachingLLM(
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mock_responder_llm, llm_parameters={}, operation="test", cache=TestCache()
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)
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response = await llm("input 1")
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assert response.output == "response to [input 1]"
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llm.set_delegate(throwing_llm)
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response = await llm("input 1")
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assert response.output == "response to [input 1]"
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async def test_composite_llm() -> None:
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"""Test a composite LLM."""
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caching = CachingLLM(
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mock_responder_llm, llm_parameters={}, operation="test", cache=TestCache()
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)
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llm = OpenAIHistoryTrackingLLM(caching)
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response = await llm("input 1")
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history: list[dict] = cast(list[dict], response.history)
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assert len(history) == 2
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response = await llm("input 1")
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history: list[dict] = cast(list[dict], response.history)
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assert len(history) == 2
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response = await llm("input 2", history=history)
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history: list[dict] = cast(list[dict], response.history)
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assert len(history) == 4
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