mirror of
https://github.com/Significant-Gravitas/AutoGPT.git
synced 2026-04-08 03:00:28 -04:00
Fix the maximum context length issue by chunking (#3222)
Co-authored-by: Reinier van der Leer <github@pwuts.nl>
This commit is contained in:
kinance
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File diff suppressed because it is too large
Load Diff
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import string
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from unittest.mock import MagicMock
|
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|
||||
import pytest
|
||||
from numpy.random import RandomState
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
from autogpt.llm.llm_utils import get_ada_embedding
|
||||
from autogpt.config import Config
|
||||
from autogpt.llm import llm_utils
|
||||
from autogpt.llm.api_manager import ApiManager
|
||||
from autogpt.llm.modelsinfo import COSTS
|
||||
from tests.utils import requires_api_key
|
||||
|
||||
|
||||
@@ -16,10 +21,42 @@ def random_large_string():
|
||||
return "".join(random.choice(list(string.ascii_lowercase), size=n_characters))
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="We have no mechanism for embedding large strings.")
|
||||
@pytest.fixture()
|
||||
def api_manager(mocker: MockerFixture):
|
||||
api_manager = ApiManager()
|
||||
mocker.patch.multiple(
|
||||
api_manager,
|
||||
total_prompt_tokens=0,
|
||||
total_completion_tokens=0,
|
||||
total_cost=0,
|
||||
)
|
||||
yield api_manager
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def spy_create_embedding(mocker: MockerFixture):
|
||||
return mocker.spy(llm_utils, "create_embedding")
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
@requires_api_key("OPENAI_API_KEY")
|
||||
def test_get_ada_embedding(
|
||||
config: Config, api_manager: ApiManager, spy_create_embedding: MagicMock
|
||||
):
|
||||
token_cost = COSTS[config.embedding_model]["prompt"]
|
||||
llm_utils.get_ada_embedding("test")
|
||||
|
||||
spy_create_embedding.assert_called_once_with("test", model=config.embedding_model)
|
||||
|
||||
assert (prompt_tokens := api_manager.get_total_prompt_tokens()) == 1
|
||||
assert api_manager.get_total_completion_tokens() == 0
|
||||
assert api_manager.get_total_cost() == (prompt_tokens * token_cost) / 1000
|
||||
|
||||
|
||||
@pytest.mark.vcr
|
||||
@requires_api_key("OPENAI_API_KEY")
|
||||
def test_get_ada_embedding_large_context(random_large_string):
|
||||
# This test should be able to mock the openai call after we have a fix. We don't need
|
||||
# to hit the API to test the logic of the function (so not using vcr). This is a quick
|
||||
# regression test to document the issue.
|
||||
get_ada_embedding(random_large_string)
|
||||
llm_utils.get_ada_embedding(random_large_string)
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
import pytest
|
||||
from openai.error import APIError, RateLimitError
|
||||
|
||||
from autogpt.llm import COSTS, get_ada_embedding
|
||||
from autogpt.llm.llm_utils import retry_openai_api
|
||||
from autogpt.llm import llm_utils
|
||||
|
||||
|
||||
@pytest.fixture(params=[RateLimitError, APIError])
|
||||
@@ -13,22 +12,12 @@ def error(request):
|
||||
return request.param("Error")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_create_embedding(mocker):
|
||||
mock_response = mocker.MagicMock()
|
||||
mock_response.usage.prompt_tokens = 5
|
||||
mock_response.__getitem__.side_effect = lambda key: [{"embedding": [0.1, 0.2, 0.3]}]
|
||||
return mocker.patch(
|
||||
"autogpt.llm.llm_utils.create_embedding", return_value=mock_response
|
||||
)
|
||||
|
||||
|
||||
def error_factory(error_instance, error_count, retry_count, warn_user=True):
|
||||
class RaisesError:
|
||||
def __init__(self):
|
||||
self.count = 0
|
||||
|
||||
@retry_openai_api(
|
||||
@llm_utils.retry_openai_api(
|
||||
num_retries=retry_count, backoff_base=0.001, warn_user=warn_user
|
||||
)
|
||||
def __call__(self):
|
||||
@@ -41,7 +30,7 @@ def error_factory(error_instance, error_count, retry_count, warn_user=True):
|
||||
|
||||
|
||||
def test_retry_open_api_no_error(capsys):
|
||||
@retry_openai_api()
|
||||
@llm_utils.retry_openai_api()
|
||||
def f():
|
||||
return 1
|
||||
|
||||
@@ -114,16 +103,31 @@ def test_retry_openapi_other_api_error(capsys):
|
||||
assert output.out == ""
|
||||
|
||||
|
||||
def test_get_ada_embedding(mock_create_embedding, api_manager):
|
||||
model = "text-embedding-ada-002"
|
||||
embedding = get_ada_embedding("test")
|
||||
mock_create_embedding.assert_called_once_with(
|
||||
"test", model="text-embedding-ada-002"
|
||||
)
|
||||
|
||||
assert embedding == [0.1, 0.2, 0.3]
|
||||
|
||||
cost = COSTS[model]["prompt"]
|
||||
assert api_manager.get_total_prompt_tokens() == 5
|
||||
assert api_manager.get_total_completion_tokens() == 0
|
||||
assert api_manager.get_total_cost() == (5 * cost) / 1000
|
||||
def test_chunked_tokens():
|
||||
text = "Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model"
|
||||
expected_output = [
|
||||
(
|
||||
13556,
|
||||
12279,
|
||||
2898,
|
||||
374,
|
||||
459,
|
||||
22772,
|
||||
1825,
|
||||
31874,
|
||||
3851,
|
||||
67908,
|
||||
279,
|
||||
17357,
|
||||
315,
|
||||
279,
|
||||
480,
|
||||
2898,
|
||||
12,
|
||||
19,
|
||||
4221,
|
||||
1646,
|
||||
)
|
||||
]
|
||||
output = list(llm_utils.chunked_tokens(text, "cl100k_base", 8191))
|
||||
assert output == expected_output
|
||||
Reference in New Issue
Block a user