mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-12 23:01:41 +00:00
test(embeddings): move encoding_format default coverage to wire-level assertions
Consolidate the new regression tests into test_openai_embedding_encoding_format_default.py, replacing mocks that pinned the old float default with respx captures of the request body, and update the stale local_testing default-float test to assert omission
This commit is contained in:
mateo-berri
parent
e22744c439
commit
c254605e92
3 changed files with 109 additions and 203 deletions
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@ -3,6 +3,8 @@ import os
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import re
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import traceback
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import httpx
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import openai
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import pytest
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from dotenv import load_dotenv
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@ -1255,56 +1257,42 @@ def test_jina_ai_img_embeddings(input_data, expected_payload_input):
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assert sent_data["input"] == expected_payload_input
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def test_encoding_format_defaults_to_float_for_openai_sdk(monkeypatch):
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def test_encoding_format_omitted_by_default_for_openai_sdk(monkeypatch):
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"""
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When encoding_format is not provided, LiteLLM sends `float` for OpenAI-path embeddings.
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When encoding_format is not provided, LiteLLM leaves it out of the upstream request.
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Optional global override: `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT`.
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"""
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monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False)
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with patch(
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"litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client"
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) as mock_get_client:
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# Create a mock client instance
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mock_client_instance = MagicMock()
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mock_get_client.return_value = mock_client_instance
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captured_bodies = []
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# Mock the embeddings.with_raw_response.create method
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mock_response = MagicMock()
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mock_response.parse.return_value = MagicMock(
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model_dump=lambda: {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"model": "text-embedding-ada-002",
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def handler(request: httpx.Request) -> httpx.Response:
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captured_bodies.append(json.loads(request.content))
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return httpx.Response(
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200,
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json={
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"object": "list",
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"data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}],
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"model": "text-embedding-ada-002",
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"usage": {"prompt_tokens": 1, "total_tokens": 1},
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}
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)
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mock_response.headers = {}
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mock_client_instance.embeddings.with_raw_response.create.return_value = (
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mock_response
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},
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)
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# Call the embedding function without encoding_format
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response = embedding(
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model="text-embedding-ada-002",
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input="Hello world",
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)
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client = openai.OpenAI(
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api_key="sk-test", http_client=httpx.Client(transport=httpx.MockTransport(handler))
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)
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# Get the call arguments to verify what was sent to OpenAI SDK
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call_args = mock_client_instance.embeddings.with_raw_response.create.call_args
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assert (
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call_args is not None
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), "OpenAI SDK embeddings.create should have been called"
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response = embedding(
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model="text-embedding-ada-002",
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input="Hello world",
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api_key="sk-test",
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client=client,
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)
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call_kwargs = call_args[1] # Get kwargs
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assert "encoding_format" in call_kwargs
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assert (
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call_kwargs["encoding_format"] == "float"
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), "encoding_format should default to float when not provided by user"
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print("✅ PASS: encoding_format='float' is correctly passed to OpenAI SDK")
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assert response.data[0]["embedding"] == [0.1, 0.2, 0.3]
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assert "encoding_format" not in captured_bodies[0], (
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"encoding_format should be omitted from the upstream request when not provided by user"
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)
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def test_encoding_format_explicit_value_preserved():
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@ -3182,63 +3182,3 @@ def test_stream_chunk_builder_defers_cost_to_logging_obj_when_usage_cost_absent(
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assert response is not None
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assert response._hidden_params.get("response_cost") is None
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def _mock_openai_embedding_route(respx_mock: respx.MockRouter) -> respx.Route:
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return respx_mock.post("https://api.openai.com/v1/embeddings").mock(
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return_value=httpx.Response(
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200,
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json={
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"object": "list",
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"data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}],
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"model": "text-embedding-3-small",
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"usage": {"prompt_tokens": 2, "total_tokens": 2},
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},
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)
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)
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def test_embedding_openai_omits_encoding_format_when_client_omits_it(respx_mock: respx.MockRouter) -> None:
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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response: Final = litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert "encoding_format" not in request_body
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assert response.data[0]["embedding"] == [0.1, 0.2, 0.3]
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def test_embedding_openai_forwards_explicit_encoding_format(respx_mock: respx.MockRouter) -> None:
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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litellm.embedding(
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model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64"
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)
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert request_body["encoding_format"] == "base64"
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def test_embedding_openai_env_var_sets_default_encoding_format(
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respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
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) -> None:
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monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float")
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert request_body["encoding_format"] == "float"
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@pytest.mark.asyncio
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async def test_aembedding_openai_omits_encoding_format_when_client_omits_it(
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respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
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) -> None:
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monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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response: Final = await litellm.aembedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert "encoding_format" not in request_body
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assert response.data[0]["embedding"] == [0.1, 0.2, 0.3]
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@ -1,124 +1,102 @@
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from unittest.mock import MagicMock, patch
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import json
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from typing import Final
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import httpx
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import pytest
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import respx
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from litellm import embedding
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import litellm
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@pytest.mark.parametrize(
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"set_env, env_value, expected",
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[
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(False, None, "float"),
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(True, "base64", "base64"),
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],
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)
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def test_openai_embedding_encoding_format_default(
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monkeypatch, set_env, env_value, expected
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):
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monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False)
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if set_env:
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monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_value)
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mock_response = MagicMock()
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mock_response.parse.return_value = MagicMock(
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model_dump=lambda: {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"model": "text-embedding-ada-002",
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"object": "list",
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"usage": {"prompt_tokens": 1, "total_tokens": 1},
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}
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def _mock_openai_embedding_route(respx_mock: respx.MockRouter) -> respx.Route:
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return respx_mock.post("https://api.openai.com/v1/embeddings").mock(
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return_value=httpx.Response(
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200,
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json={
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"object": "list",
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"data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}],
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"model": "text-embedding-3-small",
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"usage": {"prompt_tokens": 2, "total_tokens": 2},
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},
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)
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)
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mock_response.headers = {}
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with patch(
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"litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client"
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) as mock_get_client:
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mock_client_instance = MagicMock()
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mock_get_client.return_value = mock_client_instance
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mock_client_instance.embeddings.with_raw_response.create.return_value = (
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mock_response
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)
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embedding(
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model="text-embedding-ada-002",
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input="Hello world",
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)
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@pytest.fixture(autouse=True)
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def clear_default_encoding_format_env(monkeypatch: pytest.MonkeyPatch):
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monkeypatch.delenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", raising=False)
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call_kwargs = (
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mock_client_instance.embeddings.with_raw_response.create.call_args[1]
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)
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assert call_kwargs["encoding_format"] == expected
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def test_embedding_openai_omits_encoding_format_when_client_omits_it(respx_mock: respx.MockRouter) -> None:
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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response: Final = litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert "encoding_format" not in request_body
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assert response.data[0]["embedding"] == [0.1, 0.2, 0.3]
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def test_embedding_openai_forwards_explicit_encoding_format(respx_mock: respx.MockRouter) -> None:
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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litellm.embedding(
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model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64"
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)
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert request_body["encoding_format"] == "base64"
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def test_embedding_openai_explicit_encoding_format_wins_over_env_var(
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respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
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) -> None:
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monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float")
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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litellm.embedding(
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model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test", encoding_format="base64"
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)
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert request_body["encoding_format"] == "base64"
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@pytest.mark.parametrize("env_value", ["float", "base64"])
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def test_embedding_openai_env_var_sets_default_encoding_format(
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respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch, env_value: str
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) -> None:
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monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_value)
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert request_body["encoding_format"] == env_value
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@pytest.mark.parametrize("env_none", ["none", "NONE", " none "])
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def test_openai_embedding_encoding_format_env_none_omits_param(
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monkeypatch, env_none
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):
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"""LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT=none omits encoding_format (provider default)."""
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def test_embedding_openai_env_none_omits_encoding_format(
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respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch, env_none: str
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) -> None:
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monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", env_none)
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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mock_response = MagicMock()
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mock_response.parse.return_value = MagicMock(
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model_dump=lambda: {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"model": "text-embedding-ada-002",
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"object": "list",
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"usage": {"prompt_tokens": 1, "total_tokens": 1},
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}
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)
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mock_response.headers = {}
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litellm.embedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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with patch(
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"litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client"
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) as mock_get_client:
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mock_client_instance = MagicMock()
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mock_get_client.return_value = mock_client_instance
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mock_client_instance.embeddings.with_raw_response.create.return_value = (
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mock_response
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)
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embedding(
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model="text-embedding-ada-002",
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input="Hello world",
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)
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call_kwargs = (
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mock_client_instance.embeddings.with_raw_response.create.call_args[1]
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)
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assert "encoding_format" not in call_kwargs
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert "encoding_format" not in request_body
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def test_openai_embedding_encoding_format_explicit_overrides_env(monkeypatch):
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"""Request `encoding_format` wins over LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT."""
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monkeypatch.setenv("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT", "float")
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@pytest.mark.asyncio
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async def test_aembedding_openai_omits_encoding_format_when_client_omits_it(
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respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
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) -> None:
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monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
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mock_route: Final = _mock_openai_embedding_route(respx_mock)
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mock_response = MagicMock()
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mock_response.parse.return_value = MagicMock(
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model_dump=lambda: {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"model": "text-embedding-ada-002",
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"object": "list",
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"usage": {"prompt_tokens": 1, "total_tokens": 1},
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}
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)
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mock_response.headers = {}
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response: Final = await litellm.aembedding(model="openai/text-embedding-3-small", input=["hello"], api_key="sk-test")
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with patch(
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"litellm.llms.openai.openai.OpenAIChatCompletion._get_openai_client"
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) as mock_get_client:
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mock_client_instance = MagicMock()
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mock_get_client.return_value = mock_client_instance
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mock_client_instance.embeddings.with_raw_response.create.return_value = (
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mock_response
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)
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embedding(
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model="text-embedding-ada-002",
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input="Hello world",
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encoding_format="base64",
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)
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call_kwargs = (
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mock_client_instance.embeddings.with_raw_response.create.call_args[1]
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)
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assert call_kwargs["encoding_format"] == "base64"
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request_body: Final = json.loads(mock_route.calls.last.request.read())
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assert "encoding_format" not in request_body
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assert response.data[0]["embedding"] == [0.1, 0.2, 0.3]
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