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6a0d03914c
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
359 lines
12 KiB
Python
359 lines
12 KiB
Python
import json
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from datetime import datetime
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from unittest.mock import AsyncMock
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import litellm
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from unittest.mock import patch, MagicMock
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import pytest
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from test_rerank import assert_response_shape
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from base_embedding_unit_tests import BaseLLMEmbeddingTest
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from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
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from litellm.types.utils import EmbeddingResponse, Usage
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@pytest.mark.asyncio()
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async def test_infinity_rerank():
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mock_response = AsyncMock()
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def return_val():
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return {
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"id": "cmpl-mockid",
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"results": [{"index": 0, "relevance_score": 0.95}],
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"usage": {"prompt_tokens": 100, "total_tokens": 150},
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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expected_payload = {
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"model": "rerank-model",
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"query": "hello",
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"top_n": 3,
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"documents": ["hello", "world"],
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.arerank(
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model="infinity/rerank-model",
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query="hello",
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documents=["hello", "world"],
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top_n=3,
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api_base="https://api.infinity.ai",
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)
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print("async re rank response: ", response)
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# Assert
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mock_post.assert_called_once()
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print("call args", mock_post.call_args)
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args_to_api = mock_post.call_args.kwargs["data"]
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_url = mock_post.call_args.kwargs["url"]
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print("Arguments passed to API=", args_to_api)
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print("url = ", _url)
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assert _url == "https://api.infinity.ai/rerank"
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request_data = json.loads(args_to_api)
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assert request_data["query"] == expected_payload["query"]
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assert request_data["documents"] == expected_payload["documents"]
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assert request_data["top_n"] == expected_payload["top_n"]
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assert request_data["model"] == expected_payload["model"]
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assert response.id is not None
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assert response.results is not None
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assert response.meta["tokens"]["input_tokens"] == 100
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assert (
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response.meta["tokens"]["output_tokens"] == 50
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) # total_tokens - prompt_tokens
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assert_response_shape(response, custom_llm_provider="infinity")
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@pytest.mark.asyncio()
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async def test_infinity_rerank_with_return_documents():
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mock_response = AsyncMock()
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mock_response = AsyncMock()
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def return_val():
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return {
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"id": "cmpl-mockid",
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"results": [{"index": 0, "relevance_score": 0.95, "document": "hello"}],
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"usage": {"prompt_tokens": 100, "total_tokens": 150},
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.arerank(
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model="infinity/rerank-model",
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query="hello",
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documents=["hello", "world"],
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top_n=3,
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return_documents=True,
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api_base="https://api.infinity.ai",
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)
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assert response.results[0]["document"] == {"text": "hello"}
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assert_response_shape(response, custom_llm_provider="infinity")
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@pytest.mark.asyncio()
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async def test_infinity_rerank_with_env(monkeypatch):
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# Set up mock response
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mock_response = AsyncMock()
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def return_val():
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return {
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"id": "cmpl-mockid",
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"results": [{"index": 0, "relevance_score": 0.95}],
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"usage": {"prompt_tokens": 100, "total_tokens": 150},
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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# Set environment variable
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monkeypatch.setenv("INFINITY_API_BASE", "https://env.infinity.ai")
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expected_payload = {
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"model": "rerank-model",
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"query": "hello",
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"top_n": 3,
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"documents": ["hello", "world"],
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.arerank(
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model="infinity/rerank-model",
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query="hello",
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documents=["hello", "world"],
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top_n=3,
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)
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print("async re rank response: ", response)
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# Assert
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mock_post.assert_called_once()
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print("call args", mock_post.call_args)
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args_to_api = mock_post.call_args.kwargs["data"]
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_url = mock_post.call_args.kwargs["url"]
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print("Arguments passed to API=", args_to_api)
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print("url = ", _url)
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assert _url == "https://env.infinity.ai/rerank"
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request_data = json.loads(args_to_api)
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assert request_data["query"] == expected_payload["query"]
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assert request_data["documents"] == expected_payload["documents"]
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assert request_data["top_n"] == expected_payload["top_n"]
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assert request_data["model"] == expected_payload["model"]
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assert response.id is not None
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assert response.results is not None
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assert response.meta["tokens"]["input_tokens"] == 100
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assert (
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response.meta["tokens"]["output_tokens"] == 50
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) # total_tokens - prompt_tokens
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assert_response_shape(response, custom_llm_provider="infinity")
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#### Embedding Tests
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@pytest.mark.asyncio()
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async def test_infinity_embedding():
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mock_response = AsyncMock()
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def return_val():
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return {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"usage": {"prompt_tokens": 100, "total_tokens": 150},
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"model": "custom-model/embedding-v1",
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"object": "list",
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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expected_payload = {
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"model": "custom-model/embedding-v1",
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"input": ["hello world"],
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"encoding_format": "float",
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"output_dimension": 512,
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.aembedding(
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model="infinity/custom-model/embedding-v1",
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input=["hello world"],
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dimensions=512,
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encoding_format="float",
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api_base="https://api.infinity.ai/embeddings",
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)
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# Assert
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mock_post.assert_called_once()
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print("call args", mock_post.call_args)
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request_data = mock_post.call_args.kwargs["json"]
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_url = mock_post.call_args.kwargs["url"]
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assert _url == "https://api.infinity.ai/embeddings"
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assert request_data["input"] == expected_payload["input"]
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assert request_data["model"] == expected_payload["model"]
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assert request_data["output_dimension"] == expected_payload["output_dimension"]
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assert request_data["encoding_format"] == expected_payload["encoding_format"]
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assert response.data is not None
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assert response.usage.prompt_tokens == 100
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assert response.usage.total_tokens == 150
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assert response.model == "custom-model/embedding-v1"
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assert response.object == "list"
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@pytest.mark.asyncio()
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async def test_infinity_embedding_with_env(monkeypatch):
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# Set up mock response
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mock_response = AsyncMock()
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def return_val():
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return {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"usage": {"prompt_tokens": 100, "total_tokens": 150},
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"model": "custom-model/embedding-v1",
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"object": "list",
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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expected_payload = {
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"model": "custom-model/embedding-v1",
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"input": ["hello world"],
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"encoding_format": "float",
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"output_dimension": 512,
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.aembedding(
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model="infinity/custom-model/embedding-v1",
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input=["hello world"],
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dimensions=512,
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encoding_format="float",
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api_base="https://api.infinity.ai/embeddings",
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)
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# Assert
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mock_post.assert_called_once()
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print("call args", mock_post.call_args)
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request_data = mock_post.call_args.kwargs["json"]
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_url = mock_post.call_args.kwargs["url"]
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assert _url == "https://api.infinity.ai/embeddings"
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assert request_data["input"] == expected_payload["input"]
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assert request_data["model"] == expected_payload["model"]
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assert request_data["output_dimension"] == expected_payload["output_dimension"]
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assert request_data["encoding_format"] == expected_payload["encoding_format"]
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assert response.data is not None
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assert response.usage.prompt_tokens == 100
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assert response.usage.total_tokens == 150
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assert response.model == "custom-model/embedding-v1"
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assert response.object == "list"
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@pytest.mark.asyncio()
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async def test_infinity_embedding_extra_params():
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mock_response = AsyncMock()
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def return_val():
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return {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"usage": {"prompt_tokens": 100, "total_tokens": 150},
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"model": "custom-model/embedding-v1",
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"object": "list",
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.aembedding(
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model="infinity/custom-model/embedding-v1",
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input=["test input"],
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dimensions=512,
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encoding_format="float",
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modality="text",
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api_base="https://api.infinity.ai/embeddings",
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)
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mock_post.assert_called_once()
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request_data = mock_post.call_args.kwargs["json"]
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# Assert the request parameters
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assert request_data["input"] == ["test input"]
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assert request_data["model"] == "custom-model/embedding-v1"
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assert request_data["output_dimension"] == 512
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assert request_data["encoding_format"] == "float"
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assert request_data["modality"] == "text"
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@pytest.mark.asyncio()
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async def test_infinity_embedding_prompt_token_mapping():
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mock_response = AsyncMock()
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def return_val():
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return {
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"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
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"usage": {"total_tokens": 1, "prompt_tokens": 1},
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"model": "custom-model/embedding-v1",
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"object": "list",
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = await litellm.aembedding(
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model="infinity/custom-model/embedding-v1",
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input=["a"],
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dimensions=512,
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encoding_format="float",
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api_base="https://api.infinity.ai/embeddings",
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)
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mock_post.assert_called_once()
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# Assert the response
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assert response.usage.prompt_tokens == 1
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assert response.usage.total_tokens == 1
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