mirror of
https://github.com/BerriAI/litellm.git
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251467a525
* build(model_prices_and_context_window.json): mark bedrock llama as supporting vision based on docs * Add price for Cerebras llama3.3-70b (#8676) * docs(readme.md): fix contributing docs point people to new mock directory testing structure s/o @vibhavbhat * build: update contributing readme * docs(readme.md): improve docs * docs(readme.md): cleanup readme on tests/ * docs(README.md): cleanup doc * feat(infinity/): support returning documents when return_documents=True * test(test_rerank.py): add e2e testing for cohere rerank * fix: fix linting errors * fix(together_ai/): fix together ai transformation * fix: fix linting error * fix: fix linting errors * fix: fix linting errors * test: mark cohere as flaky * build: fix model supports check * test: fix test * test: mark flaky test * fix: fix test * test: fix test --------- Co-authored-by: Yury Koleda <fut.wrk@gmail.com>
184 lines
5.5 KiB
Python
184 lines
5.5 KiB
Python
import json
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import os
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import sys
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from datetime import datetime
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from unittest.mock import AsyncMock
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system-path
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import litellm
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import json
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import os
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import sys
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from datetime import datetime
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from unittest.mock import AsyncMock, patch
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import pytest
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system-path
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from test_rerank import assert_response_shape
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import litellm
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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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