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import json
import os
import sys
from datetime import datetime
from unittest.mock import AsyncMock
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system-path
import litellm
import json
import os
import sys
from datetime import datetime
from unittest.mock import patch, MagicMock, AsyncMock
import pytest
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system-path
from test_rerank import assert_response_shape
import litellm
from base_embedding_unit_tests import BaseLLMEmbeddingTest
from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
from litellm.types.utils import EmbeddingResponse, Usage
@pytest.mark.asyncio()
async def test_infinity_rerank():
mock_response = AsyncMock()
def return_val():
return {
"id": "cmpl-mockid",
"results": [{"index": 0, "relevance_score": 0.95}],
"usage": {"prompt_tokens": 100, "total_tokens": 150},
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
expected_payload = {
"model": "rerank-model",
"query": "hello",
"top_n": 3,
"documents": ["hello", "world"],
}
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.arerank(
model="infinity/rerank-model",
query="hello",
documents=["hello", "world"],
top_n=3,
api_base="https://api.infinity.ai",
)
print("async re rank response: ", response)
# Assert
mock_post.assert_called_once()
print("call args", mock_post.call_args)
args_to_api = mock_post.call_args.kwargs["data"]
_url = mock_post.call_args.kwargs["url"]
print("Arguments passed to API=", args_to_api)
print("url = ", _url)
assert _url == "https://api.infinity.ai/rerank"
request_data = json.loads(args_to_api)
assert request_data["query"] == expected_payload["query"]
assert request_data["documents"] == expected_payload["documents"]
assert request_data["top_n"] == expected_payload["top_n"]
assert request_data["model"] == expected_payload["model"]
assert response.id is not None
assert response.results is not None
assert response.meta["tokens"]["input_tokens"] == 100
assert (
response.meta["tokens"]["output_tokens"] == 50
) # total_tokens - prompt_tokens
assert_response_shape(response, custom_llm_provider="infinity")
@pytest.mark.asyncio()
async def test_infinity_rerank_with_return_documents():
mock_response = AsyncMock()
mock_response = AsyncMock()
def return_val():
return {
"id": "cmpl-mockid",
"results": [{"index": 0, "relevance_score": 0.95, "document": "hello"}],
"usage": {"prompt_tokens": 100, "total_tokens": 150},
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.arerank(
model="infinity/rerank-model",
query="hello",
documents=["hello", "world"],
top_n=3,
return_documents=True,
api_base="https://api.infinity.ai",
)
assert response.results[0]["document"] == {"text": "hello"}
assert_response_shape(response, custom_llm_provider="infinity")
@pytest.mark.asyncio()
async def test_infinity_rerank_with_env(monkeypatch):
# Set up mock response
mock_response = AsyncMock()
def return_val():
return {
"id": "cmpl-mockid",
"results": [{"index": 0, "relevance_score": 0.95}],
"usage": {"prompt_tokens": 100, "total_tokens": 150},
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
# Set environment variable
monkeypatch.setenv("INFINITY_API_BASE", "https://env.infinity.ai")
expected_payload = {
"model": "rerank-model",
"query": "hello",
"top_n": 3,
"documents": ["hello", "world"],
}
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.arerank(
model="infinity/rerank-model",
query="hello",
documents=["hello", "world"],
top_n=3,
)
print("async re rank response: ", response)
# Assert
mock_post.assert_called_once()
print("call args", mock_post.call_args)
args_to_api = mock_post.call_args.kwargs["data"]
_url = mock_post.call_args.kwargs["url"]
print("Arguments passed to API=", args_to_api)
print("url = ", _url)
assert _url == "https://env.infinity.ai/rerank"
request_data = json.loads(args_to_api)
assert request_data["query"] == expected_payload["query"]
assert request_data["documents"] == expected_payload["documents"]
assert request_data["top_n"] == expected_payload["top_n"]
assert request_data["model"] == expected_payload["model"]
assert response.id is not None
assert response.results is not None
assert response.meta["tokens"]["input_tokens"] == 100
assert (
response.meta["tokens"]["output_tokens"] == 50
) # total_tokens - prompt_tokens
assert_response_shape(response, custom_llm_provider="infinity")
#### Embedding Tests
@pytest.mark.asyncio()
async def test_infinity_embedding():
mock_response = AsyncMock()
def return_val():
return {
"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
"usage": {"prompt_tokens": 100, "total_tokens": 150},
"model": "custom-model/embedding-v1",
"object": "list"
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
expected_payload = {
"model": "custom-model/embedding-v1",
"input": ["hello world"],
"encoding_format": "float",
"output_dimension": 512
}
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.aembedding(
model="infinity/custom-model/embedding-v1",
input=["hello world"],
dimensions=512,
encoding_format="float",
api_base="https://api.infinity.ai/embeddings",
)
# Assert
mock_post.assert_called_once()
print("call args", mock_post.call_args)
request_data = mock_post.call_args.kwargs["json"]
_url = mock_post.call_args.kwargs["url"]
assert _url == "https://api.infinity.ai/embeddings"
assert request_data["input"] == expected_payload["input"]
assert request_data["model"] == expected_payload["model"]
assert request_data["output_dimension"] == expected_payload["output_dimension"]
assert request_data["encoding_format"] == expected_payload["encoding_format"]
assert response.data is not None
assert response.usage.prompt_tokens == 100
assert response.usage.total_tokens == 150
assert response.model == "custom-model/embedding-v1"
assert response.object == "list"
@pytest.mark.asyncio()
async def test_infinity_embedding_with_env(monkeypatch):
# Set up mock response
mock_response = AsyncMock()
def return_val():
return {
"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
"usage": {"prompt_tokens": 100, "total_tokens": 150},
"model": "custom-model/embedding-v1",
"object": "list"
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
expected_payload = {
"model": "custom-model/embedding-v1",
"input": ["hello world"],
"encoding_format": "float",
"output_dimension": 512
}
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.aembedding(
model="infinity/custom-model/embedding-v1",
input=["hello world"],
dimensions=512,
encoding_format="float",
api_base="https://api.infinity.ai/embeddings",
)
# Assert
mock_post.assert_called_once()
print("call args", mock_post.call_args)
request_data = mock_post.call_args.kwargs["json"]
_url = mock_post.call_args.kwargs["url"]
assert _url == "https://api.infinity.ai/embeddings"
assert request_data["input"] == expected_payload["input"]
assert request_data["model"] == expected_payload["model"]
assert request_data["output_dimension"] == expected_payload["output_dimension"]
assert request_data["encoding_format"] == expected_payload["encoding_format"]
assert response.data is not None
assert response.usage.prompt_tokens == 100
assert response.usage.total_tokens == 150
assert response.model == "custom-model/embedding-v1"
assert response.object == "list"
@pytest.mark.asyncio()
async def test_infinity_embedding_extra_params():
mock_response = AsyncMock()
def return_val():
return {
"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
"usage": {"prompt_tokens": 100, "total_tokens": 150},
"model": "custom-model/embedding-v1",
"object": "list"
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.aembedding(
model="infinity/custom-model/embedding-v1",
input=["test input"],
dimensions=512,
encoding_format="float",
modality="text",
api_base="https://api.infinity.ai/embeddings",
)
mock_post.assert_called_once()
request_data = mock_post.call_args.kwargs["json"]
# Assert the request parameters
assert request_data["input"] == ["test input"]
assert request_data["model"] == "custom-model/embedding-v1"
assert request_data["output_dimension"] == 512
assert request_data["encoding_format"] == "float"
assert request_data["modality"] == "text"
@pytest.mark.asyncio()
async def test_infinity_embedding_prompt_token_mapping():
mock_response = AsyncMock()
def return_val():
return {
"data": [{"embedding": [0.1, 0.2, 0.3], "index": 0}],
"usage": {"total_tokens": 1, "prompt_tokens": 1},
"model": "custom-model/embedding-v1",
"object": "list"
}
mock_response.json = return_val
mock_response.headers = {"key": "value"}
mock_response.status_code = 200
with patch(
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
return_value=mock_response,
) as mock_post:
response = await litellm.aembedding(
model="infinity/custom-model/embedding-v1",
input=["a"],
dimensions=512,
encoding_format="float",
api_base="https://api.infinity.ai/embeddings",
)
mock_post.assert_called_once()
# Assert the response
assert response.usage.prompt_tokens == 1
assert response.usage.total_tokens == 1
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