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# What this tests ?
## Tests /batches endpoints
import pytest
import asyncio
import aiohttp, openai
from openai import OpenAI, AsyncOpenAI
from typing import Optional, List, Union
from test_openai_files_endpoints import upload_file, delete_file
import os
import sys
import time
BASE_URL = "http://localhost:4000" # Replace with your actual base URL
API_KEY = "sk-1234" # Replace with your actual API key
from openai import OpenAI
client = OpenAI(base_url=BASE_URL, api_key=API_KEY)
@pytest.mark.asyncio
async def test_batches_operations():
_current_dir = os.path.dirname(os.path.abspath(__file__))
input_file_path = os.path.join(_current_dir, "input.jsonl")
file_obj = client.files.create(
file=open(input_file_path, "rb"),
purpose="batch",
)
batch = client.batches.create(
input_file_id=file_obj.id,
endpoint="/v1/chat/completions",
completion_window="24h",
)
assert batch.id is not None
# Test get batch
_retrieved_batch = client.batches.retrieve(batch_id=batch.id)
print("response from get batch", _retrieved_batch)
assert _retrieved_batch.id == batch.id
assert _retrieved_batch.input_file_id == file_obj.id
# Test list batches
_list_batches = client.batches.list()
print("response from list batches", _list_batches)
assert _list_batches is not None
assert len(_list_batches.data) > 0
# Clean up
# Test cancel batch
_canceled_batch = client.batches.cancel(batch_id=batch.id)
print("response from cancel batch", _canceled_batch)
assert _canceled_batch.status is not None
assert (
_canceled_batch.status == "cancelling" or _canceled_batch.status == "cancelled"
)
# finally delete the file
_deleted_file = client.files.delete(file_id=file_obj.id)
print("response from delete file", _deleted_file)
assert _deleted_file.deleted is True
def create_batch_oai_sdk(filepath: str, custom_llm_provider: str) -> str:
batch_input_file = client.files.create(
file=open(filepath, "rb"),
purpose="batch",
extra_body={"custom_llm_provider": custom_llm_provider},
)
batch_input_file_id = batch_input_file.id
print("waiting for file to be processed......")
time.sleep(5)
rq = client.batches.create(
input_file_id=batch_input_file_id,
endpoint="/v1/chat/completions",
completion_window="24h",
metadata={
"description": filepath,
},
extra_body={"custom_llm_provider": custom_llm_provider},
)
print(f"Batch submitted. ID: {rq.id}")
return rq.id
def await_batch_completion(batch_id: str, custom_llm_provider: str):
max_tries = 3
tries = 0
while tries < max_tries:
batch = client.batches.retrieve(
batch_id, extra_body={"custom_llm_provider": custom_llm_provider}
)
if batch.status == "completed":
print(f"Batch {batch_id} completed.")
return batch.id
tries += 1
print(f"waiting for batch to complete... (attempt {tries}/{max_tries})")
time.sleep(10)
print(
f"Reached maximum number of attempts ({max_tries}). Batch may still be processing."
)
def write_content_to_file(
batch_id: str, output_path: str, custom_llm_provider: str
) -> str:
batch = client.batches.retrieve(
batch_id=batch_id, extra_body={"custom_llm_provider": custom_llm_provider}
)
content = client.files.content(
file_id=batch.output_file_id,
extra_body={"custom_llm_provider": custom_llm_provider},
)
print("content from files.content", content.content)
content.write_to_file(output_path)
import jsonlines
def read_jsonl(filepath: str):
results = []
with jsonlines.open(filepath) as f:
for line in f:
results.append(line)
for item in results:
print(item)
custom_id = item["custom_id"]
print(custom_id)
def get_any_completed_batch_id_azure():
print("AZURE getting any completed batch id")
list_of_batches = client.batches.list(extra_body={"custom_llm_provider": "azure"})
print("list of batches", list_of_batches)
for batch in list_of_batches:
if batch.status == "completed":
return batch.id
return None
@pytest.mark.parametrize("custom_llm_provider", ["azure", "openai"])
def test_e2e_batches_files(custom_llm_provider):
"""
[PROD Test] Ensures OpenAI Batches + files work with OpenAI SDK
"""
input_path = (
"input.jsonl" if custom_llm_provider == "openai" else "input_azure.jsonl"
)
output_path = "out.jsonl" if custom_llm_provider == "openai" else "out_azure.jsonl"
_current_dir = os.path.dirname(os.path.abspath(__file__))
input_file_path = os.path.join(_current_dir, input_path)
output_file_path = os.path.join(_current_dir, output_path)
print("running e2e batches files with custom_llm_provider=", custom_llm_provider)
batch_id = create_batch_oai_sdk(
filepath=input_file_path, custom_llm_provider=custom_llm_provider
)
if custom_llm_provider == "azure":
# azure takes very long to complete a batch
return
else:
response_batch_id = await_batch_completion(
batch_id=batch_id, custom_llm_provider=custom_llm_provider
)
if response_batch_id is None:
return
write_content_to_file(
batch_id=batch_id,
output_path=output_file_path,
custom_llm_provider=custom_llm_provider,
)
read_jsonl(output_file_path)
@pytest.mark.skip(reason="Local only test to verify if things work well")
def test_vertex_batches_endpoint():
"""
Test VertexAI Batches Endpoint
"""
import os
oai_client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
file_name = "local_testing/vertex_batch_completions.jsonl"
_current_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(_current_dir, file_name)
file_obj = oai_client.files.create(
file=open(file_path, "rb"),
purpose="batch",
extra_body={"custom_llm_provider": "vertex_ai"},
)
print("Response from creating file=", file_obj)
batch_input_file_id = file_obj.id
assert (
batch_input_file_id is not None
), f"Failed to create file, expected a non null file_id but got {batch_input_file_id}"
create_batch_response = oai_client.batches.create(
completion_window="24h",
endpoint="/v1/chat/completions",
input_file_id=batch_input_file_id,
extra_body={"custom_llm_provider": "vertex_ai"},
metadata={"key1": "value1", "key2": "value2"},
)
print("response from create batch", create_batch_response)
pass
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