Add support for OpenAI API : offline batch(file) processing (#699)
Co-authored-by: hnyls2002 <hnyls2002@gmail.com>
This commit is contained in:
yichuan~
86
examples/usage/openai_batch_chat.py
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86
examples/usage/openai_batch_chat.py
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import json
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import os
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import time
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import openai
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from openai import OpenAI
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class OpenAIBatchProcessor:
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def __init__(self, api_key):
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# client = OpenAI(api_key=api_key)
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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self.client = client
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def process_batch(self, input_file_path, endpoint, completion_window):
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# Upload the input file
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with open(input_file_path, "rb") as file:
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uploaded_file = self.client.files.create(file=file, purpose="batch")
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# Create the batch job
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batch_job = self.client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint=endpoint,
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completion_window=completion_window,
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)
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# Monitor the batch job status
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while batch_job.status not in ["completed", "failed", "cancelled"]:
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time.sleep(3) # Wait for 3 seconds before checking the status again
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print(
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f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
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)
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batch_job = self.client.batches.retrieve(batch_job.id)
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# Check the batch job status and errors
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if batch_job.status == "failed":
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print(f"Batch job failed with status: {batch_job.status}")
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print(f"Batch job errors: {batch_job.errors}")
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return None
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# If the batch job is completed, process the results
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if batch_job.status == "completed":
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# print result of batch job
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print("batch", batch_job.request_counts)
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result_file_id = batch_job.output_file_id
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# Retrieve the file content from the server
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file_response = self.client.files.content(result_file_id)
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result_content = file_response.read() # Read the content of the file
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# Save the content to a local file
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result_file_name = "batch_job_chat_results.jsonl"
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with open(result_file_name, "wb") as file:
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file.write(result_content) # Write the binary content to the file
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# Load data from the saved JSONL file
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results = []
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with open(result_file_name, "r", encoding="utf-8") as file:
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for line in file:
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json_object = json.loads(
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line.strip()
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) # Parse each line as a JSON object
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results.append(json_object)
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return results
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else:
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print(f"Batch job failed with status: {batch_job.status}")
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return None
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# Initialize the OpenAIBatchProcessor
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api_key = os.environ.get("OPENAI_API_KEY")
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processor = OpenAIBatchProcessor(api_key)
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# Process the batch job
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input_file_path = "input.jsonl"
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endpoint = "/v1/chat/completions"
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completion_window = "24h"
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# Process the batch job
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results = processor.process_batch(input_file_path, endpoint, completion_window)
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# Print the results
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print(results)
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86
examples/usage/openai_batch_complete.py
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86
examples/usage/openai_batch_complete.py
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@@ -0,0 +1,86 @@
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import json
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import os
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import time
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import openai
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from openai import OpenAI
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class OpenAIBatchProcessor:
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def __init__(self, api_key):
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# client = OpenAI(api_key=api_key)
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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self.client = client
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def process_batch(self, input_file_path, endpoint, completion_window):
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# Upload the input file
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with open(input_file_path, "rb") as file:
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uploaded_file = self.client.files.create(file=file, purpose="batch")
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# Create the batch job
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batch_job = self.client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint=endpoint,
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completion_window=completion_window,
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)
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# Monitor the batch job status
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while batch_job.status not in ["completed", "failed", "cancelled"]:
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time.sleep(3) # Wait for 3 seconds before checking the status again
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print(
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f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
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)
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batch_job = self.client.batches.retrieve(batch_job.id)
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# Check the batch job status and errors
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if batch_job.status == "failed":
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print(f"Batch job failed with status: {batch_job.status}")
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print(f"Batch job errors: {batch_job.errors}")
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return None
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# If the batch job is completed, process the results
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if batch_job.status == "completed":
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# print result of batch job
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print("batch", batch_job.request_counts)
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result_file_id = batch_job.output_file_id
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# Retrieve the file content from the server
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file_response = self.client.files.content(result_file_id)
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result_content = file_response.read() # Read the content of the file
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# Save the content to a local file
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result_file_name = "batch_job_complete_results.jsonl"
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with open(result_file_name, "wb") as file:
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file.write(result_content) # Write the binary content to the file
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# Load data from the saved JSONL file
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results = []
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with open(result_file_name, "r", encoding="utf-8") as file:
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for line in file:
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json_object = json.loads(
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line.strip()
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) # Parse each line as a JSON object
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results.append(json_object)
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return results
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else:
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print(f"Batch job failed with status: {batch_job.status}")
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return None
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# Initialize the OpenAIBatchProcessor
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api_key = os.environ.get("OPENAI_API_KEY")
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processor = OpenAIBatchProcessor(api_key)
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# Process the batch job
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input_file_path = "input_complete.jsonl"
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endpoint = "/v1/completions"
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completion_window = "24h"
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# Process the batch job
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results = processor.process_batch(input_file_path, endpoint, completion_window)
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# Print the results
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print(results)
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@@ -13,6 +13,17 @@ response = client.completions.create(
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print(response)
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# Text completion
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response = client.completions.create(
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model="default",
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prompt="I am a robot and I want to study like humans. Now let's tell a story. Once upon a time, there was a little",
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n=1,
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temperature=0.8,
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max_tokens=32,
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)
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print(response)
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# Text completion
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response = client.completions.create(
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model="default",
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@@ -24,6 +35,17 @@ response = client.completions.create(
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print(response)
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# Text completion
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response = client.completions.create(
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model="default",
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prompt=["The name of the famous soccer player is"],
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n=1,
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temperature=0.8,
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max_tokens=128,
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)
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print(response)
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# Text completion
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response = client.completions.create(
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model="default",
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@@ -60,6 +82,21 @@ response = client.completions.create(
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)
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print(response)
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# Chat completion
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response = client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "You are a helpful AI assistant"},
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{"role": "user", "content": "List 3 countries and their capitals."},
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],
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temperature=0.8,
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max_tokens=64,
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logprobs=True,
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n=1,
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)
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print(response)
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# Chat completion
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response = client.chat.completions.create(
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model="default",
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