gpt_batch 3.0 with api-base, embedding_get, new doc

README.md

@@ -1,19 +1,73 @@
+Certainly! Here's a clean and comprehensive README for your `GPTBatcher` tool, formatted in Markdown:
+
+```markdown
 # GPT Batcher
 
-A simple tool to batch process messages using OpenAI's GPT models.
+A simple tool to batch process messages using OpenAI's GPT models. `GPTBatcher` allows for efficient handling of multiple requests simultaneously, ensuring quick responses and robust error management.
 
 ## Installation
 
-Clone this repository and run:
+To get started with `GPTBatcher`, clone this repository to your local machine. Navigate to the repository directory and install the required dependencies (if any) by running:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Quick Start
 
-## Usage
+To use `GPTBatcher`, you need to instantiate it with your OpenAI API key and the model name you wish to use. Here's a quick guide:
 
-Here's how to use the `GPTBatcher`:
+### Handling Message Lists
+
+This example demonstrates how to send a list of questions and receive answers:
 
 ```python
 from gpt_batch.batcher import GPTBatcher
 
+# Initialize the batcher
 batcher = GPTBatcher(api_key='your_key_here', model_name='gpt-3.5-turbo-1106')
-result = batcher.handle_message_list(['your', 'list', 'of', 'messages'])
+
+# Send a list of messages and receive answers
+result = batcher.handle_message_list(['question_1', 'question_2', 'question_3', 'question_4'])
+print(result)
+# Expected output: ["answer_1", "answer_2", "answer_3", "answer_4"]
+```
+
+### Handling Embedding Lists
+
+This example shows how to get embeddings for a list of strings:
+
+```python
+from gpt_batch.batcher import GPTBatcher
+
+# Reinitialize the batcher for embeddings
+batcher = GPTBatcher(api_key='your_key_here', model_name='text-embedding-3-small')
+
+# Send a list of strings and get their embeddings
+result = batcher.handle_embedding_list(['question_1', 'question_2', 'question_3', 'question_4'])
 print(result)
+# Expected output: ["embedding_1", "embedding_2", "embedding_3", "embedding_4"]
+```
+
+## Configuration
+
+The `GPTBatcher` class can be customized with several parameters to adjust its performance and behavior:
+
+- **api_key** (str): Your OpenAI API key.
+- **model_name** (str): Identifier for the GPT model version you want to use, default is 'gpt-3.5-turbo-1106'.
+- **system_prompt** (str): Initial text or question to seed the model, default is empty.
+- **temperature** (float): Adjusts the creativity of the responses, default is 1.
+- **num_workers** (int): Number of parallel workers for request handling, default is 64.
+- **timeout_duration** (int): Timeout for API responses in seconds, default is 60.
+- **retry_attempts** (int): How many times to retry a failed request, default is 2.
+- **miss_index** (list): Tracks indices of requests that failed to process correctly.
+
+For more detailed documentation on the parameters and methods, refer to the class docstring.
+
+## License
+
+Specify your licensing information here.
+
+```
 
+This README provides clear instructions on how to install and use the `GPTBatcher`, along with detailed explanations of its configuration parameters. Adjust the "License" section as necessary based on your project's licensing terms.

gpt_batch/__init__.py

@@ -1,3 +1,4 @@
 from .batcher import GPTBatcher
 
+
 __all__ = ['GPTBatcher']

gpt_batch/batcher.py

@@ -4,7 +4,31 @@
 from tqdm import tqdm
 
 class GPTBatcher:
-    def __init__(self, api_key, model_name="gpt-3.5-turbo-0125", system_prompt="",temperature=1,num_workers=64,timeout_duration=60,retry_attempts=2):
+    """
+    A class to handle batching and sending requests to the OpenAI GPT model efficiently.
+
+    Attributes:
+        client (OpenAI): The client instance to communicate with the OpenAI API using the provided API key.
+        model_name (str): The name of the GPT model to be used. Default is 'gpt-3.5-turbo-0125'.
+        system_prompt (str): Initial prompt or context to be used with the model. Default is an empty string.
+        temperature (float): Controls the randomness of the model's responses. Higher values lead to more diverse outputs. Default is 1.
+        num_workers (int): Number of worker threads used for handling concurrent requests. Default is 64.
+        timeout_duration (int): Maximum time (in seconds) to wait for a response from the API before timing out. Default is 60 seconds.
+        retry_attempts (int): Number of retries if a request fails. Default is 2.
+        miss_index (list): Tracks the indices of requests that failed to process correctly.
+
+    Parameters:
+        api_key (str): API key for authenticating requests to the OpenAI API.
+        model_name (str, optional): Specifies the GPT model version. Default is 'gpt-3.5-turbo-0125'.
+        system_prompt (str, optional): Initial text or question to seed the model with. Default is empty.
+        temperature (float, optional): Sets the creativity of the responses. Default is 1.
+        num_workers (int, optional): Number of parallel workers for request handling. Default is 64.
+        timeout_duration (int, optional): Timeout for API responses in seconds. Default is 60.
+        retry_attempts (int, optional): How many times to retry a failed request. Default is 2.
+    """
+
+    def __init__(self, api_key, model_name="gpt-3.5-turbo-0125", system_prompt="",temperature=1,num_workers=64,timeout_duration=60,retry_attempts=2,api_base_url=None):
+
         self.client = OpenAI(api_key=api_key)
         self.model_name = model_name
         self.system_prompt = system_prompt
@@ -13,6 +37,8 @@ def __init__(self, api_key, model_name="gpt-3.5-turbo-0125", system_prompt="",te
         self.timeout_duration = timeout_duration
         self.retry_attempts = retry_attempts
         self.miss_index =[]
+        if api_base_url:
+            self.client.base_url = api_base_url
 
     def get_attitude(self, ask_text):
         index, ask_text = ask_text
@@ -44,7 +70,7 @@ def process_attitude(self, message_list):
                 new_list.extend(future.result() for future in done if future.done())
                 if len(not_done) == 0:
                     break
-                future_to_message = {executor.submit(self.get_attitude, (future_to_message[future], msg), temperature): future_to_message[future] for future, msg in not_done}
+                future_to_message = {executor.submit(self.get_attitude, future_to_message[future]): future_to_message[future] for future, msg in not_done}
         executor.shutdown(wait=False)
         return new_list
 
@@ -80,6 +106,43 @@ def handle_message_list(self,message_list):
         attitude_list = self.complete_attitude_list(attitude_list, max_length)
         attitude_list = [x[1] for x in attitude_list]
         return attitude_list
+
+    def process_embedding(self,message_list):
+            new_list = []
+            executor = ThreadPoolExecutor(max_workers=self.num_workers)
+            # Split message_list into chunks
+            message_chunks = list(self.chunk_list(message_list, self.num_workers))
+            fixed_get_embedding = partial(self.get_embedding)
+            for chunk in tqdm(message_chunks, desc="Processing messages"):
+                future_to_message = {executor.submit(fixed_get_embedding, message): message for message in chunk}
+                for i in range(self.retry_attempts):
+                    done, not_done = wait(future_to_message.keys(), timeout=self.timeout_duration)
+                    for future in not_done:
+                        future.cancel()
+                    new_list.extend(future.result() for future in done if future.done())
+                    if len(not_done) == 0:
+                        break
+                    future_to_message = {executor.submit(fixed_get_embedding, future_to_message[future]): future_to_message[future] for future in not_done}
+            executor.shutdown(wait=False)
+            return new_list
+    def get_embedding(self,text):
+        index,text = text
+        response = self.client.embeddings.create(
+        input=text,
+        model=self.model_name)
+        return (index,response.data[0].embedding)
+
+    def handle_embedding_list(self,message_list):
+        indexed_list = [(index, data) for index, data in enumerate(message_list)]
+        max_length = len(indexed_list)
+        attitude_list = self.process_embedding(indexed_list)
+        attitude_list.sort(key=lambda x: x[0])
+        attitude_list = self.complete_attitude_list(attitude_list, max_length)
+        attitude_list = [x[1] for x in attitude_list]
+        return attitude_list
+
+    def get_miss_index(self):
+        return self.miss_index
 
     # Add other necessary methods similar to the above, refactored to fit within this class structure.
 

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='gpt_batch',
-    version='0.1.1',
+    version='0.1.2',
     packages=find_packages(),
     install_requires=[
         'openai', 'tqdm'

tests/test_batcher.py

@@ -18,6 +18,38 @@ def test_handle_message_list():
     assert len(results) == 2, "There should be two results, one for each message"
     assert all(len(result) >= 2 for result in results), "Each result should be at least two elements"
 
+def test_handle_embedding_list():
+    # Initialize the GPTBatcher with hypothetical valid credentials
+    #api_key = #get from system environment
+    api_key = os.getenv('TEST_KEY')
+    if not api_key:
+        raise ValueError("API key must be set in the environment variables")
+    batcher = GPTBatcher(api_key=api_key, model_name='text-embedding-3-small')
+    embedding_list = [ "I think privacy is important", "I don't think privacy is important"]
+    results = batcher.handle_embedding_list(embedding_list)
+    assert len(results) == 2, "There should be two results, one for each message"
+    assert all(len(result) >= 2 for result in results), "Each result should be at least two elements"
+
+def test_base_url():
+    # Initialize the GPTBatcher with hypothetical valid credentials
+    #api_key = #get from system environment
+    api_key = os.getenv('TEST_KEY')
+    if not api_key:
+        raise ValueError("API key must be set in the environment variables")
+    batcher = GPTBatcher(api_key=api_key, model_name='gpt-3.5-turbo-1106', api_base_url="https://api.openai.com/v2/")
+    assert batcher.client.base_url == "https://api.openai.com/v2/", "The base URL should be set to the provided value"
+
+def test_get_miss_index():
+    # Initialize the GPTBatcher with hypothetical valid credentials
+    #api_key = #get from system environment
+    api_key = os.getenv('TEST_KEY')
+    if not api_key:
+        raise ValueError("API key must be set in the environment variables")
+    batcher = GPTBatcher(api_key=api_key, model_name='gpt-3.5-turbo-1106', system_prompt="Your task is to discuss privacy questions and provide persuasive answers with supporting reasons.")
+    message_list = ["I think privacy is important", "I don't think privacy is important"]
+    results = batcher.handle_message_list(message_list)
+    miss_index = batcher.get_miss_index()
+    assert miss_index == [], "The miss index should be empty"
 # Optionally, you can add a test configuration if you have specific needs
 if __name__ == "__main__":
     pytest.main()