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| # from openai import AsyncOpenAI | |
| # import logging | |
| # from typing import List, Dict, Union | |
| # import pandas as pd | |
| # import asyncio | |
| # from src.api.exceptions import OpenAIError | |
| # # Set up structured logging | |
| # logging.basicConfig( | |
| # level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" | |
| # ) | |
| # logger = logging.getLogger(__name__) | |
| # class EmbeddingService: | |
| # def __init__( | |
| # self, | |
| # openai_api_key: str, | |
| # model: str = "text-embedding-3-small", | |
| # batch_size: int = 10, | |
| # max_concurrent_requests: int = 10, # Limit to 10 concurrent requests | |
| # ): | |
| # self.client = AsyncOpenAI(api_key=openai_api_key) | |
| # self.model = model | |
| # self.batch_size = batch_size | |
| # self.semaphore = asyncio.Semaphore(max_concurrent_requests) # Rate limiter | |
| # self.total_requests = 0 # Total number of requests to process | |
| # self.completed_requests = 0 # Number of completed requests | |
| # async def get_embedding(self, text: str) -> List[float]: | |
| # """Generate embeddings for the given text using OpenAI.""" | |
| # text = text.replace("\n", " ") | |
| # try: | |
| # async with self.semaphore: # Acquire a semaphore slot | |
| # response = await self.client.embeddings.create( | |
| # input=[text], model=self.model | |
| # ) | |
| # self.completed_requests += 1 # Increment completed requests | |
| # self._log_progress() # Log progress | |
| # return response.data[0].embedding | |
| # except Exception as e: | |
| # logger.error(f"Failed to generate embedding: {e}") | |
| # raise OpenAIError(f"OpenAI API error: {e}") | |
| # async def create_embeddings( | |
| # self, | |
| # data: Union[pd.DataFrame, List[str]], | |
| # target_column: str = None, | |
| # output_column: str = "embeddings", | |
| # ) -> Union[pd.DataFrame, List[List[float]]]: | |
| # """ | |
| # Create embeddings for either a DataFrame or a list of strings. | |
| # Args: | |
| # data: Either a DataFrame or a list of strings. | |
| # target_column: The column in the DataFrame to generate embeddings for (required if data is a DataFrame). | |
| # output_column: The column to store embeddings in the DataFrame (default: "embeddings"). | |
| # Returns: | |
| # If data is a DataFrame, returns the DataFrame with the embeddings column. | |
| # If data is a list of strings, returns a list of embeddings. | |
| # """ | |
| # if isinstance(data, pd.DataFrame): | |
| # if not target_column: | |
| # raise ValueError("target_column is required when data is a DataFrame.") | |
| # return await self._create_embeddings_for_dataframe( | |
| # data, target_column, output_column | |
| # ) | |
| # elif isinstance(data, list): | |
| # return await self._create_embeddings_for_texts(data) | |
| # else: | |
| # raise TypeError( | |
| # "data must be either a pandas DataFrame or a list of strings." | |
| # ) | |
| # async def _create_embeddings_for_dataframe( | |
| # self, df: pd.DataFrame, target_column: str, output_column: str | |
| # ) -> pd.DataFrame: | |
| # """Create embeddings for the target column in the DataFrame.""" | |
| # logger.info("Generating embeddings for DataFrame...") | |
| # self.total_requests = len(df) # Set total number of requests | |
| # self.completed_requests = 0 # Reset completed requests counter | |
| # batches = [ | |
| # df[i : i + self.batch_size] for i in range(0, len(df), self.batch_size) | |
| # ] | |
| # processed_batches = await asyncio.gather( | |
| # *[ | |
| # self._process_batch(batch, target_column, output_column) | |
| # for batch in batches | |
| # ] | |
| # ) | |
| # return pd.concat(processed_batches) | |
| # async def _create_embeddings_for_texts(self, texts: List[str]) -> List[List[float]]: | |
| # """Create embeddings for a list of strings.""" | |
| # logger.info("Generating embeddings for list of texts...") | |
| # self.total_requests = len(texts) # Set total number of requests | |
| # self.completed_requests = 0 # Reset completed requests counter | |
| # batches = [ | |
| # texts[i : i + self.batch_size] | |
| # for i in range(0, len(texts), self.batch_size) | |
| # ] | |
| # embeddings = [] | |
| # for batch in batches: | |
| # batch_embeddings = await asyncio.gather( | |
| # *[self.get_embedding(text) for text in batch] | |
| # ) | |
| # embeddings.extend(batch_embeddings) | |
| # return embeddings | |
| # async def _process_batch( | |
| # self, df_batch: pd.DataFrame, target_column: str, output_column: str | |
| # ) -> pd.DataFrame: | |
| # """Process a batch of rows to generate embeddings.""" | |
| # embeddings = await asyncio.gather( | |
| # *[self.get_embedding(row[target_column]) for _, row in df_batch.iterrows()] | |
| # ) | |
| # df_batch[output_column] = embeddings | |
| # return df_batch | |
| # def _log_progress(self): | |
| # """Log the progress of embedding generation.""" | |
| # progress = (self.completed_requests / self.total_requests) * 100 | |
| # logger.info( | |
| # f"Progress: {self.completed_requests}/{self.total_requests} ({progress:.2f}%)" | |
| # ) | |
| from openai import AsyncOpenAI | |
| import logging | |
| from typing import List, Dict, Union | |
| from datasets import Dataset | |
| import asyncio | |
| from src.api.exceptions import OpenAIError | |
| # Set up structured logging | |
| logging.basicConfig( | |
| level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" | |
| ) | |
| logger = logging.getLogger(__name__) | |
| class EmbeddingService: | |
| def __init__( | |
| self, | |
| openai_api_key: str, | |
| model: str = "text-embedding-3-small", | |
| batch_size: int = 10, | |
| max_concurrent_requests: int = 10, # Limit to 10 concurrent requests | |
| ): | |
| self.client = AsyncOpenAI(api_key=openai_api_key) | |
| self.model = model | |
| self.batch_size = batch_size | |
| self.semaphore = asyncio.Semaphore(max_concurrent_requests) # Rate limiter | |
| self.total_requests = 0 # Total number of requests to process | |
| self.completed_requests = 0 # Number of completed requests | |
| async def get_embedding(self, text: str) -> List[float]: | |
| """Generate embeddings for the given text using OpenAI.""" | |
| text = text.replace("\n", " ") | |
| try: | |
| async with self.semaphore: # Acquire a semaphore slot | |
| response = await self.client.embeddings.create( | |
| input=[text], model=self.model | |
| ) | |
| self.completed_requests += 1 # Increment completed requests | |
| self._log_progress() # Log progress | |
| return response.data[0].embedding | |
| except Exception as e: | |
| logger.error(f"Failed to generate embedding: {e}") | |
| raise OpenAIError(f"OpenAI API error: {e}") | |
| async def create_embeddings( | |
| self, | |
| data: Union[Dataset, List[str]], | |
| target_column: str = None, | |
| output_column: str = "embeddings", | |
| ) -> Union[Dataset, List[List[float]]]: | |
| """ | |
| Create embeddings for either a Dataset or a list of strings. | |
| Args: | |
| data: Either a Dataset or a list of strings. | |
| target_column: The column in the Dataset to generate embeddings for (required if data is a Dataset). | |
| output_column: The column to store embeddings in the Dataset (default: "embeddings"). | |
| Returns: | |
| If data is a Dataset, returns the Dataset with the embeddings column. | |
| If data is a list of strings, returns a list of embeddings. | |
| """ | |
| if isinstance(data, Dataset): | |
| if not target_column: | |
| raise ValueError("target_column is required when data is a Dataset.") | |
| return await self._create_embeddings_for_dataset( | |
| data, target_column, output_column | |
| ) | |
| elif isinstance(data, list): | |
| return await self._create_embeddings_for_texts(data) | |
| else: | |
| raise TypeError( | |
| "data must be either a Hugging Face Dataset or a list of strings." | |
| ) | |
| async def _create_embeddings_for_dataset( | |
| self, dataset: Dataset, target_column: str, output_column: str | |
| ) -> Dataset: | |
| """Create embeddings for the target column in the Dataset.""" | |
| logger.info("Generating embeddings for Dataset...") | |
| self.total_requests = len(dataset) # Set total number of requests | |
| self.completed_requests = 0 # Reset completed requests counter | |
| embeddings = [] | |
| for i in range(0, len(dataset), self.batch_size): | |
| batch = dataset[i : i + self.batch_size] | |
| batch_embeddings = await asyncio.gather( | |
| *[self.get_embedding(text) for text in batch[target_column]] | |
| ) | |
| embeddings.extend(batch_embeddings) | |
| dataset = dataset.add_column(output_column, embeddings) | |
| return dataset | |
| async def _create_embeddings_for_texts(self, texts: List[str]) -> List[List[float]]: | |
| """Create embeddings for a list of strings.""" | |
| logger.info("Generating embeddings for list of texts...") | |
| self.total_requests = len(texts) # Set total number of requests | |
| self.completed_requests = 0 # Reset completed requests counter | |
| batches = [ | |
| texts[i : i + self.batch_size] | |
| for i in range(0, len(texts), self.batch_size) | |
| ] | |
| embeddings = [] | |
| for batch in batches: | |
| batch_embeddings = await asyncio.gather( | |
| *[self.get_embedding(text) for text in batch] | |
| ) | |
| embeddings.extend(batch_embeddings) | |
| return embeddings | |
| def _log_progress(self): | |
| """Log the progress of embedding generation.""" | |
| progress = (self.completed_requests / self.total_requests) * 100 | |
| logger.info( | |
| f"Progress: {self.completed_requests}/{self.total_requests} ({progress:.2f}%)" | |
| ) | |