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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}%)" | |
| ) | |