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"""
Azure OpenAI embeddings with batching for cost optimization.
"""
import os
import logging
from typing import List
from openai import AzureOpenAI
from tenacity import retry, stop_after_attempt, wait_exponential
from utils.langfuse_client import observe
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
class EmbeddingGenerator:
"""Generate embeddings using Azure OpenAI with batching."""
def __init__(
self,
batch_size: int = 16,
#embedding_model: str = "text-embedding-3-small"
embedding_model=os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME")
):
"""
Initialize embedding generator.
Args:
batch_size: Number of texts to batch per request
embedding_model: Azure OpenAI embedding model deployment name
"""
self.batch_size = batch_size
self.embedding_model = embedding_model
# Validate configuration
if not self.embedding_model:
raise ValueError(
"AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable is not set. "
"This is required for generating embeddings. Please set it in your .env file."
)
api_key = os.getenv("AZURE_OPENAI_API_KEY")
endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
api_version = os.getenv("AZURE_OPENAI_API_VERSION", "2024-02-01")
if not api_key or not endpoint:
raise ValueError(
"AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT must be set. "
"Please configure them in your .env file."
)
# Initialize Azure OpenAI client
try:
self.client = AzureOpenAI(
api_key=api_key,
api_version=api_version,
azure_endpoint=endpoint
)
logger.info(f"Azure OpenAI client initialized for embeddings (deployment: {self.embedding_model})")
except Exception as e:
logger.error(f"Failed to initialize Azure OpenAI client: {str(e)}")
raise
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10)
)
def generate_embedding(self, text: str) -> List[float]:
"""
Generate embedding for a single text.
Args:
text: Text to embed
Returns:
Embedding vector
Raises:
ValueError: If input text is empty or model not configured
Exception: If embedding generation fails
"""
# Validate input
if not text or not text.strip():
raise ValueError("Input text cannot be empty or whitespace-only")
if not self.embedding_model:
raise ValueError("Embedding model not configured. Set AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable")
try:
response = self.client.embeddings.create(
input=text,
model=self.embedding_model
)
embedding = response.data[0].embedding
return embedding
except Exception as e:
error_msg = str(e)
if "404" in error_msg or "Resource not found" in error_msg:
logger.error(
f"\n{'='*80}\n"
f"❌ AZURE OPENAI EMBEDDING DEPLOYMENT NOT FOUND (404 Error)\n"
f"{'='*80}\n"
f"Deployment name: {self.embedding_model}\n"
f"Endpoint: {os.getenv('AZURE_OPENAI_ENDPOINT')}\n"
f"\n"
f"POSSIBLE CAUSES:\n"
f" 1. Deployment '{self.embedding_model}' doesn't exist in your Azure resource\n"
f" 2. Deployment name is misspelled\n"
f" 3. Using wrong Azure OpenAI resource\n"
f"\n"
f"HOW TO FIX:\n"
f" Option A: Create deployment in Azure Portal\n"
f" 1. Go to https://portal.azure.com\n"
f" 2. Navigate to your Azure OpenAI resource\n"
f" 3. Go to 'Model deployments' → 'Manage Deployments'\n"
f" 4. Create deployment with model 'text-embedding-3-small'\n"
f" and name '{self.embedding_model}'\n"
f"\n"
f" Option B: Update AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME\n"
f" 1. Check existing embedding deployments in Azure Portal\n"
f" 2. Update .env or HuggingFace Spaces secrets with correct name\n"
f" 3. Common names: text-embedding-3-small, text-embedding-ada-002\n"
f"\n"
f" Option C: Run diagnostic script\n"
f" python scripts/validate_azure_embeddings.py\n"
f"\n"
f"Original error: {error_msg}\n"
f"{'='*80}"
)
else:
logger.error(f"Error generating embedding: {error_msg}")
raise
@observe(name="generate_embeddings_batch", as_type="span")
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10)
)
def generate_embeddings_batch(self, texts: List[str]) -> List[List[float]]:
"""
Generate embeddings for multiple texts in batches.
Args:
texts: List of texts to embed
Returns:
List of embedding vectors
Raises:
ValueError: If texts is empty or model not configured
Exception: If embedding generation fails
"""
# Validate input
if not self.embedding_model:
raise ValueError("Embedding model not configured. Set AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable")
# Filter out empty strings
valid_texts = [text for text in texts if text and text.strip()]
if not valid_texts:
raise ValueError("No valid texts to embed. All texts are empty or whitespace-only")
if len(valid_texts) != len(texts):
logger.warning(f"Filtered out {len(texts) - len(valid_texts)} empty texts from batch")
all_embeddings = []
try:
# Process in batches
for i in range(0, len(valid_texts), self.batch_size):
batch = valid_texts[i:i + self.batch_size]
logger.info(f"Generating embeddings for batch {i // self.batch_size + 1}")
response = self.client.embeddings.create(
input=batch,
model=self.embedding_model
)
# Extract embeddings in correct order
batch_embeddings = [item.embedding for item in response.data]
all_embeddings.extend(batch_embeddings)
logger.info(f"Generated {len(all_embeddings)} embeddings")
return all_embeddings
except Exception as e:
error_msg = str(e)
if "404" in error_msg or "Resource not found" in error_msg:
logger.error(
f"\n{'='*80}\n"
f"❌ AZURE OPENAI EMBEDDING DEPLOYMENT NOT FOUND (404 Error)\n"
f"{'='*80}\n"
f"Deployment name: {self.embedding_model}\n"
f"Endpoint: {os.getenv('AZURE_OPENAI_ENDPOINT')}\n"
f"\n"
f"POSSIBLE CAUSES:\n"
f" 1. Deployment '{self.embedding_model}' doesn't exist in your Azure resource\n"
f" 2. Deployment name is misspelled\n"
f" 3. Using wrong Azure OpenAI resource\n"
f"\n"
f"HOW TO FIX:\n"
f" Option A: Create deployment in Azure Portal\n"
f" 1. Go to https://portal.azure.com\n"
f" 2. Navigate to your Azure OpenAI resource\n"
f" 3. Go to 'Model deployments' → 'Manage Deployments'\n"
f" 4. Create deployment with model 'text-embedding-3-small'\n"
f" and name '{self.embedding_model}'\n"
f"\n"
f" Option B: Update AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME\n"
f" 1. Check existing embedding deployments in Azure Portal\n"
f" 2. Update .env or HuggingFace Spaces secrets with correct name\n"
f" 3. Common names: text-embedding-3-small, text-embedding-ada-002\n"
f"\n"
f" Option C: Run diagnostic script\n"
f" python scripts/validate_azure_embeddings.py\n"
f"\n"
f"Original error: {error_msg}\n"
f"{'='*80}"
)
else:
logger.error(f"Error generating batch embeddings: {error_msg}")
raise
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