"""Embedding configuration and model utilities.
This module provides configuration management for embedding models,
including model dimensions and client initialization.
"""
import os
from typing import Any
def get_embedding_dimensions(model: str) -> int:
"""Get the embedding dimensions for a given OpenAI model.
Args:
model: The embedding model name
Returns:
Number of dimensions for the embedding
"""
# OpenAI embedding model dimensions
model_dimensions = {
"text-embedding-3-small": 1536,
"text-embedding-3-large": 3072,
"text-embedding-ada-002": 1536,
}
# Default to 1536 for unknown models (most common)
return model_dimensions.get(model, 1536)
def get_embedding_model() -> str:
"""Get the configured embedding model.
Returns:
Embedding model name from environment or default
"""
return os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
def get_contextual_embedding_config() -> dict[str, Any]:
"""Get contextual embedding configuration from environment.
Returns:
Dictionary with contextual embedding configuration
"""
import logging
logger = logging.getLogger(__name__)
# Model selection
model = os.getenv("CONTEXTUAL_EMBEDDING_MODEL", "gpt-4o-mini")
# Validate and set max_tokens (1-4096 range)
try:
max_tokens = int(os.getenv("CONTEXTUAL_EMBEDDING_MAX_TOKENS", "200"))
if not (1 <= max_tokens <= 4096):
logger.warning(
"CONTEXTUAL_EMBEDDING_MAX_TOKENS (%s) out of range 1-4096, using default 200",
max_tokens,
)
max_tokens = 200
except ValueError:
logger.warning(
"CONTEXTUAL_EMBEDDING_MAX_TOKENS must be an integer, using default 200",
)
max_tokens = 200
# Validate and set temperature (0.0-2.0 range)
try:
temperature = float(os.getenv("CONTEXTUAL_EMBEDDING_TEMPERATURE", "0.3"))
if not (0.0 <= temperature <= 2.0):
logger.warning(
"CONTEXTUAL_EMBEDDING_TEMPERATURE (%s) out of range 0.0-2.0, using default 0.3",
temperature,
)
temperature = 0.3
except ValueError:
logger.warning(
"CONTEXTUAL_EMBEDDING_TEMPERATURE must be a number, using default 0.3",
)
temperature = 0.3
# Validate and set max_doc_chars (positive integer)
try:
max_doc_chars = int(os.getenv("CONTEXTUAL_EMBEDDING_MAX_DOC_CHARS", "25000"))
if max_doc_chars <= 0:
logger.warning(
"CONTEXTUAL_EMBEDDING_MAX_DOC_CHARS (%s) must be positive, using default 25000",
max_doc_chars,
)
max_doc_chars = 25000
except ValueError:
logger.warning(
"CONTEXTUAL_EMBEDDING_MAX_DOC_CHARS must be a positive integer, using default 25000",
)
max_doc_chars = 25000
# Max workers for parallel processing
try:
max_workers = int(os.getenv("CONTEXTUAL_EMBEDDING_MAX_WORKERS", "10"))
if max_workers <= 0:
logger.warning(
"CONTEXTUAL_EMBEDDING_MAX_WORKERS (%s) must be positive, using default 10",
max_workers,
)
max_workers = 10
except ValueError:
logger.warning(
"CONTEXTUAL_EMBEDDING_MAX_WORKERS must be a positive integer, using default 10",
)
max_workers = 10
return {
"model": model,
"max_tokens": max_tokens,
"temperature": temperature,
"max_doc_chars": max_doc_chars,
"max_workers": max_workers,
"enabled": os.getenv("USE_CONTEXTUAL_EMBEDDINGS", "false").lower() == "true",
}
