SLA-RedM/reference-deepwiki/api/tools/embedder.py

55 lines
2.1 KiB
Python

import adalflow as adal
from api.config import configs, get_embedder_type
def get_embedder(is_local_ollama: bool = False, use_google_embedder: bool = False, embedder_type: str = None) -> adal.Embedder:
"""Get embedder based on configuration or parameters.
Args:
is_local_ollama: Legacy parameter for Ollama embedder
use_google_embedder: Legacy parameter for Google embedder
embedder_type: Direct specification of embedder type ('ollama', 'google', 'openai')
Returns:
adal.Embedder: Configured embedder instance
"""
# Determine which embedder config to use
if embedder_type:
if embedder_type == 'ollama':
embedder_config = configs["embedder_ollama"]
elif embedder_type == 'google':
embedder_config = configs["embedder_google"]
else: # default to openai
embedder_config = configs["embedder"]
elif is_local_ollama:
embedder_config = configs["embedder_ollama"]
elif use_google_embedder:
embedder_config = configs["embedder_google"]
else:
# Auto-detect based on current configuration
current_type = get_embedder_type()
if current_type == 'ollama':
embedder_config = configs["embedder_ollama"]
elif current_type == 'google':
embedder_config = configs["embedder_google"]
else:
embedder_config = configs["embedder"]
# --- Initialize Embedder ---
model_client_class = embedder_config["model_client"]
if "initialize_kwargs" in embedder_config:
model_client = model_client_class(**embedder_config["initialize_kwargs"])
else:
model_client = model_client_class()
# Create embedder with basic parameters
embedder_kwargs = {"model_client": model_client, "model_kwargs": embedder_config["model_kwargs"]}
embedder = adal.Embedder(**embedder_kwargs)
# Set batch_size as an attribute if available (not a constructor parameter)
if "batch_size" in embedder_config:
embedder.batch_size = embedder_config["batch_size"]
return embedder