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"""Embedding model management for RAG functionality."""
import os
from typing import Optional
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from src.core.config import config
from src.core.logging_config import get_logger
logger = get_logger(__name__)
class EmbeddingManager:
"""Manages embedding models for document vectorization."""
def __init__(self):
self._embedding_model: Optional[GoogleGenerativeAIEmbeddings] = None
def get_embedding_model(self) -> GoogleGenerativeAIEmbeddings:
"""Get or create the Gemini embedding model."""
if self._embedding_model is None:
try:
# Get Google API key from config/environment
google_api_key = config.api.google_api_key or os.getenv("GOOGLE_API_KEY")
if not google_api_key:
raise ValueError("Google API key not found. Please set GOOGLE_API_KEY in environment variables.")
self._embedding_model = GoogleGenerativeAIEmbeddings(
model=config.rag.embedding_model,
google_api_key=google_api_key,
task_type="RETRIEVAL_DOCUMENT"
)
logger.info(f"Gemini embedding model ({config.rag.embedding_model}) initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize Gemini embedding model: {e}")
raise
return self._embedding_model
def test_embedding_model(self) -> bool:
"""Test if the embedding model is working correctly."""
try:
embedding_model = self.get_embedding_model()
# Test with a simple text
test_text = "This is a test for embedding functionality."
embedding = embedding_model.embed_query(test_text)
# Check if we got a valid embedding (list of floats)
if isinstance(embedding, list) and len(embedding) > 0 and isinstance(embedding[0], float):
logger.info("Gemini embedding model test successful")
return True
else:
logger.error("Gemini embedding model test failed: Invalid embedding format")
return False
except Exception as e:
logger.error(f"Gemini embedding model test failed: {e}")
return False
# Global embedding manager instance
embedding_manager = EmbeddingManager() |