src/embedder.py

"""
Ultra-fast ModernBERT-like embedder (CPU-compatible)
100% local — runs on x86 / Linux / Docker.
"""
from typing import List
import torch
from transformers import AutoTokenizer, AutoModel

print("Loading ModernBERT Embed (CPU version)...")

# You can use any small, fast embedding model here
# e.g. nomic-ai/nomic-embed-text-v1.5 or sentence-transformers/all-MiniLM-L6-v2
MODEL_NAME = "nomic-ai/nomic-embed-text-v1.5"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
model.eval()

if torch.cuda.is_available():
    model = model.to("cuda")
else:
    model = model.to("cpu")

class LocalEmbedder:
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Generate normalized embeddings for a batch of texts."""
        with torch.no_grad():
            inputs = tokenizer(
                texts,
                padding=True,
                truncation=True,
                max_length=512,
                return_tensors="pt",
            ).to(model.device)

            outputs = model(**inputs)
            
            # Mean pool embeddings (like ModernBERT)
            embeddings = outputs.last_hidden_state.mean(dim=1)
            
            # Normalize for cosine similarity
            embeddings = embeddings / embeddings.norm(p=2, dim=1, keepdim=True)
            
            return embeddings.cpu().tolist()

    def embed_query(self, text: str) -> List[float]:
        """Generate normalized embedding for a single query string."""
        return self.embed_documents([text])[0]


# Global instance (for llama_index)
embedder = LocalEmbedder()
print("ModernBERT Embed ready! (CPU-compatible)")