import sys
import os
import numpy as np
from tqdm import tqdm

# Add project root to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from config import (
    NUM_CLUSTERS, FRESHNESS_SHARD_ID, MRL_DIMS, 
    EMBEDDING_MODELS, ROUTER_MODELS, COLLECTION_NAME,
    QDRANT_URL, QDRANT_API_KEY
)
from src.data_pipeline import get_embeddings, load_ms_marco
from src.router import LearnedRouter
from src.vector_db import UnifiedQdrant

def ingest_data():
    print(">>> Starting Ingestion Pipeline for Qdrant Cloud...")
    
    if QDRANT_URL == ":memory:":
        print("WARNING: QDRANT_URL is still :memory:. Please set QDRANT_URL env var for production.")
        # We continue anyway for testing logic, but warn user.

    # 1. Load Data (101k samples for production proof)
    # For demo speed, we might start with 10k, but let's aim for 20k to be significant.
    N_SAMPLES = 1000 
    print(f"Loading {N_SAMPLES} samples from MS MARCO...")
    raw_texts = load_ms_marco(N_SAMPLES)
    
    # 2. Generate Embeddings
    # Use 'nomic' or 'minilm'. Let's stick to 'minilm' for speed/reliability in this demo unless specified.
    # Config says 'nomic' is primary, but 'minilm' is baseline.
    # Let's use 'minilm' for the first pass to ensure it works, or 'nomic' if we want MRL power.
    # The prompt mentioned MRL optimization, so 'nomic' is better if we want real MRL.
    # However, 'minilm' is 384 dims. 'nomic' is 768.
    # Our config MRL_DIMS is 64.
    # Let's use 'minilm' as it's faster to download/run on CPU if needed.
    MODEL_NAME = EMBEDDING_MODELS["minilm"] 
    print(f"Generating embeddings using {MODEL_NAME}...")
    embeddings = get_embeddings(MODEL_NAME, raw_texts)
    vector_dim = embeddings.shape[1]
    
    # 3. Train Router
    # We need to train the router on this data to cluster it.
    print("Training Router...")
    router = LearnedRouter(model_type="lightgbm", n_clusters=NUM_CLUSTERS, mrl_dims=MRL_DIMS)
    router.train(embeddings)
    
    # Save Router
    os.makedirs("models", exist_ok=True)
    router.save("models/router_v1.pkl")
    
    # 4. Assign Clusters (Ground Truth for Indexing)
    print("Assigning clusters...")
    # We use the router's internal KMeans to get the "Ground Truth" cluster for each point.
    # This ensures that the data actually lives where the router *should* predict it to be (mostly).
    cluster_ids = router.kmeans.predict(embeddings)
    
    # 5. Index to Qdrant
    print("Initializing Qdrant...")
    db = UnifiedQdrant(
        collection_name=COLLECTION_NAME, 
        vector_size=vector_dim, 
        num_clusters=NUM_CLUSTERS, 
        freshness_shard_id=FRESHNESS_SHARD_ID
    )
    db.initialize()
    
    print("Indexing data...")
    # Batching is handled inside index_data somewhat, but let's pass it all
    # The index_data method groups by shard, which is efficient for custom sharding.
    
    payloads = [{"text": t, "origin": "ms_marco"} for t in raw_texts]
    
    # We can process in chunks to avoid OOM if 20k is too big for memory (it's fine for 20k).
    db.index_data(embeddings, payloads, cluster_ids)
    
    print(">>> Ingestion Complete!")

if __name__ == "__main__":
    ingest_data()