from langchain_community.vectorstores import FAISS

from embedding import LocalLMEmbeddings


def retrieve_enriched_context(query, db_path="./local_faiss_db"):
    # 1. Re-initialize the same embedding model
    embeddings_model = LocalLMEmbeddings(
        model="text-embedding-qwen3-embedding-8b", base_url="http://192.168.0.49:1234"
    )

    # 2. Load the index from disk
    # allow_dangerous_deserialization is required because FAISS uses pickle
    vectorstore = FAISS.load_local(db_path, embeddings_model, allow_dangerous_deserialization=True)

    # 3. Perform the search
    # k=4 means "bring back the top 4 most relevant chunks"
    results_with_scores = vectorstore.similarity_search_with_score(query, k=4)

    return results_with_scores


# --- Example Usage ---
query = "the party get free bread but i cant remember why?"
hits = retrieve_enriched_context(query)

for doc, score in hits:
    print(f"\n🎯 [Score: {score:.4f}]")
    print(f"📄 Content: {doc.page_content[:200]}...")
    print(f"🛠️ Metadata (Enrichment): {doc.metadata}")
#    print(f"doc: {doc}")