app.py

from src.data_loader import load_all_documents
from src.vectorstore import FaissVectorStore
from src.search import RAGSearch
from src.embedding import EmbeddingPipeline

# Example usage
if __name__ == "__main__":
    
    # docs = load_all_documents("data")

    ##FOR CHECK EMBEDDING WORK OR NOT 
    # chunks=EmbeddingPipeline().chunk_documents(docs)
    # chunkvectors= EmbeddingPipeline().embed_chunks(chunks)
    # print(chunkvectors)


    store = FaissVectorStore("faiss_store")
    # store.build_from_documents(docs) ## there is no need to run again and again untill new file not come
    store.load() 
    # print(store.query("What is skills?", top_k=3))
    rag_search = RAGSearch()
    query = "What is inside pojects?"
    summary = rag_search.search_and_summarize(query, top_k=3)
    print("Summary:", summary)