| """Run this at the start of every session to instantly restore everything.""" |
|
|
| from google.colab import drive |
| drive.mount('/content/drive') |
|
|
| import pickle, numpy as np, faiss, torch, sys |
| from sentence_transformers import SentenceTransformer, CrossEncoder |
| from google.colab import userdata |
| from groq import Groq |
| from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, AutoTokenizer, AutoModelForQuestionAnswering |
|
|
| PROJECT_ROOT = "/content/drive/MyDrive/Enterprise_Knowledge_Assistant" |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| print(f"Using device: {device}") |
|
|
| |
| index = faiss.read_index(f"{PROJECT_ROOT}/embeddings/faiss_index.bin") |
| with open(f"{PROJECT_ROOT}/embeddings/all_chunks_metadata.pkl", 'rb') as f: |
| all_chunks = pickle.load(f) |
|
|
| |
| domain_indices = {} |
| domain_faiss_folder = f"{PROJECT_ROOT}/embeddings/domain_indices" |
| for domain in ['HR', 'Legal', 'Finance', 'IT']: |
| domain_indices[domain] = faiss.read_index(f"{domain_faiss_folder}/{domain}_index.bin") |
| with open(f"{domain_faiss_folder}/domain_chunks_map.pkl", 'rb') as f: |
| domain_chunks_map = pickle.load(f) |
|
|
| |
| embedding_model = SentenceTransformer(f"{PROJECT_ROOT}/models/embedding_model", device=device) |
|
|
| |
| groq_client = Groq(api_key=userdata.get("GROQ_API_KEY")) |
|
|
| |
| router_path = f"{PROJECT_ROOT}/models/distilbert_router" |
| tokenizer = DistilBertTokenizer.from_pretrained(router_path) |
| router_model = DistilBertForSequenceClassification.from_pretrained(router_path) |
| router_model.eval() |
| with open(f"{router_path}/label_encoder.pkl", 'rb') as f: |
| label_encoder = pickle.load(f) |
|
|
| |
| qa_tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased-distilled-squad") |
| qa_model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-cased-distilled-squad") |
| qa_model.eval() |
|
|
| |
| reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') |
|
|
| |
| sys.path.append(f"{PROJECT_ROOT}/src") |
| from rag_pipeline import retrieve_relevant_chunks, generate_with_groq, ask_chatbot_v4 |
| from router_utils import classify_query |
| from hybrid_retrieval import retrieve_from_domain_index, retrieve_hybrid |
| from reranker_utils import retrieve_with_reranking |
| from extractive_qa import extract_exact_answer, ask_with_extraction |
| from eva_chatbot import ask_eva |
|
|
| conversation_history = [] |
|
|
| print("Everything reloaded successfully. Ready to chat.") |
| print(f"Total chunks in index: {index.ntotal}") |
| print(f"Domains: {list(domain_indices.keys())}") |
|
|
