src/get_medical_system.py

from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.schema import Document
from langchain.retrievers import EnsembleRetriever
from langchain_community.retrievers import BM25Retriever
from langchain_openai import ChatOpenAI
import numpy as np
from sentence_transformers import CrossEncoder
from dotenv import load_dotenv
import streamlit as st
from datasets import load_dataset
import os
import pickle
import faiss
from langchain_community.docstore.in_memory import InMemoryDocstore  # Add this import
import time

load_dotenv()

def get_vector_store():
    """Load vectorstore from pre-computed embeddings"""
    
    try:
        # Load pre-computed data
        print("📥 Loading pre-computed embeddings...")
        embeddings_array = np.load('medical_embeddings.npy')
        
        with open('medical_texts.pkl', 'rb') as f:
            texts = pickle.load(f)
        
        print(f"✅ Loaded {len(embeddings_array)} pre-computed embeddings")
        
        # Create FAISS index from pre-computed embeddings
        dimension = embeddings_array.shape[1]
        index = faiss.IndexFlatL2(dimension)
        index.add(embeddings_array.astype('float32')) # type: ignore
        
        # Create embedding function for new queries
        embeddings_function = HuggingFaceEmbeddings(
            model_name="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract"
        )
        
        # Create proper Document objects and InMemoryDocstore
        documents_dict = {}
        for i, text in enumerate(texts):
            # Create Document objects with proper metadata
            doc = Document(
                page_content=text,
                metadata={"doc_id": i, "type": "medical_qa"}
            )
            documents_dict[str(i)] = doc
        
        # Create proper docstore
        docstore = InMemoryDocstore(documents_dict)
        
        # Create index to docstore mapping
        index_to_docstore_id = {i: str(i) for i in range(len(texts))}
        
        # Create FAISS vectorstore with proper parameters
        vectorstore = FAISS(
            embedding_function=embeddings_function,
            index=index,
            docstore=docstore,
            index_to_docstore_id=index_to_docstore_id
        )
        
        return vectorstore
        
    except FileNotFoundError as e:
        print(f"❌ Pre-computed files not found: {e}")
        print("🔄 Falling back to creating embeddings...")
        return None
    
    except Exception as e:
        print(f"❌ Error loading pre-computed embeddings: {e}")
        print("🔄 Falling back to creating embeddings...")
        return None


@st.cache_resource
def load_medical_system():
    """Load the medical RAG system (cached for performance)"""
    
    with st.spinner("🔄 Loading medical knowledge base..."):
        # Load dataset
        ds = load_dataset("keivalya/MedQuad-MedicalQnADataset")
        
        # Create documents
        documents = []
        for i, item in enumerate(ds['train']): # type: ignore
            content = f"Question: {item['Question']}\nAnswer: {item['Answer']}" # type: ignore
            metadata = {
                "doc_id": i,
                "question": item['Question'], # type: ignore
                "answer": item['Answer'], # type: ignore
                "question_type": item['qtype'], # type: ignore
                "type": "qa_pair"
            }
            documents.append(Document(page_content=content, metadata=metadata))
        
        
        start = time.time()
        # Try to load existing vectorstore
        vectorstore = get_vector_store()
        end = time.time()

        if vectorstore is None:
            st.error("❌ Could not load the vectorstore. Please ensure the embeddings and text files exist.")
            st.stop()

        total_time = end - start
        st.success(f"✅ Loaded existing vectorstore in {total_time:.2f} seconds")
        
        # Create retrievers
        bm25_retriever = BM25Retriever.from_documents(documents)
        vector_retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
        
        ensemble_retriever = EnsembleRetriever(
            retrievers=[bm25_retriever, vector_retriever],
            weights=[0.3, 0.7]
        )
        
        # create LLM
        openai_key = os.getenv("OPENAI_API_KEY")
        if not openai_key:
            st.error("❌ OpenAI API key not found! Please set it in your environment variables or .streamlit/secrets.toml")
            st.stop()
        llm = ChatOpenAI(temperature=0, api_key=openai_key) # type: ignore
        
        # Create reranker
        reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
        
        return documents, ensemble_retriever, llm, reranker