src/chains/rag.py

import os
from dotenv import load_dotenv
from langchain_groq import ChatGroq
from langchain_openai import ChatOpenAI
from langchain_chroma import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.retrievers import BM25Retriever
from langchain_classic.retrievers import EnsembleRetriever
from langchain_core.documents import Document
from src.chains.prompt import DISEASE_PROMPT_TEMPLATE

load_dotenv()

llm = ChatGroq(
        model="openai/gpt-oss-20b", 
        temperature=0.2,
        api_key=os.getenv("GROQ_API_KEY"),
    ) 

print("Memuat koneksi ke Database...")
embeddings = HuggingFaceEmbeddings(model_name="Qwen/Qwen3-Embedding-0.6B")
vectorstore = Chroma(
    persist_directory="chroma_data", 
    embedding_function=embeddings,
    collection_name="chilicare_kb"
)

chain = DISEASE_PROMPT_TEMPLATE | llm

def generate_narrative(disease_name):
    print(f"Mencari data untuk label: {disease_name}...")
    
    search_query = f"Penjelasan lengkap mengenai penyebab, ciri-ciri gejala, dan cara mengatasi penyakit {disease_name} pada tanaman cabai."
    
    results = vectorstore.similarity_search(
        query=search_query, 
        k=3,  # Mengambil 3 potongan (chunks) teratas
        filter={"label": disease_name}
    )

    if not results:
        return f"Data penyakit '{disease_name}' tidak ditemukan di database."

    retrieved_context = "\n\n".join([doc.page_content for doc in results])

    print("Data ditemukan. Menghasilkan narasi dengan LLM...")

    response = chain.invoke({
        "disease_name": disease_name,
        "context": retrieved_context
    })
    
    return response.content