RAG.py

import pandas as pd
import json
from langchain.docstore.document import Document
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA


# file_path = "thyroidDF.csv"
# df = pd.read_csv(file_path)

def create_doucment(df):
    documents = [
        Document(
            metadata={"id": str(i)},
            # Serialize the dictionary to a JSON string
            page_content=json.dumps(row.to_dict())
        )
        for i, row in df.iterrows()
    ]
    return documents


def load_models_embedding():
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2")
    return embeddings

api="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22"
def load_models_llm():
    llm = HuggingFaceHub(
        repo_id="Qwen/Qwen2.5-72B-Instruct",
        # Replace with your token
        huggingfacehub_api_token=api[:-2],
        model_kwargs={"temperature": 0.5,
                      "max_length": 100}  # Faster inference
    )
    return llm


def create_database(embedding, documents):
    vector_store = Chroma.from_documents(documents, embedding=embedding)
    return vector_store

# retriever = create_database().as_retriever()


def ask_me(question, retriever, llm):

    qa_chain = RetrievalQA.from_chain_type(
        retriever=retriever,
        chain_type="stuff",
        llm=load_models_llm(),
        return_source_documents=True)

    response = qa_chain.invoke({"query": question})
    print("Answer:", response["result"])


# qa_chain = RetrievalQA.from_chain_type(
#     retriever=retriever,
#     chain_type="stuff",
#     llm=llm,
#     return_source_documents=True
# )

# question = "Can you provide the TSH, T3, and FTI values for patients aged 55?"
# # question = "What columns are in the dataset?"
# response = qa_chain.invoke({"query": question})
# print("Answer:", response["result"])