rag.py

import logging
import os
import requests
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from openai import OpenAI


from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings


class RAG:
    NO_ANSWER_MESSAGE: str = "Ho sento, no he pogut respondre la teva pregunta."

    #vectorstore = "index-intfloat_multilingual-e5-small-500-100-CA-ES" # mixed
    #vectorstore = "vectorestore" # CA only
    vectorstore = "index-BAAI_bge-m3-1500-200-recursive_splitter-CA_ES_UE"

    def __init__(self, hf_token, embeddings_model, model_name, rerank_model, rerank_number_contexts):


        self.model_name = model_name
        self.hf_token = hf_token
        self.rerank_model = rerank_model
        self.rerank_number_contexts = rerank_number_contexts
        
        # load vectore store
        embeddings = HuggingFaceEmbeddings(model_name=embeddings_model, model_kwargs={'device': 'cpu'})
        self.vectore_store = FAISS.load_local(self.vectorstore, embeddings, allow_dangerous_deserialization=True)#, allow_dangerous_deserialization=True)

        logging.info("RAG loaded!")
    
    def rerank_contexts(self, instruction, contexts, number_of_contexts=1):
        """
        Rerank the contexts based on their relevance to the given instruction.
        """

        rerank_model = self.rerank_model
        

        tokenizer = AutoTokenizer.from_pretrained(rerank_model)
        model = AutoModelForSequenceClassification.from_pretrained(rerank_model)

        def get_score(query, passage):
            """Calculate the relevance score of a passage with respect to a query."""


            inputs = tokenizer(query, passage, return_tensors='pt', truncation=True, padding=True, max_length=512)
            

            with torch.no_grad():
                outputs = model(**inputs)
            

            logits = outputs.logits
            

            score = logits.view(-1, ).float()  
            

            return score

        scores = [get_score(instruction, c[0].page_content) for c in contexts]
        combined = list(zip(contexts, scores))
        sorted_combined = sorted(combined, key=lambda x: x[1], reverse=True)
        sorted_texts, _ = zip(*sorted_combined)

        return sorted_texts[:number_of_contexts]

    def get_context(self, instruction, number_of_contexts=2):
        """Retrieve the most relevant contexts for a given instruction."""
        documentos = self.vectore_store.similarity_search_with_score(instruction, k=self.rerank_number_contexts)

        documentos = self.rerank_contexts(instruction, documentos, number_of_contexts=number_of_contexts)

        print("Reranked documents")
        return documentos
        
    def predict_dolly(self, instruction, context, model_parameters):

        api_key = os.getenv("HF_TOKEN")


        headers = {
        "Accept" : "application/json",
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json" 
        }

        query = f"### Instruction\n{instruction}\n\n### Context\n{context}\n\n### Answer\n "
        #prompt = "You are a helpful assistant. Answer the question using only the context you are provided with. If it is not possible to do it with the context, just say 'I can't answer'. <|endoftext|>"


        payload = {
        "inputs": query,
        "parameters": model_parameters
        }
        
        response = requests.post(self.model_name, headers=headers, json=payload)

        return response.json()[0]["generated_text"].split("###")[-1][8:]

    def predict_completion(self, instruction, context, model_parameters):

        client = OpenAI(
                base_url=os.getenv("MODEL"),
                api_key=os.getenv("HF_TOKEN")
            )

        query = f"Context:\n{context}\n\nQuestion:\n{instruction}"

        chat_completion = client.chat.completions.create(
            model="tgi",
            messages=[
                {"role": "user", "content": instruction}
            ],
            temperature=model_parameters["temperature"],
            max_tokens=model_parameters["max_new_tokens"],
            stream=False,
            stop=["<|im_end|>"],
            extra_body = {
                "presence_penalty": model_parameters["repetition_penalty"] - 2,
                "do_sample": False
            }
        )

        response = chat_completion.choices[0].message.content

        return response
        
    
    def beautiful_context(self, docs):

        text_context = ""

        full_context = ""
        source_context = []
        for doc in docs:
            text_context += doc[0].page_content
            full_context += doc[0].page_content + "\n"
            full_context += doc[0].metadata["Títol de la norma"] + "\n\n"
            full_context += doc[0].metadata["url"] + "\n\n"
            source_context.append(doc[0].metadata["url"])

        return text_context, full_context, source_context

    def get_response(self, prompt: str, model_parameters: dict) -> str:
        try:
            docs = self.get_context(prompt, model_parameters["NUM_CHUNKS"])
            text_context, full_context, source = self.beautiful_context(docs)

            del model_parameters["NUM_CHUNKS"]

            response = self.predict_completion(prompt, text_context, model_parameters)

            if not response:
                return self.NO_ANSWER_MESSAGE

            return response, full_context, source
        except Exception as err:
            print(err)