import os
import json
from application.retriever.base import BaseRetriever
from application.core.settings import settings
from application.vectorstore.vector_creator import VectorCreator
from application.llm.llm_creator import LLMCreator

from application.utils import count_tokens



class ClassicRAG(BaseRetriever):

    def __init__(self, question, source, chat_history, prompt, chunks=2, gpt_model='docsgpt'):
        self.question = question
        self.vectorstore = self._get_vectorstore(source=source)
        self.chat_history = chat_history
        self.prompt = prompt
        self.chunks = chunks
        self.gpt_model = gpt_model

    def _get_vectorstore(self, source):
        if "active_docs" in source:
            if source["active_docs"].split("/")[0] == "default":
                    vectorstore = ""
            elif source["active_docs"].split("/")[0] == "local":
                vectorstore = "indexes/" + source["active_docs"]
            else:
                vectorstore = "vectors/" + source["active_docs"]
            if source["active_docs"] == "default":
                vectorstore = ""
        else:
            vectorstore = ""
        vectorstore = os.path.join("application", vectorstore)
        return vectorstore

    
    def _get_data(self):
        if self.chunks == 0:
            docs = []
        else:
            docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY)
            docs = docsearch.search(self.question, k=self.chunks)
        if settings.LLM_NAME == "llama.cpp":
            docs = [docs[0]]
        return docs
    
    def gen(self):
        docs = self._get_data()
        
        # join all page_content together with a newline
        docs_together = "\n".join([doc.page_content for doc in docs])
        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
        messages_combine = [{"role": "system", "content": p_chat_combine}]
        for doc in docs:
            if doc.metadata:
                yield {"source": {"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content}} 
            else:
                yield {"source": {"title": doc.page_content, "text": doc.page_content}}

        if len(self.chat_history) > 1:
            tokens_current_history = 0
            # count tokens in history
            self.chat_history.reverse()
            for i in self.chat_history:
                if "prompt" in i and "response" in i:
                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
                    if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
                        tokens_current_history += tokens_batch
                        messages_combine.append({"role": "user", "content": i["prompt"]})
                        messages_combine.append({"role": "system", "content": i["response"]})
        messages_combine.append({"role": "user", "content": self.question})

        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)

        completion = llm.gen_stream(model=self.gpt_model,
                                    messages=messages_combine)
        for line in completion:
            yield {"answer": str(line)}
    
    def search(self):
        return self._get_data()