custom_llm.py

from typing import Any, List, Mapping, Optional

from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from typing import Literal
import requests
from langchain.prompts import PromptTemplate, ChatPromptTemplate
from operator import itemgetter

from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_models import ChatOpenAI
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_core.messages import AIMessage, HumanMessage

from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyMuPDFLoader
import os
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from datasets import load_dataset


def create_vectorstore():
    data = load_dataset("ruslanmv/ai-medical-chatbot", split='train')
    emb_model = HuggingFaceEmbeddings(model_name='sentence-transformers/paraphrase-multilingual-mpnet-base-v2', encode_kwargs={'normalize_embeddings': True})
    faiss = FAISS.from_texts(data['Doctor'], emb_model)
    return faiss



def custom_chain_with_history(llm, memory):

    prompt = PromptTemplate.from_template("""You are a helpful, respectful, and honest assistant. Always answer as helpfully as possible while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

    You have the access to the following context information:

    {context}

    
    If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
    
    {chat_history}
    ### User: {question}
    ### Assistant: """)
    
    def prompt_memory(memory):
      t = ""
      for x in memory.chat_memory.messages:
      # for x in memory.messages:
        t += f"### Assistant: {x.content}\n" if type(x) is AIMessage else f"### User: {x.content}\n"
      return "" if len(t) == 0 else t
    
    def format_docs(docs):
      print(len(docs))
      return "\n".join([f"{i+1}. {d.page_content}" for i,d in enumerate(docs)])
    
    return {
        "chat_history":lambda x:prompt_memory(x['memory']), 
        "question":lambda x:x['question'], 
        "context": itemgetter("question") | create_vectorstore().as_retriever(search_type="similarity", search_kwargs={"k": 6}) | format_docs
    } | prompt | llm


class CustomLLM(LLM):
    repo_id : str
    api_token : str
    model_type: Literal["text2text-generation", "text-generation"]
    max_new_tokens: int = None
    temperature: float = 0.001
    timeout: float = None
    top_p: float = None
    top_k : int = None
    repetition_penalty : float = None
    stop : List[str] = []


    @property
    def _llm_type(self) -> str:
        return "custom"

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:

        headers = {"Authorization": f"Bearer {self.api_token}"}
        API_URL = f"https://api-inference.huggingface.co/models/{self.repo_id}"

        parameters_dict = {
          'max_new_tokens': self.max_new_tokens,
          'temperature': self.temperature,
          'timeout': self.timeout,
          'top_p': self.top_p,
          'top_k': self.top_k,
          'repetition_penalty': self.repetition_penalty,
          'stop':self.stop
        }

        if self.model_type == 'text-generation':
          parameters_dict["return_full_text"]=False

        data = {"inputs": prompt, "parameters":parameters_dict, "options":{"wait_for_model":True}}
        data = requests.post(API_URL, headers=headers, json=data).json()
        return data[0]['generated_text']

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return  {
          'repo_id': self.repo_id,
          'model_type':self.model_type,
          'stop_sequences':self.stop,
          'max_new_tokens': self.max_new_tokens,
          'temperature': self.temperature,
          'timeout': self.timeout,
          'top_p': self.top_p,
          'top_k': self.top_k,
          'repetition_penalty': self.repetition_penalty
      }