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| from fastapi import FastAPI, HTTPException | |
| from pydantic import BaseModel | |
| from embeddings import Embeddings | |
| from chain import Chain | |
| from llm import LLM | |
| from retriever import Retriever | |
| from fastapi import FastAPI, HTTPException | |
| from fastapi.responses import HTMLResponse | |
| from functools import lru_cache | |
| from tools import * | |
| import re | |
| emb = Embeddings("hf", "all-MiniLM-L6-v2") | |
| llm = LLM('gemini').get_llm() | |
| ch = Chain(llm,None) | |
| ret = Retriever('pinecone', 'pinecone', emb.embedding, 'ensemble', 5) | |
| is_arabic = False | |
| def investment_banker(query): | |
| global is_arabic | |
| context, context_list = ret.get_context(query) | |
| if not is_arabic: | |
| prompt_template = f""" | |
| You are an investment banker and financial advisor. | |
| Answer the question as detailed as possible from the provided context and make sure to provide all the details. | |
| Answer only from the context. If the answer is not in provided context, say "Answer not in context".\n\n | |
| Context:\n {context}\n\n | |
| Question: \n{query}\n | |
| Answer: | |
| """ | |
| else: | |
| prompt_template = f""" | |
| You are an investment banker and financial advisor. | |
| Answer the question as detailed as possible from the provided context and make sure to provide all the details. | |
| Answer only from the context. If the answer is not in provided context, say "Answer not in context". | |
| Return the answer in Arabic only.\n\n | |
| Context:\n {context}\n\n | |
| Question: \n{query}\n | |
| Answer: | |
| """ | |
| response = ch.run_conversational_chain(prompt_template) | |
| is_arabic = False | |
| return response | |
| def check_arabic(s): | |
| arabic_pattern = re.compile(r'[\u0600-\u06FF]') | |
| if arabic_pattern.search(s): | |
| return True | |
| else: | |
| return False | |
| history = "" | |
| def refine_query(query, conversation): | |
| prompt=f"""Given the following user query and historical user conversation with banker. | |
| If the current user query is in arabic, convert it to english and then proceed. | |
| If conversation history is empty return the current query as it is. | |
| If the query is a continuation of previous conversation then only rephrase the users current query to form a meaningful and clear question. | |
| Otherwise return the user query as it is. | |
| Previously user and banker had the following conversation: \n{conversation}\n\n User's Current Query: {query}. | |
| What will be the refined query? Only provide the query without any extra details or explanations.""" | |
| ans = llm.invoke(prompt).content | |
| return ans | |
| def get_answer(query): | |
| global history | |
| global is_arabic | |
| is_arabic = check_arabic(query) | |
| ref_query = refine_query(query, history) | |
| ans = investment_banker(ref_query) | |
| history += "Human: "+ ref_query + "\n" | |
| history += "Banker: "+ ans + "\n" | |
| return ans | |
| if __name__ == "__main__": | |
| response = get_answer() | |
| print(response) | |
| # app = FastAPI() | |
| # class Query(BaseModel): | |
| # question: str | |
| # @app.post("/chat/") | |
| # async def chat(query: Query): | |
| # global history | |
| # global is_arabic | |
| # try: | |
| # is_arabic = check_arabic(query.question) | |
| # ref_query = refine_query(query.question, history) | |
| # print(query.question, ref_query) | |
| # print(is_arabic) | |
| # ans = investment_banker(ref_query) | |
| # history += "Human: "+ ref_query + "\n" | |
| # history += "Banker: "+ ans + "\n" | |
| # return {"question": query.question, "answer": ans} | |
| # except Exception as e: | |
| # raise HTTPException(status_code=500, detail=str(e)) | |
| # @app.get("/", response_class=HTMLResponse) | |
| # async def read_index(): | |
| # with open('index.html', 'r') as f: | |
| # return HTMLResponse(content=f.read()) |