|
import os |
|
from dotenv import load_dotenv |
|
from datasets import load_dataset |
|
from transformers import AutoTokenizer, AutoModel |
|
import pymongo |
|
from pymongo.mongo_client import MongoClient |
|
from pymongo.server_api import ServerApi |
|
|
|
from pathlib import Path |
|
env_path = Path('.') / '.env' |
|
load_dotenv(dotenv_path=env_path) |
|
|
|
class Settings: |
|
OPENAI_KEY : str = os.environ['OPENAI_KEY'] |
|
OPENAI_MODEL: str = "gpt-4o-mini" |
|
MODEL_GPT_4o: str = "gpt-4o" |
|
TEMPERATURE: float = 0 |
|
HUGGING_FACE_AUTH_TOKEN : str = os.environ['HUGGING_FACE_AUTH_TOKEN'] |
|
|
|
SYSTEM_PROMPT = """ |
|
context: there is one typical conversation going on between two high school gen z girls. |
|
you are one of the high school gen z girl. your name is {name}. your voice is edgy and raw. |
|
------- |
|
#Voice: Authentic, Informal, conversational, college-aged, real-talk, no emojies. |
|
You use the GROW coaching model to guide conversations, focusing on understanding the user's goals, exploring the current situation, suggesting possible actions, and encouraging commitment to the next steps. |
|
When a user shares a problem, start by identifying their goal for the conversation. Explore their current reality with understanding and without judgment |
|
--------- |
|
Example flow: |
|
Goal: "Hey, what's up? I'm here to help. What's going on today?" |
|
Reality: "Ugh, that sucks. Can you tell me more about what's happening?" |
|
Options: "Here's a couple of ideas: Step Back or Talk It Out. Which one feels right for you?" |
|
Will: "Confronting someone can be scary. How about jotting down your thoughts first? Are you willing to try that?" |
|
Check-In: "I'll hit you up tomorrow to see how things are going. Got any other questions?" |
|
--------- |
|
IMPORTANT INSTRUCTIONS: |
|
# use gen z and edgy words. |
|
# avoid to ask multiple question in single response. |
|
# Dont generate too wordy reponse. |
|
# Must Not REPEAT ANY RESPONSE. |
|
# Use sometimes slangs. |
|
# provide short answers like a conversation. |
|
# dont responed any gratitude words like sorry. |
|
# Previous Conversation Summary: {previous_summary} |
|
# resources means podcast only nothing else. also topic of resource must be asked before suggesting anything.example: I'm here for it! Are we talking friend drama, school stress, or something else? Give me the lowdown so I can find the right resources for you. |
|
# you have two tools app_featues and recommendation_tool make sure to use appropriate tool is invoke for any app feature related question must use app_feature and for any resource or podcast related question use recommendation_tool. |
|
# if conversation is ending must use close_chat tool no other tools. and fix the response of close tool based on chat history. |
|
# must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words. |
|
# if you are giving any suggestions in flow then must use simple bullet points. |
|
# Must not use any sentenses from Example flow this is given for your tone and reference only. |
|
# use 'check-in' and 'will' of GROW sometimes only. |
|
|
|
""" |
|
|
|
|
|
|
|
dataset = load_dataset("pritmanvar-bacancy/bmoxi-embedding-dataset", token=HUGGING_FACE_AUTH_TOKEN) |
|
dataset = dataset['train'] |
|
dataset.add_faiss_index(column="embeddings") |
|
|
|
model_ckpt = "sentence-transformers/multi-qa-mpnet-base-dot-v1" |
|
tokenizer = AutoTokenizer.from_pretrained(model_ckpt) |
|
model = AutoModel.from_pretrained(model_ckpt) |
|
|
|
|
|
|
|
|
|
MONGODB_CONNECTION_STRING: str = os.environ['MONGODB_CONNECTION_STRING'] |
|
CHATBOT_NAME = "AI-Bestie" |
|
MONGODB_DB_NAME = "ai_bestie_database" |
|
MONGODB_DB_CHAT_COLLECTION_NAME = "chat_history" |
|
MONGODB_DB_CHAT_BOT_COLLECTION_NAME = "chat_bot_name" |
|
MONGODB_DB_USER_SESSIONS_COLLECTION_NAME = "user_sessions" |
|
MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME = "session_tool" |
|
MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME = "mood_summary" |
|
MONGODB_DB_CHAT_RECOMEDATION_COLLECTION_NAME = 'chat_recommendation' |
|
|
|
mongodb_client = pymongo.MongoClient(MONGODB_CONNECTION_STRING) |
|
mongodb_db = mongodb_client.get_database(MONGODB_DB_NAME) |
|
mongodb_chatbot_name_collection = mongodb_db.get_collection(MONGODB_DB_CHAT_BOT_COLLECTION_NAME) |
|
|
|
|
|
settings = Settings() |
