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1ed187b0dd
| Author | SHA1 | Date | |
|---|---|---|---|
| 1ed187b0dd | |||
| 2feaf0cdc0 | |||
| 09d453017c |
646
bot/bot.py
646
bot/bot.py
@@ -1,14 +1,53 @@
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"""
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bot.py - Discord bot client with session management and command routing
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Features:
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- Login flow with username/password
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- Session management with JWT tokens
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- AI-powered command parsing via registry
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- Background task loop for polling
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- JurySystem DBT integration for mental health support
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"""
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import discord
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from discord.ext import tasks
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import os
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import sys
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import json
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import time
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import base64
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import requests
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import bcrypt
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import pickle
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import numpy as np
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from openai import OpenAI
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# --- Configuration ---
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CONFIG_PATH = 'config.json'
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KNOWLEDGE_BASE_PATH = 'dbt_knowledge.json'
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from bot.command_registry import get_handler, list_registered, register_module
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import ai.parser as ai_parser
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import bot.commands.routines # noqa: F401 - registers handler
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import bot.commands.medications # noqa: F401 - registers handler
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import bot.commands.knowledge # noqa: F401 - registers handler
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DISCORD_BOT_TOKEN = os.getenv("DISCORD_BOT_TOKEN")
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API_URL = os.getenv("API_URL", "http://app:5000")
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user_sessions = {}
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login_state = {}
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message_history = {}
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user_cache = {}
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CACHE_FILE = "/app/user_cache.pkl"
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intents = discord.Intents.default()
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intents.message_content = True
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client = discord.Client(intents=intents)
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# ==================== JurySystem Integration ====================
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class SimpleVectorStore:
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"""A simple in-memory vector store using NumPy."""
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def __init__(self):
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self.vectors = []
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self.metadata = []
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@@ -21,175 +60,522 @@ class SimpleVectorStore:
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if not self.vectors:
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return []
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# Convert to numpy arrays for efficient math
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query_vec = np.array(query_vector)
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doc_vecs = np.array(self.vectors)
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# Cosine Similarity: (A . B) / (||A|| * ||B||)
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# Note: Both vectors must have the same dimension (e.g., 4096)
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norms = np.linalg.norm(doc_vecs, axis=1)
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# Avoid division by zero
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valid_indices = norms > 0
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scores = np.zeros(len(doc_vecs))
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# Calculate dot product
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dot_products = np.dot(doc_vecs, query_vec)
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# Calculate cosine similarity only for valid norms
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scores[valid_indices] = dot_products[valid_indices] / (norms[valid_indices] * np.linalg.norm(query_vec))
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# Get top_k indices
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scores[valid_indices] = dot_products[valid_indices] / (
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norms[valid_indices] * np.linalg.norm(query_vec)
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)
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top_indices = np.argsort(scores)[-top_k:][::-1]
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results = []
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for idx in top_indices:
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results.append({
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"metadata": self.metadata[idx],
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"score": scores[idx]
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})
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results.append({"metadata": self.metadata[idx], "score": scores[idx]})
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return results
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class JurySystem:
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"""DBT Knowledge Base Query System"""
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def __init__(self):
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self.config = self.load_config()
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# Initialize OpenRouter Client
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config_path = os.getenv("CONFIG_PATH", "config.json")
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kb_path = os.getenv(
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"KNOWLEDGE_BASE_PATH", "bot/data/dbt_knowledge.embeddings.json"
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)
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with open(config_path, "r") as f:
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self.config = json.load(f)
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self.client = OpenAI(
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base_url="https://openrouter.ai/api/v1",
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api_key=self.config['openrouter_api_key']
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api_key=self.config["openrouter_api_key"],
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)
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self.vector_store = SimpleVectorStore()
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self.load_knowledge_base()
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self._load_knowledge_base(kb_path)
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def load_config(self):
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with open(CONFIG_PATH, 'r') as f:
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return json.load(f)
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def load_knowledge_base(self):
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"""Loads the pre-computed embeddings from the JSON file."""
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print(f"Loading knowledge base from {KNOWLEDGE_BASE_PATH}...")
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def _load_knowledge_base(self, kb_path):
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print(f"Loading DBT knowledge base from {kb_path}...")
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try:
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with open(KNOWLEDGE_BASE_PATH, 'r', encoding='utf-8') as f:
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with open(kb_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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vectors = []
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metadata = []
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for item in data:
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vectors.append(item['embedding'])
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metadata.append({
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"id": item['id'],
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"source": item['source'],
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"text": item['text']
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})
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vectors.append(item["embedding"])
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metadata.append(
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{"id": item["id"], "source": item["source"], "text": item["text"]}
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)
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self.vector_store.add(vectors, metadata)
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print(f"Loaded {len(vectors)} chunks into vector store.")
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except FileNotFoundError:
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print(f"Error: {KNOWLEDGE_BASE_PATH} not found. Did you run the embedder script?")
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exit(1)
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print(f"Loaded {len(vectors)} chunks into DBT vector store.")
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except Exception as e:
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print(f"Error loading knowledge base: {e}")
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exit(1)
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print(f"Error loading DBT knowledge base: {e}")
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raise
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def retrieve_context(self, query, top_k=5):
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print("[1. Retrieving Context...]")
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async def query(self, query_text):
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"""Query the DBT knowledge base"""
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try:
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# --- CRITICAL FIX: Use the EXACT same model as the embedder ---
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# Embedder used: "qwen/qwen3-embedding-8b" -> Dimension 4096
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# We must use the same here to avoid shape mismatch.
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# Get embedding
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response = self.client.embeddings.create(
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model="qwen/qwen3-embedding-8b",
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input=query
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model="qwen/qwen3-embedding-8b", input=query_text
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)
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query_emb = response.data[0].embedding
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# Search the vector store
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context_chunks = self.vector_store.search(query_emb, top_k=top_k)
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return context_chunks
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except Exception as e:
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print(f"Error retrieving context: {e}")
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return []
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def generate_answer(self, query, context_chunks):
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print("[2. Generating Answer...]")
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# Build the context string
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context_text = "\n\n---\n\n".join([chunk['metadata']['text'] for chunk in context_chunks])
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system_prompt = """You are a helpful AI assistant specializing in DBT (Dialectical Behavior Therapy).
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Use the provided context to answer the user's question.
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# Search
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context_chunks = self.vector_store.search(query_emb, top_k=5)
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if not context_chunks:
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return "I couldn't find relevant DBT information for that query."
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# Generate answer
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context_text = "\n\n---\n\n".join(
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[chunk["metadata"]["text"] for chunk in context_chunks]
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)
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system_prompt = """You are a helpful DBT (Dialectical Behavior Therapy) assistant.
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Use the provided context from the DBT Skills Training Handouts to answer the user's question.
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If the answer is not in the context, say you don't know based on the provided text.
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Be concise and compassionate."""
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Be concise, compassionate, and practical."""
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user_prompt = f"""Context:
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{context_text}
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user_prompt = f"Context:\n{context_text}\n\nQuestion: {query_text}"
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Question: {query}"""
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try:
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# Using a strong model for the final generation
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response = self.client.chat.completions.create(
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model="openai/gpt-4o-mini", # You can change this to "qwen/qwen-3-8b" or similar if desired
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model=self.config.get("models", {}).get(
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"generator", "openai/gpt-4o-mini"
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),
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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{"role": "user", "content": user_prompt},
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],
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temperature=0.7
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temperature=0.7,
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"Error generating answer: {e}"
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return f"Error querying DBT knowledge base: {e}"
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def process_query(self, query):
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# 1. Retrieve
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context = self.retrieve_context(query)
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if not context:
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return "I couldn't find any relevant information in the knowledge base."
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# Optional: Print sources for debugging
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print(f" Found {len(context)} relevant chunks (Top score: {context[0]['score']:.4f})")
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# 2. Generate
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answer = self.generate_answer(query, context)
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return answer
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def main():
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print("Initializing AI Jury System...")
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system = JurySystem()
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print("\nSystem Ready. Ask a question (or type 'exit').")
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while True:
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# Initialize JurySystem
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jury_system = None
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try:
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jury_system = JurySystem()
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print("✓ JurySystem (DBT) initialized successfully")
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except Exception as e:
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print(f"⚠ JurySystem initialization failed: {e}")
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# ==================== Original Bot Functions ====================
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def decodeJwtPayload(token):
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payload = token.split(".")[1]
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payload += "=" * (4 - len(payload) % 4)
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return json.loads(base64.urlsafe_b64decode(payload))
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def apiRequest(method, endpoint, token=None, data=None):
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url = f"{API_URL}{endpoint}"
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headers = {"Content-Type": "application/json"}
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if token:
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headers["Authorization"] = f"Bearer {token}"
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try:
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resp = getattr(requests, method)(url, headers=headers, json=data, timeout=10)
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try:
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user_query = input("\nYou: ").strip()
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if user_query.lower() in ['exit', 'quit']:
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print("Goodbye!")
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break
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if not user_query:
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continue
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response = system.process_query(user_query)
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print(f"\nAI: {response}")
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except KeyboardInterrupt:
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print("\nGoodbye!")
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break
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except Exception as e:
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print(f"\nAn error occurred: {e}")
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return resp.json(), resp.status_code
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except ValueError:
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return {}, resp.status_code
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except requests.RequestException:
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return {"error": "API unavailable"}, 503
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def loadCache():
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try:
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if os.path.exists(CACHE_FILE):
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with open(CACHE_FILE, "rb") as f:
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global user_cache
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user_cache = pickle.load(f)
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print(f"Loaded cache for {len(user_cache)} users")
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except Exception as e:
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print(f"Error loading cache: {e}")
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def saveCache():
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try:
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with open(CACHE_FILE, "wb") as f:
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pickle.dump(user_cache, f)
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except Exception as e:
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print(f"Error saving cache: {e}")
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def hashPassword(password):
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return bcrypt.hashpw(password.encode("utf-8"), bcrypt.gensalt()).decode("utf-8")
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def verifyPassword(password, hashed):
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return bcrypt.checkpw(password.encode("utf-8"), hashed.encode("utf-8"))
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def getCachedUser(discord_id):
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return user_cache.get(discord_id)
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def setCachedUser(discord_id, user_data):
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user_cache[discord_id] = user_data
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saveCache()
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def negotiateToken(discord_id, username, password):
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cached = getCachedUser(discord_id)
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if (
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cached
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and cached.get("username") == username
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and verifyPassword(password, cached.get("hashed_password"))
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):
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result, status = apiRequest(
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"post", "/api/login", data={"username": username, "password": password}
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)
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if status == 200 and "token" in result:
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token = result["token"]
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payload = decodeJwtPayload(token)
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user_uuid = payload["sub"]
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setCachedUser(
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discord_id,
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{
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"hashed_password": cached["hashed_password"],
|
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"user_uuid": user_uuid,
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"username": username,
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},
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)
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return token, user_uuid
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return None, None
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result, status = apiRequest(
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"post", "/api/login", data={"username": username, "password": password}
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)
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if status == 200 and "token" in result:
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token = result["token"]
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payload = decodeJwtPayload(token)
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user_uuid = payload["sub"]
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setCachedUser(
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discord_id,
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{
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"hashed_password": hashPassword(password),
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"user_uuid": user_uuid,
|
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"username": username,
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},
|
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)
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return token, user_uuid
|
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return None, None
|
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|
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|
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async def handleAuthFailure(message):
|
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discord_id = message.author.id
|
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user_sessions.pop(discord_id, None)
|
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await message.channel.send(
|
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"Your session has expired. Send any message to log in again."
|
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)
|
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|
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|
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async def handleLoginStep(message):
|
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discord_id = message.author.id
|
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state = login_state[discord_id]
|
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|
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if state["step"] == "username":
|
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state["username"] = message.content.strip()
|
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state["step"] = "password"
|
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await message.channel.send("Password?")
|
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|
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elif state["step"] == "password":
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username = state["username"]
|
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password = message.content.strip()
|
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del login_state[discord_id]
|
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|
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token, user_uuid = negotiateToken(discord_id, username, password)
|
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|
||||
if token and user_uuid:
|
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user_sessions[discord_id] = {
|
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"token": token,
|
||||
"user_uuid": user_uuid,
|
||||
"username": username,
|
||||
}
|
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registered = ", ".join(list_registered()) or "none"
|
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await message.channel.send(
|
||||
f"Welcome back **{username}**!\n\n"
|
||||
f"Registered modules: {registered}\n\n"
|
||||
f"Send 'help' for available commands."
|
||||
)
|
||||
else:
|
||||
await message.channel.send(
|
||||
"Invalid credentials. Send any message to try again."
|
||||
)
|
||||
|
||||
|
||||
async def sendHelpMessage(message):
|
||||
help_msg = """**🤖 Synculous Bot - Natural Language Commands**
|
||||
|
||||
Just talk to me naturally! Here are some examples:
|
||||
|
||||
**💊 Medications:**
|
||||
• "add lsd 50 mcg every tuesday at 4:20pm"
|
||||
• "take my wellbutrin"
|
||||
• "what meds do i have today?"
|
||||
• "show my refills"
|
||||
• "snooze my reminder for 30 minutes"
|
||||
• "check adherence"
|
||||
|
||||
**📋 Routines:**
|
||||
• "create morning routine with brush teeth, shower, eat"
|
||||
• "start my morning routine"
|
||||
• "done" (complete current step)
|
||||
• "skip" (skip current step)
|
||||
• "pause/resume" (pause or continue)
|
||||
• "what steps are in my routine?"
|
||||
• "schedule workout for monday wednesday friday at 7am"
|
||||
• "show my stats"
|
||||
|
||||
**🧠 DBT Support:**
|
||||
• "how do I use distress tolerance?"
|
||||
• "explain radical acceptance"
|
||||
• "give me a DBT skill for anger"
|
||||
• "what are the TIPP skills?"
|
||||
|
||||
**💡 Tips:**
|
||||
• I understand natural language, typos, and slang
|
||||
• If I'm unsure, I'll ask for clarification
|
||||
• For important actions, I'll ask you to confirm with "yes" or "no"
|
||||
• When you're in a routine, shortcuts like "done", "skip", "pause" work automatically"""
|
||||
await message.channel.send(help_msg)
|
||||
|
||||
|
||||
async def checkActiveSession(session):
|
||||
"""Check if user has an active routine session and return details."""
|
||||
token = session.get("token")
|
||||
if not token:
|
||||
return None
|
||||
|
||||
resp, status = apiRequest("get", "/api/sessions/active", token)
|
||||
if status == 200 and "session" in resp:
|
||||
return resp
|
||||
return None
|
||||
|
||||
|
||||
async def handleConfirmation(message, session):
|
||||
"""Handle yes/no confirmation responses. Returns True if handled."""
|
||||
discord_id = message.author.id
|
||||
user_input = message.content.lower().strip()
|
||||
|
||||
if "pending_confirmations" not in session:
|
||||
return False
|
||||
|
||||
pending = session["pending_confirmations"]
|
||||
if not pending:
|
||||
return False
|
||||
|
||||
confirmation_id = list(pending.keys())[-1]
|
||||
confirmation_data = pending[confirmation_id]
|
||||
|
||||
if user_input in ("yes", "y", "yeah", "sure", "ok", "confirm"):
|
||||
del pending[confirmation_id]
|
||||
interaction_type = confirmation_data.get("interaction_type")
|
||||
handler = get_handler(interaction_type)
|
||||
|
||||
if handler:
|
||||
fake_parsed = confirmation_data.copy()
|
||||
fake_parsed["needs_confirmation"] = False
|
||||
await handler(message, session, fake_parsed)
|
||||
return True
|
||||
|
||||
elif user_input in ("no", "n", "nah", "cancel", "abort"):
|
||||
del pending[confirmation_id]
|
||||
await message.channel.send("❌ Cancelled.")
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
async def handleActiveSessionShortcuts(message, session, active_session):
|
||||
"""Handle shortcuts like 'done', 'skip', 'next' when in active session."""
|
||||
user_input = message.content.lower().strip()
|
||||
|
||||
shortcuts = {
|
||||
"done": ("routine", "complete"),
|
||||
"finished": ("routine", "complete"),
|
||||
"complete": ("routine", "complete"),
|
||||
"next": ("routine", "complete"),
|
||||
"skip": ("routine", "skip"),
|
||||
"pass": ("routine", "skip"),
|
||||
"pause": ("routine", "pause"),
|
||||
"hold": ("routine", "pause"),
|
||||
"resume": ("routine", "resume"),
|
||||
"continue": ("routine", "resume"),
|
||||
"stop": ("routine", "cancel"),
|
||||
"quit": ("routine", "cancel"),
|
||||
"abort": ("routine", "abort"),
|
||||
}
|
||||
|
||||
if user_input in shortcuts:
|
||||
interaction_type, action = shortcuts[user_input]
|
||||
handler = get_handler(interaction_type)
|
||||
if handler:
|
||||
fake_parsed = {"action": action}
|
||||
await handler(message, session, fake_parsed)
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
async def handleDBTQuery(message):
|
||||
"""Handle DBT-related queries using JurySystem"""
|
||||
if not jury_system:
|
||||
return False
|
||||
|
||||
# Keywords that indicate a DBT query
|
||||
dbt_keywords = [
|
||||
"dbt",
|
||||
"distress tolerance",
|
||||
"emotion regulation",
|
||||
"interpersonal effectiveness",
|
||||
"mindfulness",
|
||||
"radical acceptance",
|
||||
"wise mind",
|
||||
"tipp",
|
||||
"dearman",
|
||||
"check the facts",
|
||||
"opposite action",
|
||||
"cope ahead",
|
||||
"abc please",
|
||||
"stop skill",
|
||||
"pros and cons",
|
||||
"half smile",
|
||||
"willing hands",
|
||||
]
|
||||
|
||||
user_input_lower = message.content.lower()
|
||||
is_dbt_query = any(keyword in user_input_lower for keyword in dbt_keywords)
|
||||
|
||||
if is_dbt_query:
|
||||
async with message.channel.typing():
|
||||
response = await jury_system.query(message.content)
|
||||
await message.channel.send(f"🧠 **DBT Support:**\n{response}")
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
async def routeCommand(message):
|
||||
discord_id = message.author.id
|
||||
session = user_sessions[discord_id]
|
||||
user_input = message.content.lower()
|
||||
|
||||
if "help" in user_input or "what can i say" in user_input:
|
||||
await sendHelpMessage(message)
|
||||
return
|
||||
|
||||
# Check for active session first
|
||||
active_session = await checkActiveSession(session)
|
||||
|
||||
# Handle confirmation responses
|
||||
confirmation_handled = await handleConfirmation(message, session)
|
||||
if confirmation_handled:
|
||||
return
|
||||
|
||||
# Handle shortcuts when in active session
|
||||
if active_session:
|
||||
shortcut_handled = await handleActiveSessionShortcuts(
|
||||
message, session, active_session
|
||||
)
|
||||
if shortcut_handled:
|
||||
return
|
||||
|
||||
# Check for DBT queries
|
||||
dbt_handled = await handleDBTQuery(message)
|
||||
if dbt_handled:
|
||||
return
|
||||
|
||||
async with message.channel.typing():
|
||||
history = message_history.get(discord_id, [])
|
||||
|
||||
# Add context about active session to help AI understand
|
||||
context = ""
|
||||
if active_session:
|
||||
session_data = active_session.get("session", {})
|
||||
routine_name = session_data.get("routine_name", "a routine")
|
||||
current_step = session_data.get("current_step_index", 0) + 1
|
||||
total_steps = active_session.get("total_steps", 0)
|
||||
context = f"\n[Context: User is currently in active session for '{routine_name}', on step {current_step} of {total_steps}. They can say 'done', 'skip', 'pause', 'resume', or 'stop'.]"
|
||||
|
||||
parsed = await ai_parser.parse(
|
||||
message.content + context, "command_parser", history=history
|
||||
)
|
||||
|
||||
if discord_id not in message_history:
|
||||
message_history[discord_id] = []
|
||||
message_history[discord_id].append((message.content, parsed))
|
||||
message_history[discord_id] = message_history[discord_id][-5:]
|
||||
|
||||
if "needs_clarification" in parsed:
|
||||
await message.channel.send(
|
||||
f"I'm not quite sure what you mean. {parsed['needs_clarification']}"
|
||||
)
|
||||
return
|
||||
|
||||
if "error" in parsed:
|
||||
await message.channel.send(
|
||||
f"I had trouble understanding that: {parsed['error']}"
|
||||
)
|
||||
return
|
||||
|
||||
interaction_type = parsed.get("interaction_type")
|
||||
handler = get_handler(interaction_type)
|
||||
|
||||
if handler:
|
||||
await handler(message, session, parsed)
|
||||
else:
|
||||
registered = ", ".join(list_registered()) or "none"
|
||||
await message.channel.send(
|
||||
f"Unknown command type '{interaction_type}'. Registered modules: {registered}"
|
||||
)
|
||||
|
||||
|
||||
@client.event
|
||||
async def on_ready():
|
||||
print(f"Bot logged in as {client.user}")
|
||||
loadCache()
|
||||
backgroundLoop.start()
|
||||
|
||||
|
||||
@client.event
|
||||
async def on_message(message):
|
||||
if message.author == client.user:
|
||||
return
|
||||
if not isinstance(message.channel, discord.DMChannel):
|
||||
return
|
||||
|
||||
discord_id = message.author.id
|
||||
|
||||
if discord_id in login_state:
|
||||
await handleLoginStep(message)
|
||||
return
|
||||
|
||||
if discord_id not in user_sessions:
|
||||
login_state[discord_id] = {"step": "username"}
|
||||
await message.channel.send("Welcome! Send your username to log in.")
|
||||
return
|
||||
|
||||
await routeCommand(message)
|
||||
|
||||
|
||||
@tasks.loop(seconds=60)
|
||||
async def backgroundLoop():
|
||||
"""Override this in your domain module or extend as needed."""
|
||||
pass
|
||||
|
||||
|
||||
@backgroundLoop.before_loop
|
||||
async def beforeBackgroundLoop():
|
||||
await client.wait_until_ready()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
client.run(DISCORD_BOT_TOKEN)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"openrouter_api_key": "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",
|
||||
"embedding_file": "dbt_knowledge.json",
|
||||
"embedding_file": "bot/data/dbt_knowledge.embeddings.json",
|
||||
"models": {
|
||||
"generator": "moonshotai/kimi-k2.5",
|
||||
"jury_clinical": "z-ai/glm-5",
|
||||
|
||||
Reference in New Issue
Block a user