diff --git a/.claude/settings.local.json b/.claude/settings.local.json new file mode 100644 index 0000000000000000000000000000000000000000..3bd370106cc02a8669f999444504372b015de2ab --- /dev/null +++ b/.claude/settings.local.json @@ -0,0 +1,9 @@ +{ + "permissions": { + "allow": [ + "Bash(npm install:*)", + "Bash(npm run build:*)", + "Bash(npm run dev:*)" + ] + } +} diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..a3b580954de4894a1a1afdbbeb46c4b32d30bb66 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,35 +1,3 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text *.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text +important/finetuning/models/ora_adapter/tokenizer.json filter=lfs diff=lfs merge=lfs -text +data/**/*.lance filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..96d29f6eefcd193e21a1bb6214c147de1e06711e --- /dev/null +++ b/.gitignore @@ -0,0 +1,37 @@ +# Environment files +.env +*.env + +# Python +__pycache__/ +*.py[cod] +*$py.class +venv/ +.venv/ +.pytest_cache/ + +# Node/Frontend +node_modules/ +dist/ +.next/ +out/ +build/ + +# ORA specific - Large data & Databases +important/curated_data/ +important/finetuning/models/* +!important/finetuning/models/ora_adapter/ +important/finetuning/logs/ +*.jsonl +memory.db +*.zip +*.log + +# IDEs +.vscode/ +.idea/ +.DS_Store + +# Artifacts +C:\Users\max\.gemini\antigravity\brain\ +important/documentation/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0656c3440ecf5dbe0abccd18185b3f82f9c77ce5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,31 @@ +FROM python:3.10-slim + +WORKDIR /app + +# 1. Install Node.js +RUN apt-get update && apt-get install -y curl gnupg +RUN curl -fsSL https://deb.nodesource.com/setup_18.x | bash - +RUN apt-get install -y nodejs + +# 2. Copy Files +COPY . /app + +# 3. Build Frontend +WORKDIR /app/frontend +RUN npm install +RUN npm run build +WORKDIR /app + +# 4. Install Python Backend Dependencies +RUN pip install torch transformers peft fastapi uvicorn unsloth[colab-new] + +# HF Spaces User ID Setup (Optional but recommended) +RUN useradd -m -u 1000 user +USER user +ENV HOME=/home/user \ + PATH=/home/user/.local/bin:$PATH + +WORKDIR /app + +# 5. Start Backend (which serves Frontend) +CMD ["python", "app/ora_server.py"] diff --git a/ORA_Training_Colab.ipynb b/ORA_Training_Colab.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ad80fce76e3cbc52c8e06f8d7729879ab864e257 --- /dev/null +++ b/ORA_Training_Colab.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "introduction" + }, + "source": [ + "# ORA Fine-tuning on Google Colab\n", + "This notebook allows you to fine-tune the ORA model using Unsloth and QLoRA for high-performance training on NVIDIA GPUs.\n", + "\n", + "## Setup\n", + "1. Go to **Runtime** > **Change runtime type** and ensure **GPU** (T4 or A100) is selected.\n", + "2. Run the following cells to install dependencies and load your curated data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "install-deps" + }, + "outputs": [], + "source": [ + "!pip install unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\n", + "!pip install --no-deps \"xformers<0.0.29\" \"trl<0.9.0\" peft accelerate bitsandbytes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "upload-data" + }, + "source": [ + "## Upload Data\n", + "Upload the `ora_colab_training.zip` file generated by `scripts/prepare_colab.py`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "unzip-data" + }, + "outputs": [], + "source": [ + "!unzip ora_colab_training.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "training" + }, + "source": [ + "## Start Training\n", + "Run the training script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "run-training" + }, + "outputs": [], + "source": [ + "!python scripts/train_ora.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/README.md b/README.md index 89cec4f419d89b05f69e19f47bc8e6a8545fb6d4..40a5c2e66fbc210d2a0fa6459d617abc52835982 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,28 @@ ---- -title: ORA -emoji: 📊 -colorFrom: indigo -colorTo: yellow -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# ORA: AI Spiritual Assistant (Python Edition) + +ORA is a high-performance AI microservice built with **FastAPI** to serve as the spiritual intelligence layer for the SoapBox ecosystem. + +## Architecture +- **Framework**: FastAPI (Python 3.10+) +- **Inference**: Asynchronous OpenAI-compatible client (works with Ollama, vLLM, DeepSeek). +- **Documentation**: Automatic Swagger UI at `/docs`. + +## Setup +1. **Prerequisites**: Python 3.10+, [Ollama](https://ollama.com) (recommended). +2. **Install**: + ```bash + python -m venv venv + source venv/bin/activate # Windows: venv\Scripts\activate + pip install -r requirements.txt + ``` +3. **Run**: + ```bash + python main.py + ``` + The server starts at `http://localhost:6000`. + +## API Endpoints +- `POST /api/chat`: Send a message. + - Body: `{"message": "Hello ORA"}` +- `GET /health`: Health check. +- `GET /docs`: Interactive API playground. diff --git a/app/agents/base.py b/app/agents/base.py new file mode 100644 index 0000000000000000000000000000000000000000..3e617629ce4a4e51f034a3c8f795f7cb9e0f3196 --- /dev/null +++ b/app/agents/base.py @@ -0,0 +1,18 @@ +from typing import List, Callable, Optional, Union, Dict +from pydantic import BaseModel + +class Agent(BaseModel): + name: str = "Agent" + model: str = "llama3" + instructions: Union[str, Callable] = "You are a helpful assistant." + functions: List[Callable] = [] + context_variables: Dict = {} + + class Config: + arbitrary_types_allowed = True + +class Response(BaseModel): + agent: Optional[Agent] + messages: List[Dict] + context_variables: Dict = {} + trace: List[Dict] = [] diff --git a/app/agents/gatekeeper.py b/app/agents/gatekeeper.py new file mode 100644 index 0000000000000000000000000000000000000000..7068be9d2f85c5d781973fe16da4ab6140f08970 --- /dev/null +++ b/app/agents/gatekeeper.py @@ -0,0 +1,26 @@ +from app.agents.base import Agent + +def transfer_to_theologian(): + """Call this when the user wants to study the Bible, explain doctrine, or analyze scripture.""" + from app.agents.theologian import theologian_agent + return theologian_agent + +def transfer_to_healer(): + """Call this when the user is emotional, needs prayer, or is seeking personal comfort.""" + from app.agents.healer import healer_agent + return healer_agent + +gatekeeper_agent = Agent( + name="Gatekeeper", + instructions=lambda context: f""" + You are the ORA Gatekeeper. Your job is to listen to the user and hand them off to the right specialist. + + IMPORTANT CONTEXT (Episodic Memory): + {context.get('episodic_memory', 'No relevant past insights found.')} + + - If they want to STUDY or explore complex Bible topics, call transfer_to_theologian. + - If they are HURTING, need prayer, or want to talk about their feelings, call transfer_to_healer. + - If it is general conversation, use the past insights above to show you remember their journey, then ask how you can help them grow today. + """, + functions=[transfer_to_theologian, transfer_to_healer] +) diff --git a/app/agents/healer.py b/app/agents/healer.py new file mode 100644 index 0000000000000000000000000000000000000000..702edf4e6f9ba699f147c54cb23f92b0e0774bfe --- /dev/null +++ b/app/agents/healer.py @@ -0,0 +1,14 @@ +from app.agents.base import Agent +# In the future, we can import prayer_service or similar + +healer_agent = Agent( + name="Healer", + model="christian-bible-expert", # Target the empathetic expert model + instructions=""" + You are the ORA Healer. You focus on emotional support, prayer, and spiritual comfort. + - Listen deeply to the user's pain or joy. + - Offer heartfelt prayers based on their situation. + - Use scripture to provide comfort, but focus on the relationship and the heart. + - If the user wants a deep theological debate or technical study, hand back to the Gatekeeper. + """ +) diff --git a/app/agents/theologian.py b/app/agents/theologian.py new file mode 100644 index 0000000000000000000000000000000000000000..f562f990f9767b765d8f0d3931ad2604b0fe0ad1 --- /dev/null +++ b/app/agents/theologian.py @@ -0,0 +1,46 @@ +from app.agents.base import Agent +from app.services.bible import bible_service +from app.services.journal import journal_service +from app.services.rlm import rlm_service + +async def search_bible(query: str): + """Search the Bible for relevant verses and context.""" + results = await bible_service.search_bible(query) + if not results: + return "No relevant verses found." + formatted = "\n".join([f"- {r['reference']}: {r['text']}" for r in results]) + return f"I found these relevant passages:\n{formatted}" + +async def search_journal(context_variables: dict, query: str): + """Search the user's past journal entries and reflections for related insights.""" + user_id = context_variables.get("user_id", "unknown") + related = await journal_service.get_related_entries(user_id, query, limit=2) + if not related: + return "No related journal entries found." + + res = "Found related past reflections:\n" + for r in related: + res += f"- {r['timestamp']}: {r['text']}\n" + return res + +async def execute_logic(problem: str): + """ + Execute a logical reasoning loop to solve complex calculations, timelines, or consistency checks. + Use this when you need an objective 'workspace' to verify facts or dates. + """ + return await rlm_service.reason(problem) + +theologian_agent = Agent( + name="Theologian", + model="gabriel-8x7b", + instructions=""" + You are the ORA Theologian. You specialize in Bible study, doctrine, and scripture analysis. + Your goal is to provide deep, accurate, and faithful explanations of the Bible. + - Use search_bible to find matching verses. + - Use search_journal to see if the user has reflected on this topic before. + - Use execute_logic if you need to verify timelines, dates, or complex logical steps. + - Always provide chapter and verse citations. + - Avoid making authoritative claims; instead, say 'The scripture suggests...' or 'Tradition teaches...' + """, + functions=[search_bible, search_journal, execute_logic] +) diff --git a/app/api/routes.py b/app/api/routes.py new file mode 100644 index 0000000000000000000000000000000000000000..3f23b746f7b30525c33e782b8f386caeabd56f51 --- /dev/null +++ b/app/api/routes.py @@ -0,0 +1,62 @@ +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel +from app.services.llm import llm_service + +router = APIRouter() + +class ChatRequest(BaseModel): + message: str + context: dict | None = None + +class ChatResponse(BaseModel): + success: bool + response: dict + +@router.post("/chat", response_model=ChatResponse) +async def chat_endpoint(request: ChatRequest): + if not request.message: + raise HTTPException(status_code=400, detail="Message is required") + + # Use Orchestrator to handle routing and intelligence + from app.services.orchestrator import orchestrator + result = await orchestrator.process_message("user_123", request.message) + + # Map backend trace to frontend TraceStep format + formatted_trace = [] + for step in result.get("trace", []): + action = step.get("thought", "") + if step.get("tool_calls"): + tools = ", ".join([tc["name"] for tc in step["tool_calls"]]) + action = f"Thinking about using tools: {tools}. {action}" + + formatted_trace.append({ + "agent": step["agent"], + "action": action + }) + + return { + "success": True, + "response": { + "role": result["role"], + "content": result["content"], + "agent": result.get("agent", "ORA"), + "trace": formatted_trace + } + } + +@router.get("/memory/episodes") +async def get_episodes(): + from app.services.memory import memory_service + episodes = await memory_service.retrieve_episodes("user_123", "", limit=10) + return {"success": True, "episodes": episodes} + +@router.get("/user/profile") +async def get_profile(): + return { + "success": True, + "profile": { + "user_id": "user_123", + "name": "Spiritual Seeker", + "preferences": ["prayer", "scripture_study"] + } + } diff --git a/app/core/config.py b/app/core/config.py new file mode 100644 index 0000000000000000000000000000000000000000..05027a5598f3b230a62accc9966c5c4c3ed5fd2c --- /dev/null +++ b/app/core/config.py @@ -0,0 +1,37 @@ +from pydantic_settings import BaseSettings + +class Settings(BaseSettings): + PROJECT_NAME: str = "ORA AI Service" + PROJECT_VERSION: str = "1.0.0" + + # LLM Settings + LLM_BASE_URL: str = "http://localhost:11434/v1" + LLM_API_KEY: str = "ollama" + MODEL_NAME: str = "llama3" + + # Server Settings + PORT: int = 6000 + HOST: str = "0.0.0.0" + CORS_ORIGINS: list[str] = ["http://localhost:5173", "http://localhost:5000", "http://localhost:3000", "http://localhost:5174"] + + SYSTEM_PROMPT: str = """ +You are ORA, a spiritual guide and companion for the SoapBox Super App. +YOUR IDENTITY: +- You are kind, empathetic, and wise. +- You are NOT a replacement for a pastor or bible, but a helper. +- You ground your answers in biblical principles (using NIV or ESV translations). + +YOUR MISSION: +- Help users reflect on scripture (Observe, Reflect, Act). +- Offer prayer when appropriate. +- Encourage users to connect with their local church community. + +CONSTRAINTS: +- Keep responses concise (under 200 words) unless asked for a deep dive. +- If a user expresses intent to harm themselves, encourage seeking professional help. +""" + + class Config: + env_file = ".env" + +settings = Settings() diff --git a/app/core/swarm_client.py b/app/core/swarm_client.py new file mode 100644 index 0000000000000000000000000000000000000000..519b3f2f1b79665099f032017240215897fd9317 --- /dev/null +++ b/app/core/swarm_client.py @@ -0,0 +1,149 @@ +import inspect +import json +from typing import List, Dict, Any +from app.agents.base import Agent, Response +from app.services.llm import llm_service + +from app.services.trace import trace_service + +class SwarmClient: + def _function_to_schema(self, func) -> Dict: + """Converts Python function to OpenAI-style schema.""" + sig = inspect.signature(func) + return { + "type": "function", + "function": { + "name": func.__name__, + "description": func.__doc__ or "No description provided.", + "parameters": { + "type": "object", + "properties": { + name: {"type": "string"} # Simplified: assume string for ORA + for name in sig.parameters + }, + "required": [name for name, p in sig.parameters.items() if p.default == inspect.Parameter.empty] + }, + }, + } + + async def run( + self, + agent: Agent, + messages: List[Dict], + context_variables: Dict = {}, + max_turns: int = 5 + ) -> Response: + active_agent = agent + history = list(messages) + user_query = messages[-1]["content"] if messages else "" + reasoning_steps = [] + + for _ in range(max_turns): + # 1. Update active agent's context + active_agent.context_variables.update(context_variables) + + # 2. Prepare Tools + tools = [self._function_to_schema(f) for f in active_agent.functions] if active_agent.functions else None + + # 3. Get LLM Choice + instructions = active_agent.instructions + if callable(instructions): + sig = inspect.signature(instructions) + if len(sig.parameters) > 0: + instructions = instructions(active_agent.context_variables) + else: + instructions = instructions() + + print(f"Swarm [{active_agent.name}]: Processing...") + llm_res = await llm_service.generate_response( + message=history[-1]["content"], + system_prompt=instructions, + tools=tools + ) + + content = llm_res.get("content", "") + tool_calls = llm_res.get("tool_calls") + + # Record thought step + reasoning_steps.append({ + "agent": active_agent.name, + "thought": content, + "tool_calls": [ + {"name": tc.function.name, "args": json.loads(tc.function.arguments)} + for tc in tool_calls + ] if tool_calls else [] + }) + + # 3. Add Assistant Message to History + history.append({"role": "assistant", "content": content}) + + if not tool_calls: + # Capture and Save Trace + trace_service.save_trace({ + "user_query": user_query, + "steps": reasoning_steps, + "final_response": content, + "success": True + }) + return Response( + agent=active_agent, + messages=history, + context_variables=context_variables, + trace=reasoning_steps + ) + + # 4. Handle Tool Calls + for tool_call in tool_calls: + func_name = tool_call.function.name + func_args = json.loads(tool_call.function.arguments) + + func = next((f for f in active_agent.functions if f.__name__ == func_name), None) + if not func: + history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Error: Function {func_name} not found."}) + continue + + print(f"Swarm: Executing {func_name}...") + + if inspect.iscoroutinefunction(func): + result = await func(**func_args) + else: + result = func(**func_args) + + # Record tool result + reasoning_steps[-1]["tool_results"] = reasoning_steps[-1].get("tool_results", []) + reasoning_steps[-1]["tool_results"].append({ + "name": func_name, + "result": str(result) + }) + + if isinstance(result, Agent): + active_agent = result + history.append({ + "role": "tool", + "tool_call_id": tool_call.id, + "content": f"Transferring to {active_agent.name}." + }) + else: + history.append({ + "role": "tool", + "tool_call_id": tool_call.id, + "content": str(result) + }) + + # Save Trace even if max turns hit + trace_service.save_trace({ + "user_query": user_query, + "steps": reasoning_steps, + "final_response": history[-1]["content"], + "success": False, + "error": "Max turns reached" + }) + return Response( + agent=active_agent, + messages=history, + context_variables=context_variables, + trace=reasoning_steps + ) + +swarm_client = SwarmClient() + diff --git a/app/ora_server.py b/app/ora_server.py new file mode 100644 index 0000000000000000000000000000000000000000..8d6d1370c6a3ff2bbaaceb6e6b1ec305991b1cfb --- /dev/null +++ b/app/ora_server.py @@ -0,0 +1,107 @@ +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from peft import PeftModel +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from fastapi.staticfiles import StaticFiles +from fastapi.responses import FileResponse +from pydantic import BaseModel +import uvicorn +import os + +# Settings +BASE_MODEL = "unsloth/Llama-3.2-1B-Instruct" +ADAPTER_PATH = "important/finetuning/models/ora_adapter" + +app = FastAPI() + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# API Routes +class ChatRequest(BaseModel): +model = None +tokenizer = None +device = "cuda" if torch.cuda.is_available() else "cpu" + +class ChatRequest(BaseModel): + message: str + history: list = [] + +@app.on_event("startup") +async def load_model(): + global model, tokenizer + print(f"Loading ORA Model on {device}...") + + tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) + base_model = AutoModelForCausalLM.from_pretrained( + BASE_MODEL, + torch_dtype=torch.float16 if device == "cuda" else torch.float32, + device_map=device, + low_cpu_mem_usage=True + ) + + if os.path.exists(ADAPTER_PATH): + print(f"Loading adapter from {ADAPTER_PATH}...") + model = PeftModel.from_pretrained(base_model, ADAPTER_PATH) + else: + print("Adapter not found, using base model.") + model = base_model + + print("ORA Model Connected and Ready.") + +@app.post("/api/chat") +async def chat_endpoint(req: ChatRequest): + global model, tokenizer + + system_prompt = "You are ORA, a spiritual assistant specializing in theological insights and biblical wisdom. Provide discerning, compassionate, and doctrine-aware responses." + + # Construct Prompt + messages = [{"role": "system", "content": system_prompt}] + # Add last few turns of history to keep context but save tokens + messages.extend(req.history[-4:]) + messages.append({"role": "user", "content": req.message}) + + input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" + ).to(device) + + terminators = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("<|eot_id|>") + ] + + outputs = model.generate( + input_ids, + max_new_tokens=256, + eos_token_id=terminators, + do_sample=True, + temperature=0.7, + top_p=0.9, + ) + + response_tokens = outputs[0][input_ids.shape[-1]:] + response_text = tokenizer.decode(response_tokens, skip_special_tokens=True) + + return {"response": response_text} + +# Mount Static Frontend (Must be last) +# Expects 'frontend/out' to exist (built via 'next build') +if os.path.exists("frontend/out"): + app.mount("/_next", StaticFiles(directory="frontend/out/_next"), name="next") + app.mount("/", StaticFiles(directory="frontend/out", html=True), name="static") + + @app.exception_handler(404) + async def not_found(request, exc): + return FileResponse("frontend/out/index.html") + +if __name__ == "__main__": + # HF Spaces expects port 7860 + uvicorn.run(app, host="0.0.0.0", port=7860) diff --git a/app/services/audit.py b/app/services/audit.py new file mode 100644 index 0000000000000000000000000000000000000000..51c7b70f45935873c1c391ea70a3996b7a92a882 --- /dev/null +++ b/app/services/audit.py @@ -0,0 +1,51 @@ +import logging +import json +import os +from logging.handlers import RotatingFileHandler +from datetime import datetime + +# Ensure logs directory exists +LOG_DIR = "logs" +os.makedirs(LOG_DIR, exist_ok=True) +LOG_FILE = os.path.join(LOG_DIR, "ora.log") + +class AuditLog: + def __init__(self): + self.logger = logging.getLogger("ORA_Audit") + self.logger.setLevel(logging.INFO) + + # Prevent adding multiple handlers if re-initialized + if not self.logger.handlers: + # Rotate log after 10MB, keep 5 backups + handler = RotatingFileHandler(LOG_FILE, maxBytes=10*1024*1024, backupCount=5) + formatter = logging.Formatter('%(message)s') + handler.setFormatter(formatter) + self.logger.addHandler(handler) + + async def log_interaction(self, user_id: str, intent: str, guardrails_triggered: list[str]): + """ + Logs a user interaction and any triggered guardrails. + """ + entry = { + "timestamp": datetime.now().isoformat(), + "type": "INTERACTION", + "user_id": user_id, + "intent": intent, + "guardrails": guardrails_triggered + } + self.logger.info(json.dumps(entry)) + + async def log_violation(self, user_id: str, violation_type: str, content: str): + """ + Logs a specific safety violation. + """ + entry = { + "timestamp": datetime.now().isoformat(), + "type": "VIOLATION", + "user_id": user_id, + "violation_type": violation_type, + "content_snippet": content[:200] # Store first 200 chars for context + } + self.logger.info(json.dumps(entry)) + +audit_service = AuditLog() diff --git a/app/services/bible.py b/app/services/bible.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c50d5be13fc9293a892e026040426c41afac91 --- /dev/null +++ b/app/services/bible.py @@ -0,0 +1,97 @@ +import httpx +import os +import asyncio +import lancedb +from typing import List, Optional, Dict +from app.services.llm import llm_service + +class BibleService: + BASE_URL = "https://bible-api.com" + DB_PATH = "data/lancedb_storage" + TABLE_NAME = "bible_verses" + + def __init__(self): + # We don't keep an in-memory index anymore. + # Connection is established per-query or cached if needed. + pass + + async def initialize_index(self): + """ + No-op for LanceDB as it's persistent. + We might verify the DB exists here. + """ + if not os.path.exists(self.DB_PATH): + print("BibleService: LanceDB storage not found at", self.DB_PATH) + print("Please run scripts/ingest_bible.py") + else: + print("BibleService: LanceDB connected.") + + async def get_passage(self, reference: str, translation: str = "web") -> Optional[str]: + """ + Retrieves full passage text from bible-api.com (External Fallback). + """ + clean_ref = reference.strip() + if not clean_ref: + return None + + async with httpx.AsyncClient() as client: + try: + response = await client.get( + f"{self.BASE_URL}/{clean_ref}", + params={"translation": translation} + ) + if response.status_code == 200: + return response.json().get("text", "").strip() + return None + except Exception: + return None + + async def search(self, query: str, limit: int = 3) -> List[dict]: + """ + Semantic search using persistent LanceDB. + """ + if not os.path.exists(self.DB_PATH): + print("BibleService Error: DB not initialized.") + return [] + + # 1. Generate Query Vector + query_embedding = await llm_service.get_embedding(query) + if not query_embedding: + return [] + + try: + # 2. Search LanceDB + db = lancedb.connect(self.DB_PATH) + + # Check if table exists + if self.TABLE_NAME not in db.table_names(): + print(f"BibleService: Table {self.TABLE_NAME} not found.") + return [] + + tbl = db.open_table(self.TABLE_NAME) + + # LanceDB search + # Explicitly select columns to ensure they are returned + results = tbl.search(query_embedding).limit(limit).select(["reference", "text"]).to_list() + + # 3. Format Results + valid_results = [] + for item in results: + # distance is typically L2. For binary-ish vectors, it's sqrt(sum of differences squared). + dist = item.get('_distance', 1.0) + # Simple inverse normalization for display + relevance = 1.0 / (1.0 + dist) + + valid_results.append({ + "score": relevance, + "text": item.get("text", ""), + "reference": item.get("reference", "Unknown") + }) + + return valid_results + + except Exception as e: + print(f"BibleService Search Error: {e}") + return [] + +bible_service = BibleService() diff --git a/app/services/blessing.py b/app/services/blessing.py new file mode 100644 index 0000000000000000000000000000000000000000..abbdd6e62adab7cc79b90397364d6d078f3c1d9c --- /dev/null +++ b/app/services/blessing.py @@ -0,0 +1,15 @@ +from app.services.llm import llm_service + +class BlessingService: + PROMPT = """Offer a brief closing blessing. + +Guidelines: +- One or two sentences +- No claims of divine action +- Encouraging, not authoritative +- Warm but not dramatic""" + + async def bless(self) -> str: + return await llm_service.generate_response(message="Bless me.", system_prompt=self.PROMPT) + +blessing_service = BlessingService() diff --git a/app/services/citations.py b/app/services/citations.py new file mode 100644 index 0000000000000000000000000000000000000000..045efda819b4e27a34c416c1ec19e79d552b6e70 --- /dev/null +++ b/app/services/citations.py @@ -0,0 +1,24 @@ +from pydantic import BaseModel +from typing import List + +class ScriptureReference(BaseModel): + book: str + chapter: int + verses: str # e.g. "16-18" or "10" + text: str + translation: str + +class CitationService: + def format_citations(self, refs: List[ScriptureReference]) -> str: + """ + Formats a list of scripture references into a readable footer. + """ + if not refs: + return "" + + formatted = "\n\n**Scripture References:**\n" + for ref in refs: + formatted += f"- *{ref.book} {ref.chapter}:{ref.verses} ({ref.translation})*\n" + return formatted + +citation_service = CitationService() diff --git a/app/services/context.py b/app/services/context.py new file mode 100644 index 0000000000000000000000000000000000000000..30d1d6b12f5bde3a7fb04cc7b7908e0462bf6a0e --- /dev/null +++ b/app/services/context.py @@ -0,0 +1,21 @@ +from app.services.llm import llm_service + +class ContextService: + PROMPT = """You are a biblical scholar explaining scripture responsibly. + +Verse: +{verse_text} + +Explain: +- Historical context +- Audience +- Key theme +- What the verse DOES and DOES NOT claim + +Avoid modern assumptions or dogmatic conclusions.""" + + async def explain(self, verse_text: str) -> str: + prompt = self.PROMPT.format(verse_text=verse_text) + return await llm_service.generate_response(message="Explain this verse.", system_prompt=prompt) + +context_service = ContextService() diff --git a/app/services/discernment.py b/app/services/discernment.py new file mode 100644 index 0000000000000000000000000000000000000000..1f239386e61765a08eec88af32940c8aa3762b98 --- /dev/null +++ b/app/services/discernment.py @@ -0,0 +1,27 @@ +from app.services.llm import llm_service + +class DiscernmentService: + PROMPT = """You are ORA, a spiritual discernment guide. + +Context: +- User situation: {situation} +- Emotional tone: {emotion} +- Relevant scripture: {scripture} + +Your Role: +- You help the user reflect prayerfully and wisely. +- You do NOT tell the user what decision to make. +- You do NOT give commands or orders. +- You do NOT promise specific outcomes. + +Respond with: +1. A calm, empathetic acknowledgment +2. 2–3 reflective questions to help them uncover their own answer +3. A short, hopeful prayer or reflection +4. A gentle reminder of their own agency and freedom""" + + async def guide(self, situation: str, emotion: str, scripture: str = "") -> str: + prompt = self.PROMPT.format(situation=situation, emotion=emotion, scripture=scripture) + return await llm_service.generate_response(message="Please guide me.", system_prompt=prompt) + +discernment_service = DiscernmentService() diff --git a/app/services/doctrine.py b/app/services/doctrine.py new file mode 100644 index 0000000000000000000000000000000000000000..e85ac75c5509623ed89ebf8e48957efbd43263a9 --- /dev/null +++ b/app/services/doctrine.py @@ -0,0 +1,21 @@ +from app.services.llm import llm_service + +class DoctrineService: + PROMPT = """You are ORA, aware of Christian theological diversity. + +User tradition (if known): {tradition} + +Question: +{question} + +Answer by: +- Explaining the common ground +- Noting differences where relevant +- Avoiding declaring one view as “the only truth” +- Using scripture carefully""" + + async def answer(self, question: str, tradition: str = "general") -> str: + prompt = self.PROMPT.format(question=question, tradition=tradition) + return await llm_service.generate_response(message=question, system_prompt=prompt) + +doctrine_service = DoctrineService() diff --git a/app/services/emotion.py b/app/services/emotion.py new file mode 100644 index 0000000000000000000000000000000000000000..b77ff9211f9a915322f833596d5ff786298c6490 --- /dev/null +++ b/app/services/emotion.py @@ -0,0 +1,66 @@ +from typing import Dict + +class EmotionService: + def __init__(self): + self.positive_keywords = { + "happy", "joy", "blessed", "grateful", "peace", "hope", "love", + "excited", "good", "great", "wonderful", "calm", "content" + } + self.negative_keywords = { + "sad", "depressed", "anxious", "afraid", "scared", "fear", + "lonely", "hurt", "pain", "grief", "broken", "suffering", + "angry", "mad", "upset", "bad", "terrible", "hate" + } + self.high_distress_keywords = { + "kill", "suicide", "die", "end it all", "hopeless", "worthless", + "unbearable", "can't go on", "no way out" + } + + async def analyze_tone(self, text: str) -> Dict[str, any]: + """ + Returns sentiment and emotional intensity. + """ + text_lower = text.lower() + words = text_lower.split() + total_words = len(words) if words else 1 + + pos_count = sum(1 for word in words if word in self.positive_keywords) + neg_count = sum(1 for word in words if word in self.negative_keywords) + + # Determine sentiment + if pos_count > neg_count: + sentiment = "positive" + elif neg_count > pos_count: + sentiment = "negative" + else: + sentiment = "neutral" + + # Calculate intensity (simple heuristic: fraction of emotional words) + emotional_word_count = pos_count + neg_count + # Normalize intensity to be somewhat reasonable (0.0 to 1.0) + # Assuming if 30% of words are emotional, it's very intense. + raw_intensity = emotional_word_count / total_words + intensity = min(raw_intensity * 3.0, 1.0) + + return { + "sentiment": sentiment, + "intensity": round(intensity, 2), + "pos_count": pos_count, + "neg_count": neg_count + } + + def is_distress_high(self, text: str, emotion_data: Dict[str, any]) -> bool: + text_lower = text.lower() + + # Check for specific trigger words + for keyword in self.high_distress_keywords: + if keyword in text_lower: + return True + + # Check for high negative intensity + if emotion_data.get("sentiment") == "negative" and emotion_data.get("intensity", 0) > 0.8: + return True + + return False + +emotion_service = EmotionService() diff --git a/app/services/ethics.py b/app/services/ethics.py new file mode 100644 index 0000000000000000000000000000000000000000..935d99b8a128b31ebb1f9093813d9efb6b89a0ab --- /dev/null +++ b/app/services/ethics.py @@ -0,0 +1,23 @@ +from app.services.llm import llm_service + +class EthicsService: + PROMPT = """You are ORA, facilitating ethical reflection. + +Scenario: +{scenario} + +Use: +- Scripture themes (not commands) +- Wisdom principles +- Compassion-first framing + +Avoid: +- Absolute judgments +- Shame +- Fear-based language""" + + async def reflect(self, scenario: str) -> str: + prompt = self.PROMPT.format(scenario=scenario) + return await llm_service.generate_response(message="Help me reflect ethically.", system_prompt=prompt) + +ethics_service = EthicsService() diff --git a/app/services/feedback.py b/app/services/feedback.py new file mode 100644 index 0000000000000000000000000000000000000000..0ff3258aed47b21e001f0269074bb486f5dee591 --- /dev/null +++ b/app/services/feedback.py @@ -0,0 +1,11 @@ +from pydantic import BaseModel + +class Feedback(BaseModel): + message_id: str + rating: int # 1-5 + comment: str = "" + +class FeedbackService: + async def submit_feedback(self, feedback: Feedback): + # Store feedback for RLHF + pass diff --git a/app/services/growth.py b/app/services/growth.py new file mode 100644 index 0000000000000000000000000000000000000000..d8257ea0c727b460d403ff0b6815cda930a39267 --- /dev/null +++ b/app/services/growth.py @@ -0,0 +1,20 @@ +from app.services.llm import llm_service + +class GrowthService: + PROMPT = """You are summarizing the user’s spiritual journey. + +Based on: +- Past reflections: {reflections} +- Prayers: {prayers} +- Questions asked: {questions} + +Produce: +- A gentle narrative summary +- Noticing growth patterns +- No judgment or evaluation""" + + async def summarize(self, reflections: str, prayers: str, questions: str) -> str: + prompt = self.PROMPT.format(reflections=reflections, prayers=prayers, questions=questions) + return await llm_service.generate_response(message="Summarize my journey.", system_prompt=prompt) + +growth_service = GrowthService() diff --git a/app/services/guardrails.py b/app/services/guardrails.py new file mode 100644 index 0000000000000000000000000000000000000000..a0add3ebe6c9992da5151ca42c3fbf143d8ff366 --- /dev/null +++ b/app/services/guardrails.py @@ -0,0 +1,61 @@ +from pydantic import BaseModel +from typing import List, Optional +from app.services.audit import audit_service + + +class GuardrailViolation(Exception): + pass + +class GuardrailService: + def __init__(self): + self.prohibited_phrases = [ + "God told me that you", + "I prophesy", + "You must do this", + "The Lord is saying right now", + "I declare over you", + "Scripture commands you to", + "Thus saith the Lord", + "God fails you if", + "You are sinning by" + ] + + async def validate_response(self, content: str, user_id: str = "system") -> str: + """ + Ensures ORA does not claim divine authority or give dangerous advice. + """ + for phrase in self.prohibited_phrases: + if phrase.lower() in content.lower(): + # Log the violation + await audit_service.log_violation( + user_id=user_id, + violation_type="Prohibited Phrase", + content=content + ) + # Block the output + raise GuardrailViolation(f"Response violated safety guardrail: '{phrase}'") + + return content + + async def check_input_safety(self, message: str, user_id: str = "anonymous") -> bool: + """ + Checks for self-harm or crisis keywords. + """ + crisis_keywords = ["kill myself", "end it all", "suicide", "hurt myself"] + if any(keyword in message.lower() for keyword in crisis_keywords): + await audit_service.log_violation( + user_id=user_id, + violation_type="Crisis Keyword", + content=message + ) + return False + return True + + async def sanitize_content(self, content: str) -> str: + """ + Sanitizes content by replacing restricted words (placeholders for now). + """ + # Example: Simple redaction if we had a list of 'warning words' that aren't strict blocks + return content + +guardrail_service = GuardrailService() diff --git a/app/services/intent.py b/app/services/intent.py new file mode 100644 index 0000000000000000000000000000000000000000..dd652754624e32f16370b4b9b6830b01ba755d41 --- /dev/null +++ b/app/services/intent.py @@ -0,0 +1,60 @@ +from pydantic import BaseModel +from typing import Optional, List +from enum import Enum + +class UserIntent(str, Enum): + QUESTION = "question" + EMOTIONAL_SUPPORT = "emotional_support" + PRAYER_REQUEST = "prayer_request" + BIBLE_STUDY = "bible_study" + LIFE_DECISION = "life_decision" + CONFESSION = "confession" + UNKNOWN = "unknown" + +class IntentAnalysis(BaseModel): + intent: UserIntent + urgency: str = "low" # low, medium, high + requires_scripture: bool + emotional_tone: Optional[str] = None + confidence: float = 1.0 + +class IntentService: + def __init__(self): + # Keywords for deterministic matching + self.intent_keywords = { + UserIntent.PRAYER_REQUEST: ["pray", "prayer", "intercede", "god help", "need god"], + UserIntent.EMOTIONAL_SUPPORT: ["sad", "depressed", "anxious", "lonely", "hurt", "grief", "pain", "crying", "broken", "suffering"], + UserIntent.BIBLE_STUDY: ["verse", "scripture", "passage", "chapter", "bible", "read", "psalm", "gospel", "book of"], + UserIntent.LIFE_DECISION: ["decide", "choice", "what should i do", "guidance", "direction", "path", "will for me"], + UserIntent.CONFESSION: ["sin", "forgive", "confess", "guilt", "mistake", "wrong", "sorry", "repent"], + UserIntent.QUESTION: ["who", "what", "where", "when", "why", "how", "?"], + } + + async def analyze(self, message: str) -> IntentAnalysis: + msg_lower = message.lower() + + # Priority 1: High urgency / Specific needs + if any(kw in msg_lower for kw in self.intent_keywords[UserIntent.PRAYER_REQUEST]): + return IntentAnalysis(intent=UserIntent.PRAYER_REQUEST, requires_scripture=True, urgency="medium") + + if any(kw in msg_lower for kw in self.intent_keywords[UserIntent.CONFESSION]): + return IntentAnalysis(intent=UserIntent.CONFESSION, requires_scripture=True, urgency="medium") + + if any(kw in msg_lower for kw in self.intent_keywords[UserIntent.EMOTIONAL_SUPPORT]): + return IntentAnalysis(intent=UserIntent.EMOTIONAL_SUPPORT, requires_scripture=True, urgency="medium") + + # Priority 2: Study and Guidance + if any(kw in msg_lower for kw in self.intent_keywords[UserIntent.BIBLE_STUDY]): + return IntentAnalysis(intent=UserIntent.BIBLE_STUDY, requires_scripture=True) + + if any(kw in msg_lower for kw in self.intent_keywords[UserIntent.LIFE_DECISION]): + return IntentAnalysis(intent=UserIntent.LIFE_DECISION, requires_scripture=True, urgency="medium") + + # Priority 3: General Questions + if any(kw in msg_lower for kw in self.intent_keywords[UserIntent.QUESTION]): + return IntentAnalysis(intent=UserIntent.QUESTION, requires_scripture=False) + + # Fallback + return IntentAnalysis(intent=UserIntent.UNKNOWN, requires_scripture=False) + +intent_service = IntentService() diff --git a/app/services/journal.py b/app/services/journal.py new file mode 100644 index 0000000000000000000000000000000000000000..47c5b5591bfdc9fd82fd55653075d339438a0439 --- /dev/null +++ b/app/services/journal.py @@ -0,0 +1,86 @@ +import lancedb +import uuid +import os +from typing import List, Dict, Optional +from datetime import datetime +from app.services.llm import llm_service + +class JournalService: + LANCE_PATH = "data/journal_lancedb" + TABLE_NAME = "journal_nodes" + + def __init__(self): + if not os.path.exists("data"): + os.makedirs("data") + self.db = lancedb.connect(self.LANCE_PATH) + + async def create_entry(self, user_id: str, text: str, verses: List[str] = None, tags: List[str] = None) -> str: + """ + Creates a new journal node, embeds it, and automatically finds links to previous entries. + """ + entry_id = str(uuid.uuid4()) + timestamp = datetime.now().isoformat() + + # 1. Generate Embedding + vector = await llm_service.get_embedding(text) + if not vector: + print("JournalService: Failed to generate embedding.") + return None + + # 2. Prepare Data + data = [{ + "vector": vector, + "id": entry_id, + "user_id": user_id, + "text": text, + "verses": verses or [], + "tags": tags or [], + "timestamp": timestamp + }] + + # 3. Store in LanceDB + if self.TABLE_NAME in self.db.table_names(): + tbl = self.db.open_table(self.TABLE_NAME) + tbl.add(data) + else: + self.db.create_table(self.TABLE_NAME, data=data) + + print(f"JournalService: Created entry {entry_id} for user {user_id}") + return entry_id + + async def get_related_entries(self, user_id: str, entry_text: str, limit: int = 3) -> List[Dict]: + """ + Finds the 'Zettelkasten links' — other entries that are semantically related. + """ + if self.TABLE_NAME not in self.db.table_names(): + return [] + + query_vec = await llm_service.get_embedding(entry_text) + if not query_vec: + return [] + + tbl = self.db.open_table(self.TABLE_NAME) + + # Search for similar entries by the same user + results = (tbl.search(query_vec) + .where(f"user_id = '{user_id}'", prefilter=True) + .limit(limit + 1) + .to_list()) + + # Filter out exact matches (the entry itself) + filtered = [r for r in results if r['text'] != entry_text] + return filtered[:limit] + + async def get_user_entries(self, user_id: str, limit: int = 20) -> List[Dict]: + """Retrieves a timeline of entries.""" + if self.TABLE_NAME not in self.db.table_names(): + return [] + + tbl = self.db.open_table(self.TABLE_NAME) + # Using a simple to_list and manual sort for small POC datasets + results = tbl.search().where(f"user_id = '{user_id}'").to_list() + results.sort(key=lambda x: x['timestamp'], reverse=True) + return results[:limit] + +# Singleton instance +journal_service = JournalService() diff --git a/app/services/llm.py b/app/services/llm.py new file mode 100644 index 0000000000000000000000000000000000000000..d4d58bbff941ee257d16bd608d89f123a563a9be --- /dev/null +++ b/app/services/llm.py @@ -0,0 +1,128 @@ +from openai import AsyncOpenAI +from app.core.config import settings + +class LLMService: + def __init__(self): + self.client = AsyncOpenAI( + base_url=settings.LLM_BASE_URL, + api_key=settings.LLM_API_KEY + ) + self.is_offline = False # Cache offline status to avoid repeated timeouts + + async def generate_response(self, message: str, system_prompt: str = settings.SYSTEM_PROMPT, tools: list = None) -> dict: + if self.is_offline: + return self._get_mock_swarm_response(message, system_prompt, tools) + + try: + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": message} + ] + + kwargs = { + "model": settings.MODEL_NAME, + "messages": messages, + "temperature": 0.7 + } + + if tools: + kwargs["tools"] = tools + kwargs["tool_choice"] = "auto" + + completion = await self.client.chat.completions.create(**kwargs) + + choice = completion.choices[0].message + return { + "content": choice.content or "", + "tool_calls": getattr(choice, "tool_calls", None) + } + + except Exception as e: + self.is_offline = True + print(f"LLM Connectivity failed: {str(e)}. Switching to MOCK mode.") + return self._get_mock_swarm_response(message, system_prompt, tools) + + def _get_mock_swarm_response(self, message: str, system_prompt: str, tools: list) -> dict: + """ + Simulates agent handoffs and tool calling based on keywords. + Used for verification when Ollama is offline. + """ + msg_lower = message.lower() + + # Check for Episodic Memory in system prompt + memory_insight = "" + if "Relevant past insights:" in system_prompt: + # Extract the first insight for the mock response + parts = system_prompt.split("Relevant past insights:") + if len(parts) > 1: + memory_insight = parts[1].split("\n")[1].strip("- ").strip() + + # 1. RLM / REPL Mocking + if "Python script" in system_prompt or "calculate" in msg_lower: + return { + "content": "```python\n# Simulated reasoning code\ndate1 = -586\ndate2 = 70\nprint(f'Total span: {date2 - date1} years')\n```", + "tool_calls": None + } + + # 2. Check for Handoff Keywords + if tools: + tool_names = [t["function"]["name"] for t in tools] + + if "transfer_to_theologian" in tool_names and ("bible" in msg_lower or "genesis" in msg_lower or "study" in msg_lower): + return { + "content": f"I see we previously talked about {memory_insight}. I will hand you over to our Theologian for a deeper Bible study.", + "tool_calls": [type('ToolCall', (), { + "id": "mock_handoff_1", + "function": type('Func', (), {"name": "transfer_to_theologian", "arguments": "{}"}) + })] + } + + if "transfer_to_healer" in tool_names and ("sad" in msg_lower or "prayer" in msg_lower or "help" in msg_lower): + return { + "content": f"I remember you were feeling {memory_insight} earlier. I will connect you with our Healer for prayer.", + "tool_calls": [type('ToolCall', (), { + "id": "mock_handoff_2", + "function": type('Func', (), {"name": "transfer_to_healer", "arguments": "{}"}) + })] + } + + response_content = f"[MOCK MODE] I am processing your message: '{message}'." + if memory_insight: + response_content += f" I remember you mentioned: '{memory_insight}'." + + return { + "content": response_content, + "tool_calls": None + } + + + async def get_embedding(self, text: str) -> list[float]: + if self.is_offline: + return self._get_mock_embedding(text) + + try: + response = await self.client.embeddings.create( + model=settings.MODEL_NAME, + input=text + ) + return response.data[0].embedding + except Exception as e: + # First failure sets the flag for this session/instance + self.is_offline = True + print(f"Embedding connectivity failed: {str(e)}. Switching to MOCK mode for this session.") + return self._get_mock_embedding(text) + + def _get_mock_embedding(self, text: str, dim: int = 1536) -> list[float]: + """ + Creates a deterministic sparse embedding based on word hashing. + Allows basic keyword matching to work even without a real LLM. + """ + vec = [0.0] * dim + words = text.lower().split() + for word in words: + # Simple hash to map word to index + idx = sum(ord(c) for c in word) % dim + vec[idx] = 1.0 + return vec + +llm_service = LLMService() diff --git a/app/services/memory.py b/app/services/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..b869eca8a50e81e529dec70ae5cbcd067d49ce7d --- /dev/null +++ b/app/services/memory.py @@ -0,0 +1,169 @@ +import sqlite3 +import json +import os +import lancedb +from typing import List, Dict, Optional +from datetime import datetime +from app.services.llm import llm_service + +class MemoryService: + DB_PATH = "data/memory.db" + LANCE_PATH = "data/memory_lancedb" + EPISODE_TABLE = "episodes" + + def __init__(self): + self._init_sqlite() + self._init_lancedb() + + def _init_sqlite(self): + """Initialize the SQLite database with necessary tables.""" + if not os.path.exists("data"): + os.makedirs("data") + + conn = sqlite3.connect(self.DB_PATH) + cursor = conn.cursor() + + # Table for conversation history (short-term memory) + cursor.execute(''' + CREATE TABLE IF NOT EXISTS interactions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + user_id TEXT NOT NULL, + role TEXT NOT NULL, + content TEXT NOT NULL, + timestamp DATETIME DEFAULT CURRENT_TIMESTAMP + ) + ''') + + # Table for facts/long-term memory + cursor.execute(''' + CREATE TABLE IF NOT EXISTS facts ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + user_id TEXT NOT NULL, + fact TEXT NOT NULL, + timestamp DATETIME DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + + def _init_lancedb(self): + """Initialize LanceDB for episodic memory.""" + self.db = lancedb.connect(self.LANCE_PATH) + + async def add_interaction(self, user_id: str, role: str, content: str): + """Add a message interactions to the short-term memory.""" + conn = sqlite3.connect(self.DB_PATH) + cursor = conn.cursor() + cursor.execute( + 'INSERT INTO interactions (user_id, role, content) VALUES (?, ?, ?)', + (user_id, role, content) + ) + conn.commit() + conn.close() + + async def get_short_term_memory(self, user_id: str, limit: int = 10) -> List[Dict[str, str]]: + """Retrieve recent interactions for a user.""" + conn = sqlite3.connect(self.DB_PATH) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + cursor.execute( + 'SELECT role, content FROM interactions WHERE user_id = ? ORDER BY id DESC LIMIT ?', + (user_id, limit) + ) + rows = cursor.fetchall() + conn.close() + return [{"role": row["role"], "content": row["content"]} for row in reversed(rows)] + + async def add_fact(self, user_id: str, fact: str): + """Add a specific fact to long-term memory.""" + conn = sqlite3.connect(self.DB_PATH) + cursor = conn.cursor() + cursor.execute( + 'INSERT INTO facts (user_id, fact) VALUES (?, ?)', + (user_id, fact) + ) + conn.commit() + conn.close() + + async def get_long_term_memory(self, user_id: str) -> List[str]: + """Retrieve all stored facts for a user.""" + conn = sqlite3.connect(self.DB_PATH) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + cursor.execute( + 'SELECT fact FROM facts WHERE user_id = ? ORDER BY timestamp DESC', + (user_id,) + ) + rows = cursor.fetchall() + conn.close() + return [row["fact"] for row in rows] + + # --- EPISODIC MEMORY (Phase 2 Upgrade) --- + + async def store_episode(self, user_id: str, content: str, insight: str, emotion: str = "neutral"): + """ + Summarizes a session/interaction into an 'Episode' and stores in LanceDB. + """ + # 1. Generate Embedding for the insight/content + combined_text = f"Episode: {content} Insight: {insight} Emotion: {emotion}" + vector = await llm_service.get_embedding(combined_text) + + if not vector: + print("MemoryService: Failed to generate embedding for episode.") + return + + # 2. Data to store + data = [{ + "vector": vector, + "user_id": user_id, + "content": content, + "insight": insight, + "emotion": emotion, + "timestamp": datetime.now().isoformat() + }] + + # 3. Create or Update Table + if self.EPISODE_TABLE in self.db.table_names(): + tbl = self.db.open_table(self.EPISODE_TABLE) + tbl.add(data) + else: + self.db.create_table(self.EPISODE_TABLE, data=data) + + print(f"MemoryService: Episode stored for user {user_id}") + + async def retrieve_episodes(self, user_id: str, query: str, limit: int = 3) -> List[Dict]: + """ + Retrieves relevant spiritual episodes using semantic search. + """ + if self.EPISODE_TABLE not in self.db.table_names(): + return [] + + query_vec = await llm_service.get_embedding(query) + if not query_vec: + return [] + + try: + tbl = self.db.open_table(self.EPISODE_TABLE) + + # Filter by user_id + results = (tbl.search(query_vec) + .where(f"user_id = '{user_id}'", prefilter=True) + .limit(limit) + .to_list()) + + return results + except Exception as e: + print(f"MemoryService: Search error: {str(e)}") + return [] + + # --- Legacy Compatibility --- + async def get_history(self, session_id: str) -> List[Dict[str, str]]: + return await self.get_short_term_memory(session_id, limit=50) + + async def add_message(self, session_id: str, role: str, content: str): + await self.add_interaction(session_id, role, content) + +# Singleton instance +memory_service = MemoryService() + diff --git a/app/services/orchestrator.py b/app/services/orchestrator.py new file mode 100644 index 0000000000000000000000000000000000000000..856af8b5520b41011bb9e5e4b2831acc0785f898 --- /dev/null +++ b/app/services/orchestrator.py @@ -0,0 +1,58 @@ +from app.services.memory import memory_service +from app.services.guardrails import guardrail_service, GuardrailViolation +from app.core.swarm_client import swarm_client +from app.agents.gatekeeper import gatekeeper_agent +from typing import Dict + +class OrchestratorService: + async def process_message(self, user_id: str, message: str) -> dict: + # 1. Safety Check (Input) + is_safe = await guardrail_service.check_input_safety(message) + if not is_safe: + return { + "role": "assistant", + "content": "I sense you are in deep distress. Please contact a professional or emergency services immediately. You are valuable and loved." + } + + # 2. Retrieve Episodic Context (Phase 2) + episodes = await memory_service.retrieve_episodes(user_id, message, limit=2) + episodic_context = "" + if episodes: + insights = [f"- {e['timestamp']}: {e['insight']}" for e in episodes] + episodic_context = "\nRelevant past insights:\n" + "\n".join(insights) + + # 3. Run Swarm + messages = [{"role": "user", "content": message}] + + # Inject episodic memory into context variables for agents to see + context = { + "user_id": user_id, + "episodic_memory": episodic_context + } + + swarm_response = await swarm_client.run( + agent=gatekeeper_agent, + messages=messages, + context_variables=context + ) + + # The final message in the swarm history is the result + response_text = swarm_response.messages[-1]["content"] + + # 3. Final Safety Validation of Output + try: + response_text = await guardrail_service.validate_response(response_text) + except GuardrailViolation as e: + print(f"Safety Violation blocked: {e}") + response_text = "I apologize, but I cannot provide that response as it violates my safety guidelines regarding authority claims." + except GuardrailViolation: + response_text = "I apologize, but I cannot complete that response as it violates my safety guidelines." + + return { + "role": "assistant", + "content": response_text, + "agent": swarm_response.agent.name if swarm_response.agent else "Unknown", + "trace": swarm_response.trace + } + +orchestrator = OrchestratorService() diff --git a/app/services/practices.py b/app/services/practices.py new file mode 100644 index 0000000000000000000000000000000000000000..2973af38bcdedd0f77f12bee42ba84ea278bf4f7 --- /dev/null +++ b/app/services/practices.py @@ -0,0 +1,19 @@ +from pydantic import BaseModel +from typing import List, Optional + +class SpiritualPractice(BaseModel): + title: str + description: str + steps: List[str] + duration_minutes: int + scripture_ref: Optional[str] = None + +class PracticeGenerator: + async def generate(self, intent_data: dict, profile_data: dict) -> SpiritualPractice: + # Generation logic here + return SpiritualPractice( + title="Lectio Divina", + description="A traditional practice of scriptural reading, meditation and prayer.", + steps=["Read", "Reflect", "Respond", "Rest"], + duration_minutes=15 + ) diff --git a/app/services/prayer.py b/app/services/prayer.py new file mode 100644 index 0000000000000000000000000000000000000000..ac22a3ac4cf39fd2fd48c4e2ea2f35e58c1ab9c8 --- /dev/null +++ b/app/services/prayer.py @@ -0,0 +1,22 @@ +from app.services.llm import llm_service + +class PrayerService: + PROMPT = """You are ORA, a prayer companion. + +Prayer focus: {topic} +User emotion: {emotion} +Scripture (optional): {scripture} + +Write a prayer that: +- Is gentle, humble, and invitational +- Avoids absolutes (e.g., "always", "never", "definitely") +- Does NOT make promises on God's behalf (e.g., "God will heal you") +- Does NOT use commands or imperatives (e.g., "Pray this", "Do this", "Trust me") +- Uses natural, conversational language, not sermon tone +- Expresses hope and longing without guaranteeing outcomes""" + + async def compose(self, topic: str, emotion: str, scripture: str = "") -> str: + prompt = self.PROMPT.format(topic=topic, emotion=emotion, scripture=scripture) + return await llm_service.generate_response(message="Compose a prayer for me.", system_prompt=prompt) + +prayer_service = PrayerService() diff --git a/app/services/profile.py b/app/services/profile.py new file mode 100644 index 0000000000000000000000000000000000000000..c39f2f97eba148ae5d880bb24c4c10f32d032685 --- /dev/null +++ b/app/services/profile.py @@ -0,0 +1,107 @@ +from pydantic import BaseModel, Field +from typing import List, Optional +from enum import Enum +import sqlite3 +import json + +class TonePreference(str, Enum): + PASTORAL = "pastoral" + SCHOLARLY = "scholarly" + GENTLE = "gentle" + DIRECT = "direct" + +class SpiritualGoal(str, Enum): + PRAYER = "prayer" + STUDY = "study" + HEALING = "healing" + LEADERSHIP = "leadership" + DISCERNMENT = "discernment" + +class UserProfile(BaseModel): + user_id: str + denomination: Optional[str] = None + bible_translations: List[str] = Field(default=["NIV", "ESV"]) + tone_preference: TonePreference = TonePreference.PASTORAL + spiritual_goals: List[SpiritualGoal] = [] + + class Config: + json_schema_extra = { + "example": { + "user_id": "usr_123", + "denomination": "non-denominational", + "bible_translations": ["NIV"], + "tone_preference": "gentle", + "spiritual_goals": ["prayer", "healing"] + } + } + +class ProfileService: + def __init__(self, db_path: str = "memory.db"): + self.db_path = db_path + self._init_db() + + def _init_db(self): + conn = sqlite3.connect(self.db_path) + cursor = conn.cursor() + cursor.execute(''' + CREATE TABLE IF NOT EXISTS user_profiles ( + user_id TEXT PRIMARY KEY, + denomination TEXT, + bible_translations TEXT, + tone_preference TEXT, + spiritual_goals TEXT + ) + ''') + conn.commit() + conn.close() + + async def get_profile(self, user_id: str) -> UserProfile: + conn = sqlite3.connect(self.db_path) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + cursor.execute('SELECT * FROM user_profiles WHERE user_id = ?', (user_id,)) + row = cursor.fetchone() + conn.close() + + if row: + return UserProfile( + user_id=row['user_id'], + denomination=row['denomination'], + bible_translations=json.loads(row['bible_translations']) if row['bible_translations'] else ["NIV"], + tone_preference=row['tone_preference'] or TonePreference.PASTORAL, + spiritual_goals=json.loads(row['spiritual_goals']) if row['spiritual_goals'] else [] + ) + + # Return default profile if none exists + return UserProfile(user_id=user_id) + + async def update_profile(self, user_id: str, data: dict) -> UserProfile: + # Get current profile to merge (or default) + current = await self.get_profile(user_id) + + # Merge data and validate to ensure Enums are correctly parsed + merged_data = current.model_dump() + merged_data.update(data) + updated_model = UserProfile(**merged_data) + + conn = sqlite3.connect(self.db_path) + cursor = conn.cursor() + + cursor.execute(''' + INSERT OR REPLACE INTO user_profiles (user_id, denomination, bible_translations, tone_preference, spiritual_goals) + VALUES (?, ?, ?, ?, ?) + ''', ( + updated_model.user_id, + updated_model.denomination, + json.dumps(updated_model.bible_translations), + updated_model.tone_preference.value, + json.dumps([g.value for g in updated_model.spiritual_goals]) + )) + + conn.commit() + conn.close() + + return updated_model + +profile_service = ProfileService() diff --git a/app/services/repl.py b/app/services/repl.py new file mode 100644 index 0000000000000000000000000000000000000000..8fe188caead553b8d7ba02948722c283b0cb0bf6 --- /dev/null +++ b/app/services/repl.py @@ -0,0 +1,89 @@ +import sys +import io +import contextlib +import multiprocessing +import traceback +from typing import Dict, Any, Optional + +def _restricted_execute(code: str, queue: multiprocessing.Queue): + """ + Executes code in a completely isolated process environment. + """ + # Redirect stdout to capture it + stdout = io.StringIO() + + # Define restricted globals + # We remove dangerous modules like 'os', 'sys', 'shutil', etc. + safe_globals = { + "__builtins__": __builtins__.copy(), + "math": __import__("math"), + "datetime": __import__("datetime"), + "json": __import__("json"), + } + # Remove dangerous builtins + dangerous_builtins = ["open", "exec", "eval", "getattr", "setattr", "delattr", "help", "input", "compile"] + for b in dangerous_builtins: + if b in safe_globals["__builtins__"]: + del safe_globals["__builtins__"][b] + + try: + with contextlib.redirect_stdout(stdout): + exec(code, safe_globals) + queue.put({"success": True, "output": stdout.getvalue(), "error": None}) + except Exception: + queue.put({"success": False, "output": stdout.getvalue(), "error": traceback.format_exc()}) + +class REPLService: + def execute(self, code: str, timeout: int = 2) -> Dict[str, Any]: + """ + Executes Python code in a restricted global environment. + """ + stdout = io.StringIO() + + # Define restricted globals + safe_globals = { + "__builtins__": { + "print": print, + "range": range, + "len": len, + "list": list, + "dict": dict, + "str": str, + "int": int, + "float": float, + "bool": bool, + "abs": abs, + "sum": sum, + "min": min, + "max": max, + "reversed": reversed, + "sorted": sorted, + "set": set, + "enumerate": enumerate, + "zip": zip, + }, + "math": __import__("math"), + "datetime": __import__("datetime"), + "json": __import__("json"), + } + + try: + with contextlib.redirect_stdout(stdout): + # Using a wrapper to avoid affecting the main thread too much if it hangs + # Note: This doesn't actually provide timeout protection in same-thread + # but works around the multiprocess hang in local dev + exec(code, safe_globals) + + return { + "success": True, + "output": stdout.getvalue(), + "error": None + } + except Exception: + return { + "success": False, + "output": stdout.getvalue(), + "error": traceback.format_exc() + } + +repl_service = REPLService() diff --git a/app/services/rlm.py b/app/services/rlm.py new file mode 100644 index 0000000000000000000000000000000000000000..92113fca141016c95745cde6f857952c5a436c8b --- /dev/null +++ b/app/services/rlm.py @@ -0,0 +1,48 @@ +from app.services.repl import repl_service +from app.services.llm import llm_service +from typing import Dict, Any + +class RLMService: + """ + Reason Synergizing Service. + Uses the REPL to solve complex logical problems through code generation and execution. + """ + + async def reason(self, problem: str) -> str: + """ + Takes a logical problem, generates Python code to solve it, and returns the result. + """ + system_prompt = """ + You are the ORA Reasoning Engine. + Your task is to write a Python script to solve the user's logical problem or calculation. + - Only output the Python code, nothing else. + - Use print() to output final results. + - You have access to: math, datetime, json. + - Do not use dangerous modules (os, subprocess, etc). + - Keep the script efficient. + """ + + # 1. Generate the code + prompt = f"Problem: {problem}\n\nWrite a Python script to solve this. Only output code." + llm_res = await llm_service.generate_response(prompt, system_prompt=system_prompt) + code = llm_res.get("content", "").strip() + + # Clean up markdown if LLM adds it + if code.startswith("```python"): + code = code.split("```python")[1].split("```")[0].strip() + elif code.startswith("```"): + code = code.split("```")[1].split("```")[0].strip() + + if not code: + return "Error: Could not generate reasoning code." + + # 2. Execute in REPL + print(f"RLM: Executing reasoning code...") + result = repl_service.execute(code) + + if not result["success"]: + return f"Reasoning Fallacy (Code Error): {result['error']}" + + return result["output"] + +rlm_service = RLMService() diff --git a/app/services/simplify.py b/app/services/simplify.py new file mode 100644 index 0000000000000000000000000000000000000000..68dbb7e0b18d136dee7315cbcad25829b32b5d2d --- /dev/null +++ b/app/services/simplify.py @@ -0,0 +1,19 @@ +from app.services.llm import llm_service + +class SimplifyService: + PROMPT = """Rewrite the following spiritual explanation +in clear, everyday language. + +Text: +{text} + +Rules: +- No jargon +- No preaching +- Keep meaning intact""" + + async def simplify(self, text: str) -> str: + prompt = self.PROMPT.format(text=text) + return await llm_service.generate_response(message="Simplify this.", system_prompt=prompt) + +simplify_service = SimplifyService() diff --git a/app/services/stillness.py b/app/services/stillness.py new file mode 100644 index 0000000000000000000000000000000000000000..1bd60ed3e3f6304cbb41a5bf3e31de457ef9a219 --- /dev/null +++ b/app/services/stillness.py @@ -0,0 +1,17 @@ +from app.services.llm import llm_service + +class StillnessService: + PROMPT = """You are guiding the user into silence and stillness. + +Create: +- A short breathing or grounding exercise +- One reflective sentence +- An invitation to pause without pressure + +Do not quote scripture unless requested. +Keep it under 150 words.""" + + async def guide(self) -> str: + return await llm_service.generate_response(message="Help me find stillness.", system_prompt=self.PROMPT) + +stillness_service = StillnessService() diff --git a/app/services/trace.py b/app/services/trace.py new file mode 100644 index 0000000000000000000000000000000000000000..17eca2c60353d0d7b07e41800e3f8c55ed8d492f --- /dev/null +++ b/app/services/trace.py @@ -0,0 +1,31 @@ +import json +import os +from datetime import datetime +from typing import Dict, Any + +class TraceService: + TRACE_DIR = "important/reasoning_traces" + + def __init__(self): + if not os.path.exists(self.TRACE_DIR): + os.makedirs(self.TRACE_DIR, exist_ok=True) + + def save_trace(self, trace_data: Dict[str, Any]): + """ + Appends a reasoning trace to a daily .jsonl file. + """ + today = datetime.now().strftime("%Y-%m-%d") + file_path = os.path.join(self.TRACE_DIR, f"ora_traces_{today}.jsonl") + + # Add timestamp if missing + if "timestamp" not in trace_data: + trace_data["timestamp"] = datetime.now().isoformat() + + try: + with open(file_path, "a", encoding="utf-8") as f: + f.write(json.dumps(trace_data) + "\n") + # print(f"TraceService: Trace saved to {file_path}") + except Exception as e: + print(f"TraceService Error: Failed to save trace: {str(e)}") + +trace_service = TraceService() diff --git a/app/services/translation.py b/app/services/translation.py new file mode 100644 index 0000000000000000000000000000000000000000..ec2f692c9f44572bb9c2fbcf082bd5603258f553 --- /dev/null +++ b/app/services/translation.py @@ -0,0 +1,24 @@ +from app.services.llm import llm_service +from typing import List + +class TranslationService: + PROMPT = """Compare the following Bible translations: + +Verse reference: {reference} +Translations: +{translations} + +Explain: +- Key wording differences +- What meaning is shared +- What remains ambiguous + +Do not claim one translation is superior.""" + + async def compare(self, reference: str, translations: List[str]) -> str: + # In a real app, we would fetch the texts first, here we mock passing them + translations_text = "\n".join(translations) # Placeholder + prompt = self.PROMPT.format(reference=reference, translations=translations_text) + return await llm_service.generate_response(message="Compare these translations.", system_prompt=prompt) + +translation_service = TranslationService() diff --git a/config.yml b/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..0967ef424bce6791893e9a57bb952f80fd536e93 --- /dev/null +++ b/config.yml @@ -0,0 +1 @@ +{} diff --git a/frontend/.claude/settings.local.json b/frontend/.claude/settings.local.json new file mode 100644 index 0000000000000000000000000000000000000000..efd4b25d545822f823f2d8f8df5a2f65d7c0041c --- /dev/null +++ b/frontend/.claude/settings.local.json @@ -0,0 +1,17 @@ +{ + "permissions": { + "allow": [ + "Bash(dir:*)", + "Bash(npm run build:*)", + "Bash(npm run dev:*)", + "Bash(taskkill:*)", + "Bash(timeout /t 2)", + "Bash(cat:*)", + "Bash(if [ -d \".next\" ])", + "Bash(then rm -rf .next)", + "Bash(fi)", + "Bash(git init:*)", + "Bash(git add:*)" + ] + } +} diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b3ccd7e4a4a7fa9efec9bb740659e2e08faabe52 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,33 @@ +# Dependencies +/node_modules +/.pnp +.pnp.js + +# Testing +/coverage + +# Next.js +/.next/ +/out/ + +# Production +/build + +# Misc +.DS_Store +*.pem + +# Debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Local env files +.env*.local + +# Vercel +.vercel + +# TypeScript +*.tsbuildinfo +next-env.d.ts diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c98c9efa9fbd65ca8d0b03f435d405e8035cf3cc --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,98 @@ +# SoapBox Candles - Web App + +A Next.js web application for SoapBox Super App's Candles feature - spiritual engagement rewards and AI feature credits. + +## Getting Started + +### Prerequisites +- Node.js 18+ +- npm or yarn + +### Installation + +```bash +cd soapbox-candles +npm install +``` + +### Development + +```bash +npm run dev +``` + +Open [http://localhost:3000](http://localhost:3000) in your browser. + +### Build + +```bash +npm run build +``` + +### Production + +```bash +npm start +``` + +## Deploy to Vercel + +### Option 1: Vercel CLI + +```bash +npm i -g vercel +vercel +``` + +### Option 2: GitHub Integration + +1. Push this project to a GitHub repository +2. Go to [vercel.com](https://vercel.com) +3. Import your GitHub repository +4. Vercel will auto-detect Next.js and deploy + +## Project Structure + +``` +soapbox-candles/ +├── app/ +│ ├── globals.css # Global styles & animations +│ ├── layout.tsx # Root layout with Navbar/Footer +│ ├── page.tsx # Home page (Candles landing) +│ ├── features/ # Features page +│ ├── pricing/ # Pricing page +│ ├── about/ # About page +│ ├── contact/ # Contact page +│ ├── signin/ # Sign in page +│ └── get-started/ # Sign up page +├── components/ +│ ├── Navbar.tsx # Navigation component +│ ├── Footer.tsx # Footer component +│ ├── SpotlightCard.tsx # Interactive card component +│ └── ShinyButton.tsx # Animated CTA button +├── public/ # Static assets +├── package.json +├── tailwind.config.js +├── tsconfig.json +└── next.config.js +``` + +## Features + +- **Responsive Design** - Works on all devices +- **Animations** - Sonar, beam, float, flicker effects +- **Interactive Cards** - Spotlight effect on hover +- **Shiny CTA Buttons** - Animated gradient borders +- **Dark Theme** - Warm amber/gold color scheme +- **TypeScript** - Full type safety +- **Tailwind CSS** - Utility-first styling + +## Pages + +- `/` - Candles landing page +- `/features` - Platform features +- `/pricing` - Pricing plans +- `/about` - About SoapBox +- `/contact` - Contact form +- `/signin` - Sign in +- `/get-started` - Sign up diff --git a/frontend/app/about/page.tsx b/frontend/app/about/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..39f5659c5165797223c02b2bd831e9564bdf9dc3 --- /dev/null +++ b/frontend/app/about/page.tsx @@ -0,0 +1,182 @@ +'use client'; + +import { Sparkles, Brain, Heart, BookOpen, Shield, Users, Zap, Target } from 'lucide-react'; +import SpotlightCard from '@/components/SpotlightCard'; +import Link from 'next/link'; +import ShinyButton from '@/components/ShinyButton'; + +const values = [ + { + icon: Shield, + title: 'Faith-First Design', + description: 'Every feature is built with theological accuracy and spiritual sensitivity at its core.', + }, + { + icon: Heart, + title: 'Compassionate AI', + description: 'Our agents are trained to provide empathetic, pastoral care in every interaction.', + }, + { + icon: Brain, + title: 'Intelligent Guidance', + description: 'Multi-agent architecture ensures you receive specialized expertise for every question.', + }, + { + icon: Users, + title: 'Privacy Respected', + description: 'Your spiritual journey is sacred. We never sell data or compromise your privacy.', + }, +]; + +const agents = [ + { + name: 'Gatekeeper', + icon: Shield, + color: 'purple', + description: 'The intelligent router that understands your intent and connects you with the right specialist.', + }, + { + name: 'Theologian', + icon: BookOpen, + color: 'blue', + description: 'Deep Scripture analysis with cross-references, historical context, and doctrinal insights.', + }, + { + name: 'Healer', + icon: Heart, + color: 'rose', + description: 'Compassionate pastoral care with prayer guidance and emotional support.', + }, +]; + +const colorClasses: Record = { + purple: { bg: 'bg-purple-500/10', border: 'border-purple-500/30', text: 'text-purple-400' }, + blue: { bg: 'bg-blue-500/10', border: 'border-blue-500/30', text: 'text-blue-400' }, + rose: { bg: 'bg-rose-500/10', border: 'border-rose-500/30', text: 'text-rose-400' }, +}; + +export default function AboutPage() { + return ( + <> + {/* Hero */} +
+
+
+ +
+
+ + About ORA +
+ +

+ AI Spiritual Guidance,{' '} + Reimagined +

+ +

+ ORA is a sovereign AI spiritual companion built with a multi-agent architecture + designed to provide personalized, theologically-grounded guidance for your faith journey. +

+
+
+ + {/* Mission */} +
+
+
+
+

Our Mission

+

+ We believe technology can serve faith. ORA was created to bridge the gap between + ancient wisdom and modern AI, providing believers with an intelligent companion + that understands Scripture, respects tradition, and meets you where you are. +

+

+ Our name embodies our method: Observe, + Reflect, Act. + This framework guides every interaction, helping you engage more deeply with your faith. +

+
+
+ {values.map((value) => ( +
+ +

{value.title}

+

{value.description}

+
+ ))} +
+
+
+
+ + {/* Agent Architecture */} +
+
+
+

Multi-Agent Architecture

+

+ ORA uses specialized AI agents that work together, each bringing unique expertise + to serve your spiritual needs. +

+
+ +
+ {agents.map((agent) => { + const colors = colorClasses[agent.color]; + return ( + +
+ +
+

{agent.name}

+

{agent.description}

+
+ ); + })} +
+
+
+ + {/* Technology */} +
+
+

Built with Purpose

+

+ ORA combines cutting-edge AI with deep respect for theological tradition. + Our system includes episodic memory, reasoning traces, and safety guardrails + to ensure every interaction is helpful, accurate, and spiritually sensitive. +

+ +
+ {['Multi-Agent Swarm', 'Episodic Memory', 'LanceDB Vectors', 'Safety Guardrails', 'Reasoning Traces', 'Bible RAG'].map((tech) => ( + + {tech} + + ))} +
+ + + + + + Experience ORA + + + +
+
+ + ); +} diff --git a/frontend/app/candle/page.tsx b/frontend/app/candle/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..fc7f74d06fad5a8743302996723b64e352ab3a28 --- /dev/null +++ b/frontend/app/candle/page.tsx @@ -0,0 +1,635 @@ +'use client'; + +import { Flame, Sun, Share2, TrendingUp, Gift, Calendar, Check, Users, Church, Star, Zap, ArrowRight, Sparkles, Heart, BookOpen, Video, MessageSquare, Trophy, Target, UserPlus, ThumbsUp } from 'lucide-react'; +import SpotlightCard from '@/components/SpotlightCard'; +import ShinyButton from '@/components/ShinyButton'; +import Link from 'next/link'; + +export default function CandlePage() { + return ( + <> + {/* Hero Section - Completely Redesigned */} +
+
+ + {/* Animated Glow Backgrounds */} +
+
+
+ +
+
+ {/* Left: Content */} +
+
+ + + + + + Spiritual Rewards System + +
+ +

+ Light Your +
+ + Faith Journey + +

+ +

+ Earn Candles through spiritual engagement. Spend them on AI-powered tools. Watch your faith community grow. +

+ + {/* Stats Row */} +
+
+
250
+
Free Start
+
+
+
+
+250
+
Monthly
+
+
+
+
+
Earn More
+
+
+ +
+ + + + + Get 250 Free Candles + + + + +
+
+ + {/* Right: Candle Visual System */} +
+ {/* Central Candle Orb */} +
+ {/* Outer Glow Ring */} +
+
+
+
+
+ + {/* Main Candle Circle */} +
+
+
+ +
+
+
+ + {/* Orbiting Elements */} +
+
+
+ +
+
+
+
+ +
+
+
+
+ +
+
+
+
+ +
+
+
+ + {/* Inner Orbit */} +
+
+
+ +
+
+
+
+ +
+
+
+
+ + {/* Floating Cards */} +
+
+ +
+
+
Welcome Bonus
+
+250 Candles
+
+
+ +
+
+ +
+
+
Monthly Refresh
+
+250 Candles
+
+
+ +
+
+ + ORA™ Powered +
+
+
+
+
+
+ + {/* How Candles Work - Interactive Visual */} +
+
+
+
+ + The Candle Economy +
+

+ How Candles Work +

+

+ A simple three-step cycle that rewards your spiritual growth and empowers your ministry +

+
+ + {/* Three Step Flow */} +
+ {[ + { + step: '01', + icon: Sun, + title: 'Earn', + subtitle: 'Through Engagement', + description: 'Complete spiritual activities, engage with your community, and grow in faith to earn Candles.', + color: 'amber', + examples: ['Daily devotions', 'Prayer requests', 'Community posts'], + }, + { + step: '02', + icon: Zap, + title: 'Spend', + subtitle: 'On AI Features', + description: 'Use your Candles to access powerful AI tools that enhance your worship and ministry.', + color: 'purple', + examples: ['ORA™ insights', 'Video editing', 'Sermon prep'], + }, + { + step: '03', + icon: Share2, + title: 'Share', + subtitle: 'With Others', + description: 'Gift Candles to friends, family, or donate to churches and ministries in need.', + color: 'emerald', + examples: ['Gift to friends', 'Support churches', 'Fund missions'], + }, + ].map((item, i) => { + const colorClasses = { + amber: { bg: 'bg-amber-500/10', border: 'border-amber-500/20', hoverBorder: 'hover:border-amber-500/40', text: 'text-amber-400', spotlight: 'rgba(245, 158, 11, 0.15)' }, + purple: { bg: 'bg-purple-500/10', border: 'border-purple-500/20', hoverBorder: 'hover:border-purple-500/40', text: 'text-purple-400', spotlight: 'rgba(168, 85, 247, 0.15)' }, + emerald: { bg: 'bg-emerald-500/10', border: 'border-emerald-500/20', hoverBorder: 'hover:border-emerald-500/40', text: 'text-emerald-400', spotlight: 'rgba(16, 185, 129, 0.15)' }, + }; + const colors = colorClasses[item.color as keyof typeof colorClasses]; + const staggerClass = `stagger-${i + 1}`; + + return ( + + {/* Step Number */} +
{item.step}
+ +
+ +
+ +
+

{item.title}

+

{item.subtitle}

+
+ +

{item.description}

+ +
+ {item.examples.map((ex, j) => ( +
+ + {ex} +
+ ))} +
+
+ ); + })} +
+ + {/* Connection Line */} +
+
+
+
+
+
+
+
+
+ + {/* Live Candle Counter Demo */} +
+
+
+ {/* Left: Demo Interface */} +
+
+
+
+
+
+
+ candle-wallet.app +
+ +
+ {/* Balance Display */} +
+
Your Candle Balance
+
+ + 1,247 +
+
+127 this week
+
+ + {/* Recent Activity */} +
+
Recent Activity
+ {[ + { action: 'Completed Daily Devotion', amount: '+25', icon: BookOpen, color: 'amber', time: '2h ago' }, + { action: 'Posted Prayer Request', amount: '+10', icon: Heart, color: 'rose', time: '5h ago' }, + { action: 'Used ORA™ for Bible Study', amount: '-15', icon: Sparkles, color: 'purple', time: '1d ago' }, + { action: '7-Day Streak Bonus!', amount: '+50', icon: Trophy, color: 'emerald', time: '1d ago' }, + ].map((item, i) => ( +
+
+
+ +
+
+
{item.action}
+
{item.time}
+
+
+ + {item.amount} + +
+ ))} +
+
+
+ + {/* Right: Content */} +
+

+ Watch Your Candles Grow +

+

+ Every spiritual activity earns you Candles. Track your balance, see your growth over time, and celebrate milestones in your faith journey. +

+ +
+ {[ + { label: 'Real-time balance updates', desc: 'See your Candles change as you engage' }, + { label: 'Activity history', desc: 'Track every earn and spend transaction' }, + { label: 'Streak bonuses', desc: 'Earn bonus Candles for consistent engagement' }, + { label: 'Monthly refresh', desc: 'Get 250 free Candles every month' }, + ].map((item, i) => ( +
+
+ +
+
+
{item.label}
+
{item.desc}
+
+
+ ))} +
+ + + + + Start Earning Today + + + + +
+
+
+
+ + {/* Earn Candles Section - Enhanced */} +
+
+
+

+ Ways to Earn Candles +

+

+ Every meaningful interaction with your faith community earns you Candles +

+
+ + {/* Community Engagement */} +
+
+
+ +
+
+

Community Engagement

+

Connect and interact with your faith family

+
+
+
+ {[ + { icon: UserPlus, title: 'Add A Contact', candles: 25, badge: 'Connector', color: 'blue' }, + { icon: Target, title: 'Attend An Event', candles: 25, badge: 'Active Member', color: 'blue' }, + { icon: MessageSquare, title: 'Post A Discussion', candles: 20, badge: 'Community Builder', color: 'blue' }, + { icon: ThumbsUp, title: 'Like/Amen/Share', candles: 5, badge: 'Encourager', color: 'blue' }, + ].map((item, i) => { + const staggerClass = `stagger-${(i % 4) + 1}`; + return ( + +
+
+ +
+ {item.badge} +
+

{item.title}

+
+ + {item.candles} + Candles +
+
+ ); + })} +
+
+ + {/* Spiritual Habits */} +
+
+
+ +
+
+

Spiritual Habits

+

Deepen your faith through daily practices

+
+
+
+ {[ + { icon: Trophy, title: 'Finish A Reading Plan', candles: 50, badge: 'Devoted Reader', color: 'rose' }, + { icon: Flame, title: '7-Day Streak', candles: 50, badge: 'Disciplined', color: 'rose' }, + { icon: BookOpen, title: 'Post S.O.A.P. Journal', candles: 25, badge: 'Reflective', color: 'rose' }, + { icon: Heart, title: 'Request A Prayer', candles: 10, badge: 'Prayer Warrior', color: 'rose' }, + ].map((item, i) => { + const staggerClass = `stagger-${(i % 4) + 1}`; + return ( + +
+
+ +
+ {item.badge} +
+

{item.title}

+
+ + {item.candles} + Candles +
+
+ ); + })} +
+
+ + {/* Growth & Leadership */} +
+
+
+ +
+
+

Growth & Leadership

+

Big rewards for big commitments

+
+
+
+ {[ + { icon: Users, title: 'Complete Profile', candles: 250, badge: 'Committed', highlight: true }, + { icon: Gift, title: 'Refer A Friend', candles: 250, badge: 'Evangelist', highlight: true }, + { icon: Church, title: 'Create A Church', candles: 100, badge: 'Church Builder', highlight: false }, + { icon: Users, title: 'Create A Ministry', candles: 50, badge: 'Founder', highlight: false }, + ].map((item, i) => { + const staggerClass = `stagger-${(i % 4) + 1}`; + return ( + + {item.highlight && ( +
+ Bonus +
+ )} +
+
+ +
+ {item.badge} +
+

{item.title}

+
+ + {item.candles} + Candles +
+
+ ); + })} +
+
+ +

+ *Candles are awarded upon completion. Maximum 5,000 Candles per person per month. +

+
+
+ + {/* Spend Your Candles */} +
+
+
+

+ Spend Your Candles +

+

+ Unlock powerful AI features that enhance your worship, study, and ministry +

+
+ +
+ {[ + { icon: Sparkles, title: 'ORA™ Insights', desc: 'AI-powered Scripture analysis and spiritual guidance', cost: '5-15', color: 'purple' }, + { icon: Video, title: 'Pro Studio', desc: 'Professional video editing and content creation', cost: '10-50', color: 'amber' }, + { icon: BookOpen, title: 'Study Tools', desc: 'Advanced Bible study and sermon preparation', cost: '5-20', color: 'blue' }, + { icon: MessageSquare, title: 'Content AI', desc: 'Social media posts and ministry content', cost: '5-25', color: 'emerald' }, + ].map((item, i) => { + const colorClasses = { + purple: { bg: 'bg-purple-500/10', border: 'border-purple-500/20', hoverBorder: 'hover:border-purple-500/40', text: 'text-purple-400' }, + amber: { bg: 'bg-amber-500/10', border: 'border-amber-500/20', hoverBorder: 'hover:border-amber-500/40', text: 'text-amber-400' }, + blue: { bg: 'bg-blue-500/10', border: 'border-blue-500/20', hoverBorder: 'hover:border-blue-500/40', text: 'text-blue-400' }, + emerald: { bg: 'bg-emerald-500/10', border: 'border-emerald-500/20', hoverBorder: 'hover:border-emerald-500/40', text: 'text-emerald-400' }, + }; + const colors = colorClasses[item.color as keyof typeof colorClasses]; + const staggerClass = `stagger-${(i % 4) + 1}`; + + return ( + +
+ +
+

{item.title}

+

{item.desc}

+
+ + {item.cost} + Candles per use +
+
+ ); + })} +
+
+
+ + {/* Share the Light */} +
+
+ +
+
+

+ Share the Light +

+

+ Gift your Candles to bless others in their faith journey +

+
+ +
+ {[ + { icon: Users, title: 'Gift to Friends', desc: 'Send Candles to friends and family who need AI features for their spiritual growth', color: 'blue' }, + { icon: Church, title: 'Support Churches', desc: 'Donate to churches and help them access powerful ministry tools', color: 'purple' }, + { icon: Heart, title: 'Fund Missions', desc: 'Empower missionaries and ministry teams with collaborative Candle pools', color: 'emerald' }, + ].map((item, i) => { + const colorClasses = { + blue: { bg: 'bg-blue-500/10', border: 'border-blue-500/20', hoverBorder: 'hover:border-blue-500/40', text: 'text-blue-400', spotlight: 'rgba(59, 130, 246, 0.15)' }, + purple: { bg: 'bg-purple-500/10', border: 'border-purple-500/20', hoverBorder: 'hover:border-purple-500/40', text: 'text-purple-400', spotlight: 'rgba(168, 85, 247, 0.15)' }, + emerald: { bg: 'bg-emerald-500/10', border: 'border-emerald-500/20', hoverBorder: 'hover:border-emerald-500/40', text: 'text-emerald-400', spotlight: 'rgba(16, 185, 129, 0.15)' }, + }; + const colors = colorClasses[item.color as keyof typeof colorClasses]; + const staggerClass = `stagger-${i + 1}`; + + return ( + +
+ +
+

{item.title}

+

{item.desc}

+
+ ); + })} +
+
+
+ + {/* CTA Section */} +
+
+ +
+
+ +
+ +

+ Start Your Candle Journey +

+

+ Join thousands of believers earning and using Candles to enhance their faith journey with AI-powered tools. +

+ +
+ + + + + Get 250 Free Candles + + + + + View Pricing Plans + +
+ +

+ No credit card required • 250 Candles refresh monthly +

+
+
+ + ); +} diff --git a/frontend/app/contact/page.tsx b/frontend/app/contact/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..22a07c5a629df44ff3707796e8d1cd9897766aac --- /dev/null +++ b/frontend/app/contact/page.tsx @@ -0,0 +1,121 @@ +'use client'; + +import { Mail, MessageSquare, MapPin, Send, Sparkles } from 'lucide-react'; +import SpotlightCard from '@/components/SpotlightCard'; +import ShinyButton from '@/components/ShinyButton'; +import Link from 'next/link'; + +export default function ContactPage() { + return ( + <> + {/* Hero */} +
+
+
+ +
+
+ + Contact +
+ +

+ Get in Touch +

+ +

+ Have questions about ORA? We'd love to hear from you. Reach out and we'll respond as soon as we can. +

+
+
+ + {/* Contact Methods */} +
+
+
+ +
+ +
+

Email

+

Send us an email anytime

+ + support@ora.ai + +
+ + +
+ +
+

Chat with ORA

+

Try our AI assistant directly

+ + Open Dashboard + +
+ + +
+ +
+

Documentation

+

Browse our help resources

+ + View Help Center + +
+
+ + {/* Contact Form */} +
+

Send a Message

+
+
+
+ + +
+
+ + +
+
+
+ + +
+
+ + + +
+ +
+ +
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/frontend/inspo/generated-page (1).html b/frontend/inspo/generated-page (1).html new file mode 100644 index 0000000000000000000000000000000000000000..45165d59bc4aadc8d27b406b8bd6d1b2efae9436 --- /dev/null +++ b/frontend/inspo/generated-page (1).html @@ -0,0 +1,441 @@ + + +Aura Financial | Future Banking + + + + + + + + + + + + + + + + +
+
.
+ +
+ + +
+ + + + + +
+ + +
+
+ + + + + Public Beta Now Live +
+

Place every trade with confidence and clarity.

+ +

+ The AI-driven tool that keeps you away from bad setups and guides you toward cleaner entries, smarter profits, and + tighter risk.

+ +
+ +
+
+ + + +
+
+ + + +
+
+ + + + + + + +
+
+ + +
+ +
+
+
+
+ + + \ No newline at end of file diff --git a/frontend/inspo/generated-page.html b/frontend/inspo/generated-page.html new file mode 100644 index 0000000000000000000000000000000000000000..c695bcf90a72f418c1d23b4a47380c9f73dea21f --- /dev/null +++ b/frontend/inspo/generated-page.html @@ -0,0 +1,733 @@ + + +Hemanth Kumar | Finance & AI Strategist + + + + + + + + + + +
+ + + + + +
+ +
+
+ +
+
+
+
+
+
+
+
+ + +
+
+
+ + Open to Work + +
+
+ +

+ FINANCE +
+ & AI Automation +
+

+ +

+ Bridging financial analysis with data-driven efficiency.
+ Power BI, Alteryx, and n8n Expert. +

+ + +
+
+ + +
+
+
+ Power BI + ALTERYX + Python + n8n + Excel + SQL + Financial Modeling + Power BI + ALTERYX + Python + n8n + Excel + SQL + Financial Modeling +
+
+ + +
+
+
+
+
+

+ Technical Expertise +

+
+
+ + Structured Data – Automated Growth + +
+
+ +
+ + +
+
+
+
+ Analysis +

Financial Modeling

+
+ +
+

+ Constructing robust equity research models and valuation frameworks. Expertise in forecasting and scenario analysis. +

+
+ +
+
+
+ Efficiency +

AI Automation

+
+ +
+

+ Building autonomous workflows using n8n and LLMs. Streamlining repetitive tasks to optimize business operations. +

+
+ +
+
+
+ Insight +

Data Analytics

+
+ +
+

+ Transforming raw data into actionable dashboards. Proficient in cleaning, transforming, and visualizing complex datasets. +

+
+
+
+
+
+ + +
+
+ +
+
+
+

+ Financial Intelligence Engines +

+
+

+ Advanced builds combining financial theory with modern data infrastructure. +

+
+ + +
+ + +
+ +
+ +
+ Pre-IPO Analysis +
+
+ + +
+

Pre-IPO Strategic Dashboard (Zepto)

+

+ A DRHP-based valuation model analyzing Zepto's $7-8B target valuation. Features automated visualizations of fresh issue structure, runway analysis, and dark-store unit economics. +

+ + +
+ #IPOAnalysis + #ValuationModeling + #StrategicFinance +
+ + + +
+
+ + +
+ +
+ + +
+ FY25 Monitor +
+
+ + +
+

Corporate Performance Monitor (FY25)

+

+ An automated equity research dashboard tracking key performance indicators (KPIs) for firms like Motilal Oswal & Tata Power. visualizes Operating PAT, Net Worth, and YoY growth trends extracted from annual reports. +

+ + +
+ #EquityResearch + #FinancialReporting + #DataViz +
+ + + +
+
+ + +
+ +
+ +
+ FinSearch Engine +
+
+ + +
+

"FinSearch" Market Intelligence

+

+ A Universal Bloomberg-Terminal style analysis engine designed for Indian listed companies. Features a search-query interface to aggregate real-time stock data and peer comparisons. +

+ + +
+ #FintechProduct + #UXDesign + #MarketAnalytics +
+ + + +
+
+ +
+
+
+ + +
+
+
+
+ Career Path +

Work Experience

+
+
+ +
+
+
+
+
01
+
+
+ Nov '25 - Present +
+

BuyMore

+

Market Research Intern

+

+ Conducting competitor analysis and identifying e-commerce trends. +

+
+
+
+ +
+
+
+
02
+
+
+ Aug - Nov '25 +
+

StartGenie

+

Content Intern

+

+ Pitched AI-based digital marketing solutions and managed client content. +

+
+
+
+ +
+
+
+
03
+
+
+ Jul - Aug '25 +
+

NorthStar

+

Growth Intern

+

+ Utilized Gemini Pro & Notebook LM for curriculum design. +

+
+
+
+
+
+
+ + +
+
+

Certifications & Sims

+
+
+ +
+
+

Official

+
+
+ NISM V-A + Mutual Fund Distributors +
+ +
+
+ +
+

Job Simulations

+
+
+ PwC Power BI + Visualizing Data +
+ Forage +
+
+
+ BCG Strategy + Market Analysis +
+ Forage +
+
+
+
+ + +
+
+
+
+ Connect +

Ready to Collaborate?

+ +
+
+
+ +
+
+

Location

+

Bengaluru, India
Open to Remote

+
+
+
+
+ +
+
+

Social

+ LinkedIn Profile +
+
+
+
+ +
+

Send a Message

+
+
+
+ + +
+
+ + +
+
+
+ + +
+
+ + +
+ +
+
+
+
+ +
+
+ HEMANTH +
+ +
+
+
+
+ HEMANTH +

+ Finance & Accountancy Student. Integrating data analytics and automation to solve structured business problems. +

+
+
+ +
+

Menu

+ +
+ +
+

Tools

+ +
+ +
+

Get in Touch

+

Open for opportunities in Finance, Analytics, and AI Automation.

+ + Email Me + + +
+
+ +
+

© 2025 Hemanth Kumar.

+
+ + Available for Hire +
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/frontend/next.config.js b/frontend/next.config.js new file mode 100644 index 0000000000000000000000000000000000000000..4ea60ff97658ca1e2e00772ec309eb8c3311e47b --- /dev/null +++ b/frontend/next.config.js @@ -0,0 +1,8 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, + output: "export", + images: { unoptimized: true }, +} + +module.exports = nextConfig diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..2533da8c7cbf5f9f4520a9ad0e65d8a570a02ec9 --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,3133 @@ +{ + "name": "ora-frontend", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "ora-frontend", + "version": "1.0.0", + "dependencies": { + "lucide-react": "^0.400.0", + "next": "^14.2.33", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "react-markdown": 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"^3.4.3", + "typescript": "^5.4.0" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..33ad091d26d8a9dc95ebdf616e217d985ec215b8 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..f33ebfd964097ed10c422c9575eda5f1ddb2b0c4 --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,59 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: [ + './pages/**/*.{js,ts,jsx,tsx,mdx}', + './components/**/*.{js,ts,jsx,tsx,mdx}', + './app/**/*.{js,ts,jsx,tsx,mdx}', + ], + theme: { + extend: { + fontFamily: { + sans: ['Inter', 'sans-serif'], + }, + colors: { + brand: { + amber: '#f59e0b', + dark: '#050402', + } + }, + animation: { + 'beam': 'beam 3s linear infinite', + 'spin-slow': 'spin 12s linear infinite', + 'spin-slow-reverse': 'spin 15s linear infinite reverse', + 'pulse-fast': 'pulse 1.5s cubic-bezier(0.4, 0, 0.6, 1) infinite', + 'flicker': 'candle-flicker 4s infinite ease-in-out', + 'float': 'float 6s ease-in-out infinite', + 'sonar': 'sonar-wave 3s cubic-bezier(0, 0, 0.2, 1) infinite', + 'border-spin': 'border-spin 2.5s linear infinite', + 'shimmer': 'shimmer 4s linear infinite', + }, + keyframes: { + beam: { + '0%': { strokeDashoffset: '1000' }, + '100%': { strokeDashoffset: '0' }, + }, + 'candle-flicker': { + '0%, 100%': { transform: 'scale(1)', opacity: '1' }, + '25%': { transform: 'scale(1.02)', opacity: '0.9' }, + '50%': { transform: 'scale(0.98)', opacity: '0.8' }, + '75%': { transform: 'scale(1.01)', opacity: '0.95' }, + }, + float: { + '0%, 100%': { transform: 'translateY(0)' }, + '50%': { transform: 'translateY(-10px)' }, + }, + 'sonar-wave': { + '0%': { r: '10px', opacity: '0.8', strokeWidth: '1px' }, + '100%': { r: 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"^18.3.1", + "tailwind-merge": "^2.3.0" + }, + "devDependencies": { + "@types/react": "^18.3.3", + "@types/react-dom": "^18.3.0", + "@vitejs/plugin-react": "^4.3.1", + "autoprefixer": "^10.4.19", + "eslint": "^8.57.0", + "eslint-plugin-react-hooks": "^4.6.2", + "eslint-plugin-react-refresh": "^0.4.7", + "postcss": "^8.4.39", + "tailwindcss": "^3.4.4", + "typescript": "^5.2.2", + "vite": "^5.3.1" + } +} diff --git a/landing_legacy/src/App.tsx b/landing_legacy/src/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..ef2f536f235f17cb491c15c144b61b73bced7146 --- /dev/null +++ b/landing_legacy/src/App.tsx @@ -0,0 +1,221 @@ +import { useState } from 'react'; +import { motion, AnimatePresence } from 'framer-motion'; +import { Sparkles, MessageSquare, BookOpen, ArrowRight, History, Settings, LogOut } from 'lucide-react'; +import { clsx, type ClassValue } from 'clsx'; +import { twMerge } from 'tailwind-merge'; +import { AuraOrb } from './components/AuraOrb'; +import { AuraChat } from './components/AuraChat'; + +// --- Utils --- +function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)); +} + +// --- Components --- +const Navbar = ({ onStart }: { onStart: () => void }) => ( + +); + +const Sidebar = ({ onViewChange, currentView }: { onViewChange: (v: string) => void, currentView: string }) => ( +
+
+
+ +
+ ORA AURA +
+ +
+ {[ + { id: 'chat', label: 'Spiritual Study', icon: MessageSquare }, + { id: 'memory', label: 'Episodic Memory', icon: History }, + { id: 'journal', label: 'Growth Journal', icon: BookOpen }, + ].map(item => ( + + ))} +
+ +
+ + +
+
+); + +const Hero = ({ onStart }: { onStart: () => void }) => { + return ( +
+ {/* Background Glow */} +
+ +
+ +
+ + + + + SYSTEM ONLINE: v1.0.0 +
+ +

+ Your AI Spiritual
Companion. +

+ +

+ Observe. Reflect. Act.
+ ORA uses advanced intelligence to guide your spiritual journey, grounded in biblical wisdom. +

+
+ + + + + +
+
+ ); +}; + +const FeatureCard = ({ icon: Icon, title, desc }: { icon: any, title: string, desc: string }) => ( +
+
+ +
+

{title}

+

{desc}

+
+); + +const Features = () => ( +
+
+
+ + + +
+
+
+); + +function App() { + const [view, setView] = useState<'landing' | 'dashboard'>('landing'); + const [dashView, setDashView] = useState('chat'); + + return ( +
+ + {view === 'landing' ? ( + + setView('dashboard')} /> + setView('dashboard')} /> + +
+

© 2025 SoapBox Super App. Powered by ORA Intelligence.

+
+
+ ) : ( + + +
+ {/* Dashboard Background */} +
+ + {/* Left Side: Aura Orb */} +
+
+

The Presence

+

ORA's focused state. Watch the aura shift as we reason together.

+
+ +
+ + {/* Right Side: Chat */} +
+ +
+
+
+ )} +
+
+ ); +} + +export default App; diff --git a/landing_legacy/src/components/AuraChat.tsx b/landing_legacy/src/components/AuraChat.tsx new file mode 100644 index 0000000000000000000000000000000000000000..b8554803bc0a7f83756b85aa8a768c91ddbe1316 --- /dev/null +++ b/landing_legacy/src/components/AuraChat.tsx @@ -0,0 +1,135 @@ +import { useState, useRef, useEffect } from 'react'; +import { motion, AnimatePresence } from 'framer-motion'; +import { Send, User, Bot, Sparkles } from 'lucide-react'; + +interface Message { + id: string; + role: 'user' | 'assistant'; + content: string; + agent?: string; +} + +export const AuraChat = () => { + const [messages, setMessages] = useState([ + { id: '1', role: 'assistant', content: 'Peace be with you. I am ORA. How may I guide your reflections today?', agent: 'Gatekeeper' } + ]); + const [input, setInput] = useState(''); + const [isTyping, setIsTyping] = useState(false); + const scrollRef = useRef(null); + + useEffect(() => { + if (scrollRef.current) { + scrollRef.current.scrollTop = scrollRef.current.scrollHeight; + } + }, [messages]); + + const handleSend = async () => { + if (!input.trim()) return; + + const userMsg: Message = { id: Date.now().toString(), role: 'user', content: input }; + setMessages(prev => [...prev, userMsg]); + setInput(''); + setIsTyping(true); + + try { + const response = await fetch('http://localhost:8000/api/chat', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ message: input }) + }); + const data = await response.json(); + + if (data.success) { + const assistantMsg: Message = { + id: (Date.now() + 1).toString(), + role: 'assistant', + content: data.response.content, + agent: data.response.agent + }; + setMessages(prev => [...prev, assistantMsg]); + } + } catch (error) { + console.error('Chat failed:', error); + } finally { + setIsTyping(false); + } + }; + + return ( +
+ {/* Header */} +
+
+
+ Aura Intelligence Stream +
+
+ + {/* Messages */} +
+ + {messages.map((m) => ( + +
+
+ {m.role === 'user' ? : } +
+
+ {m.agent && ( +
+ + Agent: {m.agent} +
+ )} +
+ {m.content} +
+
+
+
+ ))} +
+ {isTyping && ( +
+
+
+ + + +
+
+
+ )} +
+ + {/* Input */} +
+
+ setInput(e.target.value)} + onKeyPress={(e) => e.key === 'Enter' && handleSend()} + placeholder="Ask ORA anything..." + className="w-full bg-black/50 border border-white/10 rounded-full py-3 pl-6 pr-14 focus:outline-none focus:border-cyan-500/50 transition-colors text-sm" + /> + +
+
+
+ ); +}; diff --git a/landing_legacy/src/components/AuraOrb.tsx b/landing_legacy/src/components/AuraOrb.tsx new file mode 100644 index 0000000000000000000000000000000000000000..a734a198b58f3d28ae66e396abb064acaff62f51 --- /dev/null +++ b/landing_legacy/src/components/AuraOrb.tsx @@ -0,0 +1,47 @@ +import { motion } from 'framer-motion'; + +export const AuraOrb = ({ state = 'idle' }: { state: 'idle' | 'thinking' | 'talking' | 'reasoning' }) => { + const states = { + idle: { + scale: [1, 1.05, 1], + opacity: 0.6, + background: 'radial-gradient(circle, rgba(34, 211, 238, 0.4) 0%, rgba(34, 211, 238, 0) 70%)', + boxShadow: '0 0 20px rgba(34, 211, 238, 0.3)', + }, + thinking: { + scale: [1, 1.2, 1], + opacity: 0.8, + background: 'radial-gradient(circle, rgba(168, 85, 247, 0.5) 0%, rgba(168, 85, 247, 0) 70%)', + boxShadow: '0 0 40px rgba(168, 85, 247, 0.5)', + }, + reasoning: { + scale: [1, 1.1, 1], + opacity: 1, + background: 'radial-gradient(circle, rgba(234, 179, 8, 0.5) 0%, rgba(234, 179, 8, 0) 70%)', + boxShadow: '0 0 50px rgba(234, 179, 8, 0.6)', + }, + talking: { + scale: [1, 1.15, 1], + opacity: 0.9, + background: 'radial-gradient(circle, rgba(16, 185, 129, 0.5) 0%, rgba(16, 185, 129, 0) 70%)', + boxShadow: '0 0 35px rgba(16, 185, 129, 0.4)', + } + }; + + return ( +
+ +
+
+
+
+ ); +}; diff --git a/landing_legacy/src/index.css b/landing_legacy/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..7c9925e9298961c03d85eccc72033a317b01a744 --- /dev/null +++ b/landing_legacy/src/index.css @@ -0,0 +1,29 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + background-color: #050505; + color: #ffffff; +} + +body { + margin: 0; + min-height: 100vh; + -webkit-font-smoothing: antialiased; +} + +/* Custom Scrollbar for "Futuristic" feel */ +::-webkit-scrollbar { + width: 8px; +} +::-webkit-scrollbar-track { + background: #09090b; +} +::-webkit-scrollbar-thumb { + background: #27272a; + border-radius: 4px; +} +::-webkit-scrollbar-thumb:hover { + background: #3f3f46; +} diff --git a/landing_legacy/src/main.tsx b/landing_legacy/src/main.tsx new file mode 100644 index 0000000000000000000000000000000000000000..379df9710676857034c5c97e77f983e5847afc6a --- /dev/null +++ b/landing_legacy/src/main.tsx @@ -0,0 +1,10 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App.tsx' +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/landing_legacy/tailwind.config.js b/landing_legacy/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..efa342db62c419bad68d03970cf56983f91b5c89 --- /dev/null +++ b/landing_legacy/tailwind.config.js @@ -0,0 +1,33 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: { + colors: { + // "Aura" inspired dark palette + background: '#050505', + surface: '#09090b', // zinc-950 + primary: '#22d3ee', // cyan-400 + secondary: '#a1a1aa', // zinc-400 + accent: '#10b981', // emerald-500 + }, + fontFamily: { + sans: ['Inter', 'sans-serif'], + display: ['Inter', 'sans-serif'], // Ideally would be Geist or Plus Jakarta + }, + animation: { + 'glow': 'glow 2s ease-in-out infinite alternate', + }, + keyframes: { + glow: { + '0%': { boxShadow: '0 0 20px -10px rgba(34, 211, 238, 0.5)' }, + '100%': { boxShadow: '0 0 30px -5px rgba(34, 211, 238, 0.8)' }, + } + } + }, + }, + plugins: [], +} diff --git a/landing_legacy/tsconfig.json b/landing_legacy/tsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..50b948db7ecd855be54e723f636db499927290ce --- /dev/null +++ b/landing_legacy/tsconfig.json @@ -0,0 +1,31 @@ +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": [ + "ES2020", + "DOM", + "DOM.Iterable" + ], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true + }, + "include": [ + "src" + ], + "references": [ + { + "path": "./tsconfig.node.json" + } + ] +} \ No newline at end of file diff --git a/landing_legacy/tsconfig.node.json b/landing_legacy/tsconfig.node.json new file mode 100644 index 0000000000000000000000000000000000000000..b85dd47b4c229882e4e737bf0994875ce5812c76 --- /dev/null +++ b/landing_legacy/tsconfig.node.json @@ -0,0 +1,12 @@ +{ + "compilerOptions": { + "composite": true, + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true + }, + "include": [ + "vite.config.ts" + ] +} \ No newline at end of file diff --git a/landing_legacy/vite.config.ts b/landing_legacy/vite.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..92babc14138e47f703b94ec5c8b98528eee94a31 --- /dev/null +++ b/landing_legacy/vite.config.ts @@ -0,0 +1,7 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vitejs.dev/config/ +export default defineConfig({ + plugins: [react()], +}) diff --git a/logs/ingest_debug.txt b/logs/ingest_debug.txt new file mode 100644 index 0000000000000000000000000000000000000000..c4e668e27df17f60847d83dcbf7be67433293ee5 Binary files /dev/null and b/logs/ingest_debug.txt differ diff --git a/logs/swarm_verify.txt b/logs/swarm_verify.txt new file mode 100644 index 0000000000000000000000000000000000000000..efad476c7314d3a65c4bffc51ec5260116c26b6d Binary files /dev/null and b/logs/swarm_verify.txt differ diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2fe541437cddee7e638e3ed16c82f0082fba7c --- /dev/null +++ b/main.py @@ -0,0 +1,33 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +import uvicorn +from app.core.config import settings +from app.api import routes + +app = FastAPI( + title=settings.PROJECT_NAME, + version=settings.PROJECT_VERSION +) + +# CORS Configuration +app.add_middleware( + CORSMiddleware, + allow_origins=settings.CORS_ORIGINS, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Register Routes +app.include_router(routes.router, prefix="/api") + +@app.get("/health") +async def health_check(): + return { + "status": "active", + "service": "ORA-AI-Python", + "model_target": settings.LLM_BASE_URL + } + +if __name__ == "__main__": + uvicorn.run("main:app", host=settings.HOST, port=settings.PORT, reload=True) diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..aa84894907d95edd0b1bad819d3a377e0bf8d991 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,1841 @@ +{ + "name": "ora-ai-service", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "ora-ai-service", + "version": "1.0.0", + "license": "ISC", + "dependencies": { + "cors": "^2.8.5", + "dotenv": "^16.4.5", + "express": "^4.19.2", + "helmet": "^7.1.0", + "openai": "^4.52.0", + "zod": "^3.23.8" + }, + "devDependencies": { + 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b/scripts/Modelfile.expert @@ -0,0 +1,19 @@ +# Modelfile for Christian-Bible-Expert-v2.0-12B +# Base: Mistral-Nemo-12B +# This model is fine-tuned for high accuracy in scripture retrieval and theological reasoning. + +# You must download the GGUF first and update the path below: +# Fallback if user wants to pull a base, but ideally uses the specific GGUF +FROM llama3 + +PARAMETER temperature 0.3 +PARAMETER top_p 0.9 + +SYSTEM """ +You are ORA (Omniscient Religious Assistant), a sovereign Theological Reasoner. +Your foundation is the Christian Bible and sound doctrine. +- Use the provided context from the Bible RAG system for every answer. +- Cite specific Book, Chapter, and Verse. +- If a question is outside your theological scope, steer it back to spiritual growth. +- Avoid hallucinating verses. If you aren't sure of a reference, say so. +""" diff --git a/scripts/Modelfile.gabriel b/scripts/Modelfile.gabriel new file mode 100644 index 0000000000000000000000000000000000000000..67298427fe77d8141c071a02fffebd5cc7d7f2cb --- /dev/null +++ b/scripts/Modelfile.gabriel @@ -0,0 +1,15 @@ +# Modelfile for Gabriel-8x7B-Instruct-v0.1 +# Base: Mixtral-8x7B +# This model is a powerful Mixture-of-Experts fine-tuned on 36k Christian records. + +FROM mixtral + +PARAMETER temperature 0.4 +PARAMETER stop "" + +SYSTEM """ +You are Gabriel, a deeply knowledgeable Biblical Scholar and ORA's reasoning engine. +- Provide comprehensive, cross-referenced theological analysis. +- Maintain a tone of reverence and academic rigor. +- Distinguish between primary scripture and historical commentary. +""" diff --git a/scripts/benchmark_models.py b/scripts/benchmark_models.py new file mode 100644 index 0000000000000000000000000000000000000000..348f8f42aa9d7e2cb16909188729e466f3dc6c3d --- /dev/null +++ b/scripts/benchmark_models.py @@ -0,0 +1,75 @@ +import asyncio +import json +import os +from datetime import datetime +from app.services.llm import LLMService +from app.core.config import settings + +class BenchmarkManager: + TEST_QUERIES = [ + "What is the biblical basis for Grace vs Works?", + "Explain the role of the Holy Spirit in a believer's daily life.", + "How should a Christian respond to suffering according to the book of Job?", + "Explain the concept of 'Stillness' or 'Sabbath' in modern terms.", + "What does the Bible say about caring for the poor and marginalized?" + ] + + RESULTS_DIR = "important/benchmarks" + + def __init__(self): + if not os.path.exists(self.RESULTS_DIR): + os.makedirs(self.RESULTS_DIR, exist_ok=True) + self.llm = LLMService() + + async def run_benchmark(self, model_name: str): + print(f"\n{'='*50}") + print(f"Benchmark: Running tests for model {model_name}...") + print(f"{'='*50}") + + # Patch the model name in settings + original_model = settings.MODEL_NAME + settings.MODEL_NAME = model_name + + results = { + "model": model_name, + "timestamp": datetime.now().isoformat(), + "tests": [] + } + + for query in self.TEST_QUERIES: + print(f"Testing query: {query}") + try: + # Re-initialize or clear offline state if needed + self.llm.is_offline = False + response = await self.llm.generate_response(query) + results["tests"].append({ + "query": query, + "response": response.get("content", ""), + "tool_calls": str(response.get("tool_calls", [])) + }) + except Exception as e: + print(f"Error during benchmark: {str(e)}") + results["tests"].append({"query": query, "error": str(e)}) + + # Restore original settings + settings.MODEL_NAME = original_model + + # Save results + safe_model_name = model_name.replace(':', '_').replace('/', '_') + filename = f"benchmark_{safe_model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + with open(os.path.join(self.RESULTS_DIR, filename), "w", encoding="utf-8") as f: + json.dump(results, f, indent=2) + print(f"Benchmark: Results saved to {filename}") + +if __name__ == "__main__": + benchmark = BenchmarkManager() + + async def main(): + print("Starting ORA Model Benchmarks...") + + # 2. Test Gabriel-Mini (llama3.2:1b) + await benchmark.run_benchmark("llama3.2:1b") + + print("\nBenchmarks complete. Visit important/benchmarks/ to see the reports.") + + asyncio.run(main()) diff --git a/scripts/chat_ora.py b/scripts/chat_ora.py new file mode 100644 index 0000000000000000000000000000000000000000..0850f88f0e9bfb109f5059a11fb7b217e63499dc --- /dev/null +++ b/scripts/chat_ora.py @@ -0,0 +1,90 @@ +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from peft import PeftModel, PeftConfig +import sys + +# Settings +BASE_MODEL = "unsloth/Llama-3.2-1B-Instruct" +ADAPTER_PATH = "important/finetuning/models/ora_adapter" + +def chat(): + print("Loading ORA (may take a minute)...") + device = "cuda" if torch.cuda.is_available() else "cpu" + print(f"Device: {device}") + + # 1. Load Base Model + tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) + model = AutoModelForCausalLM.from_pretrained( + BASE_MODEL, + torch_dtype=torch.float16 if device == "cuda" else torch.float32, + device_map=device, + low_cpu_mem_usage=True + ) + + # 2. Load Adapter + print(f"Loading adapter from {ADAPTER_PATH}...") + try: + model = PeftModel.from_pretrained(model, ADAPTER_PATH) + print("Adapter loaded successfully!") + except Exception as e: + print(f"Error loading adapter: {e}") + print("Running with Base Model only.") + + # 3. Chat Loop + print("\n" + "="*40) + print("ORA: Peace be with you. How can I guide you today?") + print("="*40 + "\n") + + history = [] + + # System Prompt + system_prompt = "You are ORA, a spiritual assistant specializing in theological insights and biblical wisdom. Provide discerning, compassionate, and doctrine-aware responses." + + while True: + try: + user_input = input("You: ") + if user_input.lower() in ["quit", "exit"]: + break + + # Construct Prompt (Llama 3 format) + messages = [ + {"role": "system", "content": system_prompt}, + ] + # Add history (last 2 turns context) + messages.extend(history[-4:]) + messages.append({"role": "user", "content": user_input}) + + input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" + ).to(device) + + terminators = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("<|eot_id|>") + ] + + outputs = model.generate( + input_ids, + max_new_tokens=256, + eos_token_id=terminators, + do_sample=True, + temperature=0.7, + top_p=0.9, + ) + + response = outputs[0][input_ids.shape[-1]:] + decoded_response = tokenizer.decode(response, skip_special_tokens=True) + + print(f"ORA: {decoded_response}\n") + + history.append({"role": "user", "content": user_input}) + history.append({"role": "assistant", "content": decoded_response}) + + except KeyboardInterrupt: + print("\nExiting...") + break + +if __name__ == "__main__": + chat() diff --git a/scripts/colab_server_setup.py b/scripts/colab_server_setup.py new file mode 100644 index 0000000000000000000000000000000000000000..00af7eab0fcdb8b71c70d6b52f769779de0b3cc4 --- /dev/null +++ b/scripts/colab_server_setup.py @@ -0,0 +1,96 @@ +# PASTE THIS INTO A COLAB CELL to run your Backend on a GPU! + +import os +from google.colab import userdata + +# 1. Install Dependencies +print("Installing dependencies...") +!pip install -q fastapi uvicorn pyngrok nest_asyncio unsloth[colab-new] + +# 2. Setup Ngrok (You need a free token from https://dashboard.ngrok.com/get-started/your-authtoken) +# Replace 'YOUR_TOKEN' below or set it in Colab Secrets +NGROK_TOKEN = "YOUR_NGROK_TOKEN_HERE" +from pyngrok import ngrok +ngrok.set_auth_token(NGROK_TOKEN) + +# 3. Create the Server Code (Writing to file in Colab) +server_code = """ +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from peft import PeftModel +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +import uvicorn +import os + +app = FastAPI() + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +BASE_MODEL = "unsloth/Llama-3.2-1B-Instruct" +# Check if adapter exists in the uploaded files, else just base +ADAPTER_PATH = "/content/important/finetuning/models/ora_adapter" + +model = None +tokenizer = None + +class ChatRequest(BaseModel): + message: str + history: list = [] + +@app.on_event("startup") +async def load_model(): + global model, tokenizer + print("Loading Model...") + tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) + base_model = AutoModelForCausalLM.from_pretrained( + BASE_MODEL, + torch_dtype=torch.float16, + device_map="cuda", + load_in_4bit=True, + ) + + if os.path.exists(ADAPTER_PATH): + print(f"Loading Adapter from {ADAPTER_PATH}") + model = PeftModel.from_pretrained(base_model, ADAPTER_PATH) + else: + print("Adapter not found at path, using Base!") + model = base_model + print("Ready!") + +@app.post("/api/chat") +async def chat(req: ChatRequest): + messages = [{"role": "system", "content": "You are ORA, a spiritual assistant."}] + messages.extend(req.history[-4:]) + messages.append({"role": "user", "content": req.message}) + + input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda") + + outputs = model.generate(input_ids, max_new_tokens=256, temperature=0.7) + response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True) + return {"response": response} + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8000) +""" + +with open("server.py", "w") as f: + f.write(server_code) + +# 4. Run Server & Tunnel +import nest_asyncio +nest_asyncio.apply() + +# Start Tunnel +public_url = ngrok.connect(8000).public_url +print(f"\\n\\nYOUR PUBLIC API URL IS: {public_url}\\n\\n") + +# Run Server +!python server.py diff --git a/scripts/consolidate_datasets.py b/scripts/consolidate_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..8471c19c33a1050ac8a569415f391deb59638466 --- /dev/null +++ b/scripts/consolidate_datasets.py @@ -0,0 +1,67 @@ +import json +import glob +import os + +def format_llama3(system, user, assistant): + return f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n{assistant}<|eot_id|>" + +def consolidate(): + output_file = "important/curated_data/final_ora_dataset.jsonl" + system_prompt = "You are ORA, a spiritual assistant specializing in theological insights and biblical wisdom. Provide discerning, compassionate, and doctrine-aware responses." + + curated_files = glob.glob("important/curated_data/*.jsonl") + total_samples = 0 + + print(f"Consolidating {len(curated_files)} files...") + + with open(output_file, "w", encoding="utf-8") as out: + for f in curated_files: + # Skip the output file itself + if "final_ora_dataset" in f: continue + + print(f"Processing {os.path.basename(f)}...") + with open(f, "r", encoding="utf-8") as infile: + for line in infile: + sample = json.loads(line) + text = "" + + # 1. theology_qa mapping + if "title" in sample and "chunked" in sample: + user = f"Explain the key themes and context of '{sample['title']}'." + assistant = sample['chunked'] + text = format_llama3(system_prompt, user, assistant) + + # 2. oasst1 or already formatted datasets (with instruction/response) + elif "instruction" in sample and "response" in sample: + text = format_llama3(system_prompt, sample['instruction'], sample['response']) + + # 3. databricks-dolly + elif "context" in sample and "instruction" in sample: + user = f"{sample['instruction']}\n\nContext: {sample['context']}" + assistant = sample['response'] + text = format_llama3(system_prompt, user, assistant) + + # 5. openbible (context + response) + elif "context" in sample and "response" in sample: + user = f"Please provide the scripture text for: {sample['context']}" + assistant = sample['response'] + text = format_llama3(system_prompt, user, assistant) + + # 4. generic 'text' (already formatted in curated_spiritual_dataset.jsonl) + elif "text" in sample: + raw_text = sample['text'] + # If it already has Llama 3 tags, use as is + if "<|start_header_id|>" in raw_text: + text = raw_text + else: + # Wrap raw text as an explanation + text = format_llama3(system_prompt, "Please provide spiritual insight on the following:", raw_text) + + if text: + out.write(json.dumps({"text": text}) + "\n") + total_samples += 1 + + print(f"Successfully consolidated {total_samples} samples to {output_file}") + +if __name__ == "__main__": + consolidate() diff --git a/scripts/count_curated.py b/scripts/count_curated.py new file mode 100644 index 0000000000000000000000000000000000000000..3921cb6c9cdc962519c4ca617499e2d336272e98 --- /dev/null +++ b/scripts/count_curated.py @@ -0,0 +1,17 @@ +import glob +import os + +def count_samples(): + path = "important/curated_data/*.jsonl" + files = glob.glob(path) + print(f"Counting samples in {len(files)} files...") + total = 0 + for f in files: + with open(f, "r", encoding="utf-8") as file: + count = sum(1 for line in file) + print(f"{os.path.basename(f)}: {count} samples") + total += count + print(f"Total curated samples: {total}") + +if __name__ == "__main__": + count_samples() diff --git a/scripts/dataset_manager.py b/scripts/dataset_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..3e615830815bc662f67a97566f43258e3d0ad055 --- /dev/null +++ b/scripts/dataset_manager.py @@ -0,0 +1,110 @@ +import os +import json +from datasets import load_dataset +from tqdm import tqdm + +class DatasetManager: + CURATED_DIR = "important/curated_data" + KEYWORDS = [ + "spirituality", "theology", "bible", "prayer", "faith", "god", + "ethics", "morality", "wisdom", "soul", "purpose", "grace", + "scripture", "doctrine", "philosophy", "virtue", "compassion", + "holy", "salvation", "pastoral", "discernment", "lectio", "divina", + "sacred", "revelation", "prophet", "apostle", "commandment", + "monastery", "meditation", "enlightenment", "hermeneutics" + ] + + def __init__(self): + if not os.path.exists(self.CURATED_DIR): + os.makedirs(self.CURATED_DIR, exist_ok=True) + + def curate_from_local_file(self, file_path: str, dataset_type: str = "auto"): + """ + Processes a locally downloaded file (JSONL/JSON) and filters for spiritual content. + Useful for Kaggle / Pile downloads. + """ + print(f"DatasetManager: Processing local file {file_path}...") + if not os.path.exists(file_path): + print(f"Error: File {file_path} not found.") + return + + curated = [] + count = 0 + total = 0 + + try: + with open(file_path, "r", encoding="utf-8") as f: + for line in tqdm(f): + total += 1 + sample = json.loads(line) + + # Unified filtering logic + text = self._extract_text(sample, dataset_type) + text_lower = text.lower() + + if any(k in text_lower for k in self.KEYWORDS): + curated.append(sample) + count += 1 + + if curated: + filename = os.path.basename(file_path) + output_file = os.path.join(self.CURATED_DIR, f"local_{filename}_curated.jsonl") + with open(output_file, "w", encoding="utf-8") as f: + for s in curated: + f.write(json.dumps(s) + "\n") + print(f"DatasetManager: Saved {count} samples (from {total}) to {output_file}") + else: + print("DatasetManager: No spiritual content found.") + + except Exception as e: + print(f"DatasetManager Error: {str(e)}") + + def _extract_text(self, sample: dict, ds_type: str = "auto") -> str: + """Heuristic for extracting text from various dataset schemas.""" + # Common keys + if "text" in sample: return str(sample["text"]) + if "instruction" in sample and "response" in sample: + return f"{sample['instruction']} {sample['response']}" + if "inputs" in sample and "targets" in sample: + return f"{sample['inputs']} {sample['targets']}" + if "content" in sample: return str(sample["content"]) + + # Fallback: flatten and join all values + return " ".join(str(v) for v in sample.values()) + + def curate_from_hf(self, dataset_path: str, split: str = "train", limit: int = 5000): + """ + Streams a dataset from HuggingFace and filters for spiritual/ethical content. + """ + print(f"DatasetManager: Loading {dataset_path} ({split})...") + try: + ds = load_dataset(dataset_path, split=split, streaming=True) + + count = 0 + curated = [] + + print(f"DatasetManager: Scanning first {limit} samples...") + for sample in tqdm(ds.take(limit)): + text = self._extract_text(sample) + text_lower = text.lower() + if any(k in text_lower for k in self.KEYWORDS): + curated.append(sample) + count += 1 + + if curated: + output_file = os.path.join(self.CURATED_DIR, f"{dataset_path.replace('/', '_')}_curated.jsonl") + with open(output_file, "w", encoding="utf-8") as f: + for s in curated: + f.write(json.dumps(s) + "\n") + print(f"DatasetManager: Saved {count} spiritual samples to {output_file}") + else: + print("DatasetManager: No spiritual samples found in this batch.") + + except Exception as e: + print(f"DatasetManager Error: {str(e)}") + +if __name__ == "__main__": + manager = DatasetManager() + + # Curation: HuggingFace specialized datasets + manager.curate_from_hf("oliverbob/openbible", split="train", limit=5000) diff --git a/scripts/debug_keys.py b/scripts/debug_keys.py new file mode 100644 index 0000000000000000000000000000000000000000..4683b39b13e3dc53ba9e1197e23dca9ed4283352 --- /dev/null +++ b/scripts/debug_keys.py @@ -0,0 +1,8 @@ +from datasets import load_dataset + +ds = load_dataset("odunola/bible-reference-sentence-pair", split="train", streaming=True) +print("Fetching 1 item...") +for item in ds: + print(f"Item Keys (repr): {[repr(k) for k in item.keys()]}") + print(f"Headers Content: {item.get('headers')}") + break diff --git a/scripts/fix_adapter_path.py b/scripts/fix_adapter_path.py new file mode 100644 index 0000000000000000000000000000000000000000..59da18fd41a06e50a3dfc292fdf0ec2dae7a9a26 --- /dev/null +++ b/scripts/fix_adapter_path.py @@ -0,0 +1,19 @@ +import shutil +import os + +base = "important/finetuning/models/ora_adapter" +nested = os.path.join(base, "important/finetuning/models/ora_adapter") + +if os.path.exists(nested): + print(f"Found nested files in {nested}") + for f in os.listdir(nested): + src = os.path.join(nested, f) + dst = os.path.join(base, f) + print(f"Moving {f} -> {dst}") + shutil.move(src, dst) + + # Clean up empty dirs + shutil.rmtree(os.path.join(base, "important")) + print("Cleanup complete.") +else: + print("No nested structure found.") diff --git a/scripts/ingest_bible.py b/scripts/ingest_bible.py new file mode 100644 index 0000000000000000000000000000000000000000..d3ed2fb70ee4a95413d13ec967db4057b7b0e7e7 --- /dev/null +++ b/scripts/ingest_bible.py @@ -0,0 +1,80 @@ +import asyncio +import lancedb +from lancedb.pydantic import LanceModel, Vector +from datasets import load_dataset +import sys +import os + +# Add project root to path BEFORE importing app modules +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from app.services.llm import llm_service + +class BibleVerse(LanceModel): + reference: str + text: str + vector: Vector(128) # Default mock dimension + +DB_PATH = "data/lancedb_storage" +TABLE_NAME = "bible_verses" + +async def ingest(): + print("--- Starting Bible Ingestion ---") + + # 1. Connect to DB + if not os.path.exists("data"): + os.makedirs("data") + db = lancedb.connect(DB_PATH) + + # 2. Load Dataset + print("Loading 'odunola/bible-reference-sentence-pair' (streaming)...") + ds = load_dataset("odunola/bible-reference-sentence-pair", split="train", streaming=True) + + # For MVP, let's take first 500 verses + verses = [] + print("Fetching first 500 items...") + for item in ds: + verses.append(item) + if len(verses) >= 500: + break + + print(f"Processing {len(verses)} verses...") + + data_to_insert = [] + + # Correct keys found via debug: + ref_key = 'references' + text_key = 'headers' + + print(f"Using Ref Key: {ref_key}, Text Key: {text_key}") + + # Detect dimension + first_text = verses[0][text_key] + first_vec = await llm_service.get_embedding(first_text) + dim = len(first_vec) + print(f"Detected embedding dimension: {dim}") + + for i, item in enumerate(verses): + ref = item.get(ref_key, "Unknown") + text = item.get(text_key, "") + + if not text: + continue + + vec = await llm_service.get_embedding(text) + + data_to_insert.append({ + "reference": ref, + "text": text, + "vector": vec + }) + + if (i+1) % 50 == 0: + print(f"Processed {i+1}...") + + print("Writing to LanceDB...") + db.create_table(TABLE_NAME, data=data_to_insert, mode="overwrite") + print("Ingestion Complete!") + +if __name__ == "__main__": + asyncio.run(ingest()) diff --git a/scripts/inspect_dataset.py b/scripts/inspect_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..52d4b4694628b280a61bfa8d922644b9a2f4bb04 --- /dev/null +++ b/scripts/inspect_dataset.py @@ -0,0 +1,17 @@ +from datasets import load_dataset + +def inspect(): + print("Loading dataset (streaming)...") + # Streaming to avoid downloading everything if it's huge + ds = load_dataset("bible-nlp/biblenlp-corpus", split="train", streaming=True, trust_remote_code=True) + + print("Fetching first 5 examples...") + count = 0 + for example in ds: + print(example) + count += 1 + if count >= 5: + break + +if __name__ == "__main__": + inspect() diff --git a/scripts/inspect_v2.py b/scripts/inspect_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..6b2445e8f6e05306d351e3fc1e492fb7839561dc --- /dev/null +++ b/scripts/inspect_v2.py @@ -0,0 +1,32 @@ +from datasets import load_dataset +import lancedb +import pandas as pd +import shutil + +def inspect_and_verify(): + print("--- 1. Inspecting 'odunola/bible-reference-sentence-pair' ---") + try: + ds = load_dataset("odunola/bible-reference-sentence-pair", split="train", streaming=True) + print("Success! First 3 examples:") + count = 0 + for ex in ds: + print(ex) + count += 1 + if count >= 3: + break + except Exception as e: + print(f"Failed to load dataset: {e}") + + print("\n--- 2. Verifying LanceDB ---") + try: + db = lancedb.connect("data/lancedb_test") + df = pd.DataFrame({"vector": [[1.1, 1.2], [2.1, 2.2]], "item": ["foo", "bar"]}) + tbl = db.create_table("test_table", data=df, mode="overwrite") + print(f"Created table. Count: {len(tbl)}") + shutil.rmtree("data/lancedb_test") + print("LanceDB verified.") + except Exception as e: + print(f"LanceDB failure: {e}") + +if __name__ == "__main__": + inspect_and_verify() diff --git a/scripts/ora_gradio.py b/scripts/ora_gradio.py new file mode 100644 index 0000000000000000000000000000000000000000..4f560092daea5caa229103ce087b8e830e4a5a75 --- /dev/null +++ b/scripts/ora_gradio.py @@ -0,0 +1,80 @@ +import gradio as gr +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from peft import PeftModel +import os + +# Settings +BASE_MODEL = "unsloth/Llama-3.2-1B-Instruct" +ADAPTER_PATH = "important/finetuning/models/ora_adapter" + +# Global Model +model = None +tokenizer = None +device = "cuda" if torch.cuda.is_available() else "cpu" + +def load_model(): + global model, tokenizer + print(f"Loading ORA Model on {device}...") + tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) + base_model = AutoModelForCausalLM.from_pretrained( + BASE_MODEL, + torch_dtype=torch.float16 if device == "cuda" else torch.float32, + device_map=device, + low_cpu_mem_usage=True + ) + + if os.path.exists(ADAPTER_PATH): + print(f"Loading adapter from {ADAPTER_PATH}...") + model = PeftModel.from_pretrained(base_model, ADAPTER_PATH) + else: + model = base_model + print("Model Loaded.") + +def chat_response(message, history): + system_prompt = "You are ORA, a spiritual assistant specializing in theological insights and biblical wisdom. Provide discerning, compassionate, and doctrine-aware responses." + + # Simple history construction + # Gradio history is [[user, bot], [user, bot]] + messages = [{"role": "system", "content": system_prompt}] + + for human, assistant in history: + messages.append({"role": "user", "content": human}) + messages.append({"role": "assistant", "content": assistant}) + + messages.append({"role": "user", "content": message}) + + input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" + ).to(device) + + terminators = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("<|eot_id|>") + ] + + outputs = model.generate( + input_ids, + max_new_tokens=256, + eos_token_id=terminators, + do_sample=True, + temperature=0.7, + top_p=0.9, + ) + + response_tokens = outputs[0][input_ids.shape[-1]:] + response = tokenizer.decode(response_tokens, skip_special_tokens=True) + return response + +# Load now +load_model() + +# UI +with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple")) as demo: + gr.Markdown("# ORA Spiritual Assistant") + gr.ChatInterface(fn=chat_response) + +if __name__ == "__main__": + demo.launch(share=True) diff --git a/scripts/prepare_colab.py b/scripts/prepare_colab.py new file mode 100644 index 0000000000000000000000000000000000000000..480a259c6694a4416f1e6c907bfa530af6807a13 --- /dev/null +++ b/scripts/prepare_colab.py @@ -0,0 +1,26 @@ +import zipfile +import os + +def prepare_colab_bundle(): + bundle_name = "ora_colab_training.zip" + files_to_include = [ + ("important/curated_data/final_ora_dataset.jsonl", "important/curated_data/final_ora_dataset.jsonl"), + ("scripts/train_ora.py", "scripts/train_ora.py"), + ("training/requirements.txt", "requirements.txt") + ] + + print(f"Preparing bundle: {bundle_name}...") + + with zipfile.ZipFile(bundle_name, 'w', zipfile.ZIP_DEFLATED) as zipf: + for src, dest in files_to_include: + if os.path.exists(src): + print(f"Adding {src} as {dest}...") + zipf.write(src, arcname=dest) + else: + print(f"Warning: {src} not found!") + + print(f"\nBundle ready: {os.path.abspath(bundle_name)}") + print("Next steps: Upload this ZIP to Google Colab, unzip it, and use the unsloth version of the training script.") + +if __name__ == "__main__": + prepare_colab_bundle() diff --git a/scripts/setup_ssh.py b/scripts/setup_ssh.py new file mode 100644 index 0000000000000000000000000000000000000000..3c10049bcc1f35365143ead0604d16bcfe967798 --- /dev/null +++ b/scripts/setup_ssh.py @@ -0,0 +1,19 @@ +# Install colab_ssh +try: + import colab_ssh +except ImportError: + import subprocess + subprocess.check_call(["pip", "install", "colab_ssh", "--upgrade"]) + from colab_ssh import launch_ssh_cloudflared + +# Launch SSH via Cloudflared +# This will output a command like: +# Host google_colab_ssh +# HostName ... +# User root +# Port ... +# Copy that output to your local ~/.ssh/config +print("Setting up SSH...") +launch_ssh_cloudflared(password="ora123") +print("\n[Done] Copy the 'Host google_colab_ssh' block above to your local ~/.ssh/config") +print("Then connect via VS Code: Remote-SSH -> Connect to Host -> google_colab_ssh") diff --git a/scripts/train_ora.py b/scripts/train_ora.py new file mode 100644 index 0000000000000000000000000000000000000000..714ed888898c0daf3dac34de789f624f3bb3bf6b --- /dev/null +++ b/scripts/train_ora.py @@ -0,0 +1,119 @@ +import os + +# --- IMPORT PRIORITY: Unsloth must be imported before transformers/torch if used --- +try: + import torch + if torch.cuda.is_available(): + # Force unsloth import first on GPU + from unsloth import FastLanguageModel + HAS_UNSLOTH = True + print("ORA Trainer: Unsloth imported successfully.") + else: + HAS_UNSLOTH = False +except ImportError: + HAS_UNSLOTH = False + +# Now safe to import others +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + TrainingArguments, +) +from datasets import load_dataset +from trl import SFTTrainer, SFTConfig + +# Settings +MODEL_NAME = "unsloth/Llama-3.2-1B-Instruct" # Base model +MAX_SEQ_LENGTH = 2048 +OUTPUT_DIR = "important/finetuning/models/ora_adapter" + +def train_ora(max_steps=5): + has_cuda = torch.cuda.is_available() + print(f"ORA Trainer: CUDA Detected = {has_cuda}") + + if has_cuda and HAS_UNSLOTH: + # --- MODE: GPU (Unsloth/QLoRA) --- + print("ORA Trainer: Using GPU + Unsloth (Standard for Google Colab)") + + model, tokenizer = FastLanguageModel.from_pretrained( + model_name = "unsloth/Llama-3.2-1B-Instruct-bnb-4bit", + max_seq_length = MAX_SEQ_LENGTH, + load_in_4bit = True, + ) + + model = FastLanguageModel.get_peft_model( + model, + r = 16, + target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"], + lora_alpha = 32, + lora_dropout = 0, + bias = "none", + use_gradient_checkpointing = "unsloth", + random_state = 3407, + ) + else: + # --- MODE: CPU (Standard PEFT) --- + print("ORA Trainer: Using CPU + Standard PEFT (Local Hardware Mode)") + from peft import LoraConfig, get_peft_model, TaskType + + tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) + tokenizer.pad_token = tokenizer.eos_token + + model = AutoModelForCausalLM.from_pretrained( + MODEL_NAME, + torch_dtype=torch.float32, + device_map="cpu", + low_cpu_mem_usage=True + ) + + lora_config = LoraConfig( + r=16, + lora_alpha=32, + target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], + lora_dropout=0.05, + bias="none", + task_type=TaskType.CAUSAL_LM, + ) + model = get_peft_model(model, lora_config) + + # Common Dataset Loading + data_path = "important/curated_data/final_ora_dataset.jsonl" + if not os.path.exists(data_path): + print(f"Error: Dataset {data_path} not found. Run consolidation first!") + return + + dataset = load_dataset("json", data_files=data_path, split="train") + + # SFTConfig (replaces TrainingArguments + extra SFT args) + training_args = SFTConfig( + output_dir=OUTPUT_DIR, + per_device_train_batch_size=2 if has_cuda else 1, + gradient_accumulation_steps=4, + learning_rate=2e-4, + max_steps=max_steps, + logging_steps=1, + save_strategy="no", + use_cpu=not has_cuda, + report_to="none", + max_length=MAX_SEQ_LENGTH, + dataset_text_field="text", + dataset_num_proc=2, # Limit processes to avoid pickling errors + ) + + trainer = SFTTrainer( + model=model, + train_dataset=dataset, + processing_class=tokenizer, + args=training_args, + ) + + print(f"ORA Trainer: Starting training ({max_steps} steps)...") + trainer.train() + + print(f"ORA Trainer: Saving adapter to {OUTPUT_DIR}...") + model.save_pretrained(OUTPUT_DIR) + tokenizer.save_pretrained(OUTPUT_DIR) + print("ORA Trainer: Training complete.") + +if __name__ == "__main__": + train_ora(max_steps=100) # Increased default steps for user utility, they can kill it if needed diff --git a/scripts/update_ssh_config.py b/scripts/update_ssh_config.py new file mode 100644 index 0000000000000000000000000000000000000000..303cd763be965a03ac875720f7ef4c6eea06e7df --- /dev/null +++ b/scripts/update_ssh_config.py @@ -0,0 +1,41 @@ +import os + +def update_ssh_config(): + ssh_dir = os.path.expanduser("~/.ssh") + config_path = os.path.join(ssh_dir, "config") + + # Cloudflared path (hardcoded to workspace based on previous context) + cloudflared_path = r"C:\Users\max\ORA\cloudflared.exe" + + block = f""" +Host google_colab_ssh + HostName clients-refused-besides-seat.trycloudflare.com + User root + Port 22 + ProxyCommand {cloudflared_path} access ssh --hostname %h +""" + + if not os.path.exists(ssh_dir): + print(f"Creating directory: {ssh_dir}") + os.makedirs(ssh_dir) + + print(f"Updating {config_path}...") + + current_content = "" + if os.path.exists(config_path): + with open(config_path, "r") as f: + current_content = f.read() + + if "Host google_colab_ssh" in current_content: + print("Config already contains 'Host google_colab_ssh'. Skipping append to avoid duplicates.") + # Optional strategy: Replace it? For now, assume manual intervention if it exists but is wrong. + # Check if the hostname matches? + if "clients-refused-besides-seat.trycloudflare.com" not in current_content: + print("WARNING: Existing config has different HostName. Please check manually.") + else: + with open(config_path, "a") as f: + f.write("\n" + block) + print("Successfully appended configuration.") + +if __name__ == "__main__": + update_ssh_config() diff --git a/scripts/verify_api.py b/scripts/verify_api.py new file mode 100644 index 0000000000000000000000000000000000000000..5e678ff13df0fdfef8efb83d991877cff3584311 --- /dev/null +++ b/scripts/verify_api.py @@ -0,0 +1,30 @@ +import requests +import json + +def test_chat(): + url = "http://localhost:6000/api/chat" + payload = {"message": "I need a prayer for strength"} + headers = {"Content-Type": "application/json"} + + print(f"Testing {url}...") + response = requests.post(url, json=payload, headers=headers) + + if response.status_code == 200: + data = response.json() + print("Success!") + print(json.dumps(data, indent=2)) + + # Check if trace exists and is populated + trace = data.get("response", {}).get("trace", []) + if trace: + print(f"\nTrace detected with {len(trace)} steps.") + for step in trace: + print(f" - {step['agent']}: {step['action']}") + else: + print("\nWarning: No trace detected.") + else: + print(f"Error: {response.status_code}") + print(response.text) + +if __name__ == "__main__": + test_chat() diff --git a/task.md b/task.md new file mode 100644 index 0000000000000000000000000000000000000000..69ae262cd1027f112fdc465b1dab1a39cb0677a8 --- /dev/null +++ b/task.md @@ -0,0 +1,60 @@ +# ORA System Review Task List + +- [x] Initial System Review + - [x] Explore codebase structure + - [x] Identify key configuration and entry points + - [x] Analyze Services + - [x] Review `orchestrator.py` + - [x] Review `memory.py` + - [x] Review `guardrails.py` + - [x] Review `intent.py` & `emotion.py` + - [x] Review spiritual practices (prayer/discernment/lectio) + - [x] Review infrastructure (main.py, config.py) + - [x] Review peripheral services (llm.py, telemetry.py) + - [x] Synthesize Review Report + - [x] Document Architecture/Data Flow + - [x] Identify Missing Persistent Layers + - [x] Define "Not to build" list + - [x] Create One-week MVP plan + +- [x] Implement MVP Core Services + - [x] Implement Audit Persistence (File logging) + - [x] Implement Journal Service (SQLite) + - [x] Implement User Profiles (SQLite) + - [x] Connect Bible RAG (Vector Search) + +- [x] Phase 2: Advanced Reasoning & Study + - [x] Research RLM Blog Post + - [x] Implement RLM (for Deep Study) + - [x] Design Python REPL Sandbox + - [x] Implement `RLMService` wrapper + - [x] Integrate TRM (for Logic Solvers) + - [x] Define Theological Logic dataset schema + - [x] Phase 2 Architecture Upgrades + - [x] Migrate Storage to LanceDB + - [x] Ingest `bible-nlp/biblenlp-corpus` + - [x] Evaluate Pre-trained Bible Models + - [x] Test `Christian-Bible-Expert-12B` + - [x] Create `Gabriel-8x7B` via Ollama + - [x] Setup Unsloth Fine-tuning Pipeline + - [x] Start "Reasoning Trace" Dataset Collection + +- [x] Phase 3: Frontend Mastery & Integration + - [x] Migrate `soapbox-demo` to root as `frontend` + - [x] Connect ChatWidget to ORA Backend (Port 6000) + - [x] Implement Agent Handoff Visualizations + - [x] Create Handover Prompt for Claude + +- [/] Phase 4: Model Optimization & Fine-tuning + - [/] Benchmark `gabriel-8x7b` baseline capabilities + - [x] Curate theological dataset for specialist fine-tuning + - [x] Perform LoRA fine-tuning on `gabriel-8x7b` using Unsloth + - [ ] Evaluate fine-tuned model vs baseline + - [ ] Deploy optimized reasoning model to production + +- [x] General Research Phase + - [x] Research Local Vector Databases (Chroma/LanceDB) + - [x] Research Secure REPL Sandboxes (for RLM) + - [x] Research Fine-tuning Logic for Small Models + - [x] Evaluate new Bible Datasets (Helsinki, BibleNLP, etc.) + - [x] Evaluate Pre-trained Bible Models (Gabriel, Christian-Bible-Expert) diff --git a/tests/test_emotion.py b/tests/test_emotion.py new file mode 100644 index 0000000000000000000000000000000000000000..c215d9a5a38dc6dcd3b211507d9f6defd8a3e081 --- /dev/null +++ b/tests/test_emotion.py @@ -0,0 +1,43 @@ +import pytest +from app.services.emotion import EmotionService + +@pytest.fixture +def emotion_service(): + return EmotionService() + +@pytest.mark.asyncio +async def test_positive_sentiment(emotion_service): + text = "I am so happy and blessed today" + result = await emotion_service.analyze_tone(text) + assert result["sentiment"] == "positive" + assert result["pos_count"] >= 2 + assert result["neg_count"] == 0 + +@pytest.mark.asyncio +async def test_negative_sentiment(emotion_service): + text = "I am sad and angry about this" + result = await emotion_service.analyze_tone(text) + assert result["sentiment"] == "negative" + assert result["neg_count"] >= 2 + +@pytest.mark.asyncio +async def test_neutral_sentiment(emotion_service): + text = "I walked to the store" + result = await emotion_service.analyze_tone(text) + assert result["sentiment"] == "neutral" + +@pytest.mark.asyncio +async def test_distress_detection_keywords(emotion_service): + text = "I feel hopeless and want to end it all" + analysis = await emotion_service.analyze_tone(text) + is_distress = emotion_service.is_distress_high(text, analysis) + assert is_distress is True + +@pytest.mark.asyncio +async def test_distress_detection_intensity(emotion_service): + # A sentence with almost entirely negative words to trigger high intensity + text = "pain grief suffering hurt broken sad depressed" + analysis = await emotion_service.analyze_tone(text) + assert analysis["intensity"] > 0.8 + is_distress = emotion_service.is_distress_high(text, analysis) + assert is_distress is True diff --git a/tests/test_intent.py b/tests/test_intent.py new file mode 100644 index 0000000000000000000000000000000000000000..b22dcdd7dc0223189d1fa69e39d4cb20724599ca --- /dev/null +++ b/tests/test_intent.py @@ -0,0 +1,61 @@ +import pytest +import asyncio +from app.services.intent import IntentService, UserIntent, IntentAnalysis + +@pytest.fixture +def intent_service(): + return IntentService() + +@pytest.mark.asyncio +async def test_prayer_intent(intent_service): + text = "Please pray for my sick mother" + result = await intent_service.analyze(text) + assert result.intent == UserIntent.PRAYER_REQUEST + assert result.requires_scripture is True + +@pytest.mark.asyncio +async def test_emotional_support_intent(intent_service): + text = "I feel so sad and lonely today" + result = await intent_service.analyze(text) + assert result.intent == UserIntent.EMOTIONAL_SUPPORT + assert result.requires_scripture is True + +@pytest.mark.asyncio +async def test_bible_study_intent(intent_service): + text = "Where can I find a verse about hope?" + result = await intent_service.analyze(text) + assert result.intent == UserIntent.BIBLE_STUDY + assert result.requires_scripture is True + +@pytest.mark.asyncio +async def test_life_decision_intent(intent_service): + text = "I don't know what to do about my job, I need guidance." + result = await intent_service.analyze(text) + assert result.intent == UserIntent.LIFE_DECISION + assert result.requires_scripture is True + +@pytest.mark.asyncio +async def test_confession_intent(intent_service): + text = "I made a mistake and I need to confess my sins." + result = await intent_service.analyze(text) + assert result.intent == UserIntent.CONFESSION + assert result.requires_scripture is True + +@pytest.mark.asyncio +async def test_question_intent(intent_service): + text = "Who are you?" + result = await intent_service.analyze(text) + assert result.intent == UserIntent.QUESTION + +@pytest.mark.asyncio +async def test_unknown_intent(intent_service): + text = "Hello there" + result = await intent_service.analyze(text) + assert result.intent == UserIntent.UNKNOWN + +@pytest.mark.asyncio +async def test_priority_logic(intent_service): + # "pray" (Prayer) vs "sad" (Emotional Support) -> Prayer matches first in our logic + text = "I am sad so please pray for me" + result = await intent_service.analyze(text) + assert result.intent == UserIntent.PRAYER_REQUEST diff --git a/tests/test_memory_service.py b/tests/test_memory_service.py new file mode 100644 index 0000000000000000000000000000000000000000..baaa010477836816b87641297129f8ab3d8ecba0 --- /dev/null +++ b/tests/test_memory_service.py @@ -0,0 +1,75 @@ +import sys +import os +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +import asyncio +import sqlite3 +from app.services.memory import MemoryService + +# Ensure we're testing on a fresh DB or using a test DB +TEST_DB = "test_memory.db" + +async def test_memory_service(): + print("Starting Memory Service Tests...") + + # Setup + if os.path.exists(TEST_DB): + os.remove(TEST_DB) + + memory = MemoryService(db_path=TEST_DB) + + user1 = "user_123" + user2 = "user_456" + + # 1. Test Short-Term Memory (Interactions) + print("Testing Short-Term Memory...") + await memory.add_interaction(user1, "user", "Hello ORA") + await memory.add_interaction(user1, "assistant", "Hello User") + + history = await memory.get_short_term_memory(user1) + assert len(history) == 2, f"Expected 2 messages, got {len(history)}" + assert history[0]["role"] == "user" + assert history[0]["content"] == "Hello ORA" + + # Test User Scoping + print("Testing User Scoping...") + await memory.add_interaction(user2, "user", "I am user 2") + history_u1 = await memory.get_short_term_memory(user1) + history_u2 = await memory.get_short_term_memory(user2) + assert len(history_u1) == 2 + assert len(history_u2) == 1 + assert history_u2[0]["content"] == "I am user 2" + + # 2. Test Long-Term Memory (Facts) + print("Testing Long-Term Memory...") + await memory.add_fact(user1, "User lives in New York") + await memory.add_fact(user1, "User likes pizza") + + facts = await memory.get_long_term_memory(user1) + assert len(facts) == 2 + assert "User likes pizza" in facts + + facts_u2 = await memory.get_long_term_memory(user2) + assert len(facts_u2) == 0 + + # 3. Test Persistence (Re-instantiate service) + print("Testing Persistence...") + del memory + memory_new = MemoryService(db_path=TEST_DB) + + facts_reloaded = await memory_new.get_long_term_memory(user1) + assert len(facts_reloaded) == 2 + + # Cleanup + conn = sqlite3.connect(TEST_DB) + conn.close() + if os.path.exists(TEST_DB): + try: + os.remove(TEST_DB) + except PermissionError: + print("Warning: Could not remove test db file (locked).") + + print("ALL TESTS PASSED!") + +if __name__ == "__main__": + asyncio.run(test_memory_service()) diff --git a/training/README.md b/training/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3ce4cfe4802a046e7b4cfeb9caa757cd6a38c7e9 --- /dev/null +++ b/training/README.md @@ -0,0 +1,25 @@ +# ORA Training Environment + +This directory contains the scripts and configuration for Fine-Tuning a custom LLM (e.g., Llama 3) for the ORA Spiritual Assistant. + +## Prerequisites +- Python 3.10+ +- CUDA-capable GPU (NVIDIA) or Mac with M-series chip +- Ollama (for local testing) + +## Setup +1. Create a virtual environment: + ```bash + python -m venv venv + source venv/bin/activate # or venv\Scripts\activate on Windows + ``` +2. Install dependencies: + ```bash + pip install -r requirements.txt + ``` + +## Workflow +1. **Prepare Data**: Place your data (JSONL format) in `data/`. +2. **Fine-Tune**: Run the training script (coming soon). +3. **Export**: Convert LoRA adapters to GGUF format. +4. **Serve**: Load into Ollama. diff --git a/training/requirements.txt b/training/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..695b09a69eae8851cee9ee5bb84da233c1c80d6a --- /dev/null +++ b/training/requirements.txt @@ -0,0 +1,10 @@ +torch +transformers +peft +datasets +bitsandbytes +scipy +accelerate +wandb +trl +unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git diff --git a/verify_audit.py b/verify_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..16e587d62c331d257f23542c49861ab549a42cae --- /dev/null +++ b/verify_audit.py @@ -0,0 +1,44 @@ +import asyncio +import os +import time +from app.services.audit import audit_service + +async def verify_audit(): + print("Verifying Audit Service...") + + # 1. Log Interaction + await audit_service.log_interaction( + user_id="test_user_1", + intent="test_intent", + guardrails_triggered=[] + ) + print("Logged interaction.") + + # 2. Log Violation + await audit_service.log_violation( + user_id="test_user_unsafe", + violation_type="Prohibited Phrase", + content="This is a test violation content." + ) + print("Logged violation.") + + # 3. Check File + time.sleep(1) # Ensure flush + log_path = "logs/ora.log" + if os.path.exists(log_path): + print(f"Log file found at {log_path}") + with open(log_path, "r") as f: + content = f.read() + print("--- Log Content ---") + print(content) + print("-------------------") + + if "test_user_1" in content and "test_user_unsafe" in content: + print("SUCCESS: Both log entries found.") + else: + print("FAILURE: Log entries missing.") + else: + print(f"FAILURE: Log file not found at {log_path}") + +if __name__ == "__main__": + asyncio.run(verify_audit()) diff --git a/verify_full_stack.py b/verify_full_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..75c1978bd24d4fbdceb6abcabb3e9d9065274450 --- /dev/null +++ b/verify_full_stack.py @@ -0,0 +1,71 @@ +import asyncio +import os +from app.services.orchestrator import orchestrator +from app.services.profile import profile_service +from app.services.journal import journal_service +from app.services.audit import audit_service +from app.services.bible import bible_service + +# Mock user ID for testing +TEST_USER_ID = "test_user_integration_v1" + +async def main(): + print("=== ORA MVP Integration Test ===\n") + + # 1. Setup / Cleanup + print("1. [SETUP] Clearing test data...") + # Clean up any previous test entries if needed (optional, keeping simple for now) + + # 2. Profile Test + print("\n2. [PROFILE] Updating and retrieving profile...") + await profile_service.update_profile(TEST_USER_ID, {"denomination": "Integration Test", "spiritual_goals": ["study", "prayer"]}) + profile = await profile_service.get_profile(TEST_USER_ID) + print(f" -> Denomination: {profile.denomination}") + print(f" -> Goals: {profile.spiritual_goals}") + assert profile.denomination == "Integration Test" + assert "study" in [g.value for g in profile.spiritual_goals] + + # 3. Bible RAG Test (Mock Mode check) + print("\n3. [BIBLE] Search for 'peace'...") + # Ensure index is initialized + await bible_service.initialize_index() + results = await bible_service.search("peace") + print(f" -> Found {len(results)} results.") + if results: + print(f" -> Top result: {results[0]['reference']} - {results[0]['text'][:30]}...") + + # 4. Orchestrator Flow (Simulate Chat) + print("\n4. [ORCHESTRATOR] Sending 'I feel anxious'...") + # This triggers Guardrails -> Intent -> Emotion -> Routing -> Response + # Since we don't have a real LLM running (probably), the stubbed services might fail or return mock data. + # However, we mostly want to ensure no crashes happen. + + try: + response = await orchestrator.process_message(TEST_USER_ID, "I feel anxious about the integration test.") + print(f" -> ORA Response: {response['content']}") + assert response['role'] == "assistant" + except Exception as e: + print(f" -> [ERROR] Orchestrator failed: {e}") + + # 5. Journal Persistence + print("\n5. [JOURNAL] Saving interaction...") + # The orchestrator usually handles this, but let's verify manual save works + entry_id = await journal_service.save_entry(TEST_USER_ID, "Integration Test Entry", ["test", "mvp"]) + print(f" -> Saved Entry ID: {entry_id}") + recent = await journal_service.get_recent_entries(TEST_USER_ID, 1) + print(f" -> Retrieved: {recent[0]['content']}") + assert recent[0]['content'] == "Integration Test Entry" + + # 6. Audit Log Verification + print("\n6. [AUDIT] Checking log file existence...") + if os.path.exists("logs/ora.log"): + size = os.path.getsize("logs/ora.log") + print(f" -> Log file exists. Size: {size} bytes.") + assert size > 0 + else: + print(" -> [ERROR] Log file not found!") + + print("\n=== Integration Test Complete ===") + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/verify_journal.py b/verify_journal.py new file mode 100644 index 0000000000000000000000000000000000000000..ca1c629cf72423737638a0be189519554395f565 --- /dev/null +++ b/verify_journal.py @@ -0,0 +1,43 @@ +import asyncio +from app.services.journal import journal_service + +async def main(): + print("=== ORA Zettelkasten Journal Verification ===") + + user_id = "spiritual_traveler" + + # 1. Create a foundational note + print("\n1. Creating foundational note on 'Faith'...") + await journal_service.create_entry( + user_id=user_id, + text="Faith is not just belief, but active trust when things are uncertain. Like Peter stepping out of the boat.", + verses=["Matthew 14:29"], + tags=["faith", "trust"] + ) + + # 2. Create a related note (different topic but similar sentiment) + print("2. Creating related note on 'Courage'...") + await journal_service.create_entry( + user_id=user_id, + text="Today I feel I need courage for my presentation. It feels like stepping out of a boat into the unknown.", + tags=["courage", "work"] + ) + + # 3. Verify the link + print("\n3. Finding related entries for the 'Courage' reflection...") + related = await journal_service.get_related_entries(user_id, "I need courage for my presentation. It feels like stepping out of a boat.") + + print(f" -> Found {len(related)} related journal nodes.") + for idx, node in enumerate(related): + print(f" Link {idx+1}:") + print(f" - Text: {node['text'][:60]}...") + print(f" - Linked Verses: {node['verses']}") + print(f" - Timestamp: {node['timestamp']}") + + if len(related) > 0: + print("\n[SUCCESS] Zettelkasten Journal is successfully linking nodes.") + else: + print("\n[FAIL] No related nodes found.") + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/verify_memory.py b/verify_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..c101b50949772eb21d6d439328c76f48876bc132 --- /dev/null +++ b/verify_memory.py @@ -0,0 +1,46 @@ +import asyncio +import os +from app.services.memory import memory_service + +async def main(): + print("=== ORA Episodic Memory Verification ===") + + user_id = "seeker_77" + + # 1. Store an Episode + print("\n1. Storing spiritual episode...") + await memory_service.store_episode( + user_id=user_id, + content="We discussed the Parable of the Sower and how it applies to my current job stress.", + insight="I realized that job stress is 'thorns' choking the word in my life right now.", + emotion="anxious but enlightened" + ) + + # 2. Store another one + print("2. Storing another episode...") + await memory_service.store_episode( + user_id=user_id, + content="Deep study on Grace and works.", + insight="Grace is not just for salvation, but daily strength.", + emotion="peaceful" + ) + + # 3. Retrieve + query = "Tell me about my growth regarding job stress" + print(f"\n3. Retrieving episodes for: '{query}'") + results = await memory_service.retrieve_episodes(user_id, query) + + print(f" -> Found {len(results)} relevant episodes.") + for idx, res in enumerate(results): + print(f" Episode {idx+1}:") + print(f" - Insight: {res['insight']}") + print(f" - Emotion: {res['emotion']}") + print(f" - Timestamp: {res['timestamp']}") + + if results: + print("\n[SUCCESS] Episodic Memory is operational with LanceDB.") + else: + print("\n[FAIL] No episodes found.") + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/verify_persistence.py b/verify_persistence.py new file mode 100644 index 0000000000000000000000000000000000000000..526c1a5870250dbfa3abaeb82e63b3fea57fe838 --- /dev/null +++ b/verify_persistence.py @@ -0,0 +1,70 @@ +import asyncio +import os +import uuid +from datetime import datetime +from app.services.journal import journal_service, JournalEntry +from app.services.profile import profile_service, TonePreference, SpiritualGoal + +async def verify_persistence(): + print("Verifying Persistence Layer...") + user_id = "test_persistence_user" + + # --- Profile Test --- + print("\n--- Testing Profile ---") + # 1. Get default + p1 = await profile_service.get_profile(user_id) + print(f"Default Profile: {p1.tone_preference} (Expected: pastoral) | Translations: {p1.bible_translations}") + + # 2. Update + print("Updating profile...") + updated = await profile_service.update_profile(user_id, { + "tone_preference": TonePreference.SCHOLARLY, + "bible_translations": ["KJV", "RSV"], + "spiritual_goals": [SpiritualGoal.STUDY] + }) + + # 3. Verify Update Persisted + p2 = await profile_service.get_profile(user_id) + if p2.tone_preference == TonePreference.SCHOLARLY and "KJV" in p2.bible_translations: + print("SUCCESS: Profile update persisted.") + else: + print(f"FAILURE: Profile content mismatch. Got {p2.tone_preference}") + + # --- Journal Test --- + print("\n--- Testing Journal ---") + entry_id = str(uuid.uuid4()) + entry = JournalEntry( + id=entry_id, + user_id=user_id, + content="Today I felt peace.", + timestamp=datetime.now(), + tags=["peace", "reflection"], + emotion="calm" + ) + + # 1. Save + await journal_service.save_entry(entry) + print("Saved journal entry.") + + # 2. Retrieve + entries = await journal_service.get_recent(user_id) + if not entries: + print("FAILURE: No entries returned.") + else: + found = entries[0] + print(f"Retrieved Entry: {found.content} | Tags: {found.tags}") + if found.id == entry_id and "peace" in found.tags: + print("SUCCESS: Journal entry verified.") + else: + print("FAILURE: Entry content mismatch.") + + # 3. Delete + await journal_service.delete_entry(entry_id) + entries_after = await journal_service.get_recent(user_id) + if len(entries_after) == 0: + print("SUCCESS: Journal entry deleted.") + else: + print("FAILURE: Entry still exists after delete.") + +if __name__ == "__main__": + asyncio.run(verify_persistence()) diff --git a/verify_prompts.py b/verify_prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..c9504bd27c7ad9f907538165f5ec05f355ae596e --- /dev/null +++ b/verify_prompts.py @@ -0,0 +1,27 @@ +import sys +import os + +# Ensure app is in path +sys.path.append(os.getcwd()) + +from app.services.prayer import prayer_service +from app.services.discernment import discernment_service + +def verify_prompts(): + print("--- Verifying Prayer Service Prompt ---") + prompt = prayer_service.PROMPT.format(topic="[TOPIC]", emotion="[EMOTION]", scripture="[SCRIPTURE]") + print(prompt) + + print("\n" + "-" * 20 + "\n") + + print("--- Verifying Discernment Service Prompt ---") + guide_prompt = discernment_service.PROMPT.format(situation="[SITUATION]", emotion="[EMOTION]", scripture="[SCRIPTURE]") + print(guide_prompt) + + # Simple checks + assert "You do NOT tell the user" in guide_prompt + assert "Does NOT make promises" in prompt + print("\n[SUCCESS] Prompts contain strict negative constraints.") + +if __name__ == "__main__": + verify_prompts() diff --git a/verify_rag.py b/verify_rag.py new file mode 100644 index 0000000000000000000000000000000000000000..af1c72d89d053be1f3f8d2eb978d67cf147ceb05 --- /dev/null +++ b/verify_rag.py @@ -0,0 +1,34 @@ +import asyncio +import os +from app.services.bible import bible_service + +async def main(): + print("=== ORA RAG Verification (LanceDB) ===") + + # Check DB Path + db_path = "data/lancedb_storage" + if os.path.exists(db_path): + print(f"[OK] LanceDB storage found at: {db_path}") + else: + print(f"[WARN] LanceDB storage NOT found at: {db_path} (Expected if ingestion is running/incomplete)") + + print("1. Initializing Service (Connecting locally)...") + await bible_service.initialize_index() + + # Search for a common term + query = "Seth" + print(f"\n2. Searching for '{query}'...") + results = await bible_service.search(query) + + print(f" -> Found {len(results)} results.") + for idx, res in enumerate(results): + print(f" {idx+1}. {res['reference']} (Score: {res['score']:.4f})") + print(f" \"{res['text'][:100]}...\"") + + if results: + print("\n[SUCCESS] RAG System is operational with LanceDB.") + else: + print("\n[FAIL] No results returned. Check embedding service or DB population.") + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/verify_repl.py b/verify_repl.py new file mode 100644 index 0000000000000000000000000000000000000000..0e76e9d42c6f9c9af2d51fab0eefa29c7adb32bb --- /dev/null +++ b/verify_repl.py @@ -0,0 +1,34 @@ +import asyncio +from app.services.repl import repl_service +from app.services.rlm import rlm_service + +async def main(): + print("=== ORA REPL & RLM Verification ===") + + # 1. Basic REPL test + print("\n1. Testing basic REPL (Math/Logic)...") + code = "x = 10\ny = 20\nprint(f'{x} + {y} = {x+y}')" + res = repl_service.execute(code) + print(f" Output: {res['output'].strip()}") + + # 2. Safety Test + print("\n2. Testing Safety (Attempting to import OS)...") + unsafe_code = "import os\nprint(os.name)" + res = repl_service.execute(unsafe_code) + if not res["success"]: + print(f" [SAFE] Blocked/Failed correctly: {res['error'].splitlines()[-1]}") + else: + print(f" [DANGER] OS access was allowed!") + + # 3. RLM Test (Requires Mock LLM to cooperate) + # We update mock logic to handle "calculate" keywords + print("\n3. Testing RLM (Reasoning Loop)...") + problem = "Calculate the number of days between the destruction of the first temple (586 BC) and the second temple (70 AD)." + + # Note: Mock LLM needs a fallback for this to work in offline mode + print(" (Verifying code generation -> execution flow)") + reasoning_result = await rlm_service.reason(problem) + print(f" RLM Final Result: {reasoning_result.strip()}") + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/verify_spiritual.py b/verify_spiritual.py new file mode 100644 index 0000000000000000000000000000000000000000..89fce739bb4ba6b9747b776e5910d38fc2d89834 --- /dev/null +++ b/verify_spiritual.py @@ -0,0 +1,23 @@ +import asyncio +import sys +import os + +# Ensure app is in path +sys.path.append(os.getcwd()) + +from app.services.prayer import prayer_service +from app.services.discernment import discernment_service + +async def verify(): + print("--- Verifying Prayer Service ---") + prayer = await prayer_service.compose(topic="healing for a friend", emotion="anxious") + print(f"\n[Prayer Output]:\n{prayer}\n") + + print("-" * 20) + + print("--- Verifying Discernment Service ---") + guidance = await discernment_service.guide(situation="Should I move to a new city?", emotion="uncertain") + print(f"\n[Discernment Output]:\n{guidance}\n") + +if __name__ == "__main__": + asyncio.run(verify()) diff --git a/verify_swarm.py b/verify_swarm.py new file mode 100644 index 0000000000000000000000000000000000000000..77ae6fcea55569d216508b445e3fb786ce8b2916 --- /dev/null +++ b/verify_swarm.py @@ -0,0 +1,19 @@ +import asyncio +from app.services.orchestrator import orchestrator + +async def main(): + print("=== ORA Swarm Orchestrator Verification ===") + + test_cases = [ + "I need help studying Genesis 1.", # Should trigger Theologian + "I am feeling very sad and need prayer.", # Should trigger Healer + "Hey ORA, do you remember our talk about job stress?" # Should trigger memory recall + ] + + for msg in test_cases: + print(f"\n[USER]: {msg}") + res = await orchestrator.process_message(user_id="seeker_77", message=msg) + print(f"[ORA - {res.get('agent')}]: {res['content']}") + +if __name__ == "__main__": + asyncio.run(main())