Add CLI, Docker setup, and quick start guide
- CLI tool for testing (cli.py) - Docker Compose for easy deployment - Dockerfile for backend - QUICKSTART.md with setup instructions Ready to deploy! Run: python cli.py or docker-compose up
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QUICKSTART.md
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QUICKSTART.md
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# Grimlock Quick Start Guide
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Get Grimlock running in 5 minutes.
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## Prerequisites
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- Python 3.11+
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- Anthropic API key ([get one here](https://console.anthropic.com/))
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- OR Docker + Docker Compose
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## Option 1: Quick CLI Test (Fastest)
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```bash
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# Clone the repo (if not already)
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git clone https://gittea.979labs.com/amitis55/grimlock.git
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cd grimlock
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# Set up environment
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cp backend/.env.example backend/.env
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# Edit backend/.env and add your ANTHROPIC_API_KEY
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# Install dependencies
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pip install -r backend/requirements.txt
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# Run CLI
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python cli.py
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```
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You should see:
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```
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============================================================
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GRIMLOCK CLI
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============================================================
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Initializing...
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Context loaded: {'projects': 1, 'patterns': 0, 'anti_patterns': 0, 'cost_models': 0, 'repos': 0}
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AI client ready
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Grimlock is online. Type 'exit' to quit.
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You:
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```
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## Option 2: Run Backend Server
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```bash
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# From grimlock directory
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cd backend
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# Set up environment
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cp .env.example .env
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# Edit .env and add your ANTHROPIC_API_KEY
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# Install dependencies
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pip install -r requirements.txt
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# Run server
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python main.py
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```
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Server will start at http://localhost:8000
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Test it:
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```bash
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curl http://localhost:8000/api/health
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```
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## Option 3: Docker Compose (Production-like)
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```bash
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# From grimlock directory
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cp backend/.env.example backend/.env
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# Edit backend/.env and add your ANTHROPIC_API_KEY
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# Start services
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docker-compose up -d
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# Check logs
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docker-compose logs -f grimlock-backend
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# Test
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curl http://localhost:8000/api/health
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```
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## Testing the Chat API
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Using curl:
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```bash
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curl -X POST http://localhost:8000/api/chat \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "What is Grimlock?"}
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]
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}'
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```
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Using Python:
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```python
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import requests
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response = requests.post(
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"http://localhost:8000/api/chat",
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json={
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"messages": [
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{"role": "user", "content": "What projects does Vector Zulu have?"}
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],
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"role": "engineer"
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}
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)
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print(response.json()["response"])
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```
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## Adding Vector Zulu Context
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Add your project summaries, patterns, and other context:
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```bash
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# Create context files
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mkdir -p backend/context/projects
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mkdir -p backend/context/patterns
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mkdir -p backend/context/anti_patterns
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mkdir -p backend/context/cost_models
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# Add UTILEN project
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cp path/to/UTILEN-summary.md backend/context/projects/utilen.md
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# Add Vector Zulu platform
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cp path/to/VectorZulu-summary.md backend/context/projects/vector-zulu.md
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# Add patterns
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echo "# Multi-Tenant SaaS Pattern
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Based on UTILEN architecture:
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- FastAPI + PostgreSQL + Redis + Celery
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- Tenant isolation at DB and storage level
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- Background processing for heavy operations
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- JWT auth (avoid Keycloak)
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" > backend/context/patterns/multi-tenant-saas.md
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```
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Restart the server and Grimlock will have Vector Zulu context!
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## Example Interactions
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**Ask about projects:**
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```
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You: What projects has Vector Zulu built?
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Grimlock: Vector Zulu has built several major projects including UTILEN (an AI-powered document management system), the Vector Zulu distributed cyber range platform, and a Layer 1 blockchain with stablecoin...
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```
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**Get architecture advice:**
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```
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You: I need to build a document processing system
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Grimlock: Based on Vector Zulu patterns, this maps to the UTILEN architecture: FastAPI + PostgreSQL + Redis + Celery + MinIO + Claude Vision API...
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```
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**Role-specific responses:**
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```python
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# As engineer
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response = requests.post("http://localhost:8000/api/chat", json={
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"messages": [{"role": "user", "content": "How should I implement multi-tenancy?"}],
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"role": "engineer"
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})
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# As BD person
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response = requests.post("http://localhost:8000/api/chat", json={
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"messages": [{"role": "user", "content": "What's our pricing for UTILEN?"}],
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"role": "bd"
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})
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```
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## Next Steps
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1. **Add more context** - The more context you add, the smarter Grimlock becomes
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2. **Build the web interface** - See `frontend/` directory
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3. **Deploy to your infrastructure** - Use Docker Compose on your servers
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4. **Integrate with your systems** - Build connectors for git, databases, etc.
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## Troubleshooting
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**"ANTHROPIC_API_KEY not set"**
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- Make sure you copied `.env.example` to `.env` and added your API key
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**"Context manager not initialized"**
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- The `backend/context` directory needs to exist
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- Run `mkdir -p backend/context/{projects,patterns,anti_patterns,cost_models}`
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**"Module not found"**
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- Make sure you installed requirements: `pip install -r backend/requirements.txt`
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**Docker issues**
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- Make sure Docker daemon is running
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- Check logs: `docker-compose logs -f`
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## Support
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Questions? Open an issue on the repo or contact the Vector Zulu team.
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---
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**Built with ❤️ by Vector Zulu**
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84
cli.py
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cli.py
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#!/usr/bin/env python3
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"""
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Grimlock CLI - Simple command line interface for testing
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"""
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import asyncio
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import os
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import sys
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from pathlib import Path
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# Add backend to path
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sys.path.insert(0, str(Path(__file__).parent / "backend"))
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from core.ai_client import AIClient
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from core.context_manager import ContextManager
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from dotenv import load_dotenv
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load_dotenv("backend/.env")
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async def main():
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print("=" * 60)
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print("GRIMLOCK CLI")
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print("=" * 60)
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# Initialize
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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print("ERROR: ANTHROPIC_API_KEY not set")
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print("Copy backend/.env.example to backend/.env and add your API key")
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return
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print("\nInitializing...")
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context_manager = ContextManager("backend/context")
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context_manager.load_all_context()
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print(f"Context loaded: {context_manager.get_summary()}")
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ai_client = AIClient(api_key=api_key)
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print("AI client ready")
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# Interactive loop
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print("\nGrimlock is online. Type 'exit' to quit.\n")
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messages = []
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while True:
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try:
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user_input = input("You: ").strip()
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if not user_input:
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continue
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if user_input.lower() in ['exit', 'quit', 'q']:
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print("\nGoodbye!")
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break
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# Add user message
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messages.append({"role": "user", "content": user_input})
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# Get context
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context = context_manager.get_context_for_query(user_input)
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system_prompt = context_manager.get_system_prompt()
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if context:
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system_prompt += f"\n\n# Company Context\n{context}"
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# Get response
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print("\nGrimlock: ", end="", flush=True)
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response = await ai_client.chat(
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messages=messages,
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system_prompt=system_prompt
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)
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print(response)
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print()
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# Add assistant message
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messages.append({"role": "assistant", "content": response})
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except KeyboardInterrupt:
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print("\n\nGoodbye!")
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break
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except Exception as e:
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print(f"\nError: {e}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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docker-compose.yml
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version: '3.8'
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services:
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grimlock-backend:
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build:
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context: ./backend
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dockerfile: ../docker/Dockerfile.backend
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container_name: grimlock-backend
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ports:
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- "8000:8000"
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environment:
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- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
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- HOST=0.0.0.0
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- PORT=8000
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- DEBUG=true
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- CONTEXT_PATH=/app/context
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- AI_MODEL=claude-sonnet-4-5-20250514
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- LOG_LEVEL=INFO
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volumes:
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- ./backend/context:/app/context:ro
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- ./backend:/app:ro
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restart: unless-stopped
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command: uvicorn main:app --host 0.0.0.0 --port 8000 --reload
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networks:
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default:
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name: grimlock-network
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25
docker/Dockerfile.backend
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FROM python:3.11-slim
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WORKDIR /app
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# Install system dependencies for WeasyPrint (PDF generation)
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RUN apt-get update && apt-get install -y \
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libpango-1.0-0 \
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libpangoft2-1.0-0 \
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libgdk-pixbuf2.0-0 \
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libffi-dev \
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shared-mime-info \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements and install Python dependencies
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COPY backend/requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY backend/ .
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# Expose port
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EXPOSE 8000
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# Run the application
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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