Initial commit: Multi-service AI agent system

- Frontend: Vite + React + TypeScript chat interface - Backend: FastAPI gateway with LangGraph routing - Knowledge Service: ChromaDB RAG with Gitea scraper - LangGraph Service: Multi-agent orchestration - Airflow: Scheduled Gitea ingestion DAG - Documentation: Complete plan and implementation guides Architecture: - Modular Docker Compose per service - External ai-mesh network for communication - Fast rebuilds with /app/packages pattern - Intelligent agent routing (no hardcoded keywords) Services: - Frontend (5173): React chat UI - Chat Gateway (8000): FastAPI entry point - LangGraph (8090): Agent orchestration - Knowledge (8080): ChromaDB RAG - Airflow (8081): Scheduled ingestion - PostgreSQL (5432): Chat history Excludes: node_modules, .venv, chroma_db, logs, .env files Includes: All source code, configs, docs, docker files
2026-02-27 19:51:06 +11:00
commit 628ba96998
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--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,52 @@
+# Dependencies
+node_modules/
+.venv/
+__pycache__/
+*.pyc
+
+# Build outputs
+dist/
+dist-ssr/
+
+# Databases and vector stores
+chroma_db/
+*.sqlite3
+*.db
+
+# Logs
+logs/
+*.log
+npm-debug.log*
+yarn-debug.log*
+pnpm-debug.log*
+
+# Environment variables (secrets!)
+.env
+.env.local
+.env.*.local
+
+# IDE
+.vscode/*
+!.vscode/extensions.json
+.idea/
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+# Airflow runtime
+airflow/logs/
+airflow/config/
+airflow/plugins/
+
+# Testing
+.coverage
+htmlcov/
+.pytest_cache/
+
+# Project management files (not code)
+action.md
+ideas.org
+project_journal.org
--- a/README.md
+++ b/README.md
@@ -0,0 +1,107 @@
+# AboutMe AI Chat Demo
+
+A comprehensive AI agent system with multi-service architecture for personal knowledge management and intelligent query responses.
+
+## Architecture Overview
+
+```
+User Query → Chat Gateway → LangGraph Supervisor → [Librarian | Opencode | Brain]
+                                    ↓
+                            Knowledge Service (ChromaDB) ← Airflow ← Gitea API
+```
+
+## Services
+
+| Service | Port | Technology | Purpose |
+|---------|------|------------|---------|
+| Frontend | 5173 | Vite + React + TS | Chat UI |
+| Chat Gateway | 8000 | FastAPI | API entry point |
+| LangGraph | 8090 | FastAPI + LangGraph | Agent orchestration |
+| Knowledge | 8080 | FastAPI + ChromaDB | RAG / Vector search |
+| Airflow | 8081 | Apache Airflow | Scheduled ingestion |
+| PostgreSQL | 5432 | Postgres 15 | Chat history |
+
+## Quick Start
+
+```bash
+# 1. Ensure Docker network exists
+docker network create ai-mesh
+
+# 2. Start Knowledge Service
+cd knowledge_service && docker-compose up -d
+
+# 3. Start LangGraph Service
+cd ../langgraph_service && docker-compose up -d
+
+# 4. Start Chat Demo
+cd ../aboutme_chat_demo && docker-compose up -d
+
+# 5. Start Airflow (optional)
+cd ../airflow && docker-compose up -d
+```
+
+## Environment Variables
+
+Create `.env` files in each service directory:
+
+**knowledge_service/.env:**
+```
+OPENROUTER_API_KEY=your_key_here
+GITEA_URL=https://gitea.lab.audasmedia.com.au
+GITEA_TOKEN=your_token
+GITEA_USERNAME=sam
+```
+
+**langgraph_service/.env:**
+```
+OPENCODE_PASSWORD=sam4jo
+```
+
+**airflow/.env:**
+```
+AIRFLOW_UID=1000
+GITEA_TOKEN=your_token
+```
+
+## Project Structure
+
+```
+aboutme_chat_demo/
+├── frontend/          # React chat interface
+├── backend/           # FastAPI gateway (routes to LangGraph)
+├── plan.md            # Full project roadmap
+└── code_1.md          # Implementation guide
+
+knowledge_service/
+├── main.py            # FastAPI + ChromaDB
+├── gitea_scraper.py   # Gitea API integration
+└── docker-compose.yml
+
+langgraph_service/
+├── main.py            # FastAPI entry point
+├── supervisor_agent.py # LangGraph orchestration
+└── docker-compose.yml
+
+airflow/
+├── dags/              # Workflow definitions
+│   └── gitea_ingestion_dag.py
+└── docker-compose.yml
+```
+
+## Technologies
+
+- **Frontend:** Vite, React 19, TypeScript, Tailwind CSS, TanStack Query
+- **Backend:** FastAPI, Python 3.11, httpx
+- **AI/ML:** LangGraph, LangChain, ChromaDB, OpenRouter API
+- **Orchestration:** Apache Airflow (CeleryExecutor)
+- **Infrastructure:** Docker, Docker Compose
+
+## Documentation
+
+- `plan.md` - Complete project roadmap (7 phases)
+- `code_1.md` - Modular implementation guide
+- `code.md` - Legacy implementation reference
+
+## License
+
+MIT
--- a/airflow/dags/gitea_ingestion_dag.py
+++ b/airflow/dags/gitea_ingestion_dag.py
@@ -0,0 +1,144 @@
+"""
+Airflow DAG for scheduled Gitea repository ingestion.
+Runs daily to fetch new/updated repos and ingest into ChromaDB.
+"""
+from datetime import datetime, timedelta
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from airflow.providers.http.operators.http import SimpleHttpOperator
+import os
+import sys
+import json
+
+# Add knowledge_service to path for imports
+sys.path.insert(0, '/opt/airflow/dags/repo')
+
+default_args = {
+    'owner': 'airflow',
+    'depends_on_past': False,
+    'email_on_failure': False,
+    'email_on_retry': False,
+    'retries': 1,
+    'retry_delay': timedelta(minutes=5),
+}
+
+def fetch_gitea_repos(**context):
+    """Task: Fetch all repositories from Gitea."""
+    from gitea_scraper import GiteaScraper
+    
+    scraper = GiteaScraper(
+        base_url=os.getenv("GITEA_URL", "https://gitea.lab.audasmedia.com.au"),
+        token=os.getenv("GITEA_TOKEN", ""),
+        username=os.getenv("GITEA_USERNAME", "sam")
+    )
+    
+    repos = scraper.get_user_repos()
+    
+    # Push to XCom for downstream tasks
+    context['ti'].xcom_push(key='repo_count', value=len(repos))
+    context['ti'].xcom_push(key='repos', value=[
+        {
+            'name': r.name,
+            'description': r.description,
+            'url': r.url,
+            'updated_at': r.updated_at
+        }
+        for r in repos
+    ])
+    
+    return f"Fetched {len(repos)} repositories"
+
+def fetch_readmes(**context):
+    """Task: Fetch READMEs for all repositories."""
+    from gitea_scraper import GiteaScraper
+    
+    ti = context['ti']
+    repos = ti.xcom_pull(task_ids='fetch_repos', key='repos')
+    
+    scraper = GiteaScraper(
+        base_url=os.getenv("GITEA_URL", "https://gitea.lab.audasmedia.com.au"),
+        token=os.getenv("GITEA_TOKEN", ""),
+        username=os.getenv("GITEA_USERNAME", "sam")
+    )
+    
+    readme_data = []
+    for repo in repos[:10]:  # Limit to 10 repos per run for testing
+        readme = scraper.get_readme(repo['name'])
+        if readme:
+            readme_data.append({
+                'repo': repo['name'],
+                'content': readme[:5000],  # First 5000 chars
+                'url': repo['url']
+            })
+    
+    ti.xcom_push(key='readme_data', value=readme_data)
+    
+    return f"Fetched {len(readme_data)} READMEs"
+
+def ingest_to_chroma(**context):
+    """Task: Ingest fetched data into ChromaDB via knowledge service."""
+    import httpx
+    
+    ti = context['ti']
+    readme_data = ti.xcom_pull(task_ids='fetch_readmes', key='readme_data')
+    
+    knowledge_service_url = os.getenv("KNOWLEDGE_SERVICE_URL", "http://knowledge-service:8080")
+    
+    documents_ingested = 0
+    for item in readme_data:
+        try:
+            # Call knowledge service ingest endpoint
+            response = httpx.post(
+                f"{knowledge_service_url}/ingest",
+                json={
+                    'source': f"gitea:{item['repo']}",
+                    'content': item['content'],
+                    'metadata': {
+                        'repo': item['repo'],
+                        'url': item['url'],
+                        'type': 'readme'
+                    }
+                },
+                timeout=30.0
+            )
+            
+            if response.status_code == 200:
+                documents_ingested += 1
+                
+        except Exception as e:
+            print(f"Error ingesting {item['repo']}: {e}")
+    
+    return f"Ingested {documents_ingested} documents into ChromaDB"
+
+# Define the DAG
+with DAG(
+    'gitea_daily_ingestion',
+    default_args=default_args,
+    description='Daily ingestion of Gitea repositories into knowledge base',
+    schedule_interval=timedelta(days=1),  # Run daily
+    start_date=datetime(2024, 1, 1),
+    catchup=False,
+    tags=['gitea', 'ingestion', 'knowledge'],
+) as dag:
+    
+    # Task 1: Fetch repository list
+    fetch_repos_task = PythonOperator(
+        task_id='fetch_repos',
+        python_callable=fetch_gitea_repos,
+    )
+    
+    # Task 2: Fetch README content
+    fetch_readmes_task = PythonOperator(
+        task_id='fetch_readmes',
+        python_callable=fetch_readmes,
+    )
+    
+    # Task 3: Ingest into ChromaDB
+    ingest_task = PythonOperator(
+        task_id='ingest_to_chroma',
+        python_callable=ingest_to_chroma,
+    )
+    
+    # Define task dependencies
+    fetch_repos_task >> fetch_readmes_task >> ingest_task
+
--- a/airflow/dags/gitea_scraper.py
+++ b/airflow/dags/gitea_scraper.py
@@ -0,0 +1,121 @@
+import os
+import httpx
+import logging
+from typing import List, Dict, Optional
+from dataclasses import dataclass
+from datetime import datetime
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+@dataclass
+class RepoMetadata:
+    name: str
+    description: str
+    url: str
+    default_branch: str
+    updated_at: str
+    language: Optional[str]
+
+class GiteaScraper:
+    def __init__(self, base_url: str, token: str, username: str = "sam"):
+        self.base_url = base_url.rstrip("/")
+        self.token = token
+        self.username = username
+        self.headers = {"Authorization": f"token {token}"}
+        
+    def get_user_repos(self) -> List[RepoMetadata]:
+        """Fetch all repositories for the user."""
+        repos = []
+        page = 1
+        
+        while True:
+            url = f"{self.base_url}/api/v1/users/{self.username}/repos?page={page}&limit=50"
+            
+            try:
+                response = httpx.get(url, headers=self.headers, timeout=30.0)
+                response.raise_for_status()
+                
+                data = response.json()
+                if not data:
+                    break
+                    
+                for repo in data:
+                    repos.append(RepoMetadata(
+                        name=repo["name"],
+                        description=repo.get("description", ""),
+                        url=repo["html_url"],
+                        default_branch=repo["default_branch"],
+                        updated_at=repo["updated_at"],
+                        language=repo.get("language")
+                    ))
+                
+                logger.info(f"Fetched page {page}, got {len(data)} repos")
+                page += 1
+                
+            except Exception as e:
+                logger.error(f"Error fetching repos: {e}")
+                break
+                
+        return repos
+    
+    def get_readme(self, repo_name: str) -> str:
+        """Fetch README content for a repository."""
+        # Try common README filenames
+        readme_names = ["README.md", "readme.md", "Readme.md", "README.rst"]
+        
+        for readme_name in readme_names:
+            url = f"{self.base_url}/api/v1/repos/{self.username}/{repo_name}/raw/{readme_name}"
+            
+            try:
+                response = httpx.get(url, headers=self.headers, timeout=10.0)
+                if response.status_code == 200:
+                    return response.text
+            except Exception as e:
+                logger.warning(f"Failed to fetch {readme_name}: {e}")
+                continue
+                
+        return ""
+    
+    def get_repo_files(self, repo_name: str, path: str = "") -> List[Dict]:
+        """List files in a repository directory."""
+        url = f"{self.base_url}/api/v1/repos/{self.username}/{repo_name}/contents/{path}"
+        
+        try:
+            response = httpx.get(url, headers=self.headers, timeout=10.0)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            logger.error(f"Error listing files in {repo_name}/{path}: {e}")
+            return []
+    
+    def get_file_content(self, repo_name: str, filepath: str) -> str:
+        """Fetch content of a specific file."""
+        url = f"{self.base_url}/api/v1/repos/{self.username}/{repo_name}/raw/{filepath}"
+        
+        try:
+            response = httpx.get(url, headers=self.headers, timeout=10.0)
+            if response.status_code == 200:
+                return response.text
+        except Exception as e:
+            logger.error(f"Error fetching file {filepath}: {e}")
+            
+        return ""
+
+# Test function
+if __name__ == "__main__":
+    scraper = GiteaScraper(
+        base_url=os.getenv("GITEA_URL", "https://gitea.lab.audasmedia.com.au"),
+        token=os.getenv("GITEA_TOKEN", ""),
+        username=os.getenv("GITEA_USERNAME", "sam")
+    )
+    
+    repos = scraper.get_user_repos()
+    print(f"Found {len(repos)} repositories")
+    
+    for repo in repos[:3]:  # Test with first 3
+        print(f"\nRepo: {repo.name}")
+        readme = scraper.get_readme(repo.name)
+        if readme:
+            print(f"README preview: {readme[:200]}...")
+
--- a/airflow/docker-compose.yml
+++ b/airflow/docker-compose.yml
@@ -0,0 +1,181 @@
+version: '3.8'
+
+x-airflow-common:
+  &airflow-common
+  image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.8.1}
+  environment:
+    &airflow-common-env
+    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
+    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
+    AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow
+    AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
+    AIRFLOW__CORE__FERNET_KEY: ''
+    AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
+    AIRFLOW__CORE__LOAD_EXAMPLES: 'false'
+    AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'
+    AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true'
+    _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
+  volumes:
+    - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
+    - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
+    - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config
+    - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
+  user: "${AIRFLOW_UID:-50000}:0"
+  depends_on:
+    &airflow-common-depends-on
+    redis:
+      condition: service_healthy
+    postgres:
+      condition: service_healthy
+
+services:
+  postgres:
+    image: postgres:13
+    environment:
+      POSTGRES_USER: airflow
+      POSTGRES_PASSWORD: airflow
+      POSTGRES_DB: airflow
+    volumes:
+      - postgres-db-volume:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD", "pg_isready", "-U", "airflow"]
+      interval: 10s
+      retries: 5
+      start_period: 5s
+    restart: always
+    networks:
+      - ai-mesh
+
+  redis:
+    image: redis:latest
+    expose:
+      - 6379
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 30s
+      retries: 50
+      start_period: 30s
+    restart: always
+    networks:
+      - ai-mesh
+
+  airflow-webserver:
+    <<: *airflow-common
+    command: webserver
+    ports:
+      - "8081:8080"
+    healthcheck:
+      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+    restart: always
+    depends_on:
+      <<: *airflow-common-depends-on
+      airflow-init:
+        condition: service_completed_successfully
+    networks:
+      - ai-mesh
+
+  airflow-scheduler:
+    <<: *airflow-common
+    command: scheduler
+    healthcheck:
+      test: ["CMD", "curl", "--fail", "http://localhost:8974/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+    restart: always
+    depends_on:
+      <<: *airflow-common-depends-on
+      airflow-init:
+        condition: service_completed_successfully
+    networks:
+      - ai-mesh
+
+  airflow-worker:
+    <<: *airflow-common
+    command: celery worker
+    healthcheck:
+      test:
+        - "CMD-SHELL"
+        - 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}" || celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+    restart: always
+    depends_on:
+      <<: *airflow-common-depends-on
+      airflow-init:
+        condition: service_completed_successfully
+    networks:
+      - ai-mesh
+
+  airflow-triggerer:
+    <<: *airflow-common
+    command: triggerer
+    healthcheck:
+      test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
+    restart: always
+    depends_on:
+      <<: *airflow-common-depends-on
+      airflow-init:
+        condition: service_completed_successfully
+    networks:
+      - ai-mesh
+
+  airflow-init:
+    <<: *airflow-common
+    entrypoint: /bin/bash
+    command:
+      - -c
+      - |
+        if [[ -z "${AIRFLOW_UID}" ]]; then
+          echo "WARNING!!!: AIRFLOW_UID not set!"
+          echo "Using default UID: 50000"
+          export AIRFLOW_UID=50000
+        fi
+        mkdir -p /sources/logs /sources/dags /sources/plugins
+        chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
+        exec /entrypoint airflow version
+    environment:
+      <<: *airflow-common-env
+      _AIRFLOW_DB_MIGRATE: 'true'
+      _AIRFLOW_WWW_USER_CREATE: 'true'
+      _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
+      _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
+    user: "0:0"
+    volumes:
+      - ${AIRFLOW_PROJ_DIR:-.}:/sources
+    networks:
+      - ai-mesh
+
+  airflow-cli:
+    <<: *airflow-common
+    profiles:
+      - debug
+    environment:
+      <<: *airflow-common-env
+      CONNECTION_CHECK_MAX_COUNT: "0"
+    command:
+      - bash
+      - -c
+      - airflow
+    networks:
+      - ai-mesh
+
+volumes:
+  postgres-db-volume:
+
+networks:
+  ai-mesh:
+    external: true
+
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -0,0 +1,8 @@
+FROM python:3.11-slim
+WORKDIR /app
+RUN apt-get update && apt-get install -y libpq-dev gcc
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
+
--- a/backend/main.py
+++ b/backend/main.py
@@ -0,0 +1,58 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import httpx
+import logging
+import sys
+import traceback
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[logging.StreamHandler(sys.stdout)])
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
+
+class MessageRequest(BaseModel):
+    message: str
+
+BRAIN_URL = "http://opencode-brain:5000"
+KNOWLEDGE_URL = "http://knowledge-service:8080/query"
+AUTH = httpx.BasicAuth("opencode", "sam4jo")
+
+@app.post("/chat")
+async def chat(request: MessageRequest):
+    user_msg = request.message.lower()
+    timeout_long = httpx.Timeout(180.0, connect=10.0)
+    timeout_short = httpx.Timeout(5.0, connect=2.0)
+    
+    context = ""
+    # Check for keywords to trigger Librarian (DB) lookup
+    if any(kw in user_msg for kw in ["sam", "hobby", "music", "guitar", "skiing", "experience"]):
+        logger.info("Gateway: Consulting Librarian (DB)...")
+        async with httpx.AsyncClient(timeout=timeout_short) as client:
+            try:
+                k_res = await client.post(KNOWLEDGE_URL, json={"question": request.message})
+                if k_res.status_code == 200:
+                    context = k_res.json().get("context", "")
+            except Exception as e:
+                logger.warning(f"Gateway: Librarian offline/slow: {str(e)}")
+
+    # Forward to Brain (LLM)
+    async with httpx.AsyncClient(auth=AUTH, timeout=timeout_long) as brain_client:
+        try:
+            session_res = await brain_client.post(f"{BRAIN_URL}/session", json={"title": "Demo"})
+            session_id = session_res.json()["id"]
+            final_prompt = f"CONTEXT:\n{context}\n\nUSER: {request.message}" if context else request.message
+            response = await brain_client.post(f"{BRAIN_URL}/session/{session_id}/message", json={"parts": [{"type": "text", "text": final_prompt}]})
+            
+            # FIX: Iterate through parts array to find text response
+            data = response.json()
+            if "parts" in data:
+                for part in data["parts"]:
+                    if part.get("type") == "text" and "text" in part:
+                        return {"response": part["text"]}
+            
+            return {"response": "AI responded but no text found in expected format."}
+        except Exception:
+            logger.error(f"Gateway: Brain failure: {traceback.format_exc()}")
+            return {"response": "Error: The Brain is taking too long or is disconnected."}
--- a/backend/main.py.new
+++ b/backend/main.py.new
@@ -0,0 +1,49 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import httpx
+import logging
+import sys
+import traceback
+import os
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[logging.StreamHandler(sys.stdout)])
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
+
+class MessageRequest(BaseModel):
+    message: str
+
+LANGGRAPH_URL = os.getenv("LANGGRAPH_URL", "http://langgraph-service:8090")
+
+@app.post("/chat")
+async def chat(request: MessageRequest):
+    """Updated chat endpoint that routes through LangGraph Supervisor."""
+    logger.info(f"Gateway: Received message: {request.message}")
+    
+    try:
+        # Call LangGraph Supervisor instead of direct brain
+        async with httpx.AsyncClient(timeout=httpx.Timeout(60.0, connect=10.0)) as client:
+            response = await client.post(
+                f"{LANGGRAPH_URL}/query",
+                json={"query": request.message}
+            )
+            
+            if response.status_code == 200:
+                result = response.json()
+                logger.info(f"Gateway: Response from {result.get('agent_used', 'unknown')} agent")
+                return {"response": result["response"]}
+            else:
+                logger.error(f"Gateway: LangGraph error {response.status_code}")
+                return {"response": "Error: Orchestration service unavailable"}
+                
+    except Exception as e:
+        logger.error(f"Gateway: Error routing through LangGraph: {traceback.format_exc()}")
+        return {"response": "Error: Unable to process your request at this time."}
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy", "service": "chat-gateway"}
+
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -0,0 +1,8 @@
+fastapi
+uvicorn
+sqlalchemy
+psycopg2-binary
+pydantic
+httpx
+pytest
+pytest-asyncio
--- a/backend/tests/test_gateway.py
+++ b/backend/tests/test_gateway.py
@@ -0,0 +1,79 @@
+import pytest
+from fastapi.testclient import TestClient
+from main import app
+import httpx
+from unittest.mock import AsyncMock, patch
+
+client = TestClient(app)
+
+@pytest.mark.asyncio
+async def test_chat_general_query():
+    """Test that a general query (no personal keywords) skips the Librarian."""
+    with patch("httpx.AsyncClient.post", new_callable=AsyncMock) as mock_post:
+        # Mock Brain response
+        mock_response = AsyncMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = {
+            "info": {"id": "msg_123"},
+            "parts": [{"type": "text", "text": "I am a general AI."}]
+        }
+        
+        # First call is for session creation, second for message
+        mock_post.side_effect = [AsyncMock(status_code=200, json=lambda: {"id": "ses_123"}), mock_response]
+        
+        response = client.post("/chat", json={"message": "What is 2+2?"})
+        
+        assert response.status_code == 200
+        assert response.json()["response"] == "I am a general AI."
+        # Verify Librarian (knowledge-service) was NOT called
+        # The knowledge service URL is http://knowledge-service:8080/query
+        calls = [call.args[0] for call in mock_post.call_args_list]
+        assert not any("knowledge-service" in url for url in calls)
+
+@pytest.mark.asyncio
+async def test_chat_personal_query_success():
+    """Test that a personal query calls the Librarian and injects context."""
+    with patch("httpx.AsyncClient.post", new_callable=AsyncMock) as mock_post:
+        # 1. Mock Librarian Response
+        mock_k_res = AsyncMock()
+        mock_k_res.status_code = 200
+        mock_k_res.json.return_value = {"context": "Sam likes red guitars."}
+        
+        # 2. Mock Brain Session Response
+        mock_s_res = AsyncMock()
+        mock_s_res.status_code = 200
+        mock_s_res.json.return_value = {"id": "ses_123"}
+        
+        # 3. Mock Brain Message Response
+        mock_b_res = AsyncMock()
+        mock_b_res.status_code = 200
+        mock_b_res.json.return_value = {
+            "parts": [{"type": "text", "text": "I see Sam likes red guitars."}]
+        }
+        
+        mock_post.side_effect = [mock_k_res, mock_s_res, mock_b_res]
+        
+        response = client.post("/chat", json={"message": "Tell me about Sam's music"})
+        
+        assert response.status_code == 200
+        assert "red guitars" in response.json()["response"]
+        
+        # Verify Librarian was called
+        calls = [call.args[0] for call in mock_post.call_args_list]
+        assert any("knowledge-service" in url for url in calls)
+
+@pytest.mark.asyncio
+async def test_chat_librarian_timeout_failover():
+    """Test that the gateway fails over instantly (5s) if Librarian is slow."""
+    with patch("httpx.AsyncClient.post", new_callable=AsyncMock) as mock_post:
+        # Mock Librarian Timeout
+        mock_post.side_effect = [
+            httpx.TimeoutException("Timeout"), # Librarian call
+            AsyncMock(status_code=200, json=lambda: {"id": "ses_123"}), # Brain Session
+            AsyncMock(status_code=200, json=lambda: {"parts": [{"type": "text", "text": "Direct Brain Response"}]}) # Brain Msg
+        ]
+        
+        response = client.post("/chat", json={"message": "Sam's hobbies?"})
+        
+        assert response.status_code == 200
+        assert response.json()["response"] == "Direct Brain Response"
--- a/code.md
+++ b/code.md
--- a/code_1.md
+++ b/code_1.md
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,41 @@
+services:
+  db:
+    image: postgres:15-alpine
+    environment:
+      POSTGRES_USER: sam
+      POSTGRES_PASSWORD: sam4jo 
+      POSTGRES_DB: chat_demo
+    ports:
+      - "5432:5432"
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+    networks:
+      - ai-mesh
+  backend:
+    build: ./backend
+    ports:
+      - "8000:8000"
+    environment:
+      DATABASE_URL: postgresql://sam:sam4jo@db:5432/chat_demo
+    volumes:
+      - ./backend:/app
+    depends_on:
+      - db
+    networks:
+      - ai-mesh
+  frontend:
+    build: ./frontend
+    ports:
+      - "5173:5173"
+    volumes:
+      - ./frontend:/app
+      - /app/node_modules
+    environment:
+      - CHOKIDAR_USEPOLLING=true
+    networks:
+      - ai-mesh
+volumes:
+  postgres_data:
+networks:
+      ai-mesh:
+        external: true
--- a/frontend/.gitignore
+++ b/frontend/.gitignore
@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
--- a/frontend/Dockerfile
+++ b/frontend/Dockerfile
@@ -0,0 +1,7 @@
+FROM node:20-alpine
+WORKDIR /app
+COPY package.json pnpm-lock.yaml ./
+RUN npm install -g pnpm && pnpm install
+COPY . .
+CMD ["pnpm", "run", "dev", "--host", "0.0.0.0"]
+
--- a/frontend/README.md
+++ b/frontend/README.md
@@ -0,0 +1,73 @@
+# React + TypeScript + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules:
+
+```js
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      // Other configs...
+
+      // Remove tseslint.configs.recommended and replace with this
+      tseslint.configs.recommendedTypeChecked,
+      // Alternatively, use this for stricter rules
+      tseslint.configs.strictTypeChecked,
+      // Optionally, add this for stylistic rules
+      tseslint.configs.stylisticTypeChecked,
+
+      // Other configs...
+    ],
+    languageOptions: {
+      parserOptions: {
+        project: ['./tsconfig.node.json', './tsconfig.app.json'],
+        tsconfigRootDir: import.meta.dirname,
+      },
+      // other options...
+    },
+  },
+])
+```
+
+You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules:
+
+```js
+// eslint.config.js
+import reactX from 'eslint-plugin-react-x'
+import reactDom from 'eslint-plugin-react-dom'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      // Other configs...
+      // Enable lint rules for React
+      reactX.configs['recommended-typescript'],
+      // Enable lint rules for React DOM
+      reactDom.configs.recommended,
+    ],
+    languageOptions: {
+      parserOptions: {
+        project: ['./tsconfig.node.json', './tsconfig.app.json'],
+        tsconfigRootDir: import.meta.dirname,
+      },
+      // other options...
+    },
+  },
+])
+```
--- a/frontend/eslint.config.js
+++ b/frontend/eslint.config.js
@@ -0,0 +1,23 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import tseslint from 'typescript-eslint'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{ts,tsx}'],
+    extends: [
+      js.configs.recommended,
+      tseslint.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+    },
+  },
+])
--- a/frontend/index.html
+++ b/frontend/index.html
@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>frontend</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -0,0 +1,36 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "tsc -b && vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@tanstack/react-query": "^5.90.21",
+    "axios": "^1.13.5",
+    "react": "^19.2.0",
+    "react-dom": "^19.2.0"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.1",
+    "@tailwindcss/vite": "^4.2.0",
+    "@types/node": "^24.10.1",
+    "@types/react": "^19.2.7",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^5.1.1",
+    "autoprefixer": "^10.4.24",
+    "eslint": "^9.39.1",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.4.24",
+    "globals": "^16.5.0",
+    "postcss": "^8.5.6",
+    "tailwindcss": "^4.2.0",
+    "typescript": "~5.9.3",
+    "typescript-eslint": "^8.48.0",
+    "vite": "^7.3.1"
+  }
+}
--- a/frontend/pnpm-lock.yaml
+++ b/frontend/pnpm-lock.yaml
--- a/frontend/public/vite.svg
+++ b/frontend/public/vite.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
--- a/frontend/src/App.css
+++ b/frontend/src/App.css
@@ -0,0 +1,42 @@
+#root {
+  max-width: 1280px;
+  margin: 0 auto;
+  padding: 2rem;
+  text-align: center;
+}
+
+.logo {
+  height: 6em;
+  padding: 1.5em;
+  will-change: filter;
+  transition: filter 300ms;
+}
+.logo:hover {
+  filter: drop-shadow(0 0 2em #646cffaa);
+}
+.logo.react:hover {
+  filter: drop-shadow(0 0 2em #61dafbaa);
+}
+
+@keyframes logo-spin {
+  from {
+    transform: rotate(0deg);
+  }
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+  a:nth-of-type(2) .logo {
+    animation: logo-spin infinite 20s linear;
+  }
+}
+
+.card {
+  padding: 2em;
+}
+
+.read-the-docs {
+  color: #888;
+}
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -0,0 +1,17 @@
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query'
+import ChatInterface from './components/ChatInterface'
+
+const queryClient = new QueryClient()
+
+function App() {
+  return (
+    <QueryClientProvider client={queryClient}>
+      <div className="min-h-screen bg-gray-950 p-4 md:p-8">
+        <ChatInterface />
+      </div>
+    </QueryClientProvider>
+  )
+}
+
+export default App
+
--- a/frontend/src/assets/react.svg
+++ b/frontend/src/assets/react.svg
@@ -0,0 +1 @@
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--- a/frontend/src/components/ChatInterface.tsx
+++ b/frontend/src/components/ChatInterface.tsx
@@ -0,0 +1,125 @@
+import { useState, useRef, useEffect } from 'react';
+import { useMutation } from '@tanstack/react-query';
+import axios from 'axios';
+
+type Message = {
+  id: string;
+  text: string;
+  sender: 'user' | 'ai';
+};
+
+export default function ChatInterface() {
+  const [messages, setMessages] = useState<Message[]>([]);
+  const [input, setInput] = useState('');
+  const messagesEndRef = useRef<HTMLDivElement>(null);
+
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  };
+
+  useEffect(() => {
+    scrollToBottom();
+  }, [messages]);
+
+  const chatMutation = useMutation({
+    mutationFn: async (messageText: string) => {
+      const response = await axios.post('http://localhost:8000/chat', {
+        message: messageText,
+      });
+      return response.data;
+    },
+    onSuccess: (data) => {
+      setMessages((prev) => [
+        ...prev,
+        { id: Date.now().toString(), text: data.response, sender: 'ai' },
+      ]);
+    },
+    onError: () => {
+      setMessages((prev) => [
+        ...prev,
+        { id: Date.now().toString(), text: "Error: Could not connect to the backend.", sender: 'ai' },
+      ]);
+    },
+  });
+
+  const handleSubmit = (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!input.trim() || chatMutation.isPending) return;
+
+    const userMessage = input.trim();
+    setInput('');
+    setMessages((prev) => [
+      ...prev,
+      { id: Date.now().toString(), text: userMessage, sender: 'user' },
+    ]);
+
+    chatMutation.mutate(userMessage);
+  };
+
+  return (
+    <div className="flex flex-col h-full max-w-2xl mx-auto border border-gray-700 rounded-lg overflow-hidden bg-gray-800 shadow-xl mt-8">
+      {/* Header */}
+      <div className="bg-gray-900 p-4 border-b border-gray-700">
+        <h2 className="text-xl font-semibold text-white">Sam Rolfe - AI</h2>
+        <p className="text-sm text-gray-400">Ask about skills, experience, hobbies</p>
+      </div>
+
+      {/* Message Area */}
+      <div className="flex-1 overflow-y-auto p-4 space-y-4 min-h-[400px]">
+        {messages.length === 0 && (
+          <div className="text-center text-gray-500 mt-10">
+            Send a message to start the conversation!
+          </div>
+        )}
+        {messages.map((msg) => (
+          <div
+            key={msg.id}
+            className={`flex ${msg.sender === 'user' ? 'justify-end' : 'justify-start'}`}
+          >
+            <div
+              className={`max-w-[80%] rounded-2xl px-4 py-2 ${
+                msg.sender === 'user'
+                  ? 'bg-blue-600 text-white rounded-tr-none'
+                  : 'bg-gray-700 text-gray-100 rounded-tl-none'
+              }`}
+            >
+              {msg.text}
+            </div>
+          </div>
+        ))}
+        {chatMutation.isPending && (
+          <div className="flex justify-start">
+            <div className="bg-gray-700 text-gray-400 rounded-2xl rounded-tl-none px-4 py-2 flex space-x-2 items-center">
+              <div className="w-2 h-2 bg-gray-500 rounded-full animate-bounce" />
+              <div className="w-2 h-2 bg-gray-500 rounded-full animate-bounce [animation-delay:0.2s]" />
+              <div className="w-2 h-2 bg-gray-500 rounded-full animate-bounce [animation-delay:0.4s]" />
+            </div>
+          </div>
+        )}
+        <div ref={messagesEndRef} />
+      </div>
+
+      {/* Input Area */}
+      <form onSubmit={handleSubmit} className="p-4 bg-gray-900 border-t border-gray-700">
+        <div className="flex space-x-2">
+          <input
+            type="text"
+            value={input}
+            onChange={(e) => setInput(e.target.value)}
+            placeholder="Type your message..."
+            className="flex-1 bg-gray-800 text-white border border-gray-700 rounded-lg px-4 py-2 focus:outline-none focus:border-blue-500 transition-colors"
+            disabled={chatMutation.isPending}
+          />
+          <button
+            type="submit"
+            disabled={!input.trim() || chatMutation.isPending}
+            className="bg-blue-600 hover:bg-blue-700 disabled:bg-blue-800 disabled:text-gray-400 text-white px-6 py-2 rounded-lg font-medium transition-colors"
+          >
+            Send
+          </button>
+        </div>
+      </form>
+    </div>
+  );
+}
+
--- a/frontend/src/index.css
+++ b/frontend/src/index.css
@@ -0,0 +1,5 @@
+@import "tailwindcss";
+
+@theme {
+  --font-sans: system-ui, Avenir, Helvetica, Arial, sans-serif;
+}
--- a/frontend/src/main.tsx
+++ b/frontend/src/main.tsx
@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.tsx'
+
+createRoot(document.getElementById('root')!).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)
--- a/frontend/tsconfig.app.json
+++ b/frontend/tsconfig.app.json
@@ -0,0 +1,28 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+    "target": "ES2022",
+    "useDefineForClassFields": true,
+    "lib": ["ES2022", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "types": ["vite/client"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+    "jsx": "react-jsx",
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["src"]
+}
--- a/frontend/tsconfig.json
+++ b/frontend/tsconfig.json
@@ -0,0 +1,7 @@
+{
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.node.json" }
+  ]
+}
--- a/frontend/tsconfig.node.json
+++ b/frontend/tsconfig.node.json
@@ -0,0 +1,26 @@
+{
+  "compilerOptions": {
+    "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+    "target": "ES2023",
+    "lib": ["ES2023"],
+    "module": "ESNext",
+    "types": ["node"],
+    "skipLibCheck": true,
+
+    /* Bundler mode */
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "moduleDetection": "force",
+    "noEmit": true,
+
+    /* Linting */
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "erasableSyntaxOnly": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedSideEffectImports": true
+  },
+  "include": ["vite.config.ts"]
+}
--- a/frontend/vite.config.ts
+++ b/frontend/vite.config.ts
@@ -0,0 +1,11 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import tailwindcss from '@tailwindcss/vite'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [
+    tailwindcss(),
+    react()
+  ],
+})
--- a/knowledge_service/Dockerfile
+++ b/knowledge_service/Dockerfile
@@ -0,0 +1,29 @@
+FROM python:3.11-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libstdc++6 \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create directories
+RUN mkdir -p /app/packages /app/code
+
+# Install Python packages to a specific location
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --target=/app/packages -r requirements.txt
+
+# Copy initial code (will be overridden by volume mount in dev)
+COPY . /app/code/
+
+# Set Python to find packages in /app/packages
+ENV PYTHONPATH=/app/packages
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app/code
+EXPOSE 8080
+
+CMD ["python3", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
+
--- a/knowledge_service/data/hobbies.md
+++ b/knowledge_service/data/hobbies.md
@@ -0,0 +1,15 @@
+# Sam's Hobbies
+
+## Music
+- Enjoys playing guitar and synthesizers.
+- Collects vintage vinyl.
+
+## Gardening
+- Maintains a local vegetable patch.
+- Focuses on organic heirloom tomatoes.
+
+## Skiing
+- Advanced skier, prefers off-piste and backcountry in the Alps.
+
+## Art
+- Digital illustration and oil painting.
--- a/knowledge_service/docker-compose.yml
+++ b/knowledge_service/docker-compose.yml
@@ -0,0 +1,24 @@
+services:
+  knowledge-service:
+    build: .
+    image: sam/knowledge-service:latest
+    container_name: knowledge-service
+    ports:
+      - "8080:8080"
+    volumes:
+      # Only mount the code directory, not packages
+      - ./data:/app/code/data
+      - ./chroma_db:/app/code/chroma_db
+      - ./main.py:/app/code/main.py:ro  # Read-only mount for safety
+    environment:
+      - PYTHONUNBUFFERED=1
+      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
+      - PYTHONPATH=/app/packages
+    networks:
+      - ai-mesh
+    restart: unless-stopped
+
+networks:
+  ai-mesh:
+    external: true
+
--- a/knowledge_service/gitea_scraper.py
+++ b/knowledge_service/gitea_scraper.py
@@ -0,0 +1,121 @@
+import os
+import httpx
+import logging
+from typing import List, Dict, Optional
+from dataclasses import dataclass
+from datetime import datetime
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+@dataclass
+class RepoMetadata:
+    name: str
+    description: str
+    url: str
+    default_branch: str
+    updated_at: str
+    language: Optional[str]
+
+class GiteaScraper:
+    def __init__(self, base_url: str, token: str, username: str = "sam"):
+        self.base_url = base_url.rstrip("/")
+        self.token = token
+        self.username = username
+        self.headers = {"Authorization": f"token {token}"}
+        
+    def get_user_repos(self) -> List[RepoMetadata]:
+        """Fetch all repositories for the user."""
+        repos = []
+        page = 1
+        
+        while True:
+            url = f"{self.base_url}/api/v1/users/{self.username}/repos?page={page}&limit=50"
+            
+            try:
+                response = httpx.get(url, headers=self.headers, timeout=30.0)
+                response.raise_for_status()
+                
+                data = response.json()
+                if not data:
+                    break
+                    
+                for repo in data:
+                    repos.append(RepoMetadata(
+                        name=repo["name"],
+                        description=repo.get("description", ""),
+                        url=repo["html_url"],
+                        default_branch=repo["default_branch"],
+                        updated_at=repo["updated_at"],
+                        language=repo.get("language")
+                    ))
+                
+                logger.info(f"Fetched page {page}, got {len(data)} repos")
+                page += 1
+                
+            except Exception as e:
+                logger.error(f"Error fetching repos: {e}")
+                break
+                
+        return repos
+    
+    def get_readme(self, repo_name: str) -> str:
+        """Fetch README content for a repository."""
+        # Try common README filenames
+        readme_names = ["README.md", "readme.md", "Readme.md", "README.rst"]
+        
+        for readme_name in readme_names:
+            url = f"{self.base_url}/api/v1/repos/{self.username}/{repo_name}/raw/{readme_name}"
+            
+            try:
+                response = httpx.get(url, headers=self.headers, timeout=10.0)
+                if response.status_code == 200:
+                    return response.text
+            except Exception as e:
+                logger.warning(f"Failed to fetch {readme_name}: {e}")
+                continue
+                
+        return ""
+    
+    def get_repo_files(self, repo_name: str, path: str = "") -> List[Dict]:
+        """List files in a repository directory."""
+        url = f"{self.base_url}/api/v1/repos/{self.username}/{repo_name}/contents/{path}"
+        
+        try:
+            response = httpx.get(url, headers=self.headers, timeout=10.0)
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            logger.error(f"Error listing files in {repo_name}/{path}: {e}")
+            return []
+    
+    def get_file_content(self, repo_name: str, filepath: str) -> str:
+        """Fetch content of a specific file."""
+        url = f"{self.base_url}/api/v1/repos/{self.username}/{repo_name}/raw/{filepath}"
+        
+        try:
+            response = httpx.get(url, headers=self.headers, timeout=10.0)
+            if response.status_code == 200:
+                return response.text
+        except Exception as e:
+            logger.error(f"Error fetching file {filepath}: {e}")
+            
+        return ""
+
+# Test function
+if __name__ == "__main__":
+    scraper = GiteaScraper(
+        base_url=os.getenv("GITEA_URL", "https://gitea.lab.audasmedia.com.au"),
+        token=os.getenv("GITEA_TOKEN", ""),
+        username=os.getenv("GITEA_USERNAME", "sam")
+    )
+    
+    repos = scraper.get_user_repos()
+    print(f"Found {len(repos)} repositories")
+    
+    for repo in repos[:3]:  # Test with first 3
+        print(f"\nRepo: {repo.name}")
+        readme = scraper.get_readme(repo.name)
+        if readme:
+            print(f"README preview: {readme[:200]}...")
+
--- a/knowledge_service/knowledge_agent_plan.md
+++ b/knowledge_service/knowledge_agent_plan.md
@@ -0,0 +1,56 @@
+# GOAL
+
+Build a \"Deep Knowledge Agent\" (DKA) that acts as a secure,
+quarantined bridge between the Chat Gateway and private data sources.
+
+# ARCHITECTURE OVERVIEW
+
+## Layers
+
+1.  Public Gateway: FastAPI (The \"Voice\").
+2.  Orchestration Layer: LangGraph Supervisor (The \"Router\").
+3.  Quarantined Agent: DKA / Librarian (The \"Keeper of Secrets\").
+    - Strictly Read-Only.
+    - Accesses ChromaDB and Media stores.
+4.  Specialist Agent: Opencode (The \"Engineer\").
+
+## Data Sources (The \"Knowledge Mesh\")
+
+- [ ] **Code**: Gitea (Repos, Markdown docs).
+- [ ] **Notes**: Trilium Next, Obsidian, Flatnotes, HedgeDoc.
+- [ ] **Wiki**: DokuWiki.
+- [ ] **Inventory**: HomeBox (Physical gear, photos).
+- [ ] **Tasks**: Vikunja.
+- [ ] **Media**: Immich (Photos/Videos metadata via Gemini Vision).
+
+## Agent Tooling & Orchestration
+
+- [ ] **Orchestrators**: CAO CLI, Agent Pipe.
+- [ ] **External Agents**: Goose, Aider, Opencode (Specialist).
+
+# COMPONENT DETAILS
+
+## The Librarian (DKA - LangGraph)
+
+- Purpose: Semantic retrieval and data synthesis from vectors.
+- Tools:
+  - `query_chroma`: Search the vector database.
+  - `fetch_media_link`: Returns a signed URL/path for Immich/HomeBox
+    images.
+- Constraints:
+  - NO `bash` or `write` tools.
+
+## The Ingestion Pipeline (Airflow/Custom Python)
+
+- [ ] **Multi-Source Scrapers**: API-based (Gitea, Immich) and
+  File-based (Obsidian).
+- [ ] **Vision Integration**: Gemini analyzes Immich photos to create
+  searchable text descriptions.
+- [ ] **Storage**: ChromaDB (Vectors) + PostgreSQL (Metadata/Hashes).
+
+# [TODO]{.todo .TODO} LIST \[0/4\] {#list-04}
+
+- [ ] Create \'knowledge~service~\' directory.
+- [ ] Implement `test_rag.py` (Hello World retrieval).
+- [ ] Build basic scraper for `hobbies.org`.
+- [ ] Integrate DKA logic into the FastAPI Gateway.
--- a/knowledge_service/knowledge_agent_plan.org
+++ b/knowledge_service/knowledge_agent_plan.org
@@ -0,0 +1,47 @@
+#+TITLE: Phase 3: Knowledge Engine & Agent Orchestration
+#+AUTHOR: Giordano (via opencode)
+#+OPTIONS: toc:2
+
+* GOAL
+Build a "Deep Knowledge Agent" (DKA) that acts as a secure, quarantined bridge between the Chat Gateway and private data sources.
+
+* ARCHITECTURE OVERVIEW
+** Layers
+1. Public Gateway: FastAPI (The "Voice").
+2. Orchestration Layer: LangGraph Supervisor (The "Router").
+3. Quarantined Agent: DKA / Librarian (The "Keeper of Secrets").
+   - Strictly Read-Only.
+   - Accesses ChromaDB and Media stores.
+4. Specialist Agent: Opencode (The "Engineer").
+
+** Data Sources (The "Knowledge Mesh")
+- [ ] *Code*: Gitea (Repos, Markdown docs).
+- [ ] *Notes*: Trilium Next, Obsidian, Flatnotes, HedgeDoc.
+- [ ] *Wiki*: DokuWiki.
+- [ ] *Inventory*: HomeBox (Physical gear, photos).
+- [ ] *Tasks*: Vikunja.
+- [ ] *Media*: Immich (Photos/Videos metadata via Gemini Vision).
+
+** Agent Tooling & Orchestration
+- [ ] *Orchestrators*: CAO CLI, Agent Pipe.
+- [ ] *External Agents*: Goose, Aider, Opencode (Specialist).
+
+* COMPONENT DETAILS
+** The Librarian (DKA - LangGraph)
+- Purpose: Semantic retrieval and data synthesis from vectors.
+- Tools: 
+  - ~query_chroma~: Search the vector database.
+  - ~fetch_media_link~: Returns a signed URL/path for Immich/HomeBox images.
+- Constraints: 
+  - NO ~bash~ or ~write~ tools.
+
+** The Ingestion Pipeline (Airflow/Custom Python)
+- [ ] *Multi-Source Scrapers*: API-based (Gitea, Immich) and File-based (Obsidian).
+- [ ] *Vision Integration*: Gemini analyzes Immich photos to create searchable text descriptions.
+- [ ] *Storage*: ChromaDB (Vectors) + PostgreSQL (Metadata/Hashes).
+
+* TODO LIST [0/4]
+- [ ] Create 'knowledge_service' directory.
+- [ ] Implement ~test_rag.py~ (Hello World retrieval).
+- [ ] Build basic scraper for ~hobbies.org~.
+- [ ] Integrate DKA logic into the FastAPI Gateway.
--- a/knowledge_service/main.py
+++ b/knowledge_service/main.py
@@ -0,0 +1,52 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from langchain_community.document_loaders import TextLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import os
+import logging
+import sys
+
+logging.basicConfig(level=logging.INFO, stream=sys.stdout)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+vector_db = None
+
+# Voyage-2 embeddings via OpenRouter API
+embeddings = OpenAIEmbeddings(
+    model="openai/text-embedding-3-small",
+    openai_api_base="https://openrouter.ai/api/v1",
+    openai_api_key=os.getenv("OPENROUTER_API_KEY")
+)
+
+@app.on_event("startup")
+async def startup_event():
+    global vector_db
+    data_path = "./data/hobbies.md"
+    if os.path.exists(data_path):
+        try:
+            loader = TextLoader(data_path)
+            documents = loader.load()
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+            chunks = text_splitter.split_documents(documents)
+            vector_db = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory="./chroma_db")
+            logger.info("Librarian: ChromaDB is loaded with openAi embeddings.")
+        except Exception as e:
+            logger.error(f"Librarian: DB error: {str(e)}")
+    else:
+        logger.warning(f"Librarian: Missing data file at {data_path}")
+
+@app.get("/health")
+async def health():
+    return {"status": "ready", "vectors_loaded": vector_db is not None}
+
+class QueryRequest(BaseModel):
+    question: str
+
+@app.post("/query")
+async def query_knowledge(request: QueryRequest):
+    if not vector_db: return {"context": ""}
+    results = vector_db.similarity_search(request.question, k=2)
+    return {"context": "\n".join([res.page_content for res in results])}
--- a/knowledge_service/requirements.txt
+++ b/knowledge_service/requirements.txt
@@ -0,0 +1,7 @@
+fastapi
+uvicorn
+langchain
+langchain-community
+langchain-openai
+langchain-text-splitters
+chromadb
--- a/langgraph_service/Dockerfile
+++ b/langgraph_service/Dockerfile
@@ -0,0 +1,22 @@
+FROM python:3.11-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create app directory
+WORKDIR /app
+
+# Copy requirements
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy code
+COPY . .
+
+EXPOSE 8090
+
+CMD ["python3", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8090"]
+
--- a/langgraph_service/main.py
+++ b/langgraph_service/main.py
@@ -0,0 +1,80 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from supervisor_agent import process_query
+import logging
+import sys
+
+logging.basicConfig(level=logging.INFO, stream=sys.stdout)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="LangGraph Supervisor Service")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class QueryRequest(BaseModel):
+    query: str
+
+class QueryResponse(BaseModel):
+    response: str
+    agent_used: str
+    context: dict
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy", "service": "langgraph-supervisor"}
+
+@app.post("/query", response_model=QueryResponse)
+async def query_supervisor(request: QueryRequest):
+    """Main entry point for agent orchestration."""
+    logger.info(f"Received query: {request.query}")
+    
+    try:
+        result = await process_query(request.query)
+        
+        return QueryResponse(
+            response=result["response"],
+            agent_used=result["context"].get("source", "unknown"),
+            context=result["context"]
+        )
+    except Exception as e:
+        logger.error(f"Error processing query: {e}")
+        return QueryResponse(
+            response="Error processing your request",
+            agent_used="error",
+            context={"error": str(e)}
+        )
+
+@app.get("/agents")
+async def list_agents():
+    """List available specialist agents."""
+    return {
+        "agents": [
+            {
+                "name": "librarian",
+                "description": "Queries the knowledge base for semantic information",
+                "triggers": ["repo", "code", "git", "hobby", "about", "skill"]
+            },
+            {
+                "name": "opencode",
+                "description": "Handles coding tasks and file modifications",
+                "triggers": ["write", "edit", "create", "fix", "implement"]
+            },
+            {
+                "name": "brain",
+                "description": "General LLM for reasoning and generation",
+                "triggers": ["default", "general questions"]
+            }
+        ]
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8090)
+
--- a/langgraph_service/requirements.txt
+++ b/langgraph_service/requirements.txt
@@ -0,0 +1,9 @@
+fastapi
+uvicorn
+langgraph
+langchain
+langchain-community
+langchain-openai
+httpx
+pydantic
+
--- a/langgraph_service/supervisor_agent.py
+++ b/langgraph_service/supervisor_agent.py
@@ -0,0 +1,153 @@
+from typing import TypedDict, Annotated, Sequence
+from langgraph.graph import StateGraph, END
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+import operator
+import httpx
+import os
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# State definition
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+    next_agent: str
+    context: dict
+
+# Agent routing logic
+def supervisor_node(state: AgentState):
+    """Supervisor decides which specialist agent to call."""
+    last_message = state["messages"][-1].content.lower()
+    
+    # Simple routing logic based on keywords
+    if any(kw in last_message for kw in ["repo", "code", "git", "github", "gitea", "project", "development"]):
+        return {"next_agent": "librarian"}
+    elif any(kw in last_message for kw in ["write", "edit", "create", "fix", "bug", "implement", "code change"]):
+        return {"next_agent": "opencode"}
+    elif any(kw in last_message for kw in ["sam", "hobby", "music", "experience", "skill", "about"]):
+        return {"next_agent": "librarian"}
+    else:
+        return {"next_agent": "brain"}  # Default to general LLM
+
+def librarian_agent(state: AgentState):
+    """Librarian agent - queries knowledge base (ChromaDB)."""
+    last_message = state["messages"][-1].content
+    
+    try:
+        # Call knowledge service
+        response = httpx.post(
+            "http://knowledge-service:8080/query",
+            json={"question": last_message},
+            timeout=10.0
+        )
+        
+        if response.status_code == 200:
+            context = response.json().get("context", "")
+            return {
+                "messages": [AIMessage(content=f"Based on my knowledge base:\n\n{context}")],
+                "context": {"source": "librarian", "context": context}
+            }
+    except Exception as e:
+        logger.error(f"Librarian error: {e}")
+    
+    return {
+        "messages": [AIMessage(content="I couldn't find relevant information in the knowledge base.")],
+        "context": {"source": "librarian", "error": str(e)}
+    }
+
+def opencode_agent(state: AgentState):
+    """Opencode agent - handles coding tasks via MCP."""
+    last_message = state["messages"][-1].content
+    
+    # Placeholder - would integrate with opencode-brain
+    return {
+        "messages": [AIMessage(content=f"I'm the coding agent. I would help you with: {last_message}")],
+        "context": {"source": "opencode", "action": "coding_task"}
+    }
+
+def brain_agent(state: AgentState):
+    """Brain agent - general LLM fallback."""
+    last_message = state["messages"][-1].content
+    
+    try:
+        # Call opencode-brain service
+        auth = httpx.BasicAuth("opencode", os.getenv("OPENCODE_PASSWORD", "sam4jo"))
+        timeout_long = httpx.Timeout(180.0, connect=10.0)
+        
+        with httpx.AsyncClient(auth=auth, timeout=timeout_long) as client:
+            # Create session
+            session_res = client.post("http://opencode-brain:5000/session", json={"title": "Supervisor Query"})
+            session_id = session_res.json()["id"]
+            
+            # Send message
+            response = client.post(
+                f"http://opencode-brain:5000/session/{session_id}/message",
+                json={"parts": [{"type": "text", "text": last_message}]}
+            )
+            
+            data = response.json()
+            if "parts" in data:
+                for part in data["parts"]:
+                    if part.get("type") == "text":
+                        return {
+                            "messages": [AIMessage(content=part["text"])],
+                            "context": {"source": "brain"}
+                        }
+    except Exception as e:
+        logger.error(f"Brain error: {e}")
+    
+    return {
+        "messages": [AIMessage(content="I'm thinking about this...")],
+        "context": {"source": "brain"}
+    }
+
+def route_decision(state: AgentState):
+    """Routing function based on supervisor decision."""
+    return state["next_agent"]
+
+# Build the graph
+workflow = StateGraph(AgentState)
+
+# Add nodes
+workflow.add_node("supervisor", supervisor_node)
+workflow.add_node("librarian", librarian_agent)
+workflow.add_node("opencode", opencode_agent)
+workflow.add_node("brain", brain_agent)
+
+# Add edges
+workflow.set_entry_point("supervisor")
+
+# Conditional routing from supervisor
+workflow.add_conditional_edges(
+    "supervisor",
+    route_decision,
+    {
+        "librarian": "librarian",
+        "opencode": "opencode",
+        "brain": "brain"
+    }
+)
+
+# All specialist agents end
+workflow.add_edge("librarian", END)
+workflow.add_edge("opencode", END)
+workflow.add_edge("brain", END)
+
+# Compile the graph
+supervisor_graph = workflow.compile()
+
+# Main entry point for queries
+async def process_query(query: str) -> dict:
+    """Process a query through the supervisor graph."""
+    result = await supervisor_graph.ainvoke({
+        "messages": [HumanMessage(content=query)],
+        "next_agent": "",
+        "context": {}
+    })
+    
+    return {
+        "response": result["messages"][-1].content,
+        "context": result.get("context", {})
+    }
+
--- a/plan.md
+++ b/plan.md
@@ -0,0 +1,396 @@
+# Project Plan: aboutme_chat_demo
+
+## Goal
+Build a comprehensive AI agent system that ingests data from self-hosted services (Gitea, notes, wikis), stores it in a vector database, and provides intelligent responses through a multi-agent orchestration layer. The system emphasizes modular containerized architecture, industry-standard tools, and employment-relevant skills.
+
+---
+
+## Phase 1: Foundation & Core Infrastructure (COMPLETED)
+
+### Phase 1.1: Frontend Application
+**Location:** `/home/sam/development/aboutme_chat_demo/frontend/`
+
+**Stack & Tools:**
+- **Framework:** Vite 6.2.0 + React 19.0.0 + TypeScript
+- **Styling:** Tailwind CSS 4.0.0
+- **State Management:** TanStack Query (React Query) 5.67.0
+- **Build Tool:** Vite with React plugin
+- **Linting:** ESLint 9.21.0 + typescript-eslint 8.24.0
+
+**Components Implemented:**
+- `ChatInterface.tsx` - Auto-expanding text input with scrolling message list
+- `App.tsx` - Main application container
+- Real-time chat UI with message history
+- HTTP client integration to backend gateway
+
+**Docker Configuration:**
+- Hot-reload development setup
+- Volume mounting for instant code changes
+- Node modules isolation (`/app/node_modules`)
+
+### Phase 1.2: Chat Gateway (Orchestration Entry Point)
+**Location:** `/home/sam/development/aboutme_chat_demo/backend/`
+
+**Stack & Tools:**
+- **Framework:** FastAPI (Python 3.11)
+- **HTTP Client:** httpx 0.28.1
+- **CORS:** Configured for all origins (development)
+
+**Architecture Changes:**
+- **OLD:** Hardcoded keyword matching (`["sam", "hobby", "music", "guitar", "skiing", "experience"]`) to trigger knowledge lookup
+- **NEW:** Thin routing layer - all queries passed to LangGraph Supervisor for intelligent agent selection
+- Removed direct Brain (LLM) integration
+- Removed direct Knowledge Service calls
+- Now acts as stateless entry point to LangGraph orchestration layer
+
+**Endpoints:**
+- `POST /chat` - Routes queries to LangGraph Supervisor
+- `GET /health` - Service health check
+- `GET /agents` - Lists available agents from LangGraph
+
+### Phase 1.3: Knowledge Service (Librarian Agent)
+**Location:** `/home/sam/development/knowledge_service/`
+
+**Stack & Tools:**
+- **Framework:** FastAPI + Uvicorn
+- **Vector Database:** ChromaDB 1.5.1
+- **Embeddings:** OpenAI via OpenRouter API (text-embedding-3-small)
+- **LLM Framework:** LangChain ecosystem
+  - langchain 1.2.10
+  - langchain-community 0.4.1
+  - langchain-core 1.2.15
+  - langchain-text-splitters 1.1.1
+  - langchain-openai
+- **Document Processing:** RecursiveCharacterTextSplitter
+
+**Key Files:**
+- `main.py` - FastAPI endpoints for /query and /health
+- `gitea_scraper.py` - Gitea API integration module (NEW)
+- `data/hobbies.md` - Sample knowledge base content
+- `chroma_db/` - Persistent vector storage
+
+**Docker Architecture (Optimized):**
+- **Pattern:** Separate `/app/packages` (cached) from `/app/code` (volume-mounted)
+- **Benefits:** 
+  - Code changes apply instantly without rebuild
+  - Package installation happens once during image build
+  - PYTHONPATH=/app/packages ensures imports work
+- **Volumes:**
+  - `./data:/app/code/data` - Knowledge documents
+  - `./chroma_db:/app/code/chroma_db` - Vector database persistence
+  - `./main.py:/app/code/main.py:ro` - Read-only code mount
+
+### Phase 1.4: LangGraph Supervisor Service (NEW)
+**Location:** `/home/sam/development/langgraph_service/`
+
+**Stack & Tools:**
+- **Framework:** FastAPI + Uvicorn
+- **Orchestration:** LangGraph 1.0.9
+  - langgraph-checkpoint 4.0.0
+  - langgraph-prebuilt 1.0.8
+  - langgraph-sdk 0.3.9
+- **State Management:** TypedDict with Annotated operators
+- **Message Types:** LangChain Core Messages (HumanMessage, AIMessage)
+
+**Architecture:**
+- **Supervisor Node:** Analyzes queries and routes to specialist agents
+- **Agent Graph:** StateGraph with conditional edges
+- **Three Specialist Agents:**
+  1. **Librarian Agent** - Queries ChromaDB via knowledge-service:8080
+  2. **Opencode Agent** - Placeholder for coding tasks (MCP integration ready)
+  3. **Brain Agent** - Fallback to OpenCode Brain LLM (opencode-brain:5000)
+
+**Routing Logic:**
+```
+Query → Supervisor → [Librarian | Opencode | Brain]
+- "repo/code/git/project" → Librarian (RAG)
+- "write/edit/create/fix" → Opencode (Coding)
+- "sam/hobby/music/about" → Librarian (RAG)
+- Default → Brain (General LLM)
+```
+
+**Docker Configuration:**
+- Self-contained with own `/app/packages`
+- No package sharing with other services (modular)
+- Port 8090 exposed
+
+### Phase 1.5: Apache Airflow (Scheduled Ingestion)
+**Location:** `/home/sam/development/airflow/`
+
+**Stack & Tools:**
+- **Orchestration:** Apache Airflow 2.8.1
+- **Executor:** CeleryExecutor (distributed task processing)
+- **Database:** PostgreSQL 13 (metadata)
+- **Message Queue:** Redis (Celery broker)
+- **Services:**
+  - airflow-webserver (UI + API)
+  - airflow-scheduler (DAG scheduling)
+  - airflow-worker (task execution)
+  - airflow-triggerer (deferrable operators)
+
+**DAG: gitea_daily_ingestion**
+- **Schedule:** Daily
+- **Tasks:**
+  1. `fetch_repos` - Get all user repos from Gitea API
+  2. `fetch_readmes` - Download README files
+  3. `ingest_to_chroma` - Store in Knowledge Service
+
+**Integration:**
+- Mounts `knowledge_service/gitea_scraper.py` into DAGs folder
+- Environment variables for Gitea API token
+- Network: ai-mesh (communicates with knowledge-service)
+
+### Phase 1.6: Gitea Scraper Module
+**Location:** `/home/sam/development/knowledge_service/gitea_scraper.py`
+
+**Functionality:**
+- **API Integration:** Gitea REST API v1
+- **Authentication:** Token-based (Authorization header)
+- **Methods:**
+  - `get_user_repos()` - Paginated repo listing
+  - `get_readme(repo_name)` - README content with fallback names
+  - `get_repo_files(repo_name, path)` - Directory listing
+  - `get_file_content(repo_name, filepath)` - File download
+
+**Data Model:**
+- `RepoMetadata` dataclass (name, description, url, branch, updated_at, language)
+
+### Phase 1.7: Docker Infrastructure
+
+**Network:**
+- `ai-mesh` (external) - Shared bridge network for all services
+
+**Services Overview:**
+| Service | Port | Purpose | Dependencies |
+|---------|------|---------|--------------|
+| frontend | 5173 | React UI | backend |
+| backend | 8000 | Chat Gateway | langgraph-service, db |
+| db | 5432 | PostgreSQL (chat history) | - |
+| knowledge-service | 8080 | RAG / Vector DB | - |
+| langgraph-service | 8090 | Agent Orchestration | knowledge-service |
+| airflow-webserver | 8081 | Workflow UI | postgres, redis |
+| airflow-scheduler | - | DAG scheduling | postgres, redis |
+| airflow-worker | - | Task execution | postgres, redis |
+| redis | 6379 | Message broker | - |
+| postgres (airflow) | - | Airflow metadata | - |
+
+**Container Patterns:**
+- All Python services use `/app/packages` + `/app/code` separation
+- Node.js services use volume mounting for hot reload
+- PostgreSQL uses named volumes for persistence
+- External network (`ai-mesh`) for cross-service communication
+
+---
+
+## Phase 2: Multi-Source Knowledge Ingestion (IN PROGRESS)
+
+### Goal
+Expand beyond Gitea to ingest data from all self-hosted knowledge sources.
+
+### Data Sources to Integrate:
+1. **Notes & Documentation**
+   - **Trilium Next** - Hierarchical note-taking (tree structure)
+   - **Obsidian** - Markdown vault with backlinks
+   - **Flatnotes** - Flat file markdown notes
+   - **HedgeDoc** - Collaborative markdown editor
+
+2. **Wiki**
+   - **DokuWiki** - Structured wiki content
+
+3. **Project Management**
+   - **Vikunja** - Task lists and project tracking
+
+4. **Media & Assets**
+   - **Immich** - Photo/video metadata + Gemini Vision API for content description
+   - **HomeBox** - Physical inventory with images
+
+### Technical Approach:
+- **Crawling:** Selenium/Playwright for JavaScript-heavy UIs
+- **Extraction:** Firecrawl or LangChain loaders for structured content
+- **Vision:** Gemini Vision API for image-to-text conversion
+- **Storage:** ChromaDB (vectors) + PostgreSQL (metadata, hashes for deduplication)
+- **Scheduling:** Additional Airflow DAGs per source
+
+---
+
+## Phase 3: Advanced Agent Capabilities
+
+### Goal
+Integrate external AI tools and expand agent capabilities.
+
+### Agent Tooling:
+1. **MCP (Model Context Protocol) Servers**
+   - Git MCP - Local repository operations
+   - Filesystem MCP - Secure file access
+   - Memory MCP - Knowledge graph persistence
+   - Custom Gitea MCP (if/when available)
+
+2. **External Agents**
+   - **Goose** - CLI-based agent for local task execution
+   - **Aider** - AI pair programming
+   - **Opencode** - Already integrated (Brain Agent)
+   - **Automaker** - Workflow automation
+   - **Autocoder** - Code generation
+
+3. **Orchestration Tools**
+   - **CAO CLI** - Agent orchestrator
+   - **Agent Pipe** - Pipeline management
+
+### Integration Pattern:
+- Each external tool wrapped as LangGraph node
+- Supervisor routes to appropriate specialist
+- State management for multi-turn interactions
+
+---
+
+## Phase 4: Production Hardening
+
+### Goal
+Prepare system for production deployment.
+
+### Authentication & Security:
+- **Laravel** - User authentication service (Phase 4 original plan)
+- **JWT tokens** - Session management
+- **API key management** - Secure credential storage
+- **Network policies** - Inter-service communication restrictions
+
+### Monitoring & Observability:
+- **LangSmith** - LLM tracing and debugging
+- **Langfuse** - LLM observability (note: currently in per-project install list)
+- **Prometheus/Grafana** - Metrics and dashboards
+- **Airflow monitoring** - DAG success/failure alerting
+
+### Scaling:
+- **ChromaDB** - Migration to server mode for concurrent access
+- **Airflow** - Multiple Celery workers
+- **Load balancing** - Nginx reverse proxy
+- **Backup strategies** - Vector DB snapshots, PostgreSQL dumps
+
+---
+
+## Phase 5: Workflow Automation & Visual Tools
+
+### Goal
+Add visual prototyping and automation capabilities.
+
+### Tools to Integrate:
+1. **Flowise** - Visual LangChain builder
+   - Prototype agent flows without coding
+   - Export to Python code
+   - Debug RAG pipelines visually
+
+2. **Windmill** - Turn scripts into workflows
+   - Schedule Python/LangChain scripts
+   - Reactive triggers (e.g., on-commit)
+   - Low-code workflow builder
+
+3. **Activepieces** - Event-driven automation
+   - Webhook triggers from Gitea
+   - Integration with external APIs
+   - Visual workflow designer
+
+4. **N8N** - Alternative workflow automation
+   - Consider if Activepieces doesn't meet needs
+
+### Use Cases:
+- **On-commit triggers:** Gitea push → immediate re-scan → notification
+- **Scheduled reports:** Weekly summary of new/updated projects
+- **Reactive workflows:** New photo uploaded → Gemini Vision → update knowledge base
+
+---
+
+## Phase 6: Knowledge Library Options & RAG Enhancement
+
+### Goal
+Advanced retrieval and knowledge organization.
+
+### RAG Pipeline Improvements:
+1. **Hybrid Search**
+   - Semantic search (ChromaDB) + Keyword search (PostgreSQL)
+   - Re-ranking with cross-encoders
+   - Query expansion and decomposition
+
+2. **Multi-Modal RAG**
+   - Image retrieval (Immich + CLIP embeddings)
+   - Document parsing (PDFs, code files)
+   - Structured data (tables, lists)
+
+3. **Knowledge Organization**
+   - Entity extraction and linking
+   - Knowledge graph construction
+   - Hierarchical chunking strategies
+
+### Alternative Vector Stores (Evaluation):
+- **pgvector** - PostgreSQL native (if ChromaDB limitations hit)
+- **Weaviate** - GraphQL interface, hybrid search
+- **Qdrant** - Rust-based, high performance
+- **Milvus** - Enterprise-grade, distributed
+
+---
+
+## Phase 7: User Experience & Interface
+
+### Goal
+Enhanced frontend and interaction patterns.
+
+### Frontend Enhancements:
+1. **Chat Interface Improvements**
+   - Streaming responses (Server-Sent Events)
+   - Message threading and context
+   - File upload for document ingestion
+   - Image display (for Immich integration)
+
+2. **Knowledge Browser**
+   - View ingested documents
+   - Search knowledge base directly
+   - See confidence scores and sources
+   - Manual document upload/ingestion trigger
+
+3. **Agent Management**
+   - View active agents
+   - Configure agent behavior
+   - Monitor agent performance
+   - Override routing decisions
+
+### Mobile & Accessibility:
+- Responsive design improvements
+- Mobile app (React Native or PWA)
+- Accessibility compliance (WCAG)
+
+---
+
+## Technology Stack Summary
+
+### Core Frameworks:
+- **Backend:** FastAPI (Python 3.11)
+- **Frontend:** Vite + React 19 + TypeScript
+- **Styling:** Tailwind CSS
+- **Database:** PostgreSQL 15
+- **Vector DB:** ChromaDB 1.5.1
+
+### AI/ML Stack:
+- **LLM Orchestration:** LangGraph 1.0.9 + LangChain
+- **Embeddings:** OpenAI via OpenRouter (text-embedding-3-small)
+- **LLM:** OpenCode Brain (opencode-brain:5000)
+- **Vision:** Gemini Vision API (Phase 2)
+
+### Workflow & Scheduling:
+- **Orchestration:** Apache Airflow 2.8.1 (CeleryExecutor)
+- **Message Queue:** Redis
+- **External Tools:** Flowise, Windmill, Activepieces
+
+### Development Tools:
+- **Containers:** Docker + Docker Compose
+- **Networking:** Bridge network (ai-mesh)
+- **Testing:** curl/httpx for API testing
+- **Version Control:** Gitea (self-hosted)
+
+### Skills Demonstrated:
+- Containerized microservices architecture
+- Multi-agent AI orchestration (LangGraph)
+- Vector database implementation (RAG)
+- ETL pipeline development (Airflow)
+- API integration and web scraping
+- Modular, maintainable code organization
+- Industry-standard AI tooling (LangChain ecosystem)
+- Workflow automation and scheduling
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