sam-4screen-desktop 2026-5-16:17:16:54
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300 areas/350 AI/1778914902-WMFA.md
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created: 2026-05-16 17:02
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modified: 2026-05-16 17:02
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type: note
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tags:
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- ai dev-ops website iframe
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aliases: []
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id: 1778914902-WMFA
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---
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# [[1778914902-WMFA]]
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# Local Hybrid Vector + Graph RAG Setup via Caddy & Docker
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This document outlines the architecture and configuration files required to run a single, unified local RAG system (Vector search for static files + Graph search for Obsidian notes) served inside an iframe across three separate context-specific showcase websites (`devops.local`, `coding.local`, `ai.local`).
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---
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## 1. Network Routing (`Caddyfile`)
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This configuration uses a `Caddyfile` snippets to proxy your backend container while securely handling cross-origin iframe security rules (`Content-Security-Policy`).
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```caddy
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# Core AI RAG Application Backend
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ai.local {
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reverse_proxy localhost:8000
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header {
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# Restrict iframe rendering specifically to your 3 interest domains
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Content-Security-Policy "frame-ancestors 'self' https://devops.local https://coding.local https://ai-site.local"
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# Standard security hardening
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X-Content-Type-Options "nosniff"
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Referrer-Policy "strict-origin-when-cross-origin"
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}
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}
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# Example Configuration Blocks for Frontend Sites
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devops.local {
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root * /var/www/devops_site
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file_server
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}
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coding.local {
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root * /var/www/coding_site
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file_server
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}
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ai-site.local {
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root * /var/www/ai_site
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file_server
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}
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```
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---
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## 2. Infrastructure Layer (`docker-compose.yml`)
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The app runs out of a localized, slimmed-down Python environment container. Underlying vector files and Graph databases are explicitly mounted as **read-only** (`:ro`) to guarantee stability against prompt manipulation.
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```yaml
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version: '3.8'
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services:
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unified-ai-rag:
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image: python:3.11-slim
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container_name: local_ai_rag
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working_dir: /app
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volumes:
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# Mount application scripts
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- ./app:/app
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# Mount databases and notes securely as READ-ONLY
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- ./db/semantic_rag:/app/db/semantic_rag:ro
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- ./db/obsidian_graph.gpickle:/app/db/obsidian_graph.gpickle:ro
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ports:
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- "8000:8000"
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environment:
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- OPENAI_API_KEY=your_openai_api_key_here
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- ANTHROPIC_API_KEY=your_anthropic_api_key_here
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command: ["pip", "install", "-r", "requirements.txt", "&&", "chainlit", "run", "app.py", "--port", "8000"]
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restart: unless-stopped
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```
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---
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## 3. Web UI Application & Context Controller (`app.py`)
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This Chainlit-driven script reads incoming referrers or basic text prompts to shift its personality depending on which website the recruiter came from.
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```python
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import chainlit as cl
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import networkx as nx
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from chromadb import PersistentClient
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# 1. Initialization hooks for Vector and Graph layers (Read-Only)
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def load_databases():
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vector_client = PersistentClient(path="/app/db/semantic_rag")
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vector_layer = vector_client.get_collection(name="resume_and_docs")
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graph_layer = nx.read_gpickle("/app/db/obsidian_graph.gpickle")
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return vector_layer, graph_layer
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vector_db, graph_db = load_databases()
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@cl.on_chat_start
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async def start():
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# Detect the site referring the frame to adjust persona
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http_headers = cl.user_session.get("http_headers", {})
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referer = http_headers.get("referer", "")
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system_prompt = "You are a helpful AI assistant reviewing my portfolio data."
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welcome_msg = "Hello! Ask me any questions about my profile or experience."
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if "devops.local" in referer:
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system_prompt = "Persona: DevOps Engineer. Focus heavily on infrastructure, IoT architecture, CI/CD, and server logs."
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welcome_msg = "Welcome Recruiter! Ask me anything about my DevOps automation and IoT infrastructure."
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elif "coding.local" in referer:
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system_prompt = "Persona: Software Engineer. Emphasize backend code, software design patterns, and clean programming methodologies."
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welcome_msg = "Hello! Let's talk about my development portfolio and technical code paradigms."
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elif "ai-site.local" in referer:
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system_prompt = "Persona: AI/RAG Specialist. Discuss custom embedding techniques, semantic lookups, and graph networks."
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welcome_msg = "Greetings! Feel free to pick my brain about graph-based indexing and large language models."
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cl.user_session.set("system_prompt", system_prompt)
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await cl.Message(content=welcome_msg).send()
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@cl.on_message
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async def main(message: cl.Message):
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query = message.content
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sys_prompt = cl.user_session.get("system_prompt")
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# Executing the dual pass strategy
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# (Extract semantic chunks + pull Obsidian link neighbors from the graph)
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# Synthesize outputs down through the LLM context window here...
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response_text = f"Processed query using profile persona contextualized framework rules."
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await cl.Message(content=response_text).send()
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```
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---
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## 4. Frontend Integration (`iframe`)
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Embed this string block directly inside your target HTML sites:
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```html
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<iframe
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src="https://ai.local"
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style="width: 100%; height: 650px; border: 1px solid #ccc; border-radius: 8px;"
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allow="clipboard-read; clipboard-write">
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</iframe>
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```
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