12f7825adfae 2026-5-16:17:38:39

.obsidian/workspace.json

@@ -21,20 +21,6 @@
               "title": "tmpl_generic_note"
             }
           },
-          {
-            "id": "c0f905cef89022ce",
-            "type": "leaf",
-            "state": {
-              "type": "markdown",
-              "state": {
-                "file": "100 inbox/Untitled.md",
-                "mode": "source",
-                "source": false
-              },
-              "icon": "lucide-file",
-              "title": "Untitled"
-            }
-          },
           {
             "id": "c6fd063107736636",
             "type": "leaf",
@@ -48,6 +34,20 @@
               "icon": "lucide-file",
               "title": "1778841999-CBOE"
             }
+          },
+          {
+            "id": "d5c6d8933290711d",
+            "type": "leaf",
+            "state": {
+              "type": "markdown",
+              "state": {
+                "file": "100 inbox/Untitled 3.md",
+                "mode": "source",
+                "source": false
+              },
+              "icon": "lucide-file",
+              "title": "Untitled 3"
+            }
           }
         ],
         "currentTab": 2
@@ -224,13 +224,15 @@
       "templater-obsidian:Templater": false
     }
   },
-  "active": "7066c9273c6fb936",
+  "active": "d5c6d8933290711d",
   "lastOpenFiles": [
+    "100 inbox/Untitled 3.md",
+    "000 daily/1778841999-CBOE.md",
+    "300 areas/350 AI/1778914902-WMFA.md",
     "100 inbox/1778914902-WMFA.md",
     "100 inbox/Untitled 2.md",
     "100 inbox/Untitled 1.md",
     "300 areas/350 AI/Pi Agent Extensions & Skills.md",
-    "000 daily/1778841999-CBOE.md",
     "000 daily",
     "templates/Daily_Note_Template.md",
     "inbox/1778841999-CBOE.md",
@@ -260,7 +262,6 @@
     "300 areas/390 Dev Ops Computers.md",
     "300 areas/350 AI.md",
     "templates/ai_dev_ops.md",
-    "Pi_config_ideas.md",
+    "Pi_config_ideas.md"
-    "Welcome.md"
   ]
 }

100 inbox/1778914902-WMFA.md

@@ -1,11 +0,0 @@
----
-created: 2026-05-16 17:01
-modified: 2026-05-16 17:01
-type: note
-tags: []
-aliases: []
-id: 1778914902-WMFA
----
-# [[1778914902-WMFA]]
-
-

300 areas/350 AI/1778914902-WMFA.md

@@ -0,0 +1,153 @@
+---
+created: 2026-05-16 17:02
+modified: 2026-05-16 17:02
+type: note
+tags:
+  - ai dev-ops website iframe
+aliases: []
+id: 1778914902-WMFA
+---
+# [[1778914902-WMFA]]
+
+# Local Hybrid Vector + Graph RAG Setup via Caddy & Docker
+
+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`).
+
+---
+
+## 1. Network Routing (`Caddyfile`)
+
+This configuration uses a `Caddyfile` snippets to proxy your backend container while securely handling cross-origin iframe security rules (`Content-Security-Policy`).
+
+```caddy
+# Core AI RAG Application Backend
+ai.local {
+    reverse_proxy localhost:8000
+
+    header {
+        # Restrict iframe rendering specifically to your 3 interest domains
+        Content-Security-Policy "frame-ancestors 'self' https://devops.local https://coding.local https://ai-site.local"
+       
+        # Standard security hardening
+        X-Content-Type-Options "nosniff"
+        Referrer-Policy "strict-origin-when-cross-origin"
+    }
+}
+
+# Example Configuration Blocks for Frontend Sites
+devops.local {
+    root * /var/www/devops_site
+    file_server
+}
+
+coding.local {
+    root * /var/www/coding_site
+    file_server
+}
+
+ai-site.local {
+    root * /var/www/ai_site
+    file_server
+}
+```
+
+---
+
+## 2. Infrastructure Layer (`docker-compose.yml`)
+
+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.
+
+```yaml
+version: '3.8'
+
+services:
+  unified-ai-rag:
+    image: python:3.11-slim
+    container_name: local_ai_rag
+    working_dir: /app
+    volumes:
+      # Mount application scripts
+      - ./app:/app
+      # Mount databases and notes securely as READ-ONLY
+      - ./db/semantic_rag:/app/db/semantic_rag:ro
+      - ./db/obsidian_graph.gpickle:/app/db/obsidian_graph.gpickle:ro
+    ports:
+      - "8000:8000"
+    environment:
+      - OPENAI_API_KEY=your_openai_api_key_here
+      - ANTHROPIC_API_KEY=your_anthropic_api_key_here
+    command: ["pip", "install", "-r", "requirements.txt", "&&", "chainlit", "run", "app.py", "--port", "8000"]
+    restart: unless-stopped
+```
+
+---
+
+## 3. Web UI Application & Context Controller (`app.py`)
+
+This Chainlit-driven script reads incoming referrers or basic text prompts to shift its personality depending on which website the recruiter came from.
+
+```python
+import chainlit as cl
+import networkx as nx
+from chromadb import PersistentClient
+
+# 1. Initialization hooks for Vector and Graph layers (Read-Only)
+def load_databases():
+    vector_client = PersistentClient(path="/app/db/semantic_rag")
+    vector_layer = vector_client.get_collection(name="resume_and_docs")
+    graph_layer = nx.read_gpickle("/app/db/obsidian_graph.gpickle")
+    return vector_layer, graph_layer
+
+vector_db, graph_db = load_databases()
+
+@cl.on_chat_start
+async def start():
+    # Detect the site referring the frame to adjust persona
+    http_headers = cl.user_session.get("http_headers", {})
+    referer = http_headers.get("referer", "")
+   
+    system_prompt = "You are a helpful AI assistant reviewing my portfolio data."
+    welcome_msg = "Hello! Ask me any questions about my profile or experience."
+   
+    if "devops.local" in referer:
+        system_prompt = "Persona: DevOps Engineer. Focus heavily on infrastructure, IoT architecture, CI/CD, and server logs."
+        welcome_msg = "Welcome Recruiter! Ask me anything about my DevOps automation and IoT infrastructure."
+    elif "coding.local" in referer:
+        system_prompt = "Persona: Software Engineer. Emphasize backend code, software design patterns, and clean programming methodologies."
+        welcome_msg = "Hello! Let's talk about my development portfolio and technical code paradigms."
+    elif "ai-site.local" in referer:
+        system_prompt = "Persona: AI/RAG Specialist. Discuss custom embedding techniques, semantic lookups, and graph networks."
+        welcome_msg = "Greetings! Feel free to pick my brain about graph-based indexing and large language models."
+       
+    cl.user_session.set("system_prompt", system_prompt)
+    await cl.Message(content=welcome_msg).send()
+
+@cl.on_message
+async def main(message: cl.Message):
+    query = message.content
+    sys_prompt = cl.user_session.get("system_prompt")
+   
+    # Executing the dual pass strategy
+    # (Extract semantic chunks + pull Obsidian link neighbors from the graph)
+    # Synthesize outputs down through the LLM context window here...
+   
+    response_text = f"Processed query using profile persona contextualized framework rules."
+    await cl.Message(content=response_text).send()
+```
+
+---
+
+## 4. Frontend Integration (`iframe`)
+
+Embed this string block directly inside your target HTML sites:
+
+```html
+<iframe
+  src="https://ai.local"
+  style="width: 100%; height: 650px; border: 1px solid #ccc; border-radius: 8px;"
+  allow="clipboard-read; clipboard-write">
+</iframe>
+```
+
+ 
+