obsidian-vault/200 projects/210 AI Resume/Ai Resume RAG Architecture Ideas.md

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title	type	status	created	updated	tags	modified
Ai Resume RAG Architecture Ideas	project	active	2026-10-14 15:58	2026-09-26	architecture pipeline ideas	2026-10-14 15:58

Ai Resume RAG Architecture Ideas

Enterprise Homelab AI Knowledge Engine: Architecture & Workflows

This document outlines the blueprint for a professional-grade, resource-efficient, local-to-cloud hybrid RAG (Retrieval-Augmented Generation) infrastructure [3.2]. Built on a local Docker network, it orchestrates data pipelines from self-hosted tools (Gitea, Outline, Obsidian, Wiki.js, IoT systems) and routes semantic context to low-cost cloud LLMs.

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1. Architectural Overview & Component Responsibilities

The system decouples data extraction, stateful scheduling, data persistence, and processing layers [3.2]. This guarantees low memory overhead on the host machine while matching industry-standard data design patterns.

• Extraction Layer (n8n & Python Scrapers): Captures change events and extracts raw content via API parsing, web scraping, or webhook listeners.
• Orchestration Layer (Prefect): Manages data pipelines, handling fault tolerance, rate limiting, document chunking, and batch schema execution.
• Semantic Router Layer (LangGraph / LangChain): Coordinates state machines to receive user queries, perform pre-filtered database retrieval, and manage cloud LLM context boundaries.
• Storage Layer (Qdrant Vector Database): Persists mathematical representations (embeddings) of text blocks along with explicit schema definitions.
• Inference Layer (Cloud APIs via OpenRouter, DeepSeek, Gemini): Executes logic, token generation, and codebase debugging tasks statelessly over HTTPS.

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2. Component Workflow Implementations

A. Data Ingestion & Extraction (n8n & Python)

Instead of executing heavy brute-force loops, data extraction runs in two structural forms:

1. Event-Driven (n8n): Listens to live inbound HTTP payloads (e.g., Gitea webhooks on code push events, Outline page revisions). n8n captures the operational change event payload and acts as a trigger to offload heavy lifting to the ETL pipeline.
2. Scheduled Batch Jobs (Python Scrapers): Custom headless scripts built with modern parsers (such as Crawl4AI or BeautifulSoup4 coupled with html2text) to sweep legacy local web portals, hobby forums, or IoT data repositories. HTML is stripped of CSS stylesheets, tracking components, and sidebars, condensing raw input into text files.

B. Enterprise ETL Orchestration (Prefect)

Prefect takes raw string text payloads from the extraction layer and runs them through deterministic python validation tasks:

• Retry & Error Budgets: Implements graceful back-offs when hitting remote token pools or local database limits.
• Token-Aware Chunking: Uses text splitters (like RecursiveCharacterTextSplitter) to split long files into target dimensions (e.g., 500-1000 characters) with an intentional safety overlap (e.g., 10%–15%) to avoid cutting sentences or code definitions in half.
• Batch Pipelining: Aggregates multiple document updates into single payloads to lower total HTTP requests across network connections.

C. The Metadata Layer & Vector Storage (Qdrant)

To guarantee quick queries across massive local document footprints, data must be structurally categorized before it is saved into Qdrant. Every text block pushed to the database is accompanied by a structured metadata schema.

The Metadata Schema Format:

{
  "source": "gitea | outline | obsidian | iot | hobby-web",
  "project_name": "home-automation",
  "file_path_or_url": "https://gitea.local",
  "last_updated": "2026-10-14T15:55:00Z",
  "content_type": "source_code | documentation | log_summary"
}

Why Qdrant is Selected:

Qdrant acts as the system's indexing anchor. Written in Rust, it requires trivial idle resources (~200MB RAM), supports native Docker setups, and exposes comprehensive payload filtering capabilities. This allows the system to combine text math (vectors) with standard database query filters.

D. The Vector Embedding Lifecycle

Raw text chunks cannot be parsed directly by Qdrant. They must go through a vector tokenization loop:

1. Text Transform: Prefect submits a block of text to an external embedding endpoint (e.g., Gemini's free tier embedding endpoint or OpenRouter's text-embedding variants).
2. Numerical Array Return: The endpoint converts the text into a fixed-length numerical array (e.g., 1536 floating-point numbers reflecting semantic context).
3. Qdrant Upsert: Prefect packages the numerical vector array alongside the source text string and its corresponding metadata object, writing it to a specific storage collection block inside Qdrant.

E. Semantic Processing & Retrieval State Machines (LangGraph)

When a user asks a question, LangGraph acts as the cognitive director, controlling how data travels between the vector store and the cloud LLM.

1. Query Extraction Node: Evaluates the user's intent. If a user asks: "Fix the bug in my home-automation repository," LangGraph flags that the request is specifically tied to code infrastructure.
2. Metadata-Filtered Retrieval Node: Instead of executing a blind vector scan against millions of lines of text (which risks pulling in unrelated data from Obsidian notes or IoT metrics), LangGraph passes a specific metadata condition directly to Qdrant:
   • Condition: Find nearest vector coordinates WHERE source == "gitea" AND project_name == "home-automation".
3. Context Construction Node: Qdrant returns only the most relevant, targeted code snippets within milliseconds. LangGraph packages these exact blocks into a unified system context window payload.
4. Cloud LLM Call Node: The finalized packet is sent statelessly over a secure outbound HTTPS connection to low-cost APIs (DeepSeek-V3/R1, OpenRouter, or Gemini). The LLM processes the contextualized prompt and fires back the perfect answer instantly.

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3. Production-Ready Deployment: Docker Compose Topology

This composition maps your local storage environment seamlessly into your running n8n, Prefect, and LangGraph network spaces.

version: '3.8'

services:
  # The central vector memory engine
  qdrant:
    image: qdrant/qdrant:latest
    container_name: local-ai-qdrant
    ports:
      - "6333:6333" # Main REST/HTTP API endpoint
      - "6334:6334" # High-performance gRPC endpoint
    volumes:
      - qdrant_data_storage:/qdrant/storage
    environment:
      - QDRANT__SERVICE__ANALYTICS_TRACKING=false
    restart: unless-stopped
    networks:
      - homelab-ai-mesh

  # Your custom LangGraph running node/worker environment
  langgraph-agent-worker:
    build:
      context: ./infra-agent-code
      dockerfile: Dockerfile
    container_name: langgraph-agent-worker
    depends_on:
      - qdrant
    environment:
      - QDRANT_HOST=http://qdrant:6333
      - OPENROUTER_API_KEY=your_key_here
      - DEEPSEEK_API_KEY=your_key_here
      - GEMINI_API_KEY=your_key_here
    volumes:
      - /mnt/shared_local_data/obsidian_vault:/data/obsidian:ro # Read-only mount for safety
    restart: unless-stopped
    networks:
      - homelab-ai-mesh

volumes:
  qdrant_data_storage:

networks:
  homelab-ai-mesh:
    external: true # Bridges directly with your running n8n and Prefect network interfaces

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4. Architectural Summary for Professional Interviews

When presenting this architecture to industry engineering leads, highlight these technical choices to demonstrate production maturity:

1. Strict Resource Decoupling: "Instead of spinning up heavy local GPU servers or bloated monolithic RAG frameworks, I isolated the platform layers [3.2]. Compute costs are completely externalized to commodity cloud APIs via OpenRouter/DeepSeek, while data hygiene, scheduling, and storage remain entirely local inside Docker [3.2]."
2. Hybrid Metadata Optimization: "To ensure sub-second response speeds and completely prevent model hallucinations across distinct applications, I built custom Python ETL workflows in Prefect that tag all ingested data from Gitea, Outline, and local sites with strict metadata schemas. This allows my LangGraph pipelines to execute pre-filtered vector queries before the context hits the LLM."
3. Environment Agnostic Design: "The entire infrastructure interacts via standard OpenAI-compatible API schemas and container definitions. The codebase remains fully portable, meaning the system can scale from a single local workstation node to an enterprise Kubernetes tier simply by updating network configuration keys."