Ops Deck Lite — Code Search + Prompt Library#

Two high-impact services that make any AI agent dramatically more efficient: semantic code search and a categorized prompt library. Lightweight (~200MB RAM), local-only, zero cloud costs.

For the full operational stack (agent intel, social pipeline, dev journal, monitoring), see ops-deck.

What You Get#

1. Semantic Code Search (:5204)#

Search your entire codebase by meaning, not just text matching. Ask "authentication middleware" and find the actual auth code even if it's called verifyToken or checkSession.

• Hybrid search: vector similarity + keyword matching
• Local embeddings: qwen3-embedding:8b via Ollama (free, private)
• Code summaries: each chunk gets a natural language summary for better semantic matching
• Fast: <100ms search across 96K+ code chunks
• Nightly re-index: cron at 4am keeps the index fresh

bash

# Search
curl -s -X POST http://localhost:5204/api/search \
  -H "Content-Type: application/json" \
  -d '{"query":"database connection pooling","mode":"hybrid","limit":10}'

# Health check
curl -s http://localhost:5204/api/health

# Re-index (with summaries)
curl -X POST http://localhost:5204/api/index?summarize=true

# Filter by project
curl -s -X POST http://localhost:5204/api/search \
  -H "Content-Type: application/json" \
  -d '{"query":"error handling","mode":"hybrid","project":"my-api","limit":5}'

Modes:

• hybrid (default, best) — combines vector similarity with text matching
• code — raw code matching only
• summary — search against natural language summaries

2. Prompt Library (:5202)#

Categorized, searchable prompt templates. Stop writing the same prompts from scratch every session.

bash

# List all prompts
curl -s http://localhost:5202/api/prompts | python3 -c "
import sys,json
[print(f'{p[\"id\"]}: {p[\"title\"]} [{p[\"category\"]}]') for p in json.load(sys.stdin)]
"

# Get a specific prompt
curl -s http://localhost:5202/api/prompts/<id>

# Create a prompt
curl -s -X POST http://localhost:5202/api/prompts \
  -H "Content-Type: application/json" \
  -d '{"title":"Code Review","category":"coding","content":"Review this code for..."}'

Prerequisites#

• Node.js 18+ (for prompt library)
• Python 3.10+ with FastAPI and uvicorn (for code search)
• Ollama with qwen3-embedding:8b model
• PM2 for process management
• SQLite (for code search index, no external DB)

Setup#

1. Install dependencies#

bash

npm install -g pm2
pip install fastapi uvicorn aiofiles

# Ollama embedding model
ollama pull qwen3-embedding:8b

2. Create the Code Search service#

bash

mkdir -p pipeline/work/code-search
cd pipeline/work/code-search

# The server needs:
# - server.py (FastAPI app)
# - code_index.db (SQLite, auto-created on first index)
# - Ollama running locally for embeddings

Key code search server features:

• Walks your project directories, splits code into chunks
• Generates embeddings via Ollama API (localhost:11434)
• Stores chunks + embeddings + summaries in SQLite
• FastAPI with POST /api/search, GET /api/health, POST /api/index

3. Create the Prompt Library#

bash

mkdir -p pipeline/work/prompt-library/backend
cd pipeline/work/prompt-library/backend

# Express server with:
# - GET /api/prompts (list all)
# - GET /api/prompts/:id (get one)
# - POST /api/prompts (create)
# - PUT /api/prompts/:id (update)
# - DELETE /api/prompts/:id (delete)
# - SQLite or JSON file storage

4. PM2 config#

javascript

// ecosystem.config.cjs
module.exports = {
  apps: [
    {
      name: 'code-search',
      cwd: './pipeline/work/code-search',
      script: 'server.py',
      interpreter: 'python3',
      autorestart: true,
    },
    {
      name: 'prompt-library-api',
      cwd: './pipeline/work/prompt-library/backend',
      script: 'server.js',
      autorestart: true,
    },
  ]
};

5. Start and index#

bash

pm2 start ecosystem.config.cjs
pm2 save

# Initial code index (takes a few minutes depending on codebase size)
curl -X POST http://localhost:5204/api/index?summarize=true

# Set up nightly re-index
(crontab -l 2>/dev/null; echo "0 4 * * * curl -s -X POST http://localhost:5204/api/index?summarize=true > /dev/null") | crontab -

Agent Integration#

Add to your AGENTS.md or TOOLS.md:

markdown

## Code Search API (USE THIS FIRST)

Before you grep, before you spawn a sub-agent, before you read 10 files: HIT THIS API.

curl -s -X POST http://localhost:5204/api/search \
  -H "Content-Type: application/json" \
  -d '{"query":"your search here","mode":"hybrid","limit":10}'

## Prompt Library

Before writing a prompt from scratch, check if one exists:

curl -s http://localhost:5202/api/prompts

Resource Usage#

ServiceRAMCPUDiskCode Search150MB<1% idle50MB index per 100K chunksPrompt Library~50MB<1% idle<1MBOllama (embedding model)4GBSpikes during indexing4GB model

Total: ~200MB for the services (Ollama runs independently and is shared with other tools).

Why Not Just Grep?#

Grep finds exact text matches. Code search finds meaning:

QueryGrep findsCode Search finds"auth middleware"Files containing "auth middleware"verifyToken(), checkSession(), requireAuth()"database pooling"Files containing "database pooling"createPool(), getConnection(), pg.Pool config"error handling"Files containing "error handling"try/catch blocks, error middleware, custom Error classes

The embeddings understand code semantics. That's the whole point.