Kortix Semantic Search

Semantic Search

You have a full semantic search engine running on this machine, powered by lss (Local Semantic Search). It indexes text files, code, PDFs, DOCX, XLSX, PPTX, HTML, EML, JSON, CSV — virtually everything.

A background file-watcher daemon (lss-sync) detects file changes in real time via FSEvents/inotify and re-indexes within seconds.

How It Works

lss combines BM25 full-text search (keyword matching with custom re-scoring) and embedding similarity (semantic meaning via OpenAI or local fastembed) using Reciprocal Rank Fusion. Queries can be natural language — you don't need exact keywords.

The database lives at /config/.lss/lss.db.

What's Indexed

Indexed formats: ~80 known extensions (code, markup, config, documents). Unknown extensions are skipped. .gitignore patterns are respected.

Quick Reference

# Search EVERYTHING indexed
lss "your natural language query" -p /config/Desktop -k 10 --json

# Search only agent memory + knowledge
lss "user deployment preferences" -p /config/workspace/.kortix/ -k 5 --json

# Search a specific project directory
lss "database migration strategy" -p /config/Desktop/myproject/ -k 5 --json

# Search without triggering re-indexing (faster, uses existing index)
lss "query" -p /config/Desktop --no-index -k 10 --json

# Filter by file type
lss "auth logic" -p /config/Desktop -e .py -e .ts -k 10 --json

# Exclude file types
lss "config" -p /config/Desktop -E .json -E .yaml -k 10 --json

# Exclude content patterns
lss "user data" -p /config/Desktop -x '\d{4}-\d{2}-\d{2}' -k 10 --json

# Force re-index a path immediately
lss index /config/Desktop/important-file.md

# List all indexed files
lss ls

# Check index stats and configuration
lss status

Search Filters

Narrow results at query time without re-indexing.

Filter Strategy for Agents

Narrow first, then broaden. Start with tight extension filters, then remove them if results are insufficient:

# 1. Try narrow: only Python source files
lss "authentication flow" -p /config/Desktop/project -e .py -k 10 --json

# 2. If too few results, broaden to all code
lss "authentication flow" -p /config/Desktop/project -e .py -e .ts -e .go -e .rs -k 10 --json

# 3. If still insufficient, search everything
lss "authentication flow" -p /config/Desktop/project -k 10 --json

Exclude noise, not signal:

# Exclude generated/test code when looking for implementations
lss "rate limiting" -e .py -x "test_" -x "mock_" -x "fixture" --json -k 10

# Exclude logs and config when looking for code
lss "database connection" -E .log -E .yaml -E .json -E .toml --json -k 10

# Exclude dates/timestamps from data searches
lss "customer report" -x '\d{4}-\d{2}-\d{2}' --json -k 10

JSON Output Format

Always use --json for programmatic parsing.

lss "query" -p /config/Desktop --json -k 10

Returns an array of result arrays (one per query):

[
  {
    "query": "authentication flow",
    "hits": [
      {
        "file_path": "/config/Desktop/project/auth.py",
        "score": 0.0345,
        "snippet": "def authenticate(user, password):\n    \"\"\"Authenticate user with JWT...",
        "rank_stage": "S3_MMR",
        "indexed_at": 1738900000.0
      }
    ]
  }
]

Key fields:

• file_path — Full path to the source file
• score — Relevance score (higher is better)
• snippet — Best-matching text excerpt (~280 chars)
• rank_stage — S1=BM25 only, S3=fusion, S3_MMR=fusion+diversity

Multi-Query Decomposition

For complex questions, decompose into multiple specific queries:

# BAD: single vague query
lss "how does the system work" --json -k 10

# GOOD: decomposed into specific queries
lss "system architecture overview" "API endpoint design" "database schema" "authentication flow" --json -k 5

Or use a query file:

echo "system architecture overview" > /tmp/queries.txt
echo "API endpoint design" >> /tmp/queries.txt
echo "database schema" >> /tmp/queries.txt
lss -Q /tmp/queries.txt -p /config/Desktop/project --json -k 5

When to Use lss vs grep

Iterative Search Strategy

For large codebases, use an iterative approach:

# Step 1: Broad discovery — find relevant areas
lss "payment processing" -p /config/Desktop/project -k 20 --json

# Step 2: Narrow by extension — focus on implementation
lss "payment processing" -p /config/Desktop/project -e .py -k 10 --json

# Step 3: Narrow by path — focus on specific module
lss "payment processing" -p /config/Desktop/project/src/payments/ -k 10 --json

# Step 4: Read the actual files for full context
cat /config/Desktop/project/src/payments/processor.py

Indexing

# Index a directory (auto-triggered on first search)
lss index /config/Desktop/project/

# Index a single file
lss index /config/Desktop/important.pdf

# The daemon handles real-time updates automatically
# Use manual indexing only for immediate needs

Maintenance

# Sweep stale entries (files deleted from disk)
lss sweep --retention-days 90

# Clear all embeddings (forces re-embedding on next search)
lss sweep --clear-embeddings 0

# Full reset
lss sweep --clear-all

Rules

1. Always use --json flag when searching programmatically. Parse the JSON output.
2. Always use -p <path> to scope searches. Never search without a path scope.
3. Use -k to control result count. -k 5 for focused, -k 20 for broad exploration.
4. Decompose complex queries. Multiple specific queries beat one vague query.
5. Read source files for full context. Snippets are excerpts — always read the full file after finding a match.
6. Narrow with filters first. Use -e to target file types before broadening.
7. Don't grep when lss is better. Conceptual, fuzzy, and cross-file queries belong in lss.
8. The index auto-updates. Only use lss index for immediate needs. The daemon handles the rest.