Sage

Hybrid semantic search (dense vector + BM25) over local knowledge bases and codebases.

Stack: LlamaIndex · Qdrant (local embedded) · OpenAI embeddings · FastMCP

Setup

uv venv                    # creates .venv with Python 3.11 per .python-version
source .venv/bin/activate
uv pip install -e ".[mcp]"

cp config-example.yaml config.yaml
# edit config.yaml — add your KB paths

echo "OPENAI_API_KEY=sk-..." > .env

uv will download Python 3.11 automatically if it's not already installed.

Index

# Index all configured KBs
sage index

# Index one KB only
sage index --kb homelab

# Force full re-index (ignore cache)
sage index --force

Status

# Diff KB files vs cache without embedding
sage status

# Single KB
sage status --kb homelab

Search

# Hybrid search across all KBs
sage search "pihole DNS configuration"

# Limit to one KB
sage search "pihole" --kb homelab

# Filter by frontmatter fields
sage search "storage" --filter type=lxc --filter status=running

# More results
sage search "networking" --top-k 20

# Dense-only (no BM25)
sage search "pihole" --no-hybrid

# JSON output (for scripting / agent use)
sage search "pihole" --json

# Markdown output with full file paths (default template: blockquote)
sage search "pihole" --markdown

# Markdown table layout
sage search "pihole" --markdown --template table

# Custom Jinja2 template
sage search "pihole" --markdown --template ~/my-template.md.j2

Markdown templates

The --markdown flag renders results via a Jinja2 template. Two built-in templates are included:

Name	Description
blockquote	Each chunk indented as a blockquote under a ### heading with full file path (default)
table	Compact markdown table with score, KB, full file path, and truncated excerpt

To write a custom template, copy a built-in from sage_mcp/templates/ and pass the file path via --template. The following variables are available:

Variable	Type	Description
query	str	The search query
results	list[dict]	Each entry has score, file_path, kb, text, text_safe, metadata
duplicates_removed	int	Number of duplicate chunks filtered out

Each result's text_safe is the chunk text with newlines collapsed to spaces and pipe characters escaped — safe for use inside a Markdown table cell. Use text for blockquote or fenced-code rendering where the original formatting should be preserved.

List KBs

sage list-kbs

MCP (AI agent access)

Add to your MCP client config (use absolute paths):

{
  "mcpServers": {
    "sage-mcp": {
      "command": "/path/to/sage-mcp/.venv/bin/sage-mcp",
      "args": ["--config", "/path/to/sage-mcp/config.yaml"]
    }
  }
}

The --config flag is optional; without it the server looks for config.yaml in its working directory.

Tools exposed:

• search_kb(query, kb?, top_k?, filter_type?, filter_status?) — returns {results: [...], duplicates_removed: N}
• list_knowledge_bases() — list configured KBs

Config

Edit config.yaml to add KBs or switch the embedding backend. Use config-example.yaml as a template.

Switching to Ollama (once nomic-embed-text is running with GPU acceleration):

embedding:
  provider: ollama
  model: nomic-embed-text
  base_url: http://<ollama-ip>:11434

Then sage index --force to re-embed everything.

Incremental updates

The indexer tracks a content hash per file in pipeline_cache/<kb-name>/hashes.json. Re-running sage index only re-embeds files that have changed. Safe to run on a cron or inotify watch.