GrepAI Full Setup

Guided orchestrator for GrepAI semantic code search. Walks through prerequisites, infrastructure, embedding config, storage, MCP integration, and indexing.

Step 1: Prerequisites Check

grepai CLI

bash

which grepai

If missing, show install instructions:

text

Install grepai:

  macOS/Linux:
    brew install grepai/tap/grepai

  Or via curl:
    curl -fsSL https://get.grepai.dev | sh

  Windows (PowerShell):
    irm https://get.grepai.dev/install.ps1 | iex

Stop and ask user to install before continuing.

Docker

bash

which docker

If missing, instruct to install Docker Desktop or Docker Engine and stop.

Step 2: Storage Backend Selection

Present via AskUserQuestion:

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Which storage backend?

○ GOB (local file) — simple, zero config, single-project only (Recommended)
○ PostgreSQL + pgvector — scalable, team-ready, supports workspaces
○ Qdrant — lightweight vector DB, supports workspaces

If GOB: default storage, no extra config needed. Index stored in .grepai/index.gob.

Note: If the user plans to use workspace mode (cross-project search) later, they should pick PostgreSQL or Qdrant for the workspace backend. GOB is fine for the per-project local config — the workspace has its own separate store config.

If PostgreSQL: note the DSN for later:

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DSN: postgres://grepai:grepai@localhost:5432/grepai

Known issue: PostgreSQL + pgvector has a UTF-8 encoding bug where files containing Unicode box-drawing characters (e.g. U+2550) fail to index. GOB does not have this limitation.

If Qdrant: note the endpoint for later:

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REST API: http://localhost:6333
gRPC:     http://localhost:6334

Step 3: Docker Compose Setup

Select the template based on storage choice:

•GOB → ${CLAUDE_PLUGIN_ROOT}/templates/docker-compose-ollama.yml
•PostgreSQL → ${CLAUDE_PLUGIN_ROOT}/templates/docker-compose-postgres.yml
•Qdrant → ${CLAUDE_PLUGIN_ROOT}/templates/docker-compose-qdrant.yml

Read the selected template.

Present via AskUserQuestion:

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Where should docker-compose.yml be placed?

○ Project root (Recommended) — writes to $CLAUDE_PROJECT_DIR/docker-compose.yml
○ Custom path — you specify the location

Write the template to the chosen path.

If file already exists, warn and ask whether to overwrite or skip.

Then ask:

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Start Docker services now?

○ Yes, start services (Recommended) — runs docker compose up -d
○ No, I'll start later

If yes:

bash

docker compose -f {COMPOSE_PATH} up -d

Verify with:

bash

docker compose -f {COMPOSE_PATH} ps

Step 4: Embedding Provider Selection

Present via AskUserQuestion:

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Which embedding provider?

○ Ollama — local, private, free, works offline (Recommended)
○ OpenAI — cloud, high quality, costs ~$0.01-$6.50 per full index

If OpenAI: inform about API key setup:

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Set your OpenAI API key:
  export OPENAI_API_KEY="sk-..."

Cost estimates per full index:
  text-embedding-3-small  ~$0.01-$0.10 (small-medium repos)
  text-embedding-3-large  ~$0.05-$6.50 (depends on repo size)

If Ollama: proceed to model selection.

Step 5: Embedding Model Selection

Present via AskUserQuestion based on chosen provider.

For Ollama:

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Which embedding model?

○ nomic-embed-text — 768 dims, 274MB, fast general use (Recommended)
○ mxbai-embed-large — 1024 dims, 670MB, highest accuracy
○ bge-m3 — 1024 dims, 1.2GB, multilingual
○ nomic-embed-text-v2-moe — 768 dims, 500MB, multilingual MoE

For OpenAI:

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Which embedding model?

○ text-embedding-3-small — 1536 dims, $0.00002/1K tokens (Recommended)
○ text-embedding-3-large — 3072 dims, $0.00013/1K tokens

Then confirm before downloading (Ollama only):

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Pull embedding model now? This downloads {SIZE} to the Ollama container.

○ Yes, pull now (Recommended)
○ No, I'll pull later

If yes:

bash

docker exec ollama ollama pull {MODEL}

Step 6: Initialize grepai

Delegate to the initializing skill with collected choices:

Invoke the grepai:initializing skill and follow it exactly.

Pass context: chosen provider, model, storage backend, DSN if postgres, endpoint if qdrant.

For workspace mode: tell the initializing skill to use GOB for the local per-project config. The workspace handles its own shared store separately via ~/.grepai/workspace.yaml.

Step 7: MCP Registration

Delegate to the grepai:mcp-setup skill and follow it exactly.

Pass context: workspace name (if workspace mode was chosen), so it can offer the --workspace flag option.

Step 8: Official Skills Plugin

Inform user about the official grepai-skills plugin:

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The official grepai-skills plugin provides 27 reference skills for advanced
configuration tuning, troubleshooting, and workflow optimization.

Install via Claude Code plugin marketplace:
  /plugin marketplace add yoanbernabeu/grepai-skills
  /plugin install grepai-complete@grepai-skills

Note: These are reference skills (no /commands). The grepai MCP tools
(grepai_search, grepai_trace_*) handle search and trace natively.

Step 9: Start Indexing

Present via AskUserQuestion:

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Start grepai watch daemon now? This monitors file changes and updates the index.

○ Yes, start in background (Recommended)
○ No, I'll start later

If yes, branch based on mode:

Single project:

bash

grepai watch --background

Workspace mode:

bash

grepai watch --workspace {NAME} --background

Both per-project and workspace watchers can coexist. For workspace mode, the workspace watcher is the primary one to start.

Final Summary

Print configuration summary:

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============================================================================
GrepAI Setup Complete
============================================================================

Infrastructure:
  Docker Compose      {COMPOSE_PATH}
  Ollama              http://localhost:11434
  PostgreSQL/pgvector  localhost:5432 (only if postgres backend)
  Qdrant              localhost:6333/6334 (only if qdrant backend)

Embedding:
  Provider   {PROVIDER}
  Model      {MODEL}
  Dimensions {DIMS}

Storage:
  Backend    {BACKEND}

Integration:
  MCP server registered ({SCOPE})
  Config     .grepai/config.yaml

Workspace: {NAME} (only if workspace mode)
  CLAUDE.md workspace guidance added
  Watcher: grepai watch --workspace {NAME} --background

Commands:
  grepai status              # Check index health
  grepai watch --background  # Start file watcher (single project)
  grepai watch --workspace {NAME} --background  # Workspace watcher
  grepai index               # Full re-index
  /grepai:status             # Health check all components
============================================================================