pb-dolphin 0.2.4

Full-stack AI enablement platform

🐬 Dolphin

Hybrid search across all your repositories.

---

Dolphin indexes your repositories and lets you perform hybrid (semantic + keyword) search across them.

Quickstart

# Install
uv pip install pb-dolphin

# Set your OpenAI key (used for embeddings)
export OPENAI_API_KEY="sk-..."

# Initialize, add a repo, and search
dolphin init
dolphin add-repo my-project /path/to/project
dolphin index my-project
dolphin search "database connection pooling"

Dolphin indexes your code with language-aware chunking, embeds it, and returns ranked results.

Want live re-indexing as you edit files? Start the server:

dolphin serve

Agent Integration

A small companion MCP server is available at bunx dolphin-mcp. Add this to your AI app's MCP config:

{
  "mcpServers": {
    "dolphin": {
      "command": "bunx",
      "args": ["dolphin-mcp"]
    }
  }
}

Make sure dolphin serve is running, and your agent can now search, retrieve chunks, and read files from your indexed repos.

Additionally, a Claude skill is available in this repo's marketplace as a personal Plugin.

How it works

  You / Agent
        |
        v
  ┌───────────────────────────────────────┐
  │             Dolphin                   │
  │                                       │
  │   CLI ─── REST API ─── MCP Bridge     │
  │               |                       │
  │        ┌──────┴──────┐                │
  │        v             v                │
  │    LanceDB       SQLite               │
  │   (vectors)    (metadata + BM25)      │
  └───────────────────────────────────────┘

Indexing: Your code is scanned, split into semantic chunks using language-aware AST parsers, embedded via OpenAI, and stored in LanceDB (vectors) and SQLite (metadata + full-text).

Searching: Your query is embedded and matched against both vector similarity and BM25 keyword relevance. Results are fused with Reciprocal Rank Fusion, optionally reranked with a cross-encoder, and returned as structured snippets with file paths, line numbers, and scores.

Features

Intelligent hybrid search

• Hybrid vector + BM25 keyword search with RRF fusion
• Optional cross-encoder reranking for +20-30% ranking improvement
• MMR diversity to reduce redundant results
• Filter by repo, language, path, or glob pattern

Language-aware indexing

• AST-based chunking for Python, TypeScript, JavaScript, Markdown, SQL, and Svelte
• Fallback text chunking for everything else
• Respects .gitignore and an optional repo-specific Dolphin config (dolphin init --repo)

Live sync

• File-watching built into dolphin serve so edits are re-indexed automatically
• Git-aware: handles branch switches gracefully

Multiple interfaces

• dolphin CLI with compact, verbose, and JSON output modes
• FastAPI server with full search and retrieval endpoints
• MCP server for integration via bunx dolphin-mcp

CLI reference

Command	What it does
dolphin init	Create config at ~/.dolphin/config.toml
dolphin add-repo <name> <path>	Register a repository
dolphin index <name>	Index (or re-index) a repository
dolphin search <query>	Search across indexed repos
dolphin serve	Start API server with file-watching
dolphin status	Show indexed repos and stats
dolphin repos	List registered repositories
dolphin rm-repo <name>	Remove a repo and its data
dolphin config --show	Display current config

Search options

dolphin search "error handling" \
  --repo myapp \
  --lang py \
  --path src/ \
  --top-k 10 \
  --verbose          # or --json for scripting

Configuration

Dolphin auto-creates its config at ~/.dolphin/config.toml when you run dolphin init. The defaults work well out of the box.

default_embed_model = "small"   # "small" (faster) or "large" (better)

[retrieval]
top_k = 8

[retrieval.hybrid_search]
enabled = true
fusion_method = "rrf"

For per-repo overrides (custom ignore patterns, chunking settings), run dolphin init --repo inside a repository.

Full config reference: docs/ARCHITECTURE.md

Optional: cross-encoder reranking

For the best possible search quality, enable cross-encoder reranking. This re-scores results pairwise against your query using an ML model.

uv pip install "pb-dolphin[reranking]"

Then in ~/.dolphin/config.toml:

[retrieval.reranking]
enabled = true

Trade-offs: ~2GB disk for model weights, 2-3x slower searches.

Requirements

Dependency	Purpose
Python 3.12+	Core runtime
uv	Python package management
OpenAI API key	Embedding generation
Bun	MCP bridge runtime (optional)
Git	Repository scanning

Troubleshooting

Server not responding?

curl http://127.0.0.1:7777/v1/health   # check health
lsof -i :7777                           # check port
dolphin serve                            # start it

No search results?

dolphin status                                    # verify repos are indexed
dolphin index <repo-name> --full --force          # force re-index

MCP not connecting?

• Make sure dolphin serve is running
• Check that Bun is installed: bun --version
• Set DOLPHIN_API_URL if the server isn't at http://127.0.0.1:7777

License

MIT — Plastic Beach, LLC