@hung319/opencode-code-index

@hung319/opencode-code-index

Stop grepping for concepts. Start searching for meaning.

@hung319/opencode-code-index brings semantic understanding to your OpenCode workflow. Instead of guessing function names or grepping for keywords, ask your codebase questions in plain English.

Forked from opencode-codebase-index with optimizations for large codebases.

🚀 Why Use This?

• 🧠 Semantic Search: Finds "user authentication" logic even if the function is named check_creds.
• ⚡ Blazing Fast Indexing: Powered by a Rust native module using tree-sitter and usearch. Incremental updates take milliseconds.
• 🌿 Branch-Aware: Seamlessly handles git branch switches — reuses embeddings, filters stale results.
• 🔒 Privacy Focused: Your vector index is stored locally in your project.
• 🔌 Model Agnostic: Works out-of-the-box with GitHub Copilot, OpenAI, Gemini, or local Ollama models.

⚡ Quick Start

• Install the plugin

  npm install @hung319/opencode-code-index
  

• Add to opencode.json

  {
    "plugin": ["@hung319/opencode-code-index"]
  }
  

• Index your codebase Run /index or ask the agent to index your codebase. This only needs to be done once — subsequent updates are incremental.

• Start Searching Ask:

  "Find the function that handles credit card validation errors"

🔍 See It In Action

Scenario: You're new to a codebase and need to fix a bug in the payment flow.

Without Plugin (grep):

• grep "payment" . → 500 results (too many)
• grep "card" . → 200 results (mostly UI)
• grep "stripe" . → 50 results (maybe?)

With opencode-codebase-index: You ask: "Where is the payment validation logic?"

Plugin returns:

src/services/billing.ts:45  (Class PaymentValidator)
src/utils/stripe.ts:12      (Function validateCardToken)
src/api/checkout.ts:89      (Route handler for /pay)

🎯 When to Use What

Scenario	Tool	Why
Don't know the function name	codebase_search	Semantic search finds by meaning
Exploring unfamiliar codebase	codebase_search	Discovers related code across files
Just need to find locations	codebase_peek	Returns metadata only, saves ~90% tokens
Know exact identifier	grep	Faster, finds all occurrences
Need ALL matches	grep	Semantic returns top N only
Mixed discovery + precision	/find (hybrid)	Best of both worlds

Rule of thumb: codebase_peek to find locations → Read to examine → grep for precision.

📊 Token Usage

In our testing across open-source codebases (axios, express), we observed up to 90% reduction in token usage for conceptual queries like "find the error handling middleware".

Why It Saves Tokens

• Without plugin: Agent explores files, reads code, backtracks, explores more
• With plugin: Semantic search returns relevant code immediately → less exploration

Key Takeaways

• Significant savings possible: Up to 90% reduction in the best cases
• Results vary: Savings depend on query type, codebase structure, and agent behavior
• Best for discovery: Conceptual queries benefit most; exact identifier lookups should use grep
• Complements existing tools: Provides a faster initial signal, doesn't replace grep/explore

When the Plugin Helps Most

• Conceptual queries: "Where is the authentication logic?" (no keywords to grep for)
• Unfamiliar codebases: You don't know what to search for yet
• Large codebases: Semantic search scales better than exhaustive exploration

🛠️ How It Works

graph TD
    subgraph Indexing
    A[Source Code] -->|Tree-sitter| B[Semantic Chunks]
    B -->|Embedding Model| C[Vectors]
    C -->|uSearch| D[(Vector Store)]
    C -->|SQLite| G[(Embeddings DB)]
    B -->|BM25| E[(Inverted Index)]
    B -->|Branch Catalog| G
    end

    subgraph Searching
    Q[User Query] -->|Embedding Model| V[Query Vector]
    V -->|Cosine Similarity| D
    Q -->|BM25| E
    G -->|Branch Filter| F
    D --> F[Hybrid Fusion]
    E --> F
    F --> R[Ranked Results]
    end

• Parsing: We use tree-sitter to intelligently parse your code into meaningful blocks (functions, classes, interfaces). JSDoc comments and docstrings are automatically included with their associated code.

Supported Languages: TypeScript, JavaScript, Python, Rust, Go, Java, C#, Ruby, Bash, C, C++, JSON, TOML, YAML 2. Chunking: Large blocks are split with overlapping windows to preserve context across chunk boundaries. 3. Embedding: These blocks are converted into vector representations using your configured AI provider. 4. Storage: Embeddings are stored in SQLite (deduplicated by content hash) and vectors in usearch with F16 quantization for 50% memory savings. A branch catalog tracks which chunks exist on each branch. 5. Hybrid Search: Combines semantic similarity (vectors) with BM25 keyword matching, filtered by current branch.

Performance characteristics:

• Incremental indexing: ~50ms check time — only re-embeds changed files
• Smart chunking: Understands code structure to keep functions whole, with overlap for context
• Native speed: Core logic written in Rust for maximum performance
• Memory efficient: F16 vector quantization reduces index size by 50%
• Branch-aware: Automatically tracks which chunks exist on each git branch
• Provider validation: Detects embedding provider/model changes and requires rebuild to prevent garbage results

🌿 Branch-Aware Indexing

The plugin automatically detects git branches and optimizes indexing across branch switches.

How It Works

When you switch branches, code changes but embeddings for unchanged content remain the same. The plugin:

• Stores embeddings by content hash: Embeddings are deduplicated across branches
• Tracks branch membership: A lightweight catalog tracks which chunks exist on each branch
• Filters search results: Queries only return results relevant to the current branch

Benefits

Scenario	Without Branch Awareness	With Branch Awareness
Switch to feature branch	Re-index everything	Instant — reuse existing embeddings
Return to main	Re-index everything	Instant — catalog already exists
Search on branch	May return stale results	Only returns current branch's code

Automatic Behavior

• Branch detection: Automatically reads from .git/HEAD
• Re-indexing on switch: Triggers when you switch branches (via file watcher)
• Legacy migration: Automatically migrates old indexes on first run
• Garbage collection: Health check removes orphaned embeddings and chunks

Storage Structure

.opencode/index/
├── codebase.db           # SQLite: embeddings, chunks, branch catalog
├── vectors.usearch       # Vector index (uSearch)
├── inverted-index.json   # BM25 keyword index
└── file-hashes.json      # File change detection

🧰 Tools Available

The plugin exposes these tools to the OpenCode agent:

codebase_search

The primary tool. Searches code by describing behavior.

• Use for: Discovery, understanding flows, finding logic when you don't know the names.
• Example: "find the middleware that sanitizes input"

Writing good queries:

✅ Good queries (describe behavior)	❌ Bad queries (too vague)
"function that validates email format"	"email"
"error handling for failed API calls"	"error"
"middleware that checks authentication"	"auth middleware"
"code that calculates shipping costs"	"shipping"
"where user permissions are checked"	"permissions"

codebase_peek

Token-efficient discovery. Returns only metadata (file, line, name, type) without code content.

• Use for: Finding WHERE code is before deciding what to read. Saves ~90% tokens vs codebase_search.
• Example output:

  [1] function "validatePayment" at src/billing.ts:45-67 (score: 0.92)
  [2] class "PaymentProcessor" at src/processor.ts:12-89 (score: 0.87)
  
  Use Read tool to examine specific files.
  

• Workflow: codebase_peek → find locations → Read specific files

index_codebase

Manually trigger indexing.

• Use for: Forcing a re-index or checking stats.
• Parameters: force (rebuild all), estimateOnly (check costs), verbose (show skipped files and parse failures).

index_status

Checks if the index is ready and healthy.

index_health_check

Maintenance tool to remove stale entries from deleted files and orphaned embeddings/chunks from the database.

index_metrics

Returns collected metrics about indexing and search performance. Requires debug.enabled and debug.metrics to be true.

• Metrics include: Files indexed, chunks created, cache hit rate, search timing breakdown, GC stats, embedding API call stats.

index_logs

Returns recent debug logs with optional filtering.

• Parameters: category (optional: search, embedding, cache, gc, branch), level (optional: error, warn, info, debug), limit (default: 50).

🎮 Slash Commands

The plugin automatically registers these slash commands:

Command	Description
/search <query>	Pure Semantic Search. Best for "How does X work?"
/find <query>	Hybrid Search. Combines semantic search + grep. Best for "Find usage of X".
/index	Update Index. Forces a refresh of the codebase index.
/status	Check Status. Shows if indexed, chunk count, and provider info.

⚙️ Configuration

Zero-config by default (uses auto mode). Customize in .opencode/codebase-index.json:

{
  "embeddingProvider": "auto",
  "scope": "project",
  "indexing": {
    "autoIndex": false,
    "watchFiles": true,
    "maxFileSize": 1048576,
    "maxChunksPerFile": 100,
    "semanticOnly": false,
    "autoGc": true,
    "gcIntervalDays": 7,
    "gcOrphanThreshold": 100,
    "requireProjectMarker": true
  },
  "search": {
    "maxResults": 20,
    "minScore": 0.1,
    "hybridWeight": 0.5,
    "contextLines": 0
  },
  "debug": {
    "enabled": false,
    "logLevel": "info",
    "metrics": false
  }
}

Options Reference

Option	Default	Description
embeddingProvider	"auto"	Which AI to use: auto, github-copilot, openai, google, ollama
scope	"project"	project = index per repo, global = shared index across repos
indexing
autoIndex	false	Automatically index on plugin load
watchFiles	true	Re-index when files change
maxFileSize	1048576	Skip files larger than this (bytes). Default: 1MB
maxChunksPerFile	100	Maximum chunks to index per file (controls token costs for large files)
semanticOnly	false	When true, only index semantic nodes (functions, classes) and skip generic blocks
retries	3	Number of retry attempts for failed embedding API calls
retryDelayMs	1000	Delay between retries in milliseconds
autoGc	true	Automatically run garbage collection to remove orphaned embeddings/chunks
gcIntervalDays	7	Run GC on initialization if last GC was more than N days ago
gcOrphanThreshold	100	Run GC after indexing if orphan count exceeds this threshold
requireProjectMarker	true	Require a project marker (.git, package.json, etc.) to enable file watching and auto-indexing. Prevents accidentally indexing large directories like home. Set to false to index any directory.
search
maxResults	20	Maximum results to return
minScore	0.1	Minimum similarity score (0-1). Lower = more results
hybridWeight	0.5	Balance between keyword (1.0) and semantic (0.0) search
contextLines	0	Extra lines to include before/after each match
debug
enabled	false	Enable debug logging and metrics collection
logLevel	"info"	Log level: error, warn, info, debug
logSearch	true	Log search operations with timing breakdown
logEmbedding	true	Log embedding API calls (success, error, rate-limit)
logCache	true	Log cache hits and misses
logGc	true	Log garbage collection operations
logBranch	true	Log branch detection and switches
metrics	false	Enable metrics collection (indexing stats, search timing, cache performance)

Embedding Providers

The plugin automatically detects available credentials in this order:

• GitHub Copilot (Free if you have it)
• OpenAI (Standard Embeddings)
• Google (Gemini Embeddings)
• Ollama (Local/Private - requires nomic-embed-text)

Rate Limits by Provider

Each provider has different rate limits. The plugin automatically adjusts concurrency and delays:

Provider	Concurrency	Delay	Best For
GitHub Copilot	1	4s	Small codebases (<1k files)
OpenAI	3	500ms	Medium codebases
Google	5	200ms	Medium-large codebases
Ollama	5	None	Large codebases (10k+ files)

For large codebases, use Ollama locally to avoid rate limits:

# Install the embedding model
ollama pull nomic-embed-text

// .opencode/codebase-index.json
{
  "embeddingProvider": "ollama"
}

📈 Performance

The plugin is built for speed with a Rust native module. Here are typical performance numbers (Apple M1):

Parsing (tree-sitter)

Files	Chunks	Time
100	1,200	~7ms
500	6,000	~32ms

Vector Search (usearch)

Index Size	Search Time	Throughput
1,000 vectors	0.7ms	1,400 ops/sec
5,000 vectors	1.2ms	850 ops/sec
10,000 vectors	1.3ms	780 ops/sec

Database Operations (SQLite with batch)

Operation	1,000 items	10,000 items
Insert chunks	4ms	44ms
Add to branch	2ms	22ms
Check embedding exists	<0.01ms	<0.01ms

Batch vs Sequential Performance

Batch operations provide significant speedups:

Operation	Sequential	Batch	Speedup
Insert 1,000 chunks	38ms	4ms	~10x
Add 1,000 to branch	29ms	2ms	~14x
Insert 1,000 embeddings	59ms	40ms	~1.5x

Run benchmarks yourself: npx tsx benchmarks/run.ts

🎯 Choosing a Provider

Use this decision tree to pick the right embedding provider:

                    ┌─────────────────────────┐
                    │ Do you have Copilot?    │
                    └───────────┬─────────────┘
                          ┌─────┴─────┐
                         YES          NO
                          │            │
              ┌───────────▼───────┐    │
              │ Codebase < 1k     │    │
              │ files?            │    │
              └─────────┬─────────┘    │
                  ┌─────┴─────┐        │
                 YES          NO       │
                  │            │       │
                  ▼            │       │
           ┌──────────┐        │       │
           │ Copilot  │        │       │
           │ (free)   │        │       │
           └──────────┘        │       │
                               ▼       ▼
                    ┌─────────────────────────┐
                    │ Need fastest indexing?  │
                    └───────────┬─────────────┘
                          ┌─────┴─────┐
                         YES          NO
                          │            │
                          ▼            ▼
                   ┌──────────┐ ┌──────────────┐
                   │ Ollama   │ │ OpenAI or    │
                   │ (local)  │ │ Google       │
                   └──────────┘ └──────────────┘

Provider Comparison

Provider	Speed	Cost	Privacy	Best For
Ollama	Fastest	Free	Full	Large codebases, privacy-sensitive
GitHub Copilot	Slow (rate limited)	Free*	Cloud	Small codebases, existing subscribers
OpenAI	Medium	~$0.0001/1K tokens	Cloud	General use
Google	Fast	Free tier available	Cloud	Medium-large codebases

*Requires active Copilot subscription

Setup by Provider

Ollama (Recommended for large codebases)

ollama pull nomic-embed-text

{ "embeddingProvider": "ollama" }

OpenAI

export OPENAI_API_KEY=sk-...

{ "embeddingProvider": "openai" }

Google

export GOOGLE_API_KEY=...

{ "embeddingProvider": "google" }

GitHub Copilot No setup needed if you have an active Copilot subscription.

{ "embeddingProvider": "github-copilot" }

⚠️ Tradeoffs

Be aware of these characteristics:

Aspect	Reality
Search latency	~800-1000ms per query (embedding API call)
First index	Takes time depending on codebase size (e.g., ~30s for 500 chunks)
Requires API	Needs an embedding provider (Copilot, OpenAI, Google, or local Ollama)
Token costs	Uses embedding tokens (free with Copilot, minimal with others)
Best for	Discovery and exploration, not exhaustive matching

💻 Local Development

• Build:

  npm run build
  

• Register in Test Project (use file:// URL in opencode.json):

  {
    "plugin": [
      "file:///path/to/opencode-codebase-index"
    ]
  }
  

  This loads directly from your source directory, so changes take effect after rebuilding.

🤝 Contributing

• Fork the repository
• Create a feature branch: git checkout -b feature/my-feature
• Make your changes and add tests
• Run checks: npm run build && npm run test:run && npm run lint
• Commit: git commit -m "feat: add my feature"
• Push and open a pull request

CI will automatically run tests and type checking on your PR.

Project Structure

├── src/
│   ├── index.ts              # Plugin entry point
│   ├── config/               # Configuration schema
│   ├── embeddings/           # Provider detection and API calls
│   ├── indexer/              # Core indexing logic + inverted index
│   ├── git/                  # Git utilities (branch detection)
│   ├── tools/                # OpenCode tool definitions
│   ├── utils/                # File collection, cost estimation
│   ├── native/               # Rust native module wrapper
│   └── watcher/              # File/git change watcher
├── native/
│   └── src/                  # Rust: tree-sitter, usearch, xxhash, SQLite
├── tests/                    # Unit tests (vitest)
├── commands/                 # Slash command definitions
├── skill/                    # Agent skill guidance
└── .github/workflows/        # CI/CD (test, build, publish)

Native Module

The Rust native module handles performance-critical operations:

• tree-sitter: Language-aware code parsing with JSDoc/docstring extraction
• usearch: High-performance vector similarity search with F16 quantization
• SQLite: Persistent storage for embeddings, chunks, and branch catalog
• BM25 inverted index: Fast keyword search for hybrid retrieval
• xxhash: Fast content hashing for change detection

Rebuild with: npm run build:native (requires Rust toolchain)

License

MIT