Code indexing is the pipeline that turns your workspace into a searchable vector database. Once indexed, both Ask and Agent modes automatically retrieve relevant code as context, and you can search explicitly via the search_vector_db tool.

For how the search pipeline queries the index, see Semantic Search.

Quick start

CodeBuddy: Index Workspace for Semantic Search

This scans your workspace, chunks every supported file, generates embeddings, and stores everything in a local SQLite database. A progress notification tracks file count, chunk count, and whether embeddings are available.

Indexing pipeline

graph TB A["Workspace files"] --> B["File discovery<br/>vscode.workspace.findFiles()<br/>Filters: extension whitelist, .codebuddyignore, size limits"] B --> C["Change detection<br/>SHA-256 content hash vs file_metadata table<br/>Skip unchanged files"] C --> D["Chunking (worker thread)<br/>1000-char chunks, 200-char overlap<br/>Discards chunks < 50 characters"] D --> E["Embedding generation<br/>Batches of 5, rate-limited, with retry<br/>Falls back to text-only mode if API unavailable"] E --> F["SQLite persistence<br/>chunks table + FTS4 virtual table (auto-synced via triggers)<br/>file_metadata table for hash tracking"] F --> G["Disk save (5s debounce)<br/>.codebuddy/vector_store.db"]

File discovery

Supported languages

The indexer scans for files matching these extensions:

.ts .tsx .js .jsx .py .java .go .rs .cpp .c .h .cs .rb .php

Exclusions

Files are excluded at multiple levels:

Layer	What’s excluded
Base excludes	node_modules, .git, dist, out, build, coverage, .codebuddy
.codebuddyignore	Custom patterns (.gitignore syntax — globs, **, negation with !, directory patterns with trailing /)
Sync excludes	*.min.js, *.bundle.js, *.d.ts, .DS_Store, .vscode-test
File size	Varies by performance mode (see below)

Run CodeBuddy: Init .codebuddyignore to create a starter ignore file.

The .codebuddyignore watcher automatically reloads patterns when the file changes — no restart required.

.codebuddyignore syntax

The file uses .gitignore syntax. Place it in the workspace root.

# Ignore test fixtures
tests/fixtures/**

# Ignore generated code
src/generated/

# Ignore large data files
*.csv
*.parquet

# Re-include a specific fixture needed for indexing
!tests/fixtures/sample.ts

# Ignore a specific directory (trailing / = directory only)
tmp/

# Anchored pattern (only matches at root, not nested)
/scripts/legacy/

Syntax	Meaning	Example
*	Match any characters except /	*.log
**	Match any path depth	tests/**/snap*
!	Negation — re-include a previously excluded pattern	!src/important.ts
/ (trailing)	Match directories only	build/
/ (leading)	Anchored to root — won’t match in subdirectories	/scripts/
#	Comment line	# Ignore logs

File size limits

Performance mode	Max file size
balanced (default)	1 MB
performance	2 MB
memory	512 KB

Set via codebuddy.vectorDb.performanceMode.

Chunking

Chunking runs in a dedicated worker thread to avoid blocking the editor UI.

Parameter	Value
Chunk size	1,000 characters
Overlap	200 characters
Minimum chunk	50 characters (smaller chunks are discarded)

Each chunk records:

• ID — {filePath}::{charOffset}
• Text — the chunk content
• Line range — approximate start/end line numbers
• Type — text_chunk, function, class, method, or block
• Language — detected from the file extension

The worker uses Tree-sitter for AST-aware chunking with a text-based fallback splitter. Language detection maps extensions to parsers: .ts/.tsx → TypeScript, .py → Python, .java → Java, .go → Go, .rs → Rust, .cpp → C++, .c/.h → C, .cs → C#, .rb → Ruby, .php → PHP.

Tree-sitter AST analysis

CodeBuddy ships 8 pre-compiled WASM grammars for AST-aware chunking:

Grammar	Extensions	What it extracts
TypeScript	.ts, .tsx	Functions, classes, methods, interfaces, type aliases
JavaScript	.js, .jsx	Functions, classes, arrow functions, exports
Python	.py	Functions, classes, methods, decorators, docstrings
Java	.java	Classes, methods, interfaces, annotations
Go	.go	Functions, methods, structs, interfaces
Rust	.rs	Functions, impl blocks, structs, enums, traits
PHP	.php	Functions, classes, methods, namespaces
C/C++	.c, .h, .cpp	Functions, structs, classes, macros

For languages with dedicated analyzers (TypeScript, JavaScript, Python), the AST parser extracts:

• Function boundaries — splits at function/method definitions instead of arbitrary character offsets
• Class grouping — keeps class bodies together when they fit within a chunk
• Import regions — groups import statements into a single chunk
• Docstring preservation — attaches docstrings/JSDoc comments to their associated function

Languages without a WASM grammar fall back to the text-based splitter (character offset + overlap).

Embedding generation

Providers

Provider	Model	Notes
Gemini (default)	text-embedding-004	Default when codebuddy.vectorDb.embeddingModel = "gemini"
OpenAI	text-embedding-3-small	OpenAI-compatible endpoint
Local	Configurable	Uses local server’s /embeddings endpoint (e.g., nomic-embed-text)
Deepseek / Groq	OpenAI-compatible	Same SDK, different base URL

Anthropic does not support embeddings — automatically falls back to Gemini.

Batching and rate limiting

Parameter	Default
Batch size	5 chunks per batch
Rate limit	1,500 requests/min (40ms minimum interval)
Retries	3, with exponential backoff (delay × retryCount)
Retry delay	1,000ms base

Between batches, the indexer yields to the event loop via setImmediate() to keep the editor responsive.

Smart embedding phases

The embedding system uses different configurations depending on context:

Phase	Batch size	Max files	Delay between batches	Timeout	Retries
Immediate (on-save)	5	20	100ms	30s	3
On-demand (user query)	3	15	200ms	20s	2
Background (idle)	10	100	1,000ms	60s	1
Bulk (full index)	20	Unlimited	500ms	120s	2

Pre-flight check

Before bulk indexing, the system runs a test embedding to verify the API is reachable. If the check fails, indexing continues in text-only mode — chunks are stored without vectors, enabling keyword search but not semantic search. Re-run the index command after fixing your API key to generate embeddings.

Incremental indexing

CodeBuddy uses content hashing for efficient incremental updates:

1. On file save, the onDidSaveTextDocument listener fires
2. SHA-256 hash of the file content is compared against the file_metadata table
3. If unchanged → skip (no work done)
4. If changed → remove old chunks for this file → re-chunk → re-embed → persist

This means saving a file you didn’t actually change costs almost nothing — just a hash comparison.

What triggers re-indexing

Trigger	Scope	Behavior
File save	Single file	Immediate incremental index
Index Workspace command	Entire workspace	Bulk index, skips unchanged files
Background processing	Changed files	Debounced (default 1,000ms)

Filtered out automatically: git commit messages, log files, and paths containing node_modules, .git, or .codebuddy.

Storage

SQLite database

The index is stored in a SQLite database powered by sql.js (SQLite compiled to WASM). Location: <workspace>/.codebuddy/vector_store.db (falls back to the editor’s global storage).

Schema:

-- Chunk storage with optional vector embeddings
CREATE TABLE chunks (
    id TEXT PRIMARY KEY,
    text TEXT NOT NULL,
    vector BLOB,              -- Float32Array as binary (NULL in text-only mode)
    file_path TEXT NOT NULL,
    start_line INTEGER NOT NULL,
    end_line INTEGER NOT NULL,
    chunk_type TEXT NOT NULL DEFAULT 'text_chunk',
    language TEXT NOT NULL DEFAULT '',
    indexed_at TEXT NOT NULL
);

-- File change tracking
CREATE TABLE file_metadata (
    file_path TEXT PRIMARY KEY,
    file_hash TEXT NOT NULL,  -- SHA-256
    chunk_count INTEGER NOT NULL DEFAULT 0,
    indexed_at TEXT NOT NULL
);

FTS4 full-text index

A FTS4 virtual table is maintained in sync with the chunks table via SQL triggers:

• INSERT trigger — adds chunk text to FTS on insert
• DELETE trigger — removes from FTS on delete
• UPDATE trigger — delete + re-insert on update

On startup, if the FTS row count falls behind the chunks table (e.g., after a crash), a back-fill runs automatically. Small gaps (< 100 rows) use an anti-join INSERT; larger gaps use FTS4’s rebuild command.

Persistence

The database uses a dirty flag with a 5-second debounce save timer. Changes accumulate in memory and flush to disk periodically. On extension deactivation, any pending changes are saved immediately.

Initialization order

When the editor starts with CodeBuddy:

1. SqliteVectorStore singleton — loads or creates the SQLite database
2. HybridSearchService singleton — creates and initializes the FTS4 virtual table
3. AstIndexingService singleton — spawns the worker thread, wires the embedding service
4. ContextRetriever — wires the search pipeline
5. onDidSaveTextDocument listener — enables incremental indexing on file save
6. codebuddy.indexWorkspace command — registered for manual full re-index

Settings

All indexing settings are documented in the Settings Reference under Vector database and Hybrid search.

Key settings for indexing behavior:

Setting	Default	Effect
codebuddy.vectorDb.enabled	true	Master toggle for the entire indexing system
codebuddy.vectorDb.performanceMode	"balanced"	Controls file size limits and resource usage
codebuddy.vectorDb.enableBackgroundProcessing	true	Index changes in the background
codebuddy.vectorDb.debounceDelay	1000	Milliseconds to wait before re-indexing a changed file
codebuddy.vectorDb.batchSize	10	Files per embedding batch
codebuddy.indexCodebase	false	Feature flag for automatic indexing on startup