Struct Database 

pub struct Database { /* private fields */ }

SQLite database for code intelligence.

Implementations

impl Database

pub fn open(path: &Path) -> Result<Self>

Open or create a database at the given path.

pub fn open_in_memory() -> Result<Self>

Create an in-memory database (for testing).

pub fn has_vector_search(&self) -> bool

Check if vector search is available (sqlite-vec extension loaded and table exists).

pub fn transaction(&mut self) -> Result<Transaction<'_>>

Begin a transaction.

pub fn get_file_hash(&self, path: &str) -> Result<Option<String>>

Get the content hash for a file.

pub fn needs_update(&self, path: &str, new_hash: &str) -> Result<bool>

Check if a file needs reindexing based on hash.

pub fn upsert_file( &self, file: &FileRecord, source: Option<&[u8]>, ) -> Result<()>

Insert or update a file record.

pub fn delete_file(&self, path: &str) -> Result<()>

Delete a file and all associated data.

pub fn delete_symbols_for_file(&self, file_path: &str) -> Result<()>

Delete all symbols for a file.

pub fn insert_symbol(&self, symbol: &Symbol) -> Result<()>

Insert a symbol.

pub fn insert_edge(&self, edge: &Edge) -> Result<()>

Insert an edge.

pub fn upsert_module(&self, module: &ModuleInfo) -> Result<()>

Insert module information.

pub fn insert_symbols_batch(&self, symbols: &[Symbol]) -> Result<usize>

Insert multiple symbols in a transaction (batch insert for parallel indexing).

pub fn insert_edges_batch(&self, edges: &[Edge]) -> Result<usize>

Insert multiple edges in a transaction (batch insert for parallel indexing).

pub fn get_symbol(&self, id: &str) -> Result<Option<Symbol>>

Find a symbol by ID.

pub fn find_symbols(&self, pattern: &str, limit: i32) -> Result<Vec<Symbol>>

Find symbols by name (exact or pattern).

pub fn find_symbols_filtered( &self, pattern: &str, limit: i32, file_pattern: Option<&str>, kind_filter: Option<&str>, ) -> Result<Vec<Symbol>>

Find symbols by name with optional file path and kind filters.

• pattern: Name pattern to search for
• limit: Maximum number of results
• file_pattern: Optional file path filter (supports glob syntax: *, **)
• kind_filter: Optional symbol kind filter (function, method, struct, etc.)

Results are ordered by match quality: exact name match first, then prefix match, then substring match.

pub fn get_source(&self, symbol_id: &str) -> Result<Option<String>>

Get the source code for a symbol.

pub fn get_file_symbols(&self, file_path: &str) -> Result<Vec<Symbol>>

Get all symbols in a file.

pub fn find_symbols_in_file(&self, file_path: &str) -> Result<Vec<Symbol>>

Find symbols in a specific file (alias for get_file_symbols).

pub fn get_outgoing_edges(&self, symbol_id: &str) -> Result<Vec<Edge>>

Get edges from a symbol.

pub fn get_incoming_edges(&self, target_name: &str) -> Result<Vec<Edge>>

Get edges to a symbol (callers).

pub fn get_stats(&self) -> Result<CodebaseStats>

Get codebase statistics.

pub fn get_indexed_files(&self) -> Result<Vec<String>>

Get all indexed file paths.

pub fn semantic_search( &self, query: &str, limit: i32, ) -> Result<Vec<(Symbol, f64)>>

Semantic search using FTS5 full-text search. Searches across name, signature, brief, and docstring fields.

pub fn hybrid_search( &self, query: &str, limit: i32, ) -> Result<Vec<(Symbol, f64, String)>>

Hybrid search combining exact match with semantic search.

pub fn rebuild_fts_index(&self) -> Result<()>

Rebuild the FTS index (useful after schema changes).

pub fn store_embedding( &self, symbol_id: &str, provider: &str, model: &str, vector: &[f32], ) -> Result<()>

Store an embedding for a symbol.

This stores the embedding in two places:

1. The embeddings table (JSON format, for compatibility)
2. The symbol_vectors table (binary format, for fast KNN search via sqlite-vec)

pub fn get_embedding(&self, symbol_id: &str) -> Result<Option<Vec<f32>>>

Get the embedding for a symbol.

pub fn get_all_embeddings( &self, ) -> Result<Vec<(String, String, String, String, u32, Vec<f32>)>>

Get all embeddings with their symbol metadata.

pub fn count_embeddings(&self) -> Result<i64>

Count symbols that have embeddings.

pub fn get_embedding_metadata(&self) -> Result<Vec<(String, String, i64, i64)>>

Get metadata about stored embeddings (provider, model, dimension, count).

Returns a list of (provider, model, dimension, count) tuples for each unique combination in the embeddings table. This is useful for detecting dimension mismatches when querying with a different embedding provider.

pub fn get_symbols_without_embeddings(&self, limit: i64) -> Result<Vec<Symbol>>

Get symbols that don’t have embeddings yet.

pub fn migrate_embeddings_to_vec(&self) -> Result<usize>

Migrate existing embeddings from JSON table to vector table for fast KNN search.

This copies all embeddings with the correct dimension to the symbol_vectors table. Returns the number of embeddings migrated.

pub fn count_vector_embeddings(&self) -> Result<i64>

Get the count of embeddings in the vector table.

pub fn vector_search( &self, query_embedding: &[f32], limit: usize, ) -> Result<Vec<(String, String, String, String, u32, f32)>>

Fast vector similarity search using sqlite-vec.

Returns the top-k most similar symbols to the query embedding. This uses indexed KNN search which is O(log n) instead of O(n).

Returns (symbol_id, name, kind, file_path, line, distance) tuples. Distance is L2 distance (lower is more similar).

pub fn has_vector_embeddings(&self) -> bool

Check if the vector table has any embeddings.

pub fn delete_embeddings( &self, provider: &str, model: Option<&str>, ) -> Result<usize>

Delete embeddings for a specific provider/model.

pub fn resolve_edge_targets(&self) -> Result<usize>

Resolve target_id for edges that only have target_name.

This performs cross-file symbol resolution by matching target_name to symbols in the database. Resolution priority:

1. Context match: the call context contains the type name (e.g., “TypeScriptParser::new()”)
2. Unique: only one symbol with that name exists in the codebase
3. Same file unique: only one symbol with that name exists in the same file

We intentionally avoid aggressive same-file matching because calls like Vec::new() would incorrectly match a local new function.

Returns the number of edges that were resolved.

pub fn find_duplicates( &self, similarity_threshold: u32, min_lines: u32, ) -> Result<Vec<DuplicateResult>>

Find duplicate code blocks using content hashing and similarity.