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SQLite Memory Extension – API Reference

A SQLite extension that provides semantic memory capabilities with hybrid search (vector similarity + full-text search).

Table of Contents

Overview

sqlite-memory enables semantic search over text content stored in SQLite. It:

  1. Chunks text content using semantic parsing (markdown-aware)
  2. Generates embeddings for each chunk using the built-in llama.cpp engine ("local" provider) or the vectors.space remote service
  3. Stores embeddings and full-text content for hybrid search
  4. Searches using vector similarity combined with FTS5 full-text search

Sync Behavior

All memory_add_* functions use content-hash change detection to avoid redundant embedding computation. Each piece of content is hashed before processing — if the hash already exists in the database, the content is skipped.

Change Detection

Scenario Behavior
New content Chunked, embedded, and indexed
Unchanged content Skipped (hash match)
Modified file Old entry atomically deleted, new content reindexed
Deleted file Entry removed during directory sync

Transactional Safety

Every sync operation is wrapped in a SQLite SAVEPOINT transaction. If any step fails (embedding error, disk issue, constraint violation), the entire operation rolls back. This guarantees:

  • No partially-indexed files — content is either fully indexed or not at all
  • No orphaned chunks — embeddings and FTS entries are always consistent with dbmem_content
  • Safe to retry — a failed sync leaves the database in its previous valid state

This makes all sync functions idempotent and safe to call repeatedly (e.g., on a schedule or at application startup).

Loading the Extension

Dynamic Loading (Recommended)

.load ./memory

With sqlite-vector (Required for Search)

The extension requires sqlite-vector for vector similarity search:

.load ./vector
.load ./memory

SQL Functions

General Functions

memory_version()

Returns the extension version string.

Parameters: None

Returns: TEXT - Version string (e.g., "0.5.0")

Example:

SELECT memory_version();
-- Returns: "0.5.0"

Configuration Functions

memory_set_model(provider TEXT, model TEXT)

Configures the embedding model to use.

Parameters:

Parameter Type Description
provider TEXT "local" for built-in llama.cpp engine, or any other name (e.g., "openai") for vectors.space remote service
model TEXT For local: full path to GGUF model file. For remote: model identifier supported by vectors.space

Returns: INTEGER - 1 on success

Notes:

  • When provider is "local", the extension uses the built-in llama.cpp engine and verifies the model file exists
  • When provider is anything other than "local", the extension uses the vectors.space remote embedding service
  • Remote embedding requires a free API key from vectors.space (set via memory_set_apikey)
  • Settings are persisted in dbmem_settings table
  • For local models, the embedding engine is initialized immediately
  • Automatic reindex: If a model was previously configured and the new provider/model differs, all existing content is automatically re-embedded with the new model. File-based entries are re-read from disk; text-based entries are re-embedded from stored content. Errors on individual entries are silently skipped (best-effort)

Example:

-- Local embedding model (uses built-in llama.cpp engine)
SELECT memory_set_model('local', '/path/to/nomic-embed-text-v1.5.Q8_0.gguf');

-- Remote embedding via vectors.space (requires free API key)
SELECT memory_set_model('openai', 'text-embedding-3-small');
SELECT memory_set_apikey('your-vectorspace-api-key');

memory_set_apikey(key TEXT)

Sets the API key for the vectors.space remote embedding service.

Parameters:

Parameter Type Description
key TEXT API key obtained from vectors.space (free account)

Returns: INTEGER - 1 on success

Notes:

  • API key is stored in memory only, not persisted to disk
  • Required when using any provider other than "local"
  • Get a free API key by creating an account at vectors.space

Example:

SELECT memory_set_apikey('your-vectorspace-api-key');

memory_set_option(key TEXT, value ANY)

Sets a configuration option.

Parameters:

Parameter Type Description
key TEXT Option name (see Configuration Options)
value ANY Option value (type depends on the option)

Returns: INTEGER - 1 on success

Example:

-- Set maximum tokens per chunk
SELECT memory_set_option('max_tokens', 512);

-- Enable engine warmup
SELECT memory_set_option('engine_warmup', 1);

-- Set minimum score threshold
SELECT memory_set_option('min_score', 0.75);

memory_get_option(key TEXT)

Retrieves a configuration option value.

Parameters:

Parameter Type Description
key TEXT Option name

Returns: ANY - Option value, or NULL if not set

Example:

SELECT memory_get_option('max_tokens');
-- Returns: 400

SELECT memory_get_option('provider');
-- Returns: "local"

Memory Management Functions

memory_add_text(content TEXT [, context TEXT])

Syncs text content to memory. Duplicate content (same hash) is skipped automatically.

Parameters:

Parameter Type Required Description
content TEXT Yes Text content to store and index
context TEXT No Optional context label for grouping memories

Returns: INTEGER - 1 on success

Notes:

  • Content is chunked based on max_tokens and overlay_tokens settings
  • Each chunk is embedded and stored in dbmem_vault
  • Content hash prevents duplicate storage — calling with the same content is a no-op
  • Runs inside a SAVEPOINT transaction (see Sync Behavior)
  • Sets created_at timestamp automatically

Example:

-- Add text without context
SELECT memory_add_text('SQLite is a C-language library that implements a small, fast, self-contained SQL database engine.');

-- Add text with context
SELECT memory_add_text('Important meeting notes from 2024-01-15...', 'meetings');

memory_add_file(path TEXT [, context TEXT])

Syncs a file to memory. Unchanged files are skipped; modified files are atomically replaced.

Parameters:

Parameter Type Required Description
path TEXT Yes Full path to the file
context TEXT No Optional context label for grouping memories

Returns: INTEGER - 1 on success

Notes:

  • Only processes files matching configured extensions (default: md,mdx)
  • File path is stored in dbmem_content.path
  • If the file was previously indexed with different content, the old entry (chunks, embeddings, FTS) is deleted and new content is reindexed — all within a single SAVEPOINT transaction (see Sync Behavior)
  • Not available when compiled with DBMEM_OMIT_IO

Example:

SELECT memory_add_file('/docs/readme.md');
SELECT memory_add_file('/docs/api.md', 'documentation');

memory_add_directory(path TEXT [, context TEXT])

Synchronizes a directory with memory. Adds new files, reindexes modified files, and removes entries for deleted files.

Parameters:

Parameter Type Required Description
path TEXT Yes Full path to the directory
context TEXT No Optional context label applied to all files

Returns: INTEGER - Number of new files processed

Notes:

  • Recursively scans subdirectories
  • Only processes files matching configured extensions
  • Phase 1 — Cleanup: Removes entries for files that no longer exist on disk
  • Phase 2 — Scan: Processes all matching files:
    • New files are chunked, embedded, and added to the index
    • Unchanged files are skipped (content hash match)
    • Modified files have their old entries atomically replaced with new content
  • Each file is processed inside its own SAVEPOINT transaction (see Sync Behavior)
  • Safe to call repeatedly — only changed content triggers embedding computation
  • Not available when compiled with DBMEM_OMIT_IO

Example:

SELECT memory_add_directory('/path/to/docs');
-- Returns: 42 (number of new files processed)

SELECT memory_add_directory('/project/notes', 'project-notes');

-- Safe to call again — unchanged files are skipped
SELECT memory_add_directory('/path/to/docs');
-- Returns: 0 (nothing changed)

Deletion Functions

memory_delete(hash INTEGER)

Deletes a specific memory by its hash.

Parameters:

Parameter Type Description
hash INTEGER The hash identifier of the memory to delete

Returns: INTEGER - Number of content entries deleted (0 or 1)

Notes:

  • Atomically deletes from dbmem_content, dbmem_vault, and dbmem_vault_fts
  • Uses SAVEPOINT transaction for atomicity
  • Hash can be obtained from dbmem_content table or search results

Example:

-- Get hash from content table
SELECT hash FROM dbmem_content WHERE path LIKE '%readme%';

-- Delete by hash
SELECT memory_delete(1234567890);

memory_delete_context(context TEXT)

Deletes all memories with a specific context.

Parameters:

Parameter Type Description
context TEXT The context label to match

Returns: INTEGER - Number of content entries deleted

Notes:

  • Deletes all entries where context matches exactly
  • Cascades to chunks and FTS entries

Example:

-- Delete all memories with context 'meetings'
SELECT memory_delete_context('meetings');
-- Returns: 15

memory_clear()

Deletes all memories from the database.

Parameters: None

Returns: INTEGER - 1 on success

Notes:

  • Clears dbmem_content, dbmem_vault, and dbmem_vault_fts
  • Does not delete settings from dbmem_settings
  • Does not clear the embedding cache (dbmem_cache)
  • Uses SAVEPOINT transaction for atomicity

Example:

SELECT memory_clear();

memory_cache_clear([provider TEXT, model TEXT])

Clears the embedding cache.

Parameters:

Parameter Type Required Description
provider TEXT No Provider name to clear cache for
model TEXT No Model name to clear cache for

Returns: INTEGER - Number of cache entries deleted

Notes:

  • With 0 arguments: clears the entire embedding cache
  • With 2 arguments: clears cache entries for a specific provider/model combination
  • The embedding cache stores computed embeddings keyed by (text hash, provider, model) to avoid redundant computation
  • Safe to call at any time — does not affect stored memories

Example:

-- Clear entire cache
SELECT memory_cache_clear();

-- Clear cache for a specific provider/model
SELECT memory_cache_clear('openai', 'text-embedding-3-small');

memory_search

A virtual table for performing hybrid semantic search.

Query Format:

SELECT * FROM memory_search WHERE query = 'search text';

Columns:

Column Type Description
query TEXT (HIDDEN) Search query (required in WHERE clause)
hash INTEGER Content hash identifier
path TEXT Source file path or generated UUID for text content
context TEXT Context label (NULL if not set)
snippet TEXT Text snippet from the matching chunk
ranking REAL Combined similarity score (0.0 - 1.0)

Notes:

  • Requires sqlite-vector extension loaded first
  • Performs hybrid search combining vector similarity and FTS5
  • Results are ranked by combined score
  • Limited by max_results setting (default: 20)
  • Filtered by min_score setting (default: 0.7)
  • Updates last_accessed timestamp if update_access is enabled

Example:

-- Basic search
SELECT * FROM memory_search WHERE query = 'database indexing strategies';

-- Search with ranking filter
SELECT path, snippet, ranking
FROM memory_search
WHERE query = 'how to optimize queries'
AND ranking > 0.8;

-- Search within a specific context
SELECT * FROM memory_search
WHERE query = 'meeting action items'
AND context = 'meetings';

Configuration Options

Option Type Default Description
provider TEXT - Embedding provider ("local" for llama.cpp, otherwise vectors.space)
model TEXT - Model path (local) or identifier (remote)
dimension INTEGER - Embedding dimension (auto-detected)
max_tokens INTEGER 400 Maximum tokens per chunk
overlay_tokens INTEGER 80 Token overlap between consecutive chunks
chars_per_tokens INTEGER 4 Estimated characters per token
save_content INTEGER 1 Store original content (1=yes, 0=no)
skip_semantic INTEGER 0 Skip markdown parsing, treat as raw text
skip_html INTEGER 1 Strip HTML tags when parsing
extensions TEXT "md,mdx" Comma-separated file extensions to process
engine_warmup INTEGER 0 Warm up engine on model load (compiles GPU shaders)
max_results INTEGER 20 Maximum search results
fts_enabled INTEGER 1 Enable FTS5 in hybrid search
vector_weight REAL 0.5 Weight for vector similarity in scoring
text_weight REAL 0.5 Weight for FTS in scoring
min_score REAL 0.7 Minimum score threshold for results
update_access INTEGER 1 Update last_accessed on search
embedding_cache INTEGER 1 Cache embeddings to avoid redundant computation
cache_max_entries INTEGER 0 Max cache entries (0 = no limit). When exceeded, oldest entries are evicted
search_oversample INTEGER 0 Search oversampling multiplier (0 = no oversampling). When set, retrieves N * multiplier candidates from each index before merging down to N final results

Timestamps

The extension tracks two timestamps for each memory:

created_at

  • Set automatically when content is added via memory_add_text, memory_add_file, or memory_add_directory
  • Stored as Unix timestamp (seconds since 1970-01-01 00:00:00 UTC)
  • Never updated after initial creation

last_accessed

  • Updated when content appears in search results (if update_access=1)
  • Stored as Unix timestamp (seconds since 1970-01-01 00:00:00 UTC)
  • Can be disabled by setting update_access to 0

Displaying timestamps in local time:

SELECT
    path,
    datetime(created_at, 'unixepoch', 'localtime') as created,
    datetime(last_accessed, 'unixepoch', 'localtime') as accessed
FROM dbmem_content;

Examples

Complete Setup and Usage

-- Load extensions
.load ./vector
.load ./memory

-- Check version
SELECT memory_version();

-- Configure local embedding model
SELECT memory_set_model('local', '/models/nomic-embed-text-v1.5.Q8_0.gguf');

-- Configure options
SELECT memory_set_option('max_tokens', 512);
SELECT memory_set_option('min_score', 0.75);

-- Add content
SELECT memory_add_text('SQLite is a C library that provides a lightweight disk-based database.', 'sqlite-docs');
SELECT memory_add_directory('/docs/sqlite', 'sqlite-docs');

-- Search
SELECT path, snippet, ranking
FROM memory_search
WHERE query = 'how does SQLite store data on disk';

-- View all memories with timestamps
SELECT
    hash,
    path,
    context,
    datetime(created_at, 'unixepoch', 'localtime') as created,
    datetime(last_accessed, 'unixepoch', 'localtime') as last_used
FROM dbmem_content
ORDER BY last_accessed DESC;

-- Delete by context
SELECT memory_delete_context('old-docs');

-- Clear all
SELECT memory_clear();

Working with Contexts

-- Add memories with different contexts
SELECT memory_add_text('Meeting notes...', 'meetings');
SELECT memory_add_text('API documentation...', 'api-docs');
SELECT memory_add_text('Tutorial content...', 'tutorials');

-- Search within a context
SELECT * FROM memory_search
WHERE query = 'authentication'
AND context = 'api-docs';

-- List all contexts
SELECT context, COUNT(*) as count
FROM dbmem_content
GROUP BY context;

-- Delete a context
SELECT memory_delete_context('old-meetings');

Memory Statistics

-- Total memories and chunks
SELECT
    (SELECT COUNT(*) FROM dbmem_content) as total_memories,
    (SELECT COUNT(*) FROM dbmem_vault) as total_chunks;

-- Storage usage
SELECT
    SUM(length(embedding)) as embedding_bytes,
    SUM(length) as content_bytes
FROM dbmem_vault;

-- Memories by context
SELECT
    COALESCE(context, '(none)') as context,
    COUNT(*) as count
FROM dbmem_content
GROUP BY context;

-- Recently accessed
SELECT path, datetime(last_accessed, 'unixepoch', 'localtime') as last_used
FROM dbmem_content
WHERE last_accessed > 0
ORDER BY last_accessed DESC
LIMIT 10;

Compilation Options

Option Description
DBMEM_OMIT_IO Omit file/directory functions (for WASM)
DBMEM_OMIT_LOCAL_ENGINE Omit llama.cpp local engine (for remote-only builds)
DBMEM_OMIT_REMOTE_ENGINE Omit vectors.space remote engine (for local-only builds)
SQLITE_CORE Compile as part of SQLite core (not as loadable extension)

Error Handling

All functions return an error if:

  • Required parameters are missing or of wrong type
  • Database operations fail
  • Model file not found (for local provider)
  • Embedding dimension mismatch

Errors can be caught using standard SQLite error handling mechanisms.

-- Example error handling in application code
SELECT memory_add_text(123);  -- Error: expects TEXT parameter
SELECT memory_delete('abc');  -- Error: expects INTEGER parameter