veclite

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 Go to latest Published: Jan 25, 2026 License: MIT Imports: 13 Imported by: 0

README

VecLite

Embeddable vector database for Go with zero external dependencies.

Store vectors with metadata in a single file. Search with HNSW for fast approximate nearest neighbors.

Table of Contents

Features

  • Zero dependencies - Standard library only, no CGO
  • Single-file storage - Database persists to one .veclite file
  • HNSW indexing - Fast approximate nearest neighbor search
  • Metadata filtering - Filter results by payload fields
  • Thread-safe - Safe for concurrent read/write access
  • In-memory mode - Use :memory: for testing

Quick Start

package main

import (
    "fmt"
    "github.com/abdul-hamid-achik/veclite"
)

func main() {
    // Open database (creates if not exists)
    db, _ := veclite.Open("vectors.veclite")
    defer db.Close()

    // Get or create collection with HNSW index
    coll, _ := db.CreateCollection("embeddings",
        veclite.WithDimension(384),
        veclite.WithHNSW(16, 200),
    )

    // Insert vectors with metadata
    coll.Insert([]float32{0.1, 0.2, ...}, map[string]any{"file": "main.go"})

    // Search for similar vectors
    results, _ := coll.Search(queryVector, veclite.TopK(10))
    for _, r := range results {
        fmt.Printf("ID: %d, Score: %.4f\n", r.Record.ID, r.Score)
    }
}

Prerequisites

  • Go 1.21 or later
  • No external dependencies required

Installation

go get github.com/abdul-hamid-achik/veclite
CLI Installation
go install github.com/abdul-hamid-achik/veclite/cmd/veclite@latest

Or download from Releases.

CLI Usage

The veclite CLI provides full read/write access to VecLite database files.

veclite <command> [arguments]
Commands
Read Commands
Command Description
version Show version information
info <file> Show database summary
collections <file> List all collections
stats <file> Show detailed statistics
dump <file> Export database as JSON
get <file> <collection> Get a vector by ID
Write Commands
Command Description
create-collection <file> <name> Create a new collection
drop-collection <file> <name> Drop a collection
insert <file> <collection> Insert a vector
batch-insert <file> <collection> Insert vectors from JSON file
delete <file> <collection> Delete a vector by ID
search <file> <collection> Search for similar vectors
Server Mode
Command Description
serve <file> Start HTTP server for multi-client access
Maintenance Commands
Command Description
compact <file> Compact database and reclaim space
validate <file> Validate database integrity
benchmark <file> Run search performance benchmark
Global Flags

All commands support --json flag for JSON output (useful for scripting).

Examples
# Check version
veclite version

# View database info
veclite info data.veclite
veclite info --json data.veclite

# Create a collection with HNSW index
veclite create-collection data.veclite embeddings --dimension=384 --distance=cosine --hnsw

# Insert a vector
veclite insert data.veclite embeddings --vector='[0.1,0.2,0.3,...]' --payload='{"file":"main.go"}'

# Batch insert from JSON file
veclite batch-insert data.veclite embeddings --input=vectors.json

# Search for similar vectors
veclite search data.veclite embeddings --query='[0.1,0.2,0.3,...]' --top-k=10
veclite search data.veclite embeddings --query='[0.1,0.2,0.3,...]' --filter='type=code'

# Get a specific vector
veclite get data.veclite embeddings --id=42

# Delete a vector
veclite delete data.veclite embeddings --id=42

# Drop a collection
veclite drop-collection data.veclite embeddings

# Start HTTP server
veclite serve data.veclite --port=8080

# Validate database integrity
veclite validate data.veclite

# Compact database
veclite compact data.veclite

# Run benchmark
veclite benchmark data.veclite --collection=embeddings --queries=1000
Batch Insert File Format

The batch-insert command supports two input formats:

JSON Array:

[
  {"vector": [0.1, 0.2, 0.3], "payload": {"file": "a.go"}},
  {"vector": [0.4, 0.5, 0.6], "payload": {"file": "b.go"}}
]

JSONL (one object per line):

{"vector": [0.1, 0.2, 0.3], "payload": {"file": "a.go"}}
{"vector": [0.4, 0.5, 0.6], "payload": {"file": "b.go"}}
Output Examples

info command:

Database: data.veclite
Collections: 2
Total Records: 15000

collections command:

embeddings: 10000 records, dimension=384, distance=cosine, index=hnsw
images: 5000 records, dimension=512, distance=euclidean, index=none

search command (JSON):

[
  {"id": 42, "score": 0.9821, "payload": {"file": "main.go"}},
  {"id": 17, "score": 0.9654, "payload": {"file": "util.go"}}
]

stats command (JSON):

{
  "path": "data.veclite",
  "collections": 2,
  "total_records": 15000,
  "collection_stats": [
    {
      "name": "embeddings",
      "count": 10000,
      "dimension": 384,
      "distance_type": "cosine",
      "index_type": "hnsw"
    }
  ]
}

HTTP Server Mode

VecLite can run as an HTTP server for multi-language client access.

veclite serve data.veclite --port=8080 --cors
REST API Endpoints
Method Endpoint Description
GET /health Health check
GET /info Database info
GET /collections List collections
POST /collections Create collection
GET /collections/{name} Collection info
DELETE /collections/{name} Drop collection
POST /collections/{name}/vectors Insert vector(s)
GET /collections/{name}/vectors/{id} Get vector by ID
DELETE /collections/{name}/vectors/{id} Delete vector
POST /collections/{name}/search Search vectors
POST /sync Force sync to disk
API Examples

Create Collection:

curl -X POST http://localhost:8080/collections \
  -H "Content-Type: application/json" \
  -d '{"name": "embeddings", "dimension": 384, "distance": "cosine", "hnsw": true}'

Insert Vector:

curl -X POST http://localhost:8080/collections/embeddings/vectors \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.1, 0.2, 0.3], "payload": {"file": "main.go"}}'

Batch Insert:

curl -X POST http://localhost:8080/collections/embeddings/vectors \
  -H "Content-Type: application/json" \
  -d '{"vectors": [[0.1,0.2,0.3], [0.4,0.5,0.6]], "payloads": [{"file":"a.go"}, {"file":"b.go"}]}'

Search:

curl -X POST http://localhost:8080/collections/embeddings/search \
  -H "Content-Type: application/json" \
  -d '{"query": [0.1, 0.2, 0.3], "top_k": 10}'

Search with Filters:

curl -X POST http://localhost:8080/collections/embeddings/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": [0.1, 0.2, 0.3],
    "top_k": 10,
    "filters": [{"key": "type", "op": "eq", "value": "code"}]
  }'
Filter Operators
Operator Description
eq or = Equal
neq or != Not equal
gt or > Greater than (numeric)
gte or >= Greater than or equal (numeric)
lt or < Less than (numeric)
lte or <= Less than or equal (numeric)
glob Glob pattern match
prefix String prefix
suffix String suffix
contains String contains
exists Key exists
Python Client Example
import requests

base_url = "http://localhost:8080"

# Create collection
requests.post(f"{base_url}/collections", json={
    "name": "embeddings",
    "dimension": 384,
    "hnsw": True
})

# Insert vector
response = requests.post(f"{base_url}/collections/embeddings/vectors", json={
    "vector": [0.1] * 384,
    "payload": {"file": "main.py"}
})
print(response.json())  # {"status": "inserted", "id": 1}

# Search
response = requests.post(f"{base_url}/collections/embeddings/search", json={
    "query": [0.1] * 384,
    "top_k": 5
})
print(response.json())  # {"results": [...], "count": 5}

Library API

Opening a Database
// File-based (persistent)
db, err := veclite.Open("vectors.veclite")

// In-memory (testing)
db, err := veclite.Open(":memory:")

// With options
db, err := veclite.Open("vectors.veclite",
    veclite.WithSyncOnWrite(true),  // Sync after each write
    veclite.WithReadOnly(true),     // Read-only mode
)

defer db.Close()
Collections
// Get or create (simple)
coll := db.Collection("embeddings")

// Create with options
coll, err := db.CreateCollection("embeddings",
    veclite.WithDimension(384),                    // Fixed dimension
    veclite.WithDistanceType(veclite.DistanceCosine), // Distance metric
    veclite.WithHNSW(16, 200),                     // Enable HNSW index
)

// Get existing
coll, err := db.GetCollection("embeddings")

// List all
names := db.Collections()

// Delete
err := db.DropCollection("embeddings")
Distance Metrics
Metric Constant Best Score
Cosine Similarity DistanceCosine Higher = more similar
Dot Product DistanceDot Higher = more similar
Euclidean DistanceEuclidean Lower = more similar
Inserting Vectors
// Insert with auto-generated ID
id, err := coll.Insert(vector, map[string]any{
    "file": "main.go",
    "type": "code",
})

// Batch insert
vectors := [][]float32{v1, v2, v3}
payloads := []map[string]any{p1, p2, p3}
ids, err := coll.InsertBatch(vectors, payloads)
Upserting (Insert or Update)
// Upsert by ID (0 = auto-generate new ID)
id, err := coll.Upsert(0, vector, payload)           // Insert new
id, err := coll.Upsert(42, vector, payload)          // Update if exists, insert with ID 42 if not

// Upsert by key field (useful for incremental indexing)
id, wasInsert, err := coll.UpsertByKey("file", "main.go", vector, map[string]any{
    "file": "main.go",
    "line": 100,
})
// wasInsert is true if new record was created, false if existing was updated

// Update only the vector (keep existing payload)
err := coll.UpdateVector(id, newVector)

// Update only the payload (keep existing vector)
err := coll.Update(id, newPayload)
Searching
// Basic search
results, err := coll.Search(queryVector, veclite.TopK(10))

// With threshold
results, err := coll.Search(queryVector,
    veclite.TopK(10),
    veclite.Threshold(0.8),
)

// With HNSW tuning (higher ef = better recall, slower)
results, err := coll.Search(queryVector,
    veclite.TopK(10),
    veclite.WithEfSearch(200),
)

// Access results
for _, r := range results {
    fmt.Printf("ID: %d, Score: %.4f, Payload: %v\n",
        r.Record.ID, r.Score, r.Record.Payload)
}
Filtering

Filter results by metadata fields:

// Equal
results, _ := coll.Search(query,
    veclite.TopK(10),
    veclite.WithFilter(veclite.Equal("type", "code")),
)

// Multiple filters (AND logic)
results, _ := coll.Search(query,
    veclite.TopK(10),
    veclite.WithFilters(
        veclite.Equal("language", "go"),
        veclite.Prefix("file", "src/"),
    ),
)

Available filters:

  • Equal(key, value) - Exact match
  • NotEqual(key, value) - Not equal
  • In(key, values...) - Value in list
  • NotIn(key, values...) - Value not in list
  • Glob(key, pattern) - Glob pattern match
  • Prefix(key, prefix) - String prefix
  • Suffix(key, suffix) - String suffix
  • Contains(key, substr) - String contains
  • Exists(key) - Key exists in payload
  • And(filters...) - Combine filters with AND
  • Or(filters...) - Combine filters with OR
  • Not(filter) - Negate a filter

Range filters (numeric):

  • GreaterThan(key, value) or GT(key, value) - Greater than
  • GreaterThanOrEqual(key, value) or GTE(key, value) - Greater than or equal
  • LessThan(key, value) or LT(key, value) - Less than
  • LessThanOrEqual(key, value) or LTE(key, value) - Less than or equal
  • Between(key, min, max) - Value in range (inclusive)
// Range filter examples
results, _ := coll.Search(query,
    veclite.TopK(10),
    veclite.WithFilters(
        veclite.GT("score", 0.5),          // score > 0.5
        veclite.Between("line", 100, 500), // 100 <= line <= 500
    ),
)
HNSW Configuration
// Basic HNSW
coll, _ := db.CreateCollection("vectors",
    veclite.WithHNSW(16, 200),  // M=16, efConstruction=200
)

// Custom configuration
coll, _ := db.CreateCollection("vectors",
    veclite.WithHNSWConfig(veclite.HNSWConfig{
        M:              32,   // More connections = better recall, more memory
        EfConstruction: 400,  // Higher = better index quality, slower build
        EfSearch:       100,  // Default search quality
    }),
)

Parameter Guidelines:

Parameter Default Range Trade-off
M 16 12-48 Higher = better recall, more memory
efConstruction 200 100-500 Higher = better index, slower build
efSearch 100 50-500 Higher = better recall, slower search
Deleting Records
// Delete by ID
err := coll.Delete(42)

// Delete by filter
count, err := coll.DeleteWhere(veclite.Equal("type", "temp"))
Statistics
// Database stats
dbStats := db.Stats()
fmt.Printf("Collections: %d, Total Records: %d\n",
    dbStats.Collections, dbStats.TotalRecords)

// Collection stats
collStats := coll.Stats()
fmt.Printf("Count: %d, Dimension: %d, Index: %s\n",
    collStats.Count, collStats.Dimension, collStats.IndexType)

// Get search explanation
explanation, err := coll.SearchExplain(query, veclite.TopK(10))
fmt.Printf("Index: %s, Nodes Visited: %d, Duration: %v\n",
    explanation.IndexType,
    explanation.NodesVisited,
    explanation.Duration,
)

Performance

Benchmark results on 10,000 384-dimensional vectors:

Method Time Speedup
Brute Force ~2.5ms 1x
HNSW ~0.4ms 6x

HNSW provides >95% recall at 6-7x speedup over brute force.

Thread Safety

VecLite is safe for concurrent access:

  • Multiple goroutines can read simultaneously
  • Writes are serialized with proper locking
  • Use WithSyncOnWrite(true) for durability after each write

Persistence

Data is stored using Go's gob encoding:

  • Call db.Sync() to persist changes manually
  • Use WithSyncOnWrite(true) for automatic persistence
  • db.Close() syncs before closing

Contributing

Contributions welcome. Please:

  1. Run go test -race ./... before submitting
  2. Add tests for new features
  3. Follow existing code style

License

MIT License - see LICENSE

Documentation

Overview

Package veclite provides an embeddable vector database with zero external dependencies. It stores vectors with metadata in a single file using gob encoding.

Basic usage: 

db, err := veclite.Open("data.veclite")
if err != nil {
    log.Fatal(err)
}
defer db.Close()

coll := db.Collection("embeddings")
id, err := coll.Insert(vector, map[string]any{"file": "main.go"})

results, err := coll.Search(queryVector, veclite.TopK(10))

Index

Constants

View Source 
const (
	// DistanceCosine uses cosine similarity (higher = more similar).
	DistanceCosine = floats.DistanceCosine
	// DistanceDot uses dot product (higher = more similar).
	DistanceDot = floats.DistanceDot
	// DistanceEuclidean uses Euclidean distance (lower = more similar).
	DistanceEuclidean = floats.DistanceEuclidean
)
View Source 
const Version = "0.2.0"

Version is the library version.

Variables

View Source 
var (
	// ErrNotFound is returned when a record or collection is not found.
	ErrNotFound = errors.New("veclite: not found")

	// ErrDimensionMismatch is returned when vector dimensions don't match.
	ErrDimensionMismatch = errors.New("veclite: dimension mismatch")

	// ErrEmptyVector is returned when an empty vector is provided.
	ErrEmptyVector = errors.New("veclite: empty vector")

	// ErrCollectionExists is returned when trying to create a collection that already exists.
	ErrCollectionExists = errors.New("veclite: collection already exists")

	// ErrDatabaseClosed is returned when operations are attempted on a closed database.
	ErrDatabaseClosed = errors.New("veclite: database closed")

	// ErrInvalidPath is returned when an invalid file path is provided.
	ErrInvalidPath = errors.New("veclite: invalid path")

	// ErrCorruptedFile is returned when the database file is corrupted.
	ErrCorruptedFile = errors.New("veclite: corrupted file")

	// ErrInvalidVersion is returned when the file version is not supported.
	ErrInvalidVersion = errors.New("veclite: unsupported file version")

	// ErrBatchSizeMismatch is returned when batch operation input sizes don't match.
	ErrBatchSizeMismatch = errors.New("veclite: batch size mismatch")
)

Sentinel errors for common conditions.

Functions

This section is empty.

Types

type Collection 

type Collection struct {
	// contains filtered or unexported fields
}

Collection represents a collection of vectors with the same dimension.

func (*Collection) All 

func (c *Collection) All() []*Record

All returns all records in the collection.

func (*Collection) Clear 

func (c *Collection) Clear()

Clear removes all records from the collection.

func (*Collection) Count 

func (c *Collection) Count() int

Count returns the number of records in the collection.

func (*Collection) Delete 

func (c *Collection) Delete(id uint64) error

Delete removes a record by ID.

func (*Collection) DeleteWhere 

func (c *Collection) DeleteWhere(filters ...Filter) (int, error)

DeleteWhere removes all records matching the filters. Returns the number of deleted records.

func (*Collection) Dimension 

func (c *Collection) Dimension() int

Dimension returns the vector dimension. Returns 0 if no vectors have been inserted yet.

func (*Collection) DistanceType 

func (c *Collection) DistanceType() floats.DistanceType

DistanceType returns the distance metric type.

func (*Collection) Find 

func (c *Collection) Find(filters ...Filter) ([]*Record, error)

Find retrieves all records matching the filters.

func (*Collection) FindOne 

func (c *Collection) FindOne(filters ...Filter) (*Record, error)

FindOne retrieves the first record matching the filters.

func (*Collection) Get 

func (c *Collection) Get(id uint64) (*Record, error)

Get retrieves a record by ID.

func (*Collection) GetVector 

func (c *Collection) GetVector(id uint64) ([]float32, error)

GetVector retrieves just the vector for a record.

func (*Collection) HasIndex added in v0.2.0 

func (c *Collection) HasIndex() bool

HasIndex returns true if this collection has an index.

func (*Collection) IndexStats added in v0.2.0 

func (c *Collection) IndexStats() *hnsw.IndexStats

IndexStats returns statistics about the collection's index. Returns nil if no index is configured.

func (*Collection) IndexType added in v0.2.0 

func (c *Collection) IndexType() IndexType

IndexType returns the index type for this collection.

func (*Collection) Insert 

func (c *Collection) Insert(vector []float32, payload map[string]any) (uint64, error)

Insert adds a vector with optional payload to the collection. Returns the assigned record ID.

func (*Collection) InsertBatch 

func (c *Collection) InsertBatch(vectors [][]float32, payloads []map[string]any) ([]uint64, error)

InsertBatch adds multiple vectors with payloads to the collection. Returns the assigned record IDs. If payloads is nil or shorter than vectors, missing payloads are treated as nil.

func (*Collection) Name 

func (c *Collection) Name() string

Name returns the collection name.

func (*Collection) Search 

func (c *Collection) Search(query []float32, opts ...SearchOption) ([]Result, error)

Search finds the most similar vectors to the query vector.

func (*Collection) SearchExplain added in v0.2.0 

func (c *Collection) SearchExplain(query []float32, opts ...SearchOption) (*SearchExplanation, error)

SearchExplain performs a search and returns detailed statistics.

func (*Collection) Stats 

func (c *Collection) Stats() CollectionStats

Stats returns statistics about the collection.

func (*Collection) Update 

func (c *Collection) Update(id uint64, payload map[string]any) error

Update updates the payload for a record.

func (*Collection) UpdateVector added in v0.4.0 

func (c *Collection) UpdateVector(id uint64, vector []float32) error

UpdateVector updates the vector for a record.

func (*Collection) Upsert added in v0.4.0 

func (c *Collection) Upsert(id uint64, vector []float32, payload map[string]any) (uint64, error)

Upsert inserts a new record or updates an existing one by ID. If the ID is 0, a new record is created with an auto-generated ID. If the ID exists, the vector and payload are updated. If the ID doesn't exist, a new record is created with that ID. Returns the record ID (either the provided one or newly generated).

func (*Collection) UpsertByKey added in v0.4.0 

func (c *Collection) UpsertByKey(keyField string, keyValue any, vector []float32, payload map[string]any) (uint64, bool, error)

UpsertByKey inserts a new record or updates an existing one based on a key field. If a record with payload[keyField] == keyValue exists, it is updated. Otherwise, a new record is inserted. Returns the record ID and whether it was an insert (true) or update (false).

type CollectionOption 

type CollectionOption interface {
	// contains filtered or unexported methods
}

CollectionOption configures a collection.

func WithDimension 

func WithDimension(dim int) CollectionOption

WithDimension sets the vector dimension for the collection. If set, all vectors must match this dimension. If not set (0), the dimension is determined by the first insert.

func WithDistanceType 

func WithDistanceType(t floats.DistanceType) CollectionOption

WithDistanceType sets the distance metric for the collection. Default is cosine similarity.

func WithHNSW added in v0.2.0 

func WithHNSW(m, efConstruction int) CollectionOption

WithHNSW enables HNSW indexing for the collection. m is the maximum number of connections per node (default: 16, recommended: 12-48). efConstruction is the candidate list size during construction (default: 200).

func WithHNSWConfig added in v0.2.0 

func WithHNSWConfig(config HNSWConfig) CollectionOption

WithHNSWConfig enables HNSW indexing with custom configuration.

type CollectionSnapshot 

type CollectionSnapshot struct {
	// Name is the collection name.
	Name string

	// Dimension is the vector dimension.
	Dimension int

	// DistanceType is the distance metric.
	DistanceType floats.DistanceType

	// NextID is the next record ID to assign.
	NextID uint64

	// Records contains all records in the collection.
	Records []*RecordSnapshot

	// CreatedAt is when the collection was created.
	CreatedAt time.Time

	// UpdatedAt is when the collection was last modified.
	UpdatedAt time.Time

	// IndexType is the type of index (none, hnsw).
	IndexType IndexType

	// HNSWConfig holds the HNSW configuration (if IndexType is hnsw).
	HNSWConfig *HNSWConfig

	// HNSWSnapshot holds the HNSW index state (if IndexType is hnsw).
	HNSWSnapshot *hnsw.Snapshot
}

CollectionSnapshot is the serializable state of a collection.

func NewCollectionSnapshot 

func NewCollectionSnapshot(name string, dimension int, distanceType floats.DistanceType) *CollectionSnapshot

NewCollectionSnapshot creates a new empty collection snapshot.

type CollectionStats 

type CollectionStats struct {
	// Name is the collection name.
	Name string

	// Count is the number of records in the collection.
	Count int

	// Dimension is the vector dimension (0 if not yet set).
	Dimension int

	// DistanceType is the distance metric used.
	DistanceType string

	// IndexType is the index type (none, hnsw).
	IndexType string
}

CollectionStats contains statistics about a collection.

type DB 

type DB struct {
	// contains filtered or unexported fields
}

DB represents a VecLite database.

func Open 

func Open(path string, opts ...Option) (*DB, error)

Open opens or creates a VecLite database at the given path. Use ":memory:" for an in-memory database that won't be persisted.

func (*DB) Close 

func (db *DB) Close() error

Close closes the database, syncing any pending changes.

func (*DB) Collection 

func (db *DB) Collection(name string) *Collection

Collection returns a collection by name, creating it if it doesn't exist. This is the preferred way to get collections for most use cases.

func (*DB) Collections 

func (db *DB) Collections() []string

Collections returns the names of all collections.

func (*DB) CreateCollection 

func (db *DB) CreateCollection(name string, opts ...CollectionOption) (*Collection, error)

CreateCollection creates a new collection with the given options. Returns an error if the collection already exists.

func (*DB) DropCollection 

func (db *DB) DropCollection(name string) error

DropCollection removes a collection and all its data.

func (*DB) GetCollection 

func (db *DB) GetCollection(name string) (*Collection, error)

GetCollection returns an existing collection or ErrNotFound.

func (*DB) HasCollection 

func (db *DB) HasCollection(name string) bool

HasCollection returns true if a collection exists.

func (*DB) IsClosed 

func (db *DB) IsClosed() bool

IsClosed returns true if the database is closed.

func (*DB) Path 

func (db *DB) Path() string

Path returns the database file path.

func (*DB) Stats 

func (db *DB) Stats() DatabaseStats

Stats returns statistics about the database.

func (*DB) Sync 

func (db *DB) Sync() error

Sync writes all pending changes to storage.

type DatabaseSnapshot 

type DatabaseSnapshot struct {
	// Version is the file format version.
	Version uint32

	// Collections maps collection names to their snapshots.
	Collections map[string]*CollectionSnapshot

	// CreatedAt is when the database was created.
	CreatedAt time.Time

	// UpdatedAt is when the database was last modified.
	UpdatedAt time.Time
}

DatabaseSnapshot is the serializable state of the database.

func NewDatabaseSnapshot 

func NewDatabaseSnapshot() *DatabaseSnapshot

NewDatabaseSnapshot creates a new empty database snapshot.

type DatabaseStats 

type DatabaseStats struct {
	// Path is the database file path (":memory:" for in-memory).
	Path string

	// Collections is the number of collections.
	Collections int

	// TotalRecords is the total number of records across all collections.
	TotalRecords int

	// CollectionStats contains stats for each collection.
	CollectionStats []CollectionStats
}

DatabaseStats contains statistics about the database.

type DimensionError 

type DimensionError struct {
	Expected int
	Got      int
}

DimensionError provides details about dimension mismatches.

func (*DimensionError) Error 

func (e *DimensionError) Error() string

func (*DimensionError) Unwrap 

func (e *DimensionError) Unwrap() error

type DistanceType added in v0.2.0 

type DistanceType = floats.DistanceType

Re-export distance types for external use.

type FileStorage 

type FileStorage struct {
	// contains filtered or unexported fields
}

FileStorage is a file-based storage implementation. Uses gob encoding with atomic writes for durability.

func NewFileStorage 

func NewFileStorage(path string) *FileStorage

NewFileStorage creates a new file storage for the given path.

func (*FileStorage) Close 

func (f *FileStorage) Close() error

Close is a no-op for file storage.

func (*FileStorage) Delete 

func (f *FileStorage) Delete() error

Delete removes the database file and any backup files.

func (*FileStorage) Exists 

func (f *FileStorage) Exists() bool

Exists returns true if the database file exists.

func (*FileStorage) Load 

func (f *FileStorage) Load() (*DatabaseSnapshot, error)

Load reads the database from the file. Returns nil, nil if the file doesn't exist yet.

func (*FileStorage) Path 

func (f *FileStorage) Path() string

Path returns the file path.

func (*FileStorage) Save 

func (f *FileStorage) Save(snapshot *DatabaseSnapshot) error

Save writes the database to the file using atomic write pattern. Writes to .tmp file, then renames old to .bak, then renames .tmp to final.

type Filter 

type Filter interface {
	// Match returns true if the record matches the filter criteria.
	Match(r *Record) bool
}

Filter is an interface for filtering records based on payload values.

func And 

func And(filters ...Filter) Filter

And creates a filter that matches records matching all given filters.

func Between added in v0.4.0 

func Between(key string, min, max float64) Filter

Between creates a filter that matches records where min <= payload[key] <= max.

func Contains 

func Contains(key, substr string) Filter

Contains creates a filter that matches records where payload[key] contains the substring.

func Equal 

func Equal(key string, value any) Filter

Equal creates a filter that matches records where payload[key] equals value.

func Exists 

func Exists(key string) Filter

Exists creates a filter that matches records where payload[key] exists.

func GT added in v0.4.0 

func GT(key string, value float64) Filter

GT is an alias for GreaterThan.

func GTE added in v0.4.0 

func GTE(key string, value float64) Filter

GTE is an alias for GreaterThanOrEqual.

func Glob 

func Glob(key, pattern string) Filter

Glob creates a filter that matches records where payload[key] matches the glob pattern.

func GreaterThan added in v0.4.0 

func GreaterThan(key string, value float64) Filter

GreaterThan creates a filter that matches records where payload[key] > value.

func GreaterThanOrEqual added in v0.4.0 

func GreaterThanOrEqual(key string, value float64) Filter

GreaterThanOrEqual creates a filter that matches records where payload[key] >= value.

func In 

func In(key string, values ...any) Filter

In creates a filter that matches records where payload[key] is in the given values.

func LT added in v0.4.0 

func LT(key string, value float64) Filter

LT is an alias for LessThan.

func LTE added in v0.4.0 

func LTE(key string, value float64) Filter

LTE is an alias for LessThanOrEqual.

func LessThan added in v0.4.0 

func LessThan(key string, value float64) Filter

LessThan creates a filter that matches records where payload[key] < value.

func LessThanOrEqual added in v0.4.0 

func LessThanOrEqual(key string, value float64) Filter

LessThanOrEqual creates a filter that matches records where payload[key] <= value.

func Not 

func Not(filter Filter) Filter

Not creates a filter that negates the given filter.

func NotEqual 

func NotEqual(key string, value any) Filter

NotEqual creates a filter that matches records where payload[key] does not equal value.

func NotIn 

func NotIn(key string, values ...any) Filter

NotIn creates a filter that matches records where payload[key] is not in the given values.

func Or 

func Or(filters ...Filter) Filter

Or creates a filter that matches records matching any given filter.

func Prefix 

func Prefix(key, prefix string) Filter

Prefix creates a filter that matches records where payload[key] has the given prefix.

func Suffix 

func Suffix(key, suffix string) Filter

Suffix creates a filter that matches records where payload[key] has the given suffix.

type FilterFunc 

type FilterFunc func(r *Record) bool

FilterFunc is a function adapter for the Filter interface.

func (FilterFunc) Match 

func (f FilterFunc) Match(r *Record) bool

Match implements Filter interface.

type HNSWConfig added in v0.2.0 

type HNSWConfig struct {
	// M is the maximum number of connections per node.
	M int
	// EfConstruction is the size of the candidate list during index construction.
	EfConstruction int
	// EfSearch is the default size of the candidate list during search.
	EfSearch int
}

HNSWConfig holds HNSW index configuration.

type HNSWIndex added in v0.2.0 

type HNSWIndex struct {
	// contains filtered or unexported fields
}

HNSWIndex wraps the HNSW index to implement the Index interface.

func NewHNSWIndex added in v0.2.0 

func NewHNSWIndex(dimension int, distanceType floats.DistanceType, m, efConstruction int) *HNSWIndex

NewHNSWIndex creates a new HNSW index with the given parameters.

func NewHNSWIndexWithConfig added in v0.2.0 

func NewHNSWIndexWithConfig(dimension int, distanceType floats.DistanceType, config hnsw.Config) *HNSWIndex

NewHNSWIndexWithConfig creates a new HNSW index with a custom configuration.

func (*HNSWIndex) Count added in v0.2.0 

func (h *HNSWIndex) Count() int

Count returns the number of vectors in the index.

func (*HNSWIndex) Delete added in v0.2.0 

func (h *HNSWIndex) Delete(id uint64) error

Delete removes a vector from the index (soft delete).

func (*HNSWIndex) HardDelete added in v0.4.0 

func (h *HNSWIndex) HardDelete(id uint64) error

HardDelete removes a vector completely from the index. This is needed for update operations where we re-insert with the same ID.

func (*HNSWIndex) Insert added in v0.2.0 

func (h *HNSWIndex) Insert(id uint64, vector []float32) error

Insert adds a vector with the given ID to the index.

func (*HNSWIndex) Internal added in v0.2.0 

func (h *HNSWIndex) Internal() *hnsw.Index

Internal returns the underlying HNSW index (for serialization).

func (*HNSWIndex) Search added in v0.2.0 

func (h *HNSWIndex) Search(query []float32, k int) ([]IndexResult, error)

Search finds the k nearest neighbors to the query vector.

func (*HNSWIndex) SearchWithEf added in v0.2.0 

func (h *HNSWIndex) SearchWithEf(query []float32, k int, ef int) ([]IndexResult, error)

SearchWithEf searches with a custom ef parameter.

func (*HNSWIndex) SetInternal added in v0.2.0 

func (h *HNSWIndex) SetInternal(idx *hnsw.Index)

SetInternal sets the underlying HNSW index (for deserialization).

func (*HNSWIndex) Stats added in v0.2.0 

func (h *HNSWIndex) Stats() hnsw.IndexStats

Stats returns statistics about the index.

func (*HNSWIndex) Type added in v0.2.0 

func (h *HNSWIndex) Type() string

Type returns "hnsw".

type Index added in v0.2.0 

type Index interface {
	// Insert adds a vector with the given ID to the index.
	Insert(id uint64, vector []float32) error

	// Delete removes a vector from the index.
	Delete(id uint64) error

	// Search finds the k nearest neighbors to the query vector.
	// Returns IDs and distances/similarities.
	Search(query []float32, k int) ([]IndexResult, error)

	// SearchWithEf searches with a custom ef parameter (for HNSW).
	// For indexes that don't support ef, this should behave like Search.
	SearchWithEf(query []float32, k int, ef int) ([]IndexResult, error)

	// Count returns the number of vectors in the index.
	Count() int

	// Type returns the index type name.
	Type() string
}

Index is the interface for vector search indexes. Implementations can provide different algorithms (brute-force, HNSW, etc.).

type IndexResult added in v0.2.0 

type IndexResult struct {
	ID       uint64
	Distance float32
}

IndexResult represents a search result from an index.

type IndexType added in v0.2.0 

type IndexType string

IndexType represents the type of index. 

const (
	// IndexTypeNone means no index (brute force search).
	IndexTypeNone IndexType = "none"

	// IndexTypeHNSW means HNSW approximate nearest neighbor index.
	IndexTypeHNSW IndexType = "hnsw"
)

type MemoryStorage 

type MemoryStorage struct {
	// contains filtered or unexported fields
}

MemoryStorage is an in-memory storage implementation. Data is not persisted and will be lost when the database is closed.

func NewMemoryStorage 

func NewMemoryStorage() *MemoryStorage

NewMemoryStorage creates a new in-memory storage.

func (*MemoryStorage) Close 

func (m *MemoryStorage) Close() error

Close is a no-op for memory storage.

func (*MemoryStorage) Load 

func (m *MemoryStorage) Load() (*DatabaseSnapshot, error)

Load returns the stored snapshot or nil if none exists.

func (*MemoryStorage) Save 

func (m *MemoryStorage) Save(snapshot *DatabaseSnapshot) error

Save stores the snapshot in memory.

type NotFoundError 

type NotFoundError struct {
	Type string // "record", "collection", etc.
	ID   string // Identifier that was not found
}

NotFoundError provides details about what was not found.

func (*NotFoundError) Error 

func (e *NotFoundError) Error() string

func (*NotFoundError) Unwrap 

func (e *NotFoundError) Unwrap() error

type Option 

type Option interface {
	// contains filtered or unexported methods
}

Option configures the database.

func WithReadOnly 

func WithReadOnly(enabled bool) Option

WithReadOnly opens the database in read-only mode. Write operations will return an error.

func WithSyncOnWrite 

func WithSyncOnWrite(enabled bool) Option

WithSyncOnWrite enables automatic sync after each write operation. This is slower but ensures durability.

type Record 

type Record struct {
	// ID is the unique identifier for this record.
	ID uint64

	// Vector is the embedding vector.
	Vector []float32

	// Payload contains arbitrary metadata associated with the vector.
	Payload map[string]any

	// CreatedAt is when the record was inserted.
	CreatedAt time.Time

	// UpdatedAt is when the record was last updated.
	UpdatedAt time.Time
}

Record represents a stored vector with its metadata.

func (*Record) Clone 

func (r *Record) Clone() *Record

Clone creates a deep copy of the record.

type RecordSnapshot 

type RecordSnapshot struct {
	// ID is the record's unique identifier.
	ID uint64

	// Vector is the embedding vector.
	Vector []float32

	// Payload contains arbitrary metadata.
	Payload map[string]any

	// CreatedAt is when the record was inserted.
	CreatedAt time.Time

	// UpdatedAt is when the record was last updated.
	UpdatedAt time.Time
}

RecordSnapshot is the serializable state of a record.

type Result 

type Result struct {
	// Record is the matched record.
	Record *Record

	// Score is the similarity/distance score.
	// For cosine/dot: higher is more similar.
	// For euclidean: lower is more similar.
	Score float32
}

Result represents a search result with its similarity score.

type SearchExplanation added in v0.2.0 

type SearchExplanation struct {
	// Results contains the search results.
	Results []Result

	// IndexType is the type of index used (none, hnsw).
	IndexType string

	// NodesVisited is the number of nodes visited during search.
	// Only populated for HNSW searches.
	NodesVisited int

	// LayersVisited is the number of HNSW layers visited.
	// Only populated for HNSW searches.
	LayersVisited int

	// Duration is how long the search took.
	Duration time.Duration

	// BruteForce indicates whether brute-force search was used.
	BruteForce bool
}

SearchExplanation provides details about how a search was performed.

type SearchOption 

type SearchOption interface {
	// contains filtered or unexported methods
}

SearchOption configures search behavior.

func Threshold 

func Threshold(t float32) SearchOption

Threshold sets the minimum similarity score for results. For cosine/dot: results with score >= threshold are returned. For euclidean: results with score <= threshold are returned.

func TopK 

func TopK(k int) SearchOption

TopK sets the maximum number of results to return. Default is 10.

func WithEfSearch added in v0.2.0 

func WithEfSearch(ef int) SearchOption

WithEfSearch sets the efSearch parameter for HNSW search. Higher values improve recall at the cost of speed. Has no effect on collections without HNSW index.

func WithFilter 

func WithFilter(f Filter) SearchOption

WithFilter adds a filter to the search. Multiple filters are combined with AND logic.

func WithFilters 

func WithFilters(filters ...Filter) SearchOption

WithFilters adds multiple filters to the search. All filters are combined with AND logic.

type Storage 

type Storage interface {
	// Load reads the database from storage.
	// Returns nil, nil if the database doesn't exist yet.
	Load() (*DatabaseSnapshot, error)

	// Save writes the database to storage.
	Save(snapshot *DatabaseSnapshot) error

	// Close releases any resources held by the storage.
	Close() error
}

Storage is the interface for database persistence.

type StorageError 

type StorageError struct {
	Op  string // Operation that failed
	Err error  // Underlying error
}

StorageError wraps storage-related errors with context.

func (*StorageError) Error 

func (e *StorageError) Error() string

func (*StorageError) Unwrap 

func (e *StorageError) Unwrap() error

Source Files

View all Source files

Directories

Path Synopsis
cmd
veclite command
Command veclite provides a CLI for interacting with VecLite databases.
 Command veclite provides a CLI for interacting with VecLite databases.
internal
floats
Package floats provides optimized floating-point vector operations.
 Package floats provides optimized floating-point vector operations.
hnsw
Package hnsw implements the Hierarchical Navigable Small World graph algorithm for approximate nearest neighbor search.
 Package hnsw implements the Hierarchical Navigable Small World graph algorithm for approximate nearest neighbor search.

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