LatticeDB Python Bindings

Python bindings for LatticeDB, an embedded single-file property-graph database with native vector and BM25 full-text search.

Installation

pip install latticedb

Published wheels are expected to bundle the native shared library on supported platforms.

If you are installing from a source checkout, the package build can either:

• bundle a prebuilt liblattice from LATTICE_BUNDLE_LIB_DIR / LATTICE_BUNDLE_LIB_PATH, or
• build liblattice with Zig during the wheel build

For example, to bundle a staged installed library into a locally built wheel:

export LATTICE_BUNDLE_LIB_DIR=/tmp/lattice-install/lib
pip wheel . -w dist
pip install dist/latticedb-*.whl

At runtime, explicit library discovery overrides still work via LATTICE_LIB_PATH, LATTICE_PREFIX, and pkg-config.

Migration note: embedding helpers now live in the dedicated latticedb.embedding module. See ../../docs/client_api_migration.md for the preferred API names and deprecated compatibility aliases.

Installed-prefix workflow:

zig build install --prefix /tmp/lattice-install
export LATTICE_PREFIX=/tmp/lattice-install

Alternatively, discovery can use pkg-config:

export PKG_CONFIG_PATH=/tmp/lattice-install/lib/pkgconfig

Quick Start

import numpy as np
from latticedb import Database

with Database("knowledge.db", create=True, enable_vectors=True, vector_dimensions=4) as db:
    # Create nodes, edges, and index content
    db.create_node_fts_index("Person", "bio")

with db.write() as txn:
        alice = txn.create_node(
            labels=["Person"],
            properties={"name": "Alice", "age": 30},
        )
        bob = txn.create_node(
            labels=["Person"],
            properties={"name": "Bob", "age": 25},
        )
        txn.create_edge(alice.id, bob.id, "KNOWS")

        # Writing the indexed property is what makes it searchable.
        txn.set_property(alice.id, "bio", "Alice works on machine learning research")
        txn.set_property(bob.id, "bio", "Bob studies deep learning and neural networks")

        # Store vector embeddings
        txn.set_vector(alice.id, "embedding", np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32))
        txn.set_vector(bob.id, "embedding", np.array([0.0, 1.0, 0.0, 0.0], dtype=np.float32))

        txn.commit()

    # Query with Cypher
    result = db.query("MATCH (n:Person) WHERE n.age > 20 RETURN n.name, n.age")
    for row in result:
        print(row)

    # Vector similarity search
    query_vec = np.array([0.9, 0.1, 0.0, 0.0], dtype=np.float32)
    for r in db.vector_search(query_vec, k=2):
        print(f"Node {r.node_id}: distance={r.distance:.4f}")

    # Full-text search
    for r in db.fts_search("Person", "bio", "machine learning"):
        print(f"Node {r.node_id}: score={r.score:.4f}")

    # Fuzzy search (typo-tolerant)
    for r in db.fts_search_fuzzy("Person", "bio", "machin lerning"):
        print(f"Node {r.node_id}: score={r.score:.4f}")

API Reference

Database

Database(
    path: str | Path,
    *,
    create: bool = False,        # Create if doesn't exist
    read_only: bool = False,     # Open in read-only mode
    cache_size_mb: int = 100,    # Page cache size
    enable_vectors: bool | None = None, # Preferred vector config flag
    enable_vector: bool | None = None,  # Deprecated compatibility alias
    vector_dimensions: int = 128 # Vector dimensions
)

Methods

• open() / close() - Open/close the database (also works as context manager)
• read() - Start a read-only transaction (context manager)
• write() - Start a read-write transaction (context manager)
• query(cypher, parameters=None) - Execute a Cypher query
• vector_search(vector, k=10, ef_search=64) - k-NN vector search
• fts_search(query, limit=10) - Full-text search
• fts_search_fuzzy(query, limit=10, max_distance=0, min_term_length=0) - Fuzzy full-text search
• create_node_property_index(label, property_key) / drop_node_property_index(...) - Manage explicit node equality indexes
• create_edge_property_index(edge_type, property_key) / drop_edge_property_index(...) - Manage explicit edge equality indexes
• read_stream(stream, after_sequence=0, limit=100, timeout_ms=0) - Read durable stream records by cursor
• get_stream_offset(stream, consumer) - Read a committed consumer offset
• changes(after_sequence=0, limit=100, timeout_ms=0) - Read the built-in graph changefeed
• cache_clear() - Clear the query cache
• cache_stats() - Get cache hit/miss statistics

Transaction

Read Operations

• get_node(node_id) - Get a node by ID, returns Node or None
• node_exists(node_id) - Check if a node exists
• get_property(node_id, key) - Get a property value
• get_outgoing_edges(node_id) - Get outgoing edges from a node
• get_incoming_edges(node_id) - Get incoming edges to a node
• find_nodes_by_label_property(label, property_key, value, limit=100) - Indexed node equality lookup
• find_edges_by_type_property(edge_type, property_key, value, limit=100) - Indexed edge equality lookup
• is_read_only / is_active - Transaction state

Write Operations

• create_node(labels=[], properties=None) - Create a node
• delete_node(node_id) - Delete a node
• set_property(node_id, key, value) - Set a property on a node
• set_vector(node_id, key, vector) - Set a vector embedding
• batch_insert_vectors(label, vectors) - Insert vector-bearing nodes in one call
• batch_insert(label, vectors) - Deprecated compatibility alias for batch_insert_vectors
• db.create_node_fts_index(label, property) - Declare a full-text index; writing that property keeps it current
• create_edge(source_id, target_id, edge_type, properties=None) - Create an edge
• delete_edge(source_id, target_id, edge_type) - Delete an edge
• set_edge_property(edge_id, key, value) - Set an edge property by stable edge ID
• get_edge_property(edge_id, key) - Get an edge property by stable edge ID
• remove_edge_property(edge_id, key) - Remove an edge property by stable edge ID
• publish_stream(stream, payload, kind="message") - Publish a durable stream record
• set_stream_offset(stream, consumer, sequence) - Commit a durable consumer offset
• trim_stream(stream, through_sequence) - Delete stream records through a sequence
• commit() / rollback() - Commit or rollback the transaction

Bulk Vector Insertion

Insert many nodes with vectors in a single efficient call:

import numpy as np

with Database("vectors.db", create=True, enable_vectors=True, vector_dimensions=128) as db:
    with db.write() as txn:
        vectors = np.random.rand(1000, 128).astype(np.float32)
        node_ids = txn.batch_insert_vectors("Document", vectors)
        print(f"Created {len(node_ids)} nodes")
        txn.commit()

Property Indexes

Property equality indexes are explicit and durable. Create them outside an active write transaction; lookup fails instead of silently scanning when the requested index does not exist.

db.create_node_property_index("Person", "email")

with db.read() as txn:
    node_ids = txn.find_nodes_by_label_property(
        "Person", "email", "alice@example.com", limit=10
    )

# Inline Cypher equality can use the same index.
rows = db.query(
    "MATCH (p:Person {email: $email}) RETURN p",
    {"email": "alice@example.com"},
)

Full-Text Search

Exact Search

results = db.fts_search("Person", "bio", "machine learning", limit=10)
for r in results:
    print(f"Node {r.node_id}: score={r.score:.4f}")

Fuzzy Search (Typo-Tolerant)

# Finds "machine learning" even with typos
results = db.fts_search_fuzzy("Person", "bio", "machne lerning", limit=10)

# Control fuzzy matching sensitivity
results = db.fts_search_fuzzy(
    "machne",
    limit=10,
    max_distance=2,      # Max edit distance (default: 2)
    min_term_length=4,   # Min term length for fuzzy matching (default: 4)
)

Embeddings

LatticeDB includes a built-in hash embedding function and an HTTP client for external embedding services. For new code, prefer the dedicated latticedb.embedding module. The package root still exposes deprecated compatibility aliases.

Hash Embeddings (Built-in)

Deterministic, no external service needed. Useful for testing or simple keyword-based similarity:

from latticedb.embedding import hash_embed

vec = hash_embed("hello world", dimensions=128)
print(vec.shape)  # (128,)

HTTP Embedding Client

Connect to Ollama, OpenAI, or compatible APIs:

from latticedb.embedding import EmbeddingClient, EmbeddingApiFormat

# Ollama (default)
with EmbeddingClient("http://localhost:11434") as client:
    vec = client.embed("hello world")

# OpenAI-compatible API
with EmbeddingClient(
    "https://api.openai.com/v1",
    model="text-embedding-3-small",
    api_format=EmbeddingApiFormat.OPENAI,
    api_key="sk-...",
) as client:
    vec = client.embed("hello world")

Edge Traversal

with db.read() as txn:
    outgoing = txn.get_outgoing_edges(node_id)
    for edge in outgoing:
        print(f"{edge.source_id} --[{edge.edge_type}]--> {edge.target_id}")

    incoming = txn.get_incoming_edges(node_id)
    for edge in incoming:
        print(f"{edge.source_id} --[{edge.edge_type}]--> {edge.target_id}")

Cypher Queries

# Pattern matching
result = db.query("MATCH (n:Person) RETURN n.name")

# With parameters
result = db.query(
    "MATCH (n:Person) WHERE n.name = $name RETURN n",
    parameters={"name": "Alice"},
)

# Vector similarity in Cypher
result = db.query(
    "MATCH (n:Document) WHERE n.embedding <=> $vec < 0.5 RETURN n.title",
    parameters={"vec": query_vector},
)

# Full-text search in Cypher
result = db.query(
    'MATCH (n:Document) WHERE n.content @@ "machine learning" RETURN n.title'
)

# Data mutation
db.query("CREATE (n:Person {name: 'Charlie', age: 35})")
db.query("MATCH (n:Person {name: 'Charlie'}) SET n.age = 36")
db.query("MATCH (n:Person {name: 'Charlie'}) DETACH DELETE n")

Query Cache

# Get cache statistics
stats = db.cache_stats()
print(f"Entries: {stats['entries']}, Hits: {stats['hits']}, Misses: {stats['misses']}")

# Clear the cache
db.cache_clear()

Durable Streams and Changefeeds

Streams are durable named event logs stored inside the database file. Records are published in write transactions, sequence numbers are per stream, and reads use an explicit cursor. Reads do not acknowledge records; commit offsets separately when your consumer has processed a batch.

with Database("events.db", create=True) as db:
    with db.write() as txn:
        txn.publish_stream("jobs", {"id": 1, "status": "queued"}, kind="job.queued")
        txn.commit()

    records = db.read_stream("jobs", after_sequence=0, limit=100, timeout_ms=0)

    with db.write() as txn:
        txn.set_stream_offset("jobs", "worker-a", records[-1].sequence)
        txn.trim_stream("jobs", records[-1].sequence - 1)
        txn.commit()

db.changes() reads the reserved __lattice_changes stream. It emits semantic graph events such as node.insert, node.property_set, edge.delete, and edge.property_remove, with payloads represented as normal Python values.

Supported Property Types

• None - Null value
• bool - Boolean
• int - 64-bit integer
• float - 64-bit float
• str - UTF-8 string
• bytes - Binary data
• NumPy ndarray (float32) - Vector embeddings

Nested list and dict values are not currently exposed by the public bindings/C API.

Error Handling

from latticedb import LatticeError, LatticeNotFoundError, LatticeIOError

try:
    with Database("nonexistent.db") as db:
        pass
except LatticeNotFoundError:
    print("Database not found")
except LatticeIOError:
    print("I/O error")
except LatticeError as e:
    print(f"Error: {e}")

Requirements

• Python 3.9+
• NumPy (for vector operations)
• The native LatticeDB library (liblattice.dylib / liblattice.so)

License

MIT