latticedb 0.3.0

Embedded knowledge graph database for AI and RAG applications

LatticeDB Python Bindings

Python bindings for LatticeDB, an embedded knowledge graph database for AI/RAG applications.

Installation

pip install latticedb

The native shared library (liblattice.dylib / liblattice.so) must be available on the system. Install it via the install script or build from source with zig build shared.

Quick Start

import numpy as np
from latticedb import Database

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

        # Index text for full-text search
        txn.fts_index(alice.id, "Alice works on machine learning research")
        txn.fts_index(bob.id, "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("machine learning"):
        print(f"Node {r.node_id}: score={r.score:.4f}")

    # Fuzzy search (typo-tolerant)
    for r in db.fts_search_fuzzy("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_vector: bool = False, # Enable vector storage
    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
• 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
• 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(label, vectors) - Batch insert nodes with vectors (see below)
• fts_index(node_id, text) - Index text for full-text search
• create_edge(source_id, target_id, edge_type) - Create an edge
• delete_edge(source_id, target_id, edge_type) - Delete an edge
• commit() / rollback() - Commit or rollback the transaction

Batch Insert

Insert many nodes with vectors in a single efficient call:

import numpy as np

with Database("vectors.db", create=True, enable_vector=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("Document", vectors)
        print(f"Created {len(node_ids)} nodes")
        txn.commit()

Full-Text Search

Exact Search

results = db.fts_search("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("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.

Hash Embeddings (Built-in)

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

from latticedb 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 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()

Supported Property Types

• None - Null value
• bool - Boolean
• int - 64-bit integer
• float - 64-bit float
• str - UTF-8 string
• bytes - Binary data

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