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LATTICE_BUNDLE_LIB_DIR=/tmp/lattice-install/lib\nnpm run bundle:native\n```\n\nIf `LATTICE_BUNDLE_LIB_DIR` / `LATTICE_BUNDLE_LIB_PATH` is not set, `npm run bundle:native` will build the current platform library with Zig.\n\nAt runtime, explicit library discovery overrides still work via `LATTICE_LIB_PATH`, `LATTICE_PREFIX`, and `pkg-config`.\n\nMigration note: embedding helpers now live in the dedicated `@hajewski/latticedb/embedding` entrypoint. See [../../docs/client_api_migration.md](../../docs/client_api_migration.md) for the preferred API names and deprecated compatibility aliases.\n\nInstalled-prefix workflow:\n\n```bash\nzig build install --prefix /tmp/lattice-install\nexport LATTICE_PREFIX=/tmp/lattice-install\n```\n\nAlternatively, discovery can use `pkg-config`:\n\n```bash\nexport PKG_CONFIG_PATH=/tmp/lattice-install/lib/pkgconfig\n```\n\n## Quick Start\n\n```typescript\nimport { Database } from \"@hajewski/latticedb\";\n\nconst db = new Database(\"knowledge.db\", {\n  create: true,\n  enableVectors: true,\n  vectorDimensions: 4,\n});\nawait db.open();\n\n// Create nodes, edges, and index content\nawait db.createNodeFtsIndex(\"Person\", \"bio\");\n\nawait db.write(async (txn) => {\n  const alice = await txn.createNode({\n    labels: [\"Person\"],\n    properties: { name: \"Alice\", age: 30 },\n  });\n\n  const bob = await txn.createNode({\n    labels: [\"Person\"],\n    properties: { name: \"Bob\", age: 25 },\n  });\n\n  await txn.createEdge(alice.id, bob.id, \"KNOWS\");\n\n  // Writing the indexed property is what makes it searchable.\n  await txn.setProperty(alice.id, \"bio\", \"Alice works on machine learning research\");\n  await txn.setProperty(bob.id, \"bio\", \"Bob studies deep learning and neural networks\");\n\n  // Store vector embeddings\n  await txn.setVector(\n    alice.id,\n    \"embedding\",\n    new Float32Array([1.0, 0.0, 0.0, 0.0])\n  );\n  await txn.setVector(\n    bob.id,\n    \"embedding\",\n    new Float32Array([0.0, 1.0, 0.0, 0.0])\n  );\n});\n\n// Query with Cypher\nconst result = await db.query(\n  \"MATCH (n:Person) WHERE n.age > 20 RETURN n.name, n.age\"\n);\nfor (const row of result.rows) {\n  console.log(row);\n}\n\n// Vector similarity search\nconst results = await db.vectorSearch(\n  new Float32Array([0.9, 0.1, 0.0, 0.0]),\n  { k: 2 }\n);\nfor (const r of results) {\n  console.log(`Node ${r.nodeId}: distance=${r.distance.toFixed(4)}`);\n}\n\n// Full-text search\nconst ftsResults = await db.ftsSearch(\"Person\", \"bio\", \"machine learning\");\nfor (const r of ftsResults) {\n  console.log(`Node ${r.nodeId}: score=${r.score.toFixed(4)}`);\n}\n\n// Fuzzy search (typo-tolerant)\nconst fuzzyResults = await db.ftsSearchFuzzy(\"Person\", \"bio\", \"machin lerning\");\nfor (const r of fuzzyResults) {\n  console.log(`Node ${r.nodeId}: score=${r.score.toFixed(4)}`);\n}\n\nawait db.close();\n```\n\n## Features\n\n- **Property Graph** - Nodes and edges with labels and properties\n- **Vector Search** - HNSW-based k-NN search for embeddings\n- **Full-Text Search** - BM25-ranked search with tokenization\n- **Fuzzy Search** - Typo-tolerant full-text search with configurable edit distance\n- **Bulk Vector Insertion** - Efficient insertion of vector-bearing nodes\n- **Embeddings** - Built-in hash embeddings and HTTP client for external services\n- **Cypher Queries** - Pattern matching with `<=>` (vector) and `@@` (FTS) extensions\n- **Transactions** - ACID-compliant read/write transactions\n- **Query Cache** - Automatic caching of parsed queries\n- **TypeScript** - Full type definitions included\n\n## API Reference\n\n### Database\n\n```typescript\nconst db = new Database(path: string, options?: DatabaseOptions);\n\ninterface DatabaseOptions {\n  create?: boolean;          // Create if not exists (default: false)\n  readOnly?: boolean;        // Open read-only (default: false)\n  cacheSizeMb?: number;      // Cache size in MB (default: 100)\n  enableVectors?: boolean;   // Preferred vector config flag\n  enableVector?: boolean;    // Deprecated compatibility alias\n  vectorDimensions?: number; // Vector dimensions (default: 128)\n}\n```\n\n#### Methods\n\n- `await db.open()` - Open the database connection\n- `await db.close()` - Close the database connection\n- `await db.read(fn)` - Execute a read-only transaction\n- `await db.write(fn)` - Execute a read-write transaction\n- `await db.query(cypher, params?)` - Execute a Cypher query\n- `await db.vectorSearch(vector, options?)` - k-NN vector search\n- `await db.ftsSearch(query, options?)` - Full-text search\n- `await db.ftsSearchFuzzy(query, options?)` - Fuzzy full-text search\n- `await db.createNodePropertyIndex(label, property)` / `dropNodePropertyIndex(...)` - Manage explicit node equality indexes\n- `await db.createEdgePropertyIndex(edgeType, property)` / `dropEdgePropertyIndex(...)` - Manage explicit edge equality indexes\n- `await db.readStream(stream, options?)` - Read durable stream records by cursor\n- `await db.getStreamOffset(stream, consumer)` - Read a committed consumer offset\n- `await db.changes(options?)` - Read the built-in graph changefeed\n- `await db.cacheClear()` - Clear the query cache\n- `await db.cacheStats()` - Get cache hit/miss statistics\n- `db.isOpen()` - Check if database is open\n- `db.getPath()` - Get database file path\n\n### Transaction\n\n#### Read Operations\n\n- `await txn.getNode(nodeId)` - Get a node by ID, returns `Node` or `null`\n- `await txn.nodeExists(nodeId)` - Check if a node exists\n- `await txn.getProperty(nodeId, key)` - Get a property value\n- `await txn.getOutgoingEdges(nodeId)` - Get outgoing edges from a node\n- `await txn.getIncomingEdges(nodeId)` - Get incoming edges to a node\n- `await txn.findNodesByLabelProperty(label, property, value, limit?)` - Indexed node equality lookup\n- `await txn.findEdgesByTypeProperty(edgeType, property, value, limit?)` - Indexed edge equality lookup\n- `txn.isReadOnly()` / `txn.isActive()` - Transaction state\n\n#### Write Operations\n\n- `await txn.createNode({ labels, properties })` - Create a node\n- `await txn.deleteNode(nodeId)` - Delete a node\n- `await txn.setProperty(nodeId, key, value)` - Set a property\n- `await txn.setVector(nodeId, key, vector)` - Set a vector embedding\n- `await txn.batchInsertVectors(label, vectors)` - Insert vector-bearing nodes in one call\n- `await txn.batchInsert(label, vectors)` - Deprecated compatibility alias for `batchInsertVectors`\n- `await db.createNodeFtsIndex(label, property)` - Declare a full-text index; writing that property keeps it current\n- `await txn.createEdge(sourceId, targetId, edgeType, options?)` - Create an edge\n- `await txn.deleteEdge(sourceId, targetId, edgeType)` - Delete an edge\n- `await txn.setEdgeProperty(edgeId, key, value)` - Set an edge property by stable edge ID\n- `await txn.getEdgeProperty(edgeId, key)` - Get an edge property by stable edge ID\n- `await txn.removeEdgeProperty(edgeId, key)` - Remove an edge property by stable edge ID\n- `txn.publishStream(stream, payload, kind?)` - Publish a durable stream record\n- `txn.setStreamOffset(stream, consumer, sequence)` - Commit a durable consumer offset\n- `txn.trimStream(stream, throughSequence)` - Delete stream records through a sequence\n- `txn.commit()` / `txn.rollback()` - Commit or rollback\n\n### Bulk Vector Insertion\n\nInsert many nodes with vectors in a single efficient call:\n\n```typescript\nimport { Database } from \"@hajewski/latticedb\";\n\nconst db = new Database(\"vectors.db\", {\n  create: true,\n  enableVectors: true,\n  vectorDimensions: 128,\n});\nawait db.open();\n\nawait db.write(async (txn) => {\n  const vectors = Array.from({ length: 1000 }, () =>\n    Float32Array.from({ length: 128 }, () => Math.random())\n  );\n  const nodeIds = await txn.batchInsertVectors(\"Document\", vectors);\n  console.log(`Created ${nodeIds.length} nodes`);\n});\n\nawait db.close();\n```\n\n### Property Indexes\n\nProperty equality indexes are explicit and durable. Create them outside an\nactive write transaction; lookup fails instead of silently scanning when the\nrequested index does not exist.\n\n```typescript\nawait db.createNodePropertyIndex(\"Person\", \"email\");\n\nconst nodeIds = await db.read((txn) =>\n  txn.findNodesByLabelProperty(\"Person\", \"email\", \"alice@example.com\", 10)\n);\n\n// Inline Cypher equality can use the same index.\nconst rows = await db.query(\n  \"MATCH (p:Person {email: $email}) RETURN p\",\n  { email: \"alice@example.com\" }\n);\n```\n\n### Full-Text Search\n\n#### Exact Search\n\n```typescript\nconst results = await db.ftsSearch(\"Person\", \"bio\", \"machine learning\", { limit: 10 });\nfor (const r of results) {\n  console.log(`Node ${r.nodeId}: score=${r.score.toFixed(4)}`);\n}\n```\n\n#### Fuzzy Search (Typo-Tolerant)\n\n```typescript\n// Finds \"machine learning\" even with typos\nconst results = await db.ftsSearchFuzzy(\"Person\", \"bio\", \"machne lerning\", { limit: 10 });\n\n// Control fuzzy matching sensitivity\nconst precise = await db.ftsSearchFuzzy(\"Person\", \"bio\", \"machne\", {\n  limit: 10,\n  maxDistance: 2, // Max edit distance (default: 0 = auto)\n  minTermLength: 4, // Min term length for fuzzy matching (default: 0 = auto)\n});\n```\n\n### Embeddings\n\nLatticeDB includes a built-in hash embedding function and an HTTP client for external embedding services. For new code, prefer the dedicated `@hajewski/latticedb/embedding` entrypoint. The package root still exposes deprecated compatibility aliases.\n\n#### Hash Embeddings (Built-in)\n\nDeterministic, no external service needed. Useful for testing or simple keyword-based similarity:\n\n```typescript\nimport { hashEmbed } from \"@hajewski/latticedb/embedding\";\n\nconst vec = hashEmbed(\"hello world\", 128);\nconsole.log(vec.length); // 128\n```\n\n#### HTTP Embedding Client\n\nConnect to Ollama, OpenAI, or compatible APIs:\n\n```typescript\nimport { EmbeddingClient, EmbeddingApiFormat } from \"@hajewski/latticedb/embedding\";\n\n// Ollama (default)\nconst client = new EmbeddingClient({\n  endpoint: \"http://localhost:11434\",\n});\nconst vec = client.embed(\"hello world\");\nclient.close();\n\n// OpenAI-compatible API\nconst openaiClient = new EmbeddingClient({\n  endpoint: \"https://api.openai.com/v1\",\n  model: \"text-embedding-3-small\",\n  apiFormat: EmbeddingApiFormat.OpenAI,\n  apiKey: \"sk-...\",\n});\nconst embedding = openaiClient.embed(\"hello world\");\nopenaiClient.close();\n```\n\n### Edge Traversal\n\n```typescript\nawait db.read(async (txn) => {\n  const outgoing = await txn.getOutgoingEdges(nodeId);\n  for (const edge of outgoing) {\n    console.log(`${edge.sourceId} --[${edge.type}]--> ${edge.targetId}`);\n  }\n\n  const incoming = await txn.getIncomingEdges(nodeId);\n  for (const edge of incoming) {\n    console.log(`${edge.sourceId} --[${edge.type}]--> ${edge.targetId}`);\n  }\n});\n```\n\n### Cypher Queries\n\n```typescript\n// Pattern matching\nconst result = await db.query(\"MATCH (n:Person) RETURN n.name\");\n\n// With parameters\nconst result = await db.query(\n  \"MATCH (n:Person) WHERE n.name = $name RETURN n\",\n  { name: \"Alice\" }\n);\n\n// Vector similarity in Cypher\nconst result = await db.query(\n  \"MATCH (n:Document) WHERE n.embedding <=> $vec < 0.5 RETURN n.title\",\n  { vec: new Float32Array([0.1, 0.2, 0.3, 0.4]) }\n);\n\n// Full-text search in Cypher\nconst result = await db.query(\n  'MATCH (n:Document) WHERE n.content @@ \"machine learning\" RETURN n.title'\n);\n\n// Data mutation\nawait db.query('CREATE (n:Person {name: \"Charlie\", age: 35})');\nawait db.query('MATCH (n:Person {name: \"Charlie\"}) SET n.age = 36');\nawait db.query('MATCH (n:Person {name: \"Charlie\"}) DETACH DELETE n');\n```\n\n### Query Cache\n\n```typescript\n// Get cache statistics\nconst stats = await db.cacheStats();\nconsole.log(\n  `Entries: ${stats.entries}, Hits: ${stats.hits}, Misses: ${stats.misses}`\n);\n\n// Clear the cache\nawait db.cacheClear();\n```\n\n### Durable Streams and Changefeeds\n\nStreams are durable named event logs stored inside the database file. Records are\npublished in write transactions, sequence numbers are per stream, and reads use\nan explicit cursor. Reads do not acknowledge records; commit offsets separately\nwhen your consumer has processed a batch.\n\n```typescript\nconst db = new Database(\"events.db\", { create: true });\nawait db.open();\n\nawait db.write(async (txn) => {\n  txn.publishStream(\"jobs\", { id: 1, status: \"queued\" }, \"job.queued\");\n});\n\nconst records = await db.readStream(\"jobs\", {\n  afterSequence: 0n,\n  limit: 100,\n  timeoutMs: 0,\n});\n\nawait db.write(async (txn) => {\n  txn.setStreamOffset(\"jobs\", \"worker-a\", records.at(-1)!.sequence);\n  txn.trimStream(\"jobs\", records.at(-1)!.sequence - 1n);\n});\n```\n\n`db.changes()` reads the reserved `__lattice_changes` stream. It emits semantic\ngraph events such as `node.insert`, `node.property_set`, `edge.delete`, and\n`edge.property_remove`, with payloads represented as normal TypeScript values.\n\n## Supported Property Types\n\n- `null` - Null value\n- `boolean` - Boolean\n- `number` - Integer or float\n- `string` - UTF-8 string\n- `Uint8Array` - Binary data\n- `Float32Array` - Vector embeddings\n\nNested arrays/objects are not currently exposed by the public bindings/C API.\n\n## Error Handling\n\n```typescript\nimport { Database, isLibraryAvailable } from \"@hajewski/latticedb\";\n\n// Check if native library is available\nif (!isLibraryAvailable()) {\n  console.error(\"LatticeDB native library not found\");\n  process.exit(1);\n}\n\ntry {\n  const db = new Database(\"test.db\", { create: true });\n  await db.open();\n  // ...\n  await db.close();\n} catch (error) {\n  console.error(\"Database error:\", error);\n}\n```\n\n## Building from Source\n\nRequires Node.js 18+ and the LatticeDB native library.\n\n```bash\n# From the latticedb root directory\nzig build shared\n\n# Build the TypeScript bindings\ncd bindings/typescript\nnpm install\nnpm run build\n\n# Run tests\nnpm test\n```\n\n## Requirements\n\n- Node.js 18+\n- The native LatticeDB library (`liblattice.dylib` / `liblattice.so`)\n\n## License\n\nMIT\n","readmeFilename":"README.md"}