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knowledge graph SDK on top of Neo4j with vector search, graph traversal, and pluggable embedders/chunkers","maintainers":[{"name":"nickpape","email":"nickpape@outlook.com"}],"readme":"# @grackle-ai/knowledge-core\n\nA domain-agnostic knowledge graph SDK built on [Neo4j](https://neo4j.com/). Provides pluggable text embedding, content chunking, an ingestion pipeline, node/edge CRUD, semantic vector search, and multi-hop graph traversal -- everything needed to build a knowledge graph with semantic search capabilities.\n\nPart of the [Grackle](https://github.com/nick-pape/grackle) platform, where it powers the shared knowledge layer that lets AI agents build and query a persistent understanding of codebases, decisions, and context. However, `@grackle-ai/knowledge-core` is a standalone library with no dependency on the rest of Grackle -- you can use it in any Node.js application that needs a knowledge graph with vector search.\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Prerequisites](#prerequisites)\n- [Quick Start](#quick-start)\n- [Architecture Overview](#architecture-overview)\n- [Key Concepts](#key-concepts)\n  - [Node Kinds](#node-kinds)\n  - [Edge Types](#edge-types)\n  - [Workspace Scoping](#workspace-scoping)\n- [API Reference](#api-reference)\n  - [Connection Management](#connection-management)\n  - [Schema Initialization](#schema-initialization)\n  - [Embedders](#embedders)\n  - [Chunkers](#chunkers)\n  - [Ingestion Pipeline](#ingestion-pipeline)\n  - [Node Operations](#node-operations)\n  - [Edge Operations](#edge-operations)\n  - [Semantic Search](#semantic-search)\n  - [Graph Expansion](#graph-expansion)\n  - [Listing Recent Nodes](#listing-recent-nodes)\n  - [Type Guards](#type-guards)\n  - [Logging](#logging)\n- [Configuration](#configuration)\n  - [Environment Variables](#environment-variables)\n  - [Constants](#constants)\n- [Custom Embedders and Chunkers](#custom-embedders-and-chunkers)\n- [License](#license)\n\n## Installation\n\n```bash\nnpm install @grackle-ai/knowledge-core\n```\n\nRequires **Node.js >= 22**.\n\n## Prerequisites\n\n### Neo4j Database\n\nThis library requires a running [Neo4j](https://neo4j.com/) instance (version 5.x) with vector index support. The simplest way to run one locally is with Docker:\n\n```bash\ndocker run -d \\\n  --name grackle-neo4j \\\n  -p 7474:7474 \\\n  -p 7687:7687 \\\n  -e NEO4J_AUTH=neo4j/grackle-dev \\\n  -v neo4j-data:/data \\\n  neo4j:5.25.1-community\n```\n\nThis starts Neo4j with:\n\n| Port | Protocol | Purpose                                          |\n| ---- | -------- | ------------------------------------------------ |\n| 7474 | HTTP     | Neo4j Browser UI (visit `http://localhost:7474`) |\n| 7687 | Bolt     | Driver protocol (what this library connects to)  |\n\nThe default credentials used by the library in development are `neo4j` / `grackle-dev`. In production, you **must** set the password via the `GRACKLE_NEO4J_PASSWORD` environment variable -- the library refuses to use the default password when `NODE_ENV=production`.\n\n#### Docker Compose\n\nIf you are using Docker Compose, here is a service definition:\n\n```yaml\nservices:\n  neo4j:\n    image: neo4j:5.25.1-community\n    ports:\n      - \"7474:7474\"\n      - \"7687:7687\"\n    environment:\n      - NEO4J_AUTH=neo4j/grackle-dev\n    volumes:\n      - neo4j-data:/data\n    healthcheck:\n      test: [\"CMD\", \"neo4j\", \"status\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n      start_period: 30s\n\nvolumes:\n  neo4j-data:\n```\n\n#### Verifying Neo4j is Ready\n\nAfter starting Neo4j, wait for it to become healthy before connecting. The health check above handles this in Docker Compose. For standalone Docker, you can poll:\n\n```bash\n# Wait until Neo4j responds to a status check\nuntil docker exec grackle-neo4j neo4j status 2>/dev/null; do sleep 2; done\n```\n\n## Quick Start\n\n```typescript\nimport {\n  openNeo4j,\n  closeNeo4j,\n  initSchema,\n  createLocalEmbedder,\n  createPassThroughChunker,\n  ingest,\n  createNativeNode,\n  knowledgeSearch,\n  NATIVE_CATEGORY,\n} from \"@grackle-ai/knowledge-core\";\n\n// 1. Connect to Neo4j\nawait openNeo4j({ url: \"bolt://127.0.0.1:7687\" });\n\n// 2. Initialize schema (idempotent -- safe to call on every startup)\nawait initSchema();\n\n// 3. Create an embedder and chunker\nconst embedder = createLocalEmbedder();\nconst chunker = createPassThroughChunker();\n\n// 4. Ingest some content (chunk + embed in one step)\nconst [embedded] = await ingest(\n  \"WebSocket reconnection uses exponential backoff with jitter\",\n  chunker,\n  embedder,\n);\n\n// 5. Store the node in the graph\nconst nodeId = await createNativeNode({\n  category: NATIVE_CATEGORY.INSIGHT,\n  title: \"WebSocket reconnection strategy\",\n  content: \"WebSocket reconnection uses exponential backoff with jitter\",\n  tags: [\"websocket\", \"networking\"],\n  embedding: embedded.vector,\n  workspaceId: \"\",\n});\n\n// 6. Search the graph semantically\nconst results = await knowledgeSearch(\"how does reconnection work?\", embedder, {\n  limit: 5,\n  minScore: 0.5,\n});\n\nfor (const result of results) {\n  console.log(`${result.score.toFixed(3)} - ${result.node.id}`);\n}\n\n// 7. Clean up\nawait closeNeo4j();\n```\n\n## Architecture Overview\n\n```mermaid\ngraph TD\n    subgraph \"Ingestion\"\n        Content[\"Raw Content\"] --> Chunker[\"Chunker\"]\n        Chunker --> Chunks[\"Chunks\"]\n        Chunks --> Embedder[\"Embedder\"]\n        Embedder --> EmbeddedChunks[\"Embedded Chunks\"]\n    end\n\n    subgraph \"Storage (Neo4j)\"\n        EmbeddedChunks --> NodeStore[\"Node Store\"]\n        NodeStore --> KG[\"Knowledge Graph\"]\n        EdgeStore[\"Edge Store\"] --> KG\n    end\n\n    subgraph \"Retrieval\"\n        Query[\"Search Query\"] --> QueryEmbed[\"Embedder\"]\n        QueryEmbed --> VectorSearch[\"Vector Search\"]\n        VectorSearch --> KG\n        KG --> Results[\"Search Results\"]\n        Results --> Expand[\"Graph Expansion\"]\n        Expand --> KG\n    end\n```\n\nThe library is organized into three layers:\n\n1. **Ingestion** -- Content is split into chunks by a `Chunker`, then each chunk is converted to a vector embedding by an `Embedder`. The `ingest()` pipeline composes these two steps.\n\n2. **Storage** -- Nodes and edges are persisted in Neo4j. Nodes carry their embedding vectors, enabling vector similarity search via Neo4j's built-in vector index. Two node kinds exist: **reference nodes** (pointers to external entities) and **native nodes** (self-contained content).\n\n3. **Retrieval** -- Semantic search embeds a query string and runs k-NN vector search against the Neo4j vector index. Results include similarity scores and immediate edges. Graph expansion traverses relationships from result nodes to discover connected context within N hops.\n\n## Key Concepts\n\n### Node Kinds\n\nEvery node in the knowledge graph is one of two kinds, forming a discriminated union:\n\n#### Reference Nodes\n\nA **reference node** points to an entity that lives elsewhere (e.g., a task in a relational database, a session log, a finding). It does not duplicate content -- it stores a `sourceType` and `sourceId` that identify the external entity, plus a `label` for display and an `embedding` for search.\n\n```typescript\nimport { NODE_KIND, REFERENCE_SOURCE } from \"@grackle-ai/knowledge-core\";\n\n// Built-in reference source types (you can use any string):\nREFERENCE_SOURCE.TASK; // \"task\"\nREFERENCE_SOURCE.SESSION; // \"session\"\nREFERENCE_SOURCE.FINDING; // \"finding\"\nREFERENCE_SOURCE.WORKSPACE; // \"workspace\"\n\n// Custom source types are fully supported:\nconst sourceType: string = \"ado-work-item\"; // any string works\n```\n\n**`ReferenceNode` properties:**\n\n| Property      | Type          | Description                                                     |\n| ------------- | ------------- | --------------------------------------------------------------- |\n| `id`          | `string`      | UUID, auto-generated                                            |\n| `kind`        | `\"reference\"` | Discriminator                                                   |\n| `sourceType`  | `string`      | Entity type (e.g., `\"task\"`, `\"finding\"`, or any custom string) |\n| `sourceId`    | `string`      | ID of the entity in the external system                         |\n| `label`       | `string`      | Human-readable label (e.g., task title)                         |\n| `embedding`   | `number[]`    | Dense vector for similarity search                              |\n| `workspaceId` | `string`      | Workspace scope (empty string = global)                         |\n| `createdAt`   | `string`      | ISO 8601 timestamp, auto-generated                              |\n| `updatedAt`   | `string`      | ISO 8601 timestamp, auto-maintained                             |\n\n#### Native Nodes\n\nA **native node** owns its content directly. It exists only in the knowledge graph and is suitable for insights, decisions, concepts, code snippets, or any freeform knowledge.\n\n```typescript\nimport { NATIVE_CATEGORY } from \"@grackle-ai/knowledge-core\";\n\n// Built-in native categories (you can use any string):\nNATIVE_CATEGORY.DECISION; // \"decision\"\nNATIVE_CATEGORY.INSIGHT; // \"insight\"\nNATIVE_CATEGORY.CONCEPT; // \"concept\"\nNATIVE_CATEGORY.SNIPPET; // \"snippet\"\n\n// Custom categories are fully supported:\nconst category: string = \"research-note\";\n```\n\n**`NativeNode` properties:**\n\n| Property      | Type       | Description                                                         |\n| ------------- | ---------- | ------------------------------------------------------------------- |\n| `id`          | `string`   | UUID, auto-generated                                                |\n| `kind`        | `\"native\"` | Discriminator                                                       |\n| `category`    | `string`   | Subcategory (e.g., `\"decision\"`, `\"insight\"`, or any custom string) |\n| `title`       | `string`   | Title or summary                                                    |\n| `content`     | `string`   | Full content owned by this node                                     |\n| `tags`        | `string[]` | Free-form tags for categorization                                   |\n| `embedding`   | `number[]` | Dense vector for similarity search                                  |\n| `workspaceId` | `string`   | Workspace scope (empty string = global)                             |\n| `createdAt`   | `string`   | ISO 8601 timestamp, auto-generated                                  |\n| `updatedAt`   | `string`   | ISO 8601 timestamp, auto-maintained                                 |\n\n### Edge Types\n\nEdges are directional typed relationships between nodes. Five relationship types are supported:\n\n| Edge Type                | Value            | Semantics                         |\n| ------------------------ | ---------------- | --------------------------------- |\n| `EDGE_TYPE.RELATES_TO`   | `\"RELATES_TO\"`   | General association               |\n| `EDGE_TYPE.DEPENDS_ON`   | `\"DEPENDS_ON\"`   | Dependency relationship           |\n| `EDGE_TYPE.DERIVED_FROM` | `\"DERIVED_FROM\"` | One node was derived from another |\n| `EDGE_TYPE.MENTIONS`     | `\"MENTIONS\"`     | One node references another       |\n| `EDGE_TYPE.PART_OF`      | `\"PART_OF\"`      | Containment / composition         |\n\nEdges can carry optional JSON metadata (e.g., a confidence score or a context snippet):\n\n```typescript\nimport { createEdge, EDGE_TYPE } from \"@grackle-ai/knowledge-core\";\n\nawait createEdge(nodeA, nodeB, EDGE_TYPE.DEPENDS_ON, {\n  confidence: 0.92,\n  reason: \"Identified in code review\",\n});\n```\n\n**`KnowledgeEdge` properties:**\n\n| Property    | Type                                   | Description            |\n| ----------- | -------------------------------------- | ---------------------- |\n| `fromId`    | `string`                               | Source node ID         |\n| `toId`      | `string`                               | Target node ID         |\n| `type`      | `EdgeType`                             | Relationship type      |\n| `metadata`  | `Record<string, unknown> \\| undefined` | Optional JSON metadata |\n| `createdAt` | `string`                               | ISO 8601 timestamp     |\n\n### Workspace Scoping\n\nEvery node has a `workspaceId` field. Setting it to a non-empty string scopes the node to that workspace. Setting it to an empty string (`\"\"`) makes the node global. Search and listing operations accept an optional `workspaceId` filter to restrict results to a specific workspace.\n\n## API Reference\n\n### Connection Management\n\nThe library manages a **singleton Neo4j driver**. Call `openNeo4j()` once at application startup and `closeNeo4j()` on shutdown.\n\n#### `openNeo4j(config?: Neo4jClientConfig): Promise<void>`\n\nOpen a connection to Neo4j. Reads configuration from environment variables first, then from the config parameter, then from built-in defaults. Idempotent -- returns silently if already connected.\n\n```typescript\ninterface Neo4jClientConfig {\n  /** Bolt URL (default: \"bolt://127.0.0.1:7687\") */\n  url?: string;\n  /** Username (default: \"neo4j\") */\n  username?: string;\n  /** Password (default: \"grackle-dev\"; REQUIRED in production) */\n  password?: string;\n  /** Database name (default: \"neo4j\") */\n  database?: string;\n}\n```\n\nResolution order for each field: **environment variable > config parameter > default constant**.\n\n| Field      | Environment Variable     | Default                  |\n| ---------- | ------------------------ | ------------------------ |\n| `url`      | `GRACKLE_NEO4J_URL`      | `bolt://127.0.0.1:7687`  |\n| `username` | `GRACKLE_NEO4J_USER`     | `neo4j`                  |\n| `password` | `GRACKLE_NEO4J_PASSWORD` | `grackle-dev` (dev only) |\n| `database` | `GRACKLE_NEO4J_DATABASE` | `neo4j`                  |\n\n**Production safety:** When `NODE_ENV=production`, the library throws if no explicit password is provided (via env var or config). The default development password is never used in production.\n\n```typescript\n// Minimal (uses all defaults):\nawait openNeo4j();\n\n// Explicit config:\nawait openNeo4j({\n  url: \"bolt://my-neo4j-host:7687\",\n  username: \"admin\",\n  password: \"secure-password\",\n  database: \"knowledge\",\n});\n```\n\n#### `closeNeo4j(): Promise<void>`\n\nGracefully close the Neo4j connection and release resources. Safe to call multiple times or when no connection is open. Waits for any in-flight initialization to settle before closing.\n\n```typescript\nawait closeNeo4j();\n```\n\n#### `healthCheck(): Promise<boolean>`\n\nCheck Neo4j connectivity. Returns `true` if the connection is healthy, `false` otherwise (including when not initialized).\n\n```typescript\nconst isHealthy = await healthCheck();\n```\n\n#### `getSession(): Session`\n\nGet a Neo4j session for running queries. Sessions are lightweight and should be short-lived -- open one per logical unit of work and close it when done. Throws if `openNeo4j()` has not been called.\n\n```typescript\nconst session = getSession();\ntry {\n  const result = await session.run(\"MATCH (n) RETURN count(n) AS count\");\n  console.log(result.records[0].get(\"count\"));\n} finally {\n  await session.close();\n}\n```\n\n#### `getDriver(): Driver`\n\nGet the raw Neo4j `Driver` instance. Prefer `getSession()` for most use cases. Use the driver directly only for `driver.executeQuery()` one-shot queries. Throws if `openNeo4j()` has not been called.\n\n### Schema Initialization\n\n#### `initSchema(): Promise<void>`\n\nInitialize the Neo4j schema: constraints, property indexes, and the vector index. All statements use `IF NOT EXISTS` and are idempotent -- safe to call on every application startup. Must be called after `openNeo4j()`.\n\nThe following schema objects are created:\n\n| Statement         | Purpose                                                                            |\n| ----------------- | ---------------------------------------------------------------------------------- |\n| `UNIQUE_NODE_ID`  | Uniqueness constraint on `KnowledgeNode.id`                                        |\n| `INDEX_KIND`      | Index on `kind` for efficient filtering by node type                               |\n| `INDEX_WORKSPACE` | Index on `workspaceId` for scoped queries                                          |\n| `INDEX_SOURCE`    | Composite index on `(sourceType, sourceId)` for reference lookups                  |\n| `VECTOR_INDEX`    | Vector index on `embedding` for cosine similarity search (dimensions configurable) |\n\n```typescript\nconst embedder = createLocalEmbedder();\nawait openNeo4j();\nawait initSchema(embedder.dimensions); // creates a 384-dim vector index\n```\n\n#### `buildSchemaStatements(dimensions?: number): Record<string, string>`\n\nBuild the Cypher schema statements with a custom vector index dimensionality. Defaults to `EMBEDDING_DIMENSIONS` (1536) if omitted.\n\n#### `SCHEMA_STATEMENTS: Record<string, string>`\n\nThe raw Cypher statements using the default 1536 dimensions. Prefer `buildSchemaStatements(dimensions)` for custom dimensions.\n\n### Embedders\n\nAn embedder converts text into dense vector embeddings for similarity search. The library ships with a local ONNX-based embedder and defines a pluggable `Embedder` interface so you can bring your own (e.g., OpenAI, Cohere).\n\n#### `Embedder` Interface\n\n```typescript\ninterface EmbeddingResult {\n  /** The input text that was embedded. */\n  text: string;\n  /** The embedding vector. */\n  vector: number[];\n}\n\ninterface Embedder {\n  /** Embed a single text string. */\n  embed(text: string): Promise<EmbeddingResult>;\n  /** Embed multiple texts in batch. */\n  embedBatch(texts: string[]): Promise<EmbeddingResult[]>;\n  /** Dimensionality of vectors produced by this embedder. */\n  readonly dimensions: number;\n}\n```\n\n#### `createLocalEmbedder(options?: EmbedderOptions): Embedder`\n\nCreate a local embedder that runs ONNX inference via `@huggingface/transformers`. Downloads and caches a HuggingFace model on first use, then runs inference locally on CPU -- **no API keys or external services required**.\n\nThe underlying pipeline is lazily initialized on the first call to `embed()` or `embedBatch()`, so construction is non-blocking.\n\n```typescript\ninterface EmbedderOptions {\n  /** HuggingFace model ID (default: \"Xenova/all-MiniLM-L6-v2\") */\n  modelId?: string;\n  /** Expected embedding dimensions (default: 384 for the default model) */\n  dimensions?: number;\n}\n```\n\n**Default model:** [`Xenova/all-MiniLM-L6-v2`](https://huggingface.co/Xenova/all-MiniLM-L6-v2) -- a small, fast, general-purpose English sentence embedding model that produces 384-dimensional vectors.\n\n```typescript\n// Use the default model:\nconst embedder = createLocalEmbedder();\nconsole.log(embedder.dimensions); // 384\n\n// Use a custom model:\nconst customEmbedder = createLocalEmbedder({\n  modelId: \"Xenova/bge-small-en-v1.5\",\n  dimensions: 384,\n});\n```\n\n**Dimension matching:** `initSchema()` accepts an optional `dimensions` parameter that controls the vector index size. Pass your embedder's dimensions to ensure they match:\n\n```typescript\nconst embedder = createLocalEmbedder();\nawait initSchema(embedder.dimensions); // creates a 384-dim vector index\n```\n\nIf omitted, `initSchema()` defaults to `EMBEDDING_DIMENSIONS` (1536). Use `buildSchemaStatements(dimensions)` for custom dimensions when building statements manually.\n\n### Chunkers\n\nA chunker splits content into smaller pieces suitable for embedding. The library ships with two chunkers and defines a pluggable `Chunker` interface.\n\n#### `Chunker` Interface\n\n```typescript\ninterface Chunk {\n  /** The text content of this chunk. */\n  text: string;\n  /** Zero-based index within the source content. */\n  index: number;\n  /** Optional metadata carried through from the source. */\n  metadata?: Record<string, unknown>;\n}\n\ninterface Chunker {\n  /** Split content into one or more chunks. */\n  chunk(content: string, metadata?: Record<string, unknown>): Chunk[];\n}\n```\n\n#### `createPassThroughChunker(): Chunker`\n\nCreate a chunker that returns the entire input as a single chunk unchanged. Suitable for short content (findings, decisions, insights) that does not need splitting.\n\n```typescript\nconst chunker = createPassThroughChunker();\nconst chunks = chunker.chunk(\"A short insight about architecture.\");\n// => [{ text: \"A short insight about architecture.\", index: 0 }]\n```\n\n#### `createTranscriptChunker(options?: TranscriptChunkerOptions): Chunker`\n\nCreate a chunker that splits JSONL session transcripts by conversation turn. Designed for Grackle's session `stream.jsonl` logs, but usable with any JSONL content that follows the log entry format.\n\nA \"turn\" starts at a `user_input` event and includes all subsequent events until the next `user_input`. Events before the first `user_input` are grouped into turn 0. Turns exceeding `maxChunkSize` are split into sub-chunks at line boundaries.\n\n```typescript\ninterface TranscriptChunkerOptions {\n  /** Max characters per chunk before splitting (default: 4000). */\n  maxChunkSize?: number;\n  /** Event types to skip (default: [\"status\", \"signal\", \"usage\", \"system\"]). */\n  skipEventTypes?: string[];\n}\n```\n\nEach chunk's `metadata` includes:\n\n| Field            | Type       | Description                                       |\n| ---------------- | ---------- | ------------------------------------------------- |\n| `turnIndex`      | `number`   | Zero-based turn number                            |\n| `timestampStart` | `string`   | ISO 8601 timestamp of the first event in the turn |\n| `timestampEnd`   | `string`   | ISO 8601 timestamp of the last event in the turn  |\n| `eventTypes`     | `string[]` | Distinct event types present in the turn          |\n\nEvents are rendered with human-readable labels: `user_input` becomes `\"User:\"`, `text` becomes `\"Assistant:\"`, `tool_use` becomes `\"Tool:\"`, `tool_result` becomes `\"Result:\"`, `error` becomes `\"Error:\"`, `finding` becomes `\"Finding:\"`, and `subtask_create` becomes `\"Subtask:\"`.\n\n```typescript\nconst chunker = createTranscriptChunker({ maxChunkSize: 2000 });\n\nconst jsonl = [\n  '{\"session_id\":\"s1\",\"type\":\"user_input\",\"timestamp\":\"2026-01-01T10:00:00Z\",\"content\":\"Fix the login bug\"}',\n  '{\"session_id\":\"s1\",\"type\":\"text\",\"timestamp\":\"2026-01-01T10:00:05Z\",\"content\":\"I found the issue in auth.ts...\"}',\n  '{\"session_id\":\"s1\",\"type\":\"user_input\",\"timestamp\":\"2026-01-01T10:01:00Z\",\"content\":\"Now write tests\"}',\n  '{\"session_id\":\"s1\",\"type\":\"text\",\"timestamp\":\"2026-01-01T10:01:10Z\",\"content\":\"Here are the test cases...\"}',\n].join(\"\\n\");\n\nconst chunks = chunker.chunk(jsonl, { sessionId: \"s1\" });\n// => 2 chunks, one per conversation turn\n```\n\n### Ingestion Pipeline\n\n#### `ingest(content, chunker, embedder, metadata?): Promise<EmbeddedChunk[]>`\n\nChunk content and embed each chunk in a single pipeline. This is the primary entry point for getting content into a form ready for storage.\n\n```typescript\ninterface EmbeddedChunk extends Chunk {\n  /** The embedding vector for this chunk's text. */\n  vector: number[];\n}\n```\n\n| Parameter  | Type                      | Description                             |\n| ---------- | ------------------------- | --------------------------------------- |\n| `content`  | `string`                  | Raw text content to ingest              |\n| `chunker`  | `Chunker`                 | Splits content into chunks              |\n| `embedder` | `Embedder`                | Produces embedding vectors              |\n| `metadata` | `Record<string, unknown>` | Optional metadata passed to the chunker |\n\nReturns an array of chunks, each augmented with its embedding vector. Returns an empty array if the chunker produces no chunks.\n\n```typescript\nconst embedder = createLocalEmbedder();\nconst chunker = createPassThroughChunker();\n\nconst embeddedChunks = await ingest(\n  \"We decided to use Neo4j for the knowledge graph because of its native vector index support.\",\n  chunker,\n  embedder,\n  { source: \"architecture-decision\" },\n);\n\n// Each chunk now has a .vector property ready for createNativeNode() or createReferenceNode()\nfor (const chunk of embeddedChunks) {\n  await createNativeNode({\n    category: \"decision\",\n    title: \"Use Neo4j for knowledge graph\",\n    content: chunk.text,\n    tags: [\"architecture\", \"database\"],\n    embedding: chunk.vector,\n    workspaceId: \"my-workspace\",\n  });\n}\n```\n\n### Node Operations\n\nAll node operations use short-lived Neo4j sessions that are automatically closed after each operation.\n\n#### `createReferenceNode(input: CreateReferenceNodeInput): Promise<string>`\n\nCreate a reference node. Generates a UUID and timestamps automatically. Returns the new node's ID.\n\n```typescript\ninterface CreateReferenceNodeInput {\n  sourceType: string; // e.g., \"task\", \"finding\", or any custom type\n  sourceId: string; // ID in the external system\n  label: string; // Human-readable label\n  embedding: number[]; // Vector embedding\n  workspaceId: string; // Workspace scope (\"\" = global)\n}\n```\n\n```typescript\nconst nodeId = await createReferenceNode({\n  sourceType: REFERENCE_SOURCE.TASK,\n  sourceId: \"task-42\",\n  label: \"Fix authentication middleware\",\n  embedding: (await embedder.embed(\"Fix authentication middleware\")).vector,\n  workspaceId: \"workspace-1\",\n});\n```\n\n#### `createNativeNode(input: CreateNativeNodeInput): Promise<string>`\n\nCreate a native node. Generates a UUID and timestamps automatically. Returns the new node's ID.\n\n```typescript\ninterface CreateNativeNodeInput {\n  category: string; // e.g., \"decision\", \"insight\", or any custom category\n  title: string; // Title or summary\n  content: string; // Full content\n  tags: string[]; // Free-form tags\n  embedding: number[]; // Vector embedding\n  workspaceId: string; // Workspace scope (\"\" = global)\n}\n```\n\n```typescript\nconst nodeId = await createNativeNode({\n  category: NATIVE_CATEGORY.DECISION,\n  title: \"Use Neo4j for knowledge graph\",\n  content: \"We chose Neo4j because of native vector index support and Cypher query language.\",\n  tags: [\"architecture\", \"database\"],\n  embedding: (await embedder.embed(\"Use Neo4j for knowledge graph\")).vector,\n  workspaceId: \"\",\n});\n```\n\n#### `getNode(id: string): Promise<NodeWithEdges | undefined>`\n\nGet a node by ID, including all its edges (both incoming and outgoing). Returns `undefined` if the node does not exist.\n\n```typescript\ninterface NodeWithEdges {\n  node: KnowledgeNode; // The node (ReferenceNode | NativeNode)\n  edges: KnowledgeEdge[]; // All connected edges\n}\n```\n\n```typescript\nconst result = await getNode(\"some-uuid\");\nif (result) {\n  console.log(result.node.kind); // \"reference\" or \"native\"\n  console.log(result.edges.length);\n}\n```\n\n#### `updateNode(id: string, updates: UpdateNodeInput): Promise<KnowledgeNode | undefined>`\n\nUpdate a node's mutable properties. Cannot change `kind`, `id`, `createdAt`, or `workspaceId`. Automatically updates the `updatedAt` timestamp. Returns the updated node, or `undefined` if the node was not found.\n\n```typescript\n// For reference nodes:\ninterface UpdateReferenceNodeInput {\n  label?: string;\n  sourceId?: string;\n  embedding?: number[];\n}\n\n// For native nodes:\ninterface UpdateNativeNodeInput {\n  title?: string;\n  content?: string;\n  tags?: string[];\n  embedding?: number[];\n}\n```\n\n```typescript\nconst updated = await updateNode(\"some-uuid\", {\n  title: \"Updated title\",\n  tags: [\"new-tag\"],\n});\n```\n\n#### `deleteNode(id: string): Promise<boolean>`\n\nDelete a node and all its edges (`DETACH DELETE`). Returns `true` if a node was deleted, `false` if the node was not found.\n\n```typescript\nconst wasDeleted = await deleteNode(\"some-uuid\");\n```\n\n#### `recordToNode(properties: Record<string, unknown>): KnowledgeNode`\n\nConvert raw Neo4j node properties to a typed `KnowledgeNode`. Handles the discriminated union based on the `kind` property. Useful when running custom Cypher queries.\n\n#### `recordToEdge(raw: Record<string, unknown>): KnowledgeEdge`\n\nConvert a raw edge object from Cypher results to a typed `KnowledgeEdge`. Parses JSON metadata. Useful when running custom Cypher queries.\n\n### Edge Operations\n\n#### `createEdge(fromId, toId, type, metadata?): Promise<KnowledgeEdge>`\n\nCreate a directed, typed edge between two nodes.\n\n| Parameter  | Type                      | Description                                                              |\n| ---------- | ------------------------- | ------------------------------------------------------------------------ |\n| `fromId`   | `string`                  | Source node ID                                                           |\n| `toId`     | `string`                  | Target node ID                                                           |\n| `type`     | `EdgeType`                | One of `RELATES_TO`, `DEPENDS_ON`, `DERIVED_FROM`, `MENTIONS`, `PART_OF` |\n| `metadata` | `Record<string, unknown>` | Optional metadata stored as JSON on the relationship                     |\n\nThrows if either node does not exist or if the edge type is invalid.\n\n```typescript\nconst edge = await createEdge(nodeIdA, nodeIdB, EDGE_TYPE.DERIVED_FROM, { confidence: 0.95 });\n```\n\n#### `removeEdge(fromId, toId, type): Promise<boolean>`\n\nRemove a directed edge between two nodes. Returns `true` if an edge was removed, `false` if no matching edge existed. Throws if the edge type is invalid.\n\n```typescript\nconst wasRemoved = await removeEdge(nodeIdA, nodeIdB, EDGE_TYPE.DERIVED_FROM);\n```\n\n### Semantic Search\n\n#### `knowledgeSearch(query, embedder, options?): Promise<SearchResult[]>`\n\nSearch the knowledge graph by semantic similarity. Embeds the query text, runs k-NN vector search against Neo4j's vector index, and returns ranked results with similarity scores and immediate edges.\n\n```typescript\ninterface SearchOptions {\n  /** Max results to return (default: 10). */\n  limit?: number;\n  /** Filter to specific node kinds (\"reference\", \"native\", or both). */\n  nodeKinds?: NodeKind[];\n  /** Minimum similarity score threshold (default: 0). */\n  minScore?: number;\n  /** Filter to a specific workspace. */\n  workspaceId?: string;\n}\n\ninterface SearchResult {\n  /** The matched node. */\n  node: KnowledgeNode;\n  /** Cosine similarity score (0-1). */\n  score: number;\n  /** Edges connected to this node. */\n  edges: KnowledgeEdge[];\n}\n```\n\nWhen filters (`nodeKinds`, `workspaceId`) are applied, the library automatically over-fetches candidates (3x the limit) to compensate for post-filtering, ensuring you still get enough results.\n\n```typescript\n// Basic search\nconst results = await knowledgeSearch(\"authentication middleware\", embedder);\n\n// Filtered search\nconst results = await knowledgeSearch(\"authentication\", embedder, {\n  limit: 5,\n  minScore: 0.7,\n  nodeKinds: [\"native\"],\n  workspaceId: \"workspace-1\",\n});\n\nfor (const { node, score, edges } of results) {\n  console.log(`[${score.toFixed(3)}] ${node.id}`);\n  if (node.kind === \"native\") {\n    console.log(`  Title: ${node.title}`);\n  }\n  console.log(`  Edges: ${edges.length}`);\n}\n```\n\n### Graph Expansion\n\nGraph expansion traverses relationships from a starting node to discover connected context. This is useful for enriching search results with related knowledge.\n\n#### `expandNode(nodeId, options?): Promise<ExpansionResult>`\n\nExpand a single node: return its neighbors within N hops.\n\n```typescript\ninterface ExpandOptions {\n  /** Max traversal depth (default: 1). */\n  depth?: number;\n  /** Only follow edges of these types. If empty/undefined, follow all. */\n  edgeTypes?: EdgeType[];\n}\n\ninterface ExpansionResult {\n  /** All neighbor nodes found (deduplicated), excluding the start node. */\n  nodes: KnowledgeNode[];\n  /** All edges traversed. */\n  edges: KnowledgeEdge[];\n}\n```\n\n```typescript\n// Get immediate neighbors\nconst { nodes, edges } = await expandNode(\"some-uuid\");\n\n// Multi-hop expansion, following only specific edge types\nconst { nodes, edges } = await expandNode(\"some-uuid\", {\n  depth: 3,\n  edgeTypes: [EDGE_TYPE.DEPENDS_ON, EDGE_TYPE.PART_OF],\n});\n```\n\n#### `expandResults(results, options?): Promise<ExpansionResult>`\n\nExpand all nodes from search results and merge the subgraphs. Runs `expandNode()` for each search result and deduplicates nodes and edges. The original search result nodes are excluded from the neighbor list.\n\n```typescript\nconst searchResults = await knowledgeSearch(\"authentication\", embedder);\nconst { nodes: neighbors, edges } = await expandResults(searchResults, { depth: 2 });\n\n// neighbors contains nodes connected to the search results (but not the results themselves)\n```\n\n### Listing Recent Nodes\n\n#### `listRecentNodes(limit?, workspaceId?): Promise<RecentNodesResult>`\n\nList the most recently updated knowledge graph nodes. Returns nodes ordered by `updatedAt` descending, along with all edges between the returned nodes. Used by explorer/dashboard UIs.\n\n```typescript\ninterface RecentNodesResult {\n  /** The most recently updated nodes. */\n  nodes: KnowledgeNode[];\n  /** All edges between the returned nodes. */\n  edges: KnowledgeEdge[];\n}\n```\n\n| Parameter     | Type     | Default     | Description                       |\n| ------------- | -------- | ----------- | --------------------------------- |\n| `limit`       | `number` | `20`        | Maximum number of nodes to return |\n| `workspaceId` | `string` | `undefined` | Optional workspace filter         |\n\n```typescript\n// Global recent nodes\nconst { nodes, edges } = await listRecentNodes(10);\n\n// Workspace-scoped recent nodes\nconst { nodes, edges } = await listRecentNodes(20, \"workspace-1\");\n```\n\n### Type Guards\n\n#### `isReferenceNode(node: KnowledgeNode): node is ReferenceNode`\n\nReturns `true` if the node is a reference node. Narrows the type for TypeScript.\n\n#### `isNativeNode(node: KnowledgeNode): node is NativeNode`\n\nReturns `true` if the node is a native node. Narrows the type for TypeScript.\n\n```typescript\nimport { isReferenceNode, isNativeNode } from \"@grackle-ai/knowledge-core\";\n\nconst result = await getNode(\"some-uuid\");\nif (result) {\n  if (isReferenceNode(result.node)) {\n    console.log(result.node.sourceType, result.node.sourceId);\n  }\n  if (isNativeNode(result.node)) {\n    console.log(result.node.title, result.node.content);\n  }\n}\n```\n\n### Logging\n\nThe library uses [pino](https://getpino.io/) for structured logging. The logger instance is exported if you need to adjust settings or pipe logs.\n\n```typescript\nimport { logger } from \"@grackle-ai/knowledge-core\";\n\n// Logger name: \"grackle-knowledge\"\n// Default level: \"info\" (override with LOG_LEVEL env var)\n```\n\n## Configuration\n\n### Environment Variables\n\n| Variable                 | Purpose                                                             | Default                 |\n| ------------------------ | ------------------------------------------------------------------- | ----------------------- |\n| `GRACKLE_NEO4J_URL`      | Neo4j Bolt connection URL                                           | `bolt://127.0.0.1:7687` |\n| `GRACKLE_NEO4J_USER`     | Neo4j username                                                      | `neo4j`                 |\n| `GRACKLE_NEO4J_PASSWORD` | Neo4j password (**required** in production)                         | `grackle-dev`           |\n| `GRACKLE_NEO4J_DATABASE` | Neo4j database name                                                 | `neo4j`                 |\n| `NODE_ENV`               | When `\"production\"`, requires explicit password                     | --                      |\n| `LOG_LEVEL`              | Pino log level (`trace`, `debug`, `info`, `warn`, `error`, `fatal`) | `info`                  |\n\n### Constants\n\nThese are exported for reference and customization. They control the Neo4j connection pool and the vector index configuration.\n\n| Constant                               | Value                         | Description                                      |\n| -------------------------------------- | ----------------------------- | ------------------------------------------------ |\n| `DEFAULT_NEO4J_URL`                    | `\"bolt://127.0.0.1:7687\"`     | Default Bolt URL                                 |\n| `DEFAULT_NEO4J_USER`                   | `\"neo4j\"`                     | Default username                                 |\n| `DEFAULT_NEO4J_DATABASE`               | `\"neo4j\"`                     | Default database name                            |\n| `NEO4J_MAX_POOL_SIZE`                  | `50`                          | Max connections in the Neo4j driver pool         |\n| `NEO4J_CONNECTION_ACQUISITION_TIMEOUT` | `30000`                       | Timeout (ms) for acquiring a pool connection     |\n| `NODE_LABEL`                           | `\"KnowledgeNode\"`             | Neo4j label applied to all knowledge graph nodes |\n| `VECTOR_INDEX_NAME`                    | `\"knowledge_embedding_index\"` | Name of the vector index                         |\n| `EMBEDDING_DIMENSIONS`                 | `1536`                        | Dimensionality of the vector index               |\n| `VECTOR_SIMILARITY_FUNCTION`           | `\"cosine\"`                    | Similarity function used by the vector index     |\n\n## Custom Embedders and Chunkers\n\nThe `Embedder` and `Chunker` interfaces are intentionally minimal so you can plug in any implementation.\n\n### Custom Embedder Example (OpenAI)\n\n```typescript\nimport type { Embedder, EmbeddingResult } from \"@grackle-ai/knowledge-core\";\n\nfunction createOpenAIEmbedder(apiKey: string): Embedder {\n  return {\n    dimensions: 1536,\n\n    async embed(text: string): Promise<EmbeddingResult> {\n      const response = await fetch(\"https://api.openai.com/v1/embeddings\", {\n        method: \"POST\",\n        headers: {\n          Authorization: `Bearer ${apiKey}`,\n          \"Content-Type\": \"application/json\",\n        },\n        body: JSON.stringify({\n          model: \"text-embedding-3-small\",\n          input: text,\n        }),\n      });\n      const data = await response.json();\n      return { text, vector: data.data[0].embedding };\n    },\n\n    async embedBatch(texts: string[]): Promise<EmbeddingResult[]> {\n      const response = await fetch(\"https://api.openai.com/v1/embeddings\", {\n        method: \"POST\",\n        headers: {\n          Authorization: `Bearer ${apiKey}`,\n          \"Content-Type\": \"application/json\",\n        },\n        body: JSON.stringify({\n          model: \"text-embedding-3-small\",\n          input: texts,\n        }),\n      });\n      const data = await response.json();\n      return data.data.map((d: { embedding: number[] }, i: number) => ({\n        text: texts[i],\n        vector: d.embedding,\n      }));\n    },\n  };\n}\n```\n\n### Custom Chunker Example (Fixed-Size)\n\n```typescript\nimport type { Chunker, Chunk } from \"@grackle-ai/knowledge-core\";\n\nfunction createFixedSizeChunker(maxChars: number = 1000): Chunker {\n  return {\n    chunk(content: string, metadata?: Record<string, unknown>): Chunk[] {\n      const chunks: Chunk[] = [];\n      for (let i = 0; i < content.length; i += maxChars) {\n        chunks.push({\n          text: content.slice(i, i + maxChars),\n          index: chunks.length,\n          metadata,\n        });\n      }\n      return chunks.length > 0 ? chunks : [{ text: content, index: 0, metadata }];\n    },\n  };\n}\n```\n\n## License\n\nMIT\n","readmeFilename":"README.md"}