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TypeScript framework for building hybrid GraphRAG applications using SAP HANA Cloud as the unified backend for knowledge graphs (RDF) and vector embeddings","maintainers":[{"name":"skye0402","email":"skyegu75@gmail.com"}],"readme":"# hana-kgvector\n\nA TypeScript framework for building **hybrid GraphRAG** applications using SAP HANA Cloud as the unified backend for knowledge graphs (RDF) and vector embeddings.\n\n![hana-kgvector](doc-assets/hana-kg-vector-title.png)\n**In a Nutshell:** Think of `hana-kgvector` as a super-smart librarian cat. It uses SAP HANA as a giant brain that stores data in two ways: a messy pile of \"fuzzy ideas\" (Vectors) and a neat corkboard of \"connected facts\" (Knowledge Graph). When you ask a question, it checks both the fuzzy pile and the neat board to sew together the perfect answer.\n\n## Features\n\n* **Unified Storage**: SAP HANA Cloud for both RDF triples (Knowledge Graph Engine) and vector embeddings (Vector Engine)\n \n* **Hybrid Retrieval**: Combine vector similarity search (for vague semantic matches) with graph traversal (for precise factual connections)\n \n* **Multimodal RAG Support**: Index mixed-media documents. Retrieve images or diagrams based on the semantic relevance of their surrounding text by linking them structurally in the graph.\n \n* **PropertyGraphIndex**: LlamaIndex-inspired API for building and querying property graphs\n \n* **Schema-Guided Extraction**: Extract entities and relations from documents using LLMs based on strict rules\n \n* **Multi-Tenancy**: Isolate data using separate graph names for different domains\n \n* **LLM Agnostic**: Works with any LLM via LiteLLM proxy (OpenAI, Anthropic, Azure, etc.)\n\n> πŸ“š **New to hana-kgvector?** Check out the [Step-by-Step Tutorial](./TUTORIAL.md) for a complete guide.\n\n> πŸš€ **Ready for real-world examples?** See the [hana-kgvector-examples](https://github.com/skye0402/hana-kgvector-examples) repository for:\n> - **Multi-Document Chat** - Full-featured Q&A with image processing and cross-document queries\n> - **Graph Visualizer** - Interactive web UI to explore your knowledge graph\n> - **PDF Chat** - Simple single-document example to get started\n\n## Installation\n\n```bash\npnpm add hana-kgvector\n# or\nnpm install hana-kgvector\n```\n\n## Quick Start\n\n### 1. Setup Environment\n\nCreate a `.env.local` file:\n\n```env\n# SAP HANA Cloud\nHANA_HOST=your-hana-instance.hanacloud.ondemand.com:443\nHANA_USER=your_user\nHANA_PASSWORD=your_password\n\n# LiteLLM Proxy\nLITELLM_PROXY_URL=http://localhost:4000\nLITELLM_API_KEY=your_key\n\n# Models\nDEFAULT_LLM_MODEL=gpt-4o-mini\nDEFAULT_EMBEDDING_MODEL=text-embedding-3-small\n```\n\n### 2. Create a PropertyGraphIndex\n\n```typescript\nimport {\n createHanaConnection,\n HanaPropertyGraphStore,\n PropertyGraphIndex,\n SchemaLLMPathExtractor,\n ImplicitPathExtractor,\n} from \"hana-kgvector\";\nimport OpenAI from \"openai\";\n\n// Load environment variables (user should handle this in their application)\n// Example: dotenv.config({ path: \".env.local\" });\n\n// Connect to HANA\nconst conn = await createHanaConnection({\n host: process.env.HANA_HOST!,\n user: process.env.HANA_USER!,\n password: process.env.HANA_PASSWORD!,\n});\n\n// Create OpenAI client (via LiteLLM)\nconst openai = new OpenAI({\n apiKey: process.env.LITELLM_API_KEY,\n baseURL: process.env.LITELLM_PROXY_URL,\n});\n\n// Create embed model adapter\nconst embedModel = {\n async getTextEmbedding(text: string) {\n const res = await openai.embeddings.create({\n model: process.env.DEFAULT_EMBEDDING_MODEL ?? \"text-embedding-3-small\",\n input: text,\n encoding_format: \"base64\", // Required for some LiteLLM proxy configurations\n });\n return res.data[0].embedding;\n },\n async getTextEmbeddingBatch(texts: string[]) {\n if (texts.length === 0) return [];\n const res = await openai.embeddings.create({\n model: process.env.DEFAULT_EMBEDDING_MODEL ?? \"text-embedding-3-small\",\n input: texts,\n encoding_format: \"base64\",\n });\n return res.data.map((d) => d.embedding);\n },\n};\n\n// Create LLM client adapter\nconst llmClient = {\n async structuredPredict(schema: any, prompt: string): Promise {\n const res = await openai.chat.completions.create({\n model: process.env.DEFAULT_LLM_MODEL ?? \"gpt-4o-mini\",\n messages: [{ role: \"user\", content: prompt }],\n response_format: { type: \"json_object\" },\n });\n let content = res.choices[0]?.message?.content ?? \"{}\";\n // Strip markdown code blocks if present (some LLMs wrap JSON in ```json...```)\n content = content.replace(/^```(?:json)?\\s*\\n?/i, \"\").replace(/\\n?```\\s*$/i, \"\").trim();\n return JSON.parse(content);\n },\n};\n\n// Create HANA-backed graph store\nconst graphStore = new HanaPropertyGraphStore(conn, {\n graphName: \"my_knowledge_graph\", // RDF named graph identifier\n // vectorDimension is auto-detected from first embedding\n});\n\n// Create PropertyGraphIndex with extractors\nconst index = new PropertyGraphIndex({\n propertyGraphStore: graphStore,\n embedModel,\n kgExtractors: [\n new SchemaLLMPathExtractor({\n llm: llmClient,\n schema: {\n entityTypes: [\"PERSON\", \"ORGANIZATION\", \"LOCATION\", \"PRODUCT\"],\n relationTypes: [\"WORKS_AT\", \"LOCATED_IN\", \"PRODUCES\", \"KNOWS\"],\n validationSchema: [\n [\"PERSON\", \"WORKS_AT\", \"ORGANIZATION\"],\n [\"PERSON\", \"KNOWS\", \"PERSON\"],\n [\"ORGANIZATION\", \"LOCATED_IN\", \"LOCATION\"],\n [\"ORGANIZATION\", \"PRODUCES\", \"PRODUCT\"],\n ],\n },\n }),\n new ImplicitPathExtractor(),\n ],\n embedKgNodes: true,\n});\n```\n\n### 3. Insert Documents\n\n```typescript\nawait index.insert([\n {\n id: \"doc_1\",\n text: \"Alice works at SAP in Walldorf. She collaborates with Bob.\",\n metadata: { documentId: \"company_info\" },\n },\n {\n id: \"doc_2\", \n text: \"SAP produces enterprise software and is headquartered in Germany.\",\n metadata: { documentId: \"company_info\" },\n },\n]);\n```\n\n### 4. Query the Graph\n\n```typescript\n// Simple query\nconst results = await index.query(\"Who works at SAP?\");\n\nfor (const result of results) {\n console.log(`[${result.score.toFixed(3)}] ${result.node.text}`);\n}\n\n// Advanced: Use retriever directly\nimport { VectorContextRetriever } from \"hana-kgvector\";\n\nconst retriever = new VectorContextRetriever({\n graphStore,\n embedModel,\n similarityTopK: 5,\n pathDepth: 2, // Traverse 2 hops from matched nodes\n});\n\nconst nodes = await retriever.retrieve({ queryStr: \"SAP employees\" });\n```\n\n## Architecture\n\n```\nβ”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”\nβ”‚ hana-kgvector β”‚\nβ”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€\nβ”‚ β”‚\nβ”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚\nβ”‚ β”‚ PropertyGraphIndex β”‚ β”‚ Extractors β”‚ β”‚ Retrievers β”‚ β”‚\nβ”‚ β”‚ - insert() β”‚ β”‚ - SchemaLLM β”‚ β”‚ - Vector β”‚ β”‚\nβ”‚ β”‚ - query() β”‚ β”‚ - Implicit β”‚ β”‚ - PGRetriever β”‚ β”‚\nβ”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚\nβ”‚ β”‚ β”‚\nβ”‚ β–Ό β”‚\nβ”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚\nβ”‚ β”‚ HanaPropertyGraphStore β”‚ β”‚\nβ”‚ β”‚ - upsertNodes() - vectorQuery() - getRelMap() β”‚ β”‚\nβ”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚\nβ”‚ β”‚ β”‚\nβ”‚ β–Ό β”‚\nβ”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚\nβ”‚ β”‚ HANA Vector Engine β”‚ β”‚ HANA KG Engine β”‚ β”‚\nβ”‚ β”‚ (REAL_VECTOR) β”‚ β”‚ (SPARQL_EXECUTE) β”‚ β”‚\nβ”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚\nβ”‚ β”‚\nβ””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜\n```\n\n## Core Components\n\n### HANA-Native GraphRAG APIs\n\nFor production-style HANA multimodel applications, prefer the HANA-native APIs. They keep vectors in a dedicated chunk table and use the HANA Knowledge Graph Engine as the source of truth for relationships.\n\n```typescript\nimport {\n HanaNativeVectorStore,\n HanaNativeKnowledgeGraphStore,\n HanaNativeGraphRagIndex,\n} from \"hana-kgvector\";\n\nconst vectorStore = new HanaNativeVectorStore(conn, { graphName: \"abap_ext_demo\" });\nconst knowledgeGraphStore = new HanaNativeKnowledgeGraphStore(conn, { graphName: \"abap_ext_demo\" });\n\nconst index = new HanaNativeGraphRagIndex({\n vectorStore,\n knowledgeGraphStore,\n embedModel, // any SDK/provider that implements getTextEmbedding + getTextEmbeddingBatch\n});\n\nawait index.insertGraph({ entities, relations, chunks });\nconst results = await index.query(\"Which reports read MARA?\", { pathDepth: 2 });\n```\n\nThe native APIs are LLM/SDK agnostic. Applications provide embeddings through the same minimal `EmbedModel` adapter used by the legacy API.\n\n### PropertyGraphIndex\n\nMain entry point for building and querying knowledge graphs.\n\n```typescript\nconst index = new PropertyGraphIndex({\n propertyGraphStore: graphStore, // Required: HANA-backed store\n embedModel, // Optional: for vector search\n kgExtractors: [...], // Optional: extraction pipeline\n embedKgNodes: true, // Embed entity nodes\n});\n```\n\n### HanaPropertyGraphStore\n\nCompatibility implementation of the PropertyGraphStore interface. It remains supported for existing applications and examples.\n\n```typescript\nconst store = new HanaPropertyGraphStore(conn, {\n graphName: \"my_graph\", // RDF named graph identifier\n vectorTableName: \"MY_VECTORS\", // Optional: custom table name\n // vectorDimension auto-detected from embeddings (supports 1536, 3072, etc.)\n});\n```\n\n### Extractors\n\nTransform text nodes into entities and relations.\n\n| Extractor | Description |\n|-----------|-------------|\n| `SchemaLLMPathExtractor` | Schema-guided extraction with LLM |\n| `ImplicitPathExtractor` | Extract structure-based relations (CHUNK β†’ DOCUMENT) |\n| `AdjacencyLinker` | Create structural edges between adjacent chunks (same page, sequential) |\n\n### Retrievers\n\nRetrieve relevant context from the graph.\n\n| Retriever | Description |\n|-----------|-------------|\n| `VectorContextRetriever` | Vector similarity β†’ graph traversal |\n| `PGRetriever` | Orchestrates multiple sub-retrievers |\n\n## Configuration Reference\n\n### HanaPropertyGraphStore Options\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `graphName` | `string` | Required | RDF named graph identifier (e.g., `\"my_knowledge_graph\"`) |\n| `vectorTableName` | `string` | Auto-generated | Custom table name for vector storage |\n| `documentNodesTableName` | `string` | Auto-generated | Custom table name for document nodes |\n| `sqlBatchSize` | `number` | `500` | Prepared statement batch size for vector/document table writes. Can also be set with `HANA_KGVECTOR_SQL_BATCH_SIZE` |\n| `resetTables` | `boolean` | `false` | Drop and recreate tables on init (dev/test only) |\n\n### Graph Discovery\n\nIf you're using a shared HANA schema (e.g. for demos or multiple apps), you can discover existing graphs created with hana-kgvector's table naming conventions:\n\n```typescript\nimport { createHanaConnection, listGraphs, getGraphTables } from \"hana-kgvector\";\n\nconst conn = await createHanaConnection({\n host: process.env.HANA_HOST!,\n port: parseInt(process.env.HANA_PORT || \"443\"),\n user: process.env.HANA_USER!,\n password: process.env.HANA_PASSWORD!,\n});\n\nconst graphs = await listGraphs(conn, {\n // schema: \"MY_SCHEMA\", // optional (defaults to CURRENT_SCHEMA)\n // includeCounts: true, // optional (row counts; can be expensive)\n require: [\"VECTORS\", \"NODES\"], // optional filter\n});\n\nfor (const g of graphs) {\n console.log(g.graphName, g.hasVectors, g.hasNodes, g.hasImages);\n console.log(getGraphTables(g.graphName));\n}\n```\n\n### PropertyGraphIndex Options\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `propertyGraphStore` | `PropertyGraphStore` | Required | HANA-backed graph store instance |\n| `embedModel` | `EmbedModel` | - | Embedding model for vector search |\n| `kgExtractors` | `TransformComponent[]` | `[ImplicitPathExtractor]` | Pipeline of entity/relation extractors. Pass `[]` for embed-only mode (vector search without KG extraction). |\n| `embedKgNodes` | `boolean` | `true` | Generate embeddings for extracted entity nodes |\n| `showProgress` | `boolean` | `false` | Log progress during extraction |\n| `maxEmbedTokens` | `number` | `8000` | Max tokens per text sent to the embedding model. Texts exceeding this are truncated using tiktoken (`cl100k_base` encoding). Default includes a safety buffer for 8192-token models like `text-embedding-3-small`. |\n\n### Query/Retrieval Options\n\nThese options can be passed to `index.query()` or `index.asRetriever()`:\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `similarityTopK` | `number` | `4` | Number of top similar nodes to retrieve via vector search |\n| `pathDepth` | `number` | `1` | Graph traversal depth (hops) from matched nodes |\n| `limit` | `number` | `30` | Maximum triplets/results to return after graph expansion |\n| `similarityScore` | `number` | - | Minimum similarity threshold (0.0-1.0) to filter results |\n| `crossCheckBoost` | `boolean` | `true` | Enable cross-check boosting (see below) |\n| `crossCheckBoostFactor` | `number` | `1.25` | Score multiplier for cross-check matches |\n| `includeStructuralEdges` | `boolean` | `true` | Traverse structural adjacency edges (ON_SAME_PAGE, ADJACENT_TO) |\n| `structuralDepth` | `number` | `1` | Depth for structural edge traversal |\n\n**Example:**\n\n```typescript\n// Retrieve more results with deeper graph traversal\nconst results = await index.query(\"Tech companies in California\", {\n similarityTopK: 10, // More initial matches\n pathDepth: 2, // Traverse 2 hops\n limit: 50, // Return up to 50 results\n similarityScore: 0.5, // Only results with score >= 0.5\n crossCheckBoost: true, // Enable provenance-based boosting\n});\n```\n\n### Cross-Check Boosting\n\nCross-check boosting is an advanced retrieval feature that improves result quality by combining vector similarity with graph provenance:\n\n1. **Vector search** finds semantically similar entity nodes\n2. **Graph traversal** expands to find related facts/triplets\n3. **Cross-check**: If a graph fact originated from the same document as a vector-matched entity, its score is boosted\n\nThis rewards results that are **both semantically relevant AND have explicit graph connections**, improving precision for complex queries.\n\n```typescript\n// Disable cross-check boosting for raw vector scores\nconst results = await index.query(\"Apple CEO\", {\n crossCheckBoost: false,\n});\n\n// Increase boost factor for stronger provenance preference\nconst results = await index.query(\"Apple CEO\", {\n crossCheckBoostFactor: 1.5, // 50% boost instead of default 25%\n});\n```\n\n### SchemaLLMPathExtractor Options\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `llm` | `LLMClient` | Required | LLM client for entity extraction. Uses the `Parseable` interface (`{ parse(data: unknown): T }`) β€” compatible with both Zod 3 and Zod 4. |\n| `schema.entityTypes` | `string[]` | Required | Allowed entity types (e.g., `[\"PERSON\", \"ORG\"]`) |\n| `schema.relationTypes` | `string[]` | Required | Allowed relation types (e.g., `[\"WORKS_AT\"]`) |\n| `schema.validationSchema` | `[string,string,string][]` | - | Valid triplet patterns (e.g., `[\"PERSON\", \"WORKS_AT\", \"ORG\"]`) |\n| `maxTripletsPerChunk` | `number` | `10` | Max entities/relations to extract per document |\n| `strict` | `boolean` | `true` | Only allow relations defined in validationSchema |\n| `extractPromptTemplate` | `string` | Built-in | Custom prompt template for extraction |\n\n### VectorContextRetriever Options\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `graphStore` | `PropertyGraphStore` | Required | Graph store instance |\n| `embedModel` | `EmbedModel` | Required | Embedding model for query embedding |\n| `similarityTopK` | `number` | `4` | Number of top similar nodes |\n| `pathDepth` | `number` | `1` | Graph traversal depth |\n| `limit` | `number` | `30` | Max results after expansion |\n| `similarityScore` | `number` | - | Minimum similarity threshold |\n| `includeText` | `boolean` | `true` | Include source text in results |\n| `crossCheckBoost` | `boolean` | `true` | Enable cross-check boosting |\n| `crossCheckBoostFactor` | `number` | `1.25` | Score multiplier for provenance matches |\n| `includeStructuralEdges` | `boolean` | `true` | Traverse structural adjacency edges |\n| `structuralDepth` | `number` | `1` | Depth for structural edge traversal |\n\n## Structural Adjacency (Multimodal Support)\n\nFor documents with mixed content (text, images, tables), use `AdjacencyLinker` to create structural edges between chunks:\n\n```typescript\nimport { AdjacencyLinker } from \"hana-kgvector\";\n\nconst index = new PropertyGraphIndex({\n propertyGraphStore: graphStore,\n embedModel,\n kgExtractors: [\n new SchemaLLMPathExtractor({ llm: llmClient, schema }),\n new ImplicitPathExtractor(),\n new AdjacencyLinker({ // Must come AFTER ImplicitPathExtractor\n linkSamePage: true, // Link chunks on same page\n linkAdjacent: true, // Link sequential chunks\n adjacentDistance: 1, // How many chunks ahead to link\n crossTypeOnly: false, // Set true to only link text↔image\n }),\n ],\n});\n```\n\nThis enables image/table chunks to be retrieved when nearby text matches a query, via graph traversal of `ON_SAME_PAGE` and `ADJACENT_TO` edges.\n\n**Required metadata** for adjacency linking:\n- `documentId` β€” groups chunks by document\n- `pageNumber` β€” for same-page linking \n- `chunkIndex` β€” for adjacent-chunk linking\n- `contentType` β€” (optional) for `crossTypeOnly` mode\n\n## Embed-Only Mode (Vector Search without KG Extraction)\n\nIf you manage entities and relationships externally (e.g., from a programmatic parser), you can use `PropertyGraphIndex` purely for vector search by passing an empty extractors array:\n\n```typescript\nconst index = new PropertyGraphIndex({\n propertyGraphStore: graphStore,\n embedModel,\n kgExtractors: [], // No LLM-based extraction\n});\n\n// Insert documents β€” only embeddings and document nodes are stored\nawait index.insert([\n { id: \"doc_1\", text: \"Alice works at SAP.\", metadata: {} },\n { id: \"doc_2\", text: \"SAP is headquartered in Walldorf.\", metadata: {} },\n]);\n\n// Query via vector similarity β€” returns matching document chunks directly\nconst results = await index.query(\"Where is SAP located?\");\nfor (const r of results) {\n console.log(`[${r.score.toFixed(3)}] ${r.node.text}`);\n}\n\n// Entities/relations can be inserted separately via the store\nawait graphStore.upsertNodes([...]);\nawait graphStore.upsertRelations([...]);\n```\n\nWhen `kgExtractors` is empty, `query()` returns document chunks ranked by vector similarity. When entities and relations are also present, `query()` returns both document matches and graph-expanded triplets, merged and sorted by score.\n\n## Storage Architecture\n\n### HANA-Native Storage\n\nThe native path uses two storage areas per graph:\n\n| Storage | Table / Graph | Contents |\n|---------|---------------|----------|\n| **Chunks** | `{graphName}_CHUNKS` | Source text chunks, metadata, hashes, and `REAL_VECTOR` embeddings. |\n| **RDF graph** | `` | Entities, typed relations, relation properties, and links from entities to source chunks. |\n\nRetrieval runs vector search over chunks and expands the result through the RDF graph with HANA Knowledge Graph queries.\n\n### Legacy Storage\n\nEach graph creates three storage areas:\n\n| Storage | Table | Contents |\n|---------|-------|----------|\n| **`_VECTORS`** | `{graphName}_VECTORS` | Unified query table. Stores entities (`NODE_TYPE='entity'`), document vectors (`NODE_TYPE='document'`), and relations (`NODE_TYPE='relation'` with indexed `source_id`/`target_id` columns). |\n| **`_NODES`** | `{graphName}_NODES` | Document node storage with full text, metadata, and content hashes for deduplication. |\n| **RDF graph** | SPARQL workspace | SPARQL-accessible triples (backward compat). Relations are also written here when HANA KG Engine is available. |\n\n`vectorQuery()` searches `_VECTORS` for rows with non-null embeddings (both entities and documents). `getRelMap()` queries `_VECTORS` for `NODE_TYPE='relation'` rows using indexed `source_id`/`target_id` lookups, with a SPARQL fallback for backward compatibility.\n\n## Multi-Tenancy\n\nIsolate data for different domains using separate graph names:\n\n```typescript\n// Tenant 1: Finance data\nconst financeStore = new HanaPropertyGraphStore(conn, {\n graphName: \"finance_contracts\",\n});\nconst financeIndex = new PropertyGraphIndex({\n propertyGraphStore: financeStore,\n embedModel,\n kgExtractors: [...],\n});\n\n// Tenant 2: HR data (completely isolated)\nconst hrStore = new HanaPropertyGraphStore(conn, {\n graphName: \"hr_data\",\n});\nconst hrIndex = new PropertyGraphIndex({\n propertyGraphStore: hrStore,\n embedModel,\n kgExtractors: [...],\n});\n```\n\nEach `graphName` creates:\n- A separate RDF named graph for knowledge graph data\n- A separate vector table for embeddings\n\n## Low-Level Access\n\n### Direct SPARQL Access\n\n```typescript\nimport { HanaSparqlStore } from \"hana-kgvector\";\n\nconst sparql = new HanaSparqlStore(conn);\n\n// Execute SPARQL query\nconst result = await sparql.execute({\n sparql: `SELECT ?s ?p ?o FROM WHERE { ?s ?p ?o } LIMIT 10`,\n});\n\n// Load Turtle data\nawait sparql.loadTurtle({\n turtle: ` .`,\n graphName: \"urn:hkv:my_graph\",\n});\n```\n\n## Requirements\n\n- **Node.js** 20+\n- **SAP HANA Cloud** with:\n - Vector Engine enabled (GA since Q1 2024)\n - Knowledge Graph Engine enabled (GA since Q1 2025)\n - Minimum 3 vCPUs / 48 GB memory\n- **LiteLLM Proxy** (recommended) or direct LLM API access\n\n## Scripts\n\n```bash\n# Build\npnpm run build\n\n# Test\npnpm run test\n\n# Validate HANA connection\npnpm run phase0:hana\n\n# Validate LiteLLM connection\npnpm run phase0:litellm\n\n# Run PropertyGraphIndex smoke test\npnpm run smoke:pg\n\n# Run quality test suite (comprehensive testing)\npnpm exec tsx scripts/test-quality.ts\n```\n\n## License\n\nMIT\n\n## Contributing\n\nContributions welcome! Please read the PRD.md for architectural decisions and design principles.\n","readmeFilename":"README.md"}