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RAG pipeline tools - universal chunking, embedding, vector store interfaces","maintainers":[{"name":"sergemso","email":"strebulaev@gmail.com"}],"readme":"# @vivantel/virage-core\n\n[![CI](https://github.com/vivantel/virage/actions/workflows/ci-rag-core.yaml/badge.svg)](https://github.com/vivantel/virage/actions/workflows/ci-rag-core.yaml)\n[![npm version](https://img.shields.io/npm/v/@vivantel/virage-core.svg)](https://www.npmjs.com/package/@vivantel/virage-core)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nPipeline orchestrator, provider interfaces, and CLI for Git-aware RAG indexing.\n\n## Installation\n\n```bash\nnpm install @vivantel/virage-core\n```\n\n## Quick start\n\n```bash\nnpx virage init    # generate virage.config.json interactively\nnpx virage         # run the pipeline\n```\n\n## What it does\n\nFour pipeline stages run in sequence:\n\n1. **GitTracker** — finds files matching your chunker patterns, applies `exclude` glob filters, and detects changes via commit hashes\n2. **ChunkProcessor** — splits each file into `Chunk[]` using your configured chunker plugin (runs files in parallel, concurrency = CPU core count by default)\n3. **EmbedderProcessor** — embeds `chunk.denseText` into `chunk.denseVector` (skips unchanged content via `denseTextHash`); detects model changes and auto-invalidates stale embeddings\n4. **Uploader** — syncs the vector store: deletes stale documents, upserts new ones\n\n## Four-artifact chunk model\n\nEvery chunk produced by a `FileChunker` plugin carries four text artifacts:\n\n| Field | Purpose |\n| ----- | ------- |\n| `denseText` | Breadcrumb prefix + full body — sent to the embedding model |\n| `denseVector` | Embedding of `denseText` — stored in vector index for ANN search |\n| `sparseText` | Raw body without breadcrumb — used for BM25 / FTS lexical search |\n| `contextText` | Full body + boundary padding from neighbouring windows — passed to LLM |\n| `denseTextHash` | `sha256(denseText).slice(0,16)` — 16-char hex primary cache key |\n\n```typescript\ninterface Chunk {\n  denseText: string;\n  sparseText: string;\n  contextText: string;\n  denseTextHash: string;\n  metadata: ChunkMeta;\n  sourceFile: string;\n  commitHash: string;\n}\n```\n\n## Config format\n\nConfiguration lives in `virage.config.json`. The `chunking` section groups chunker definitions and global file exclusions:\n\n```json\n{\n  \"chunking\": {\n    \"exclude\": [\"**/vendor/**\", \"**/*.min.js\", \"**/dist/**\"],\n    \"chunkers\": [\n      {\n        \"patterns\": [\"**/*.md\"],\n        \"plugin\": \"@vivantel/virage-chunker-ce-md\",\n        \"strategyOptions\": { \"maxTokens\": 512, \"overlap\": 0.15 }\n      },\n      {\n        \"patterns\": [\"src/**/*.ts\", \"src/**/*.tsx\"],\n        \"plugin\": \"@vivantel/virage-code-chunk-chunker\",\n        \"strategyOptions\": { \"maxTokens\": 512 }\n      }\n    ]\n  }\n}\n```\n\n`chunking.exclude` accepts glob patterns (using `minimatch` semantics). Files matching any pattern are skipped at both the scanning and chunking stages. `virage init` seeds this list with ecosystem-specific defaults for Node.js, .NET, Java, Go, and C/C++.\n\n## Provider interfaces\n\nImplement these three interfaces to integrate any backend:\n\n### `FileChunker`\n\n```typescript\ninterface FileChunker {\n  name: string;\n  version: string;        // semver — used to build sparseTextId and contextTextHash\n  patterns: string[];\n  sparseTextId: string;   // stable fingerprint for session-level sparse cache\n  contextTextHash: string; // stable fingerprint for session-level context cache\n  chunk(filePath: string, commitHash: string): Promise<Chunk[]>;\n  canProcess?(filePath: string): Promise<boolean>;\n}\n```\n\nAll chunker plugins are external npm packages. Built-in strategies have been removed. Use `@vivantel/virage-code-chunk-chunker` for source code or one of the CE/EE chunker packages for documents.\n\n### `EmbeddingProvider`\n\n```typescript\ninterface EmbeddingProvider {\n  name: string;\n  dimensions: number;\n  model?: string; // used for cache invalidation\n  embed(text: string): Promise<number[]>;\n  embedBatch?(texts: string[]): Promise<number[][]>;\n}\n```\n\n### `VectorStore`\n\n```typescript\ninterface VectorStore {\n  name: string;\n  initialize(): Promise<void>;\n  upsert(docs: VectorDocument[]): Promise<void>;\n  deleteBySourceFile(files: string[]): Promise<void>;\n  getCurrentState(): Promise<Map<string, string>>;\n  search(embedding: number[], topK: number): Promise<VectorSearchResult[]>;\n}\n```\n\n## Chunk utility helpers\n\n```typescript\nimport { makeDenseText, makeSparseText, computeDenseTextHash } from \"@vivantel/virage-core\";\n\n// Build the denseText string\nconst denseText = makeDenseText([\"Chapter 1\", \"Section 2\"], \"This is the body.\");\n// → \"Chapter 1 › Section 2. This is the body.\"\n\n// Compute the 16-char hex cache key\nconst hash = computeDenseTextHash(denseText);\n// → \"a3f7c2e1b4d90581\"\n```\n\n## Caching behaviour\n\n- **Per-chunk cache**: embedder checks `denseTextHash` before calling the embedding model. If the hash is already in the SQLite `chunks` table, the existing `denseVector` is reused.\n- **Session-level cache**: `sparseTextId` and `contextTextHash` are stored in the `meta` table. If unchanged between runs, the FTS rebuild and context refresh are skipped respectively.\n- **Schema migration**: if the `chunks` table has a `content` column (old schema), the table is dropped and a warning is logged. Re-index after upgrading.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}