v2 restructure: Go client, Docker engine, release tooling
- Remove v1 Python CLI (src/kb_search/, tests/, root pyproject.toml, uv.lock, .venv) - Add Go client with cross-platform build (client/) - Add FastAPI engine with NVIDIA and multi-stage ROCm Dockerfiles (engine/) - Add VERSION files for client and engine, wired into builds - Add release.sh for automated build, tag, release, and Docker push - Update README with build/release docs and ROCm migration note - Clean up .gitignore for v2 project structure Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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# Engine API
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## Purpose
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The engine API provides an HTTP interface for knowledge base operations including search, document ingestion, document management, tag management, and system status.
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## Requirements
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### Requirement: Engine startup and model loading
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The engine SHALL load the embedding model eagerly at startup before accepting HTTP requests. The engine SHALL expose a health endpoint that returns unhealthy until the model is fully loaded and the database is initialised.
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#### Scenario: Cold start with model download
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- **WHEN** the engine starts for the first time with no cached model
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- **THEN** it SHALL download the configured embedding model, load it into memory (GPU if available, CPU otherwise), enable WAL mode on the SQLite database, and begin accepting requests only after all initialisation completes
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#### Scenario: Health check during startup
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- **WHEN** a client sends `GET /api/v1/health` before the model is loaded
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- **THEN** the engine SHALL respond with HTTP 503 and `{"status": "starting"}`
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#### Scenario: Health check after startup
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- **WHEN** a client sends `GET /api/v1/health` after initialisation completes
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- **THEN** the engine SHALL respond with HTTP 200 and `{"status": "healthy"}`
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---
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### Requirement: Hybrid search
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The engine SHALL provide hybrid search combining BM25 full-text search (via FTS5) and vector similarity search (via sqlite-vec), merged using Reciprocal Rank Fusion. Search SHALL complete in under 100ms when the model is warm.
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#### Scenario: Hybrid search with results
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- **WHEN** a client sends `POST /api/v1/search` with body `{"query": "how to change oil", "top": 5}`
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- **THEN** the engine SHALL embed the query using the resident model, run both FTS5 and vector searches, merge results via RRF, and return a JSON response with matched chunks including scores, document metadata, and tags
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#### Scenario: Search with filters
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- **WHEN** a client sends `POST /api/v1/search` with body `{"query": "brakes", "tags": ["maintenance"], "doc_type": "pdf", "top": 3}`
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- **THEN** the engine SHALL apply tag and document type filters to both FTS5 and vector results before merging
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#### Scenario: Search with mode override
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- **WHEN** a client sends `POST /api/v1/search` with body `{"query": "error log", "fts_only": true}`
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- **THEN** the engine SHALL return only FTS5 results without running vector search
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#### Scenario: Empty knowledge base
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- **WHEN** a client searches against an empty database
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- **THEN** the engine SHALL return HTTP 200 with `{"query": "...", "results": [], "total_matches": 0}`
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---
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### Requirement: Async ingestion via job queue
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The engine SHALL accept file uploads and text notes for ingestion asynchronously. Uploaded content SHALL be written to a staging area and a job record created in the database. The engine SHALL return HTTP 202 immediately. A background worker SHALL process queued jobs sequentially.
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#### Scenario: Upload a PDF file
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- **WHEN** a client sends `POST /api/v1/jobs` with a multipart form containing a PDF file and optional fields (tags, doc_type)
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- **THEN** the engine SHALL write the file to the staging directory, create a job record with status `queued`, and return HTTP 202 with `{"job_id": "<id>", "status": "queued", "filename": "report.pdf"}`
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#### Scenario: Upload a text note
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- **WHEN** a client sends `POST /api/v1/jobs` with a multipart form containing a `note` text field and optional `title` field
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- **THEN** the engine SHALL write the note content to a staging file, create a job record with status `queued`, and return HTTP 202 with the job ID
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#### Scenario: Upload multiple files in sequence
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- **WHEN** a client sends multiple `POST /api/v1/jobs` requests in quick succession
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- **THEN** the engine SHALL queue each job independently and the background worker SHALL process them in FIFO order
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#### Scenario: Duplicate content detection
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- **WHEN** a client uploads a file whose content hash matches an already-ingested document
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- **THEN** the engine SHALL return HTTP 202 but the background worker SHALL mark the job as `skipped` with reason `duplicate`
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#### Scenario: Upload failure due to unsupported file type
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- **WHEN** a client uploads a file with an unsupported extension
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- **THEN** the engine SHALL return HTTP 422 with an error message listing supported types
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---
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### Requirement: Job status tracking
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The engine SHALL maintain job records in SQLite with status tracking. Jobs SHALL transition through states: `queued` → `processing` → `done` | `failed` | `skipped`.
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#### Scenario: List all jobs
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- **WHEN** a client sends `GET /api/v1/jobs`
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- **THEN** the engine SHALL return a JSON array of job records ordered by creation time (newest first), each including job_id, filename, status, created_at, and completed_at
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#### Scenario: Filter jobs by status
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- **WHEN** a client sends `GET /api/v1/jobs?status=failed`
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- **THEN** the engine SHALL return only jobs with the specified status
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#### Scenario: Get job details
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- **WHEN** a client sends `GET /api/v1/jobs/{id}`
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- **THEN** the engine SHALL return the full job record including status, filename, error message (if failed), document_id (if done), chunk count, and timing information
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#### Scenario: Job not found
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- **WHEN** a client sends `GET /api/v1/jobs/{id}` with a non-existent ID
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- **THEN** the engine SHALL return HTTP 404
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---
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### Requirement: Background ingestion worker
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The engine SHALL run a background worker that processes queued jobs. The worker SHALL process one job at a time. For each job, it SHALL: detect document type, run the appropriate chunking pipeline (Docling for PDFs, header-based for Markdown, AST-based for code, whole-text for notes), generate embeddings using the resident model, and insert chunks and vectors into the database.
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#### Scenario: Successful PDF ingestion
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- **WHEN** the background worker picks up a queued PDF job
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- **THEN** it SHALL update the job status to `processing`, run Docling conversion and chunking, embed all chunks, insert document and chunks into the database, update the job status to `done` with the resulting document_id and chunk count, and delete the staged file
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#### Scenario: Ingestion failure
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- **WHEN** the background worker encounters an error during processing (e.g., corrupt PDF)
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- **THEN** it SHALL update the job status to `failed` with the error message, delete the staged file, and continue processing the next queued job
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#### Scenario: Search during active ingestion
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- **WHEN** a search request arrives while the background worker is processing a job
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- **THEN** the search SHALL execute without blocking (SQLite WAL mode) and return results from already-ingested documents
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---
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### Requirement: Document management
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The engine SHALL provide endpoints to list, inspect, and remove ingested documents.
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#### Scenario: List documents
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- **WHEN** a client sends `GET /api/v1/documents`
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- **THEN** the engine SHALL return a JSON array of documents with id, title, doc_type, tags, chunk_count, and created_at
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#### Scenario: List documents with filters
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- **WHEN** a client sends `GET /api/v1/documents?type=pdf&tags=manual`
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- **THEN** the engine SHALL return only documents matching all specified filters
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#### Scenario: Get document details
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- **WHEN** a client sends `GET /api/v1/documents/{id}`
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- **THEN** the engine SHALL return the full document record including all chunks and their text content
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#### Scenario: Remove a document
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- **WHEN** a client sends `DELETE /api/v1/documents/{id}`
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- **THEN** the engine SHALL delete the document, all its chunks, associated embeddings, and tag associations, and return HTTP 200 with a confirmation
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#### Scenario: Remove non-existent document
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- **WHEN** a client sends `DELETE /api/v1/documents/{id}` with a non-existent ID
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- **THEN** the engine SHALL return HTTP 404
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---
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### Requirement: Tag management
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The engine SHALL provide endpoints to list all tags and manage tags on documents.
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#### Scenario: List all tags
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- **WHEN** a client sends `GET /api/v1/tags`
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- **THEN** the engine SHALL return a JSON array of tags with name and document count
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#### Scenario: Add tags to a document
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- **WHEN** a client sends `PUT /api/v1/documents/{id}/tags` with body `{"add": ["manual", "v2"]}`
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- **THEN** the engine SHALL add the specified tags to the document and return the updated tag list
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#### Scenario: Remove tags from a document
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- **WHEN** a client sends `PUT /api/v1/documents/{id}/tags` with body `{"remove": ["draft"]}`
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- **THEN** the engine SHALL remove the specified tags from the document and return the updated tag list
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---
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### Requirement: Engine status and reindex
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The engine SHALL provide status information and support re-embedding all chunks.
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#### Scenario: Get engine status
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- **WHEN** a client sends `GET /api/v1/status`
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- **THEN** the engine SHALL return JSON with model_name, embedding_dim, GPU device info, database stats (document count by type, total chunks, DB size), and queue stats (queued/processing job count)
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#### Scenario: Trigger reindex
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- **WHEN** a client sends `POST /api/v1/reindex`
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- **THEN** the engine SHALL re-embed all existing chunks using the currently loaded model and return progress information. This operation SHALL NOT block search queries.
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---
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### Requirement: API authentication
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The engine SHALL support optional API key authentication via Bearer token. When `KB_API_KEY` is set, all requests MUST include a matching `Authorization: Bearer <key>` header. When `KB_API_KEY` is not set, authentication SHALL be disabled.
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#### Scenario: Valid API key
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- **WHEN** `KB_API_KEY` is set and a request includes a matching Bearer token
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- **THEN** the engine SHALL process the request normally
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#### Scenario: Missing API key when required
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- **WHEN** `KB_API_KEY` is set and a request has no Authorization header
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- **THEN** the engine SHALL return HTTP 401 `{"error": "authentication required"}`
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#### Scenario: Invalid API key
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- **WHEN** `KB_API_KEY` is set and a request includes a non-matching Bearer token
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- **THEN** the engine SHALL return HTTP 401 `{"error": "invalid api key"}`
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#### Scenario: Auth disabled
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- **WHEN** `KB_API_KEY` is not set
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- **THEN** the engine SHALL process all requests without requiring authentication
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---
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### Requirement: Engine configuration via environment variables
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The engine SHALL be configured via environment variables. No config file is read by the engine — all configuration comes from the environment (set via compose.yaml or Docker run).
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#### Scenario: Default configuration
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- **WHEN** the engine starts with no environment variables set
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- **THEN** it SHALL use defaults: data directory `/data`, model `all-MiniLM-L6-v2`, device `auto`, no API key required
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#### Scenario: Custom model
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- **WHEN** `KB_MODEL` is set to `BAAI/bge-small-en-v1.5`
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- **THEN** the engine SHALL download and load that model instead of the default
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