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914c7be288
Add complete batches API implementation with protocol, providers, and tests: Core Infrastructure: - Add batches API protocol using OpenAI Batch types directly - Add Api.batches enum value and protocol mapping in resolver - Add OpenAI "batch" file purpose support - Include proper error handling (ConflictError, ResourceNotFoundError) Reference Provider: - Add ReferenceBatchesImpl with full CRUD operations (create, retrieve, cancel, list) - Implement background batch processing with configurable concurrency - Add SQLite KVStore backend for persistence - Support /v1/chat/completions endpoint with request validation Comprehensive Test Suite: - Add unit tests for provider implementation with validation - Add integration tests for end-to-end batch processing workflows - Add error handling tests for validation, malformed inputs, and edge cases Configuration: - Add max_concurrent_batches and max_concurrent_requests_per_batch options - Add provider documentation with sample configurations Test with - ``` $ uv run llama stack build --image-type venv --providers inference=YOU_PICK,files=inline::localfs,batches=inline::reference --run & $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run pytest tests/unit/providers/batches tests/integration/batches --text-model YOU_PICK ``` addresses #3066 --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
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APIs
A Llama Stack API is described as a collection of REST endpoints. We currently support the following APIs:
- Inference: run inference with a LLM
- Safety: apply safety policies to the output at a Systems (not only model) level
- Agents: run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
- DatasetIO: interface with datasets and data loaders
- Scoring: evaluate outputs of the system
- Eval: generate outputs (via Inference or Agents) and perform scoring
- VectorIO: perform operations on vector stores, such as adding documents, searching, and deleting documents
- Telemetry: collect telemetry data from the system
- Post Training: fine-tune a model
- Tool Runtime: interact with various tools and protocols
- Responses: generate responses from an LLM using this OpenAI compatible API.
We are working on adding a few more APIs to complete the application lifecycle. These will include:
- Batch Inference: run inference on a dataset of inputs
- Batch Agents: run agents on a dataset of inputs
- Synthetic Data Generation: generate synthetic data for model development
- Batches: OpenAI-compatible batch management for inference