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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
21 lines
1.2 KiB
Markdown
21 lines
1.2 KiB
Markdown
## APIs
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A Llama Stack API is described as a collection of REST endpoints. We currently support the following APIs:
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- **Inference**: run inference with a LLM
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- **Safety**: apply safety policies to the output at a Systems (not only model) level
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- **Agents**: run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.
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- **DatasetIO**: interface with datasets and data loaders
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- **Scoring**: evaluate outputs of the system
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- **Eval**: generate outputs (via Inference or Agents) and perform scoring
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- **VectorIO**: perform operations on vector stores, such as adding documents, searching, and deleting documents
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- **Telemetry**: collect telemetry data from the system
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- **Post Training**: fine-tune a model
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- **Tool Runtime**: interact with various tools and protocols
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- **Responses**: generate responses from an LLM using this OpenAI compatible API.
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We are working on adding a few more APIs to complete the application lifecycle. These will include:
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- **Batch Inference**: run inference on a dataset of inputs
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- **Batch Agents**: run agents on a dataset of inputs
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- **Synthetic Data Generation**: generate synthetic data for model development
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- **Batches**: OpenAI-compatible batch management for inference
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