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feat: split API and provider specs into separate llama-stack-api pkg (#3895)
# What does this PR do? Extract API definitions and provider specifications into a standalone llama-stack-api package that can be published to PyPI independently of the main llama-stack server. see: https://github.com/llamastack/llama-stack/pull/2978 and https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942 Motivation External providers currently import from llama-stack, which overrides the installed version and causes dependency conflicts. This separation allows external providers to: - Install only the type definitions they need without server dependencies - Avoid version conflicts with the installed llama-stack package - Be versioned and released independently This enables us to re-enable external provider module tests that were previously blocked by these import conflicts. Changes - Created llama-stack-api package with minimal dependencies (pydantic, jsonschema) - Moved APIs, providers datatypes, strong_typing, and schema_utils - Updated all imports from llama_stack.* to llama_stack_api.* - Configured local editable install for development workflow - Updated linting and type-checking configuration for both packages Next Steps - Publish llama-stack-api to PyPI - Update external provider dependencies - Re-enable external provider module tests Pre-cursor PRs to this one: - #4093 - #3954 - #4064 These PRs moved key pieces _out_ of the Api pkg, limiting the scope of change here. relates to #3237 ## Test Plan Package builds successfully and can be imported independently. All pre-commit hooks pass with expected exclusions maintained. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>
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358 changed files with 2337 additions and 1424 deletions
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@ -9,9 +9,8 @@ from unittest.mock import AsyncMock, MagicMock, patch
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import numpy as np
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import pytest
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from llama_stack_api import Chunk, ChunkMetadata, QueryChunksResponse, VectorStore
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from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
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from llama_stack.apis.vector_stores import VectorStore
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from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
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@ -9,11 +9,8 @@ from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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from llama_stack_api import Chunk, Files, HealthStatus, QueryChunksResponse, VectorStore
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from llama_stack.apis.files import Files
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
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from llama_stack.apis.vector_stores import VectorStore
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from llama_stack.providers.datatypes import HealthStatus
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.inline.vector_io.faiss.faiss import (
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FaissIndex,
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@ -8,8 +8,8 @@ import asyncio
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import numpy as np
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import pytest
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from llama_stack_api import Chunk, QueryChunksResponse
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
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from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
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SQLiteVecIndex,
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SQLiteVecVectorIOAdapter,
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@ -10,17 +10,17 @@ from unittest.mock import AsyncMock, patch
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import numpy as np
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import pytest
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from llama_stack.apis.common.errors import VectorStoreNotFoundError
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from llama_stack.apis.vector_io import (
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from llama_stack_api import (
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Chunk,
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OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
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OpenAICreateVectorStoreRequestWithExtraBody,
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QueryChunksResponse,
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VectorStore,
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VectorStoreChunkingStrategyAuto,
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VectorStoreFileObject,
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VectorStoreNotFoundError,
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)
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from llama_stack.apis.vector_stores import VectorStore
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from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import VECTOR_DBS_PREFIX
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# This test is a unit test for the inline VectorIO providers. This should only contain
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@ -222,7 +222,7 @@ async def test_insert_chunks_missing_db_raises(vector_io_adapter):
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async def test_insert_chunks_with_missing_document_id(vector_io_adapter):
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"""Ensure no KeyError when document_id is missing or in different places."""
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from llama_stack.apis.vector_io import Chunk, ChunkMetadata
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from llama_stack_api import Chunk, ChunkMetadata
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fake_index = AsyncMock()
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vector_io_adapter.cache["db1"] = fake_index
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@ -255,7 +255,7 @@ async def test_insert_chunks_with_missing_document_id(vector_io_adapter):
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async def test_document_id_with_invalid_type_raises_error():
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"""Ensure TypeError is raised when document_id is not a string."""
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from llama_stack.apis.vector_io import Chunk
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from llama_stack_api import Chunk
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# Integer document_id should raise TypeError
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from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
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@ -4,7 +4,8 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.apis.vector_io import Chunk, ChunkMetadata
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from llama_stack_api import Chunk, ChunkMetadata
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from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
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# This test is a unit test for the chunk_utils.py helpers. This should only contain
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