chore: Added openai compatible vector io endpoints for chromadb (#2489)

# What does this PR do?
This PR implements the openai compatible endpoints for chromadb

Closes #2462 

## Test Plan
Ran ollama llama stack server and ran the command
`pytest -sv --stack-config=http://localhost:8321
tests/integration/vector_io/test_openai_vector_stores.py
--embedding-model all-MiniLM-L6-v2`
8 failed, 27 passed, 8 skipped, 1 xfailed
The failed ones are regarding files api

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: sarthakdeshpande <sarthak.deshpande@engati.com>
Co-authored-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>

docs/source/providers/vector_io/inline_chromadb.md

@@ -42,11 +42,15 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.CHROMADB_PATH}
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db
 
 ```
 

docs/source/providers/vector_io/remote_chromadb.md

@@ -41,11 +41,15 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `url` | `str \| None` | No | PydanticUndefined |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration
 
 ```yaml
 url: ${env.CHROMADB_URL}
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_remote_registry.db
 
 ```
 

llama_stack/providers/inline/vector_io/chroma/__init__.py

@@ -16,6 +16,6 @@ async def get_provider_impl(config: ChromaVectorIOConfig, deps: dict[Api, Any]):
         ChromaVectorIOAdapter,
     )
 
-    impl = ChromaVectorIOAdapter(config, deps[Api.inference])
+    impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

llama_stack/providers/inline/vector_io/chroma/config.py

@@ -6,12 +6,25 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+
+from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
+from llama_stack.schema_utils import json_schema_type
 
 
+@json_schema_type
 class ChromaVectorIOConfig(BaseModel):
     db_path: str
+    kvstore: KVStoreConfig = Field(description="Config for KV store backend")
 
     @classmethod
-    def sample_run_config(cls, db_path: str = "${env.CHROMADB_PATH}", **kwargs: Any) -> dict[str, Any]:
-        return {"db_path": db_path}
+    def sample_run_config(
+        cls, __distro_dir__: str, db_path: str = "${env.CHROMADB_PATH}", **kwargs: Any
+    ) -> dict[str, Any]:
+        return {
+            "db_path": db_path,
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="chroma_inline_registry.db",
+            ),
+        }

llama_stack/providers/remote/vector_io/chroma/__init__.py

@@ -12,6 +12,6 @@ from .config import ChromaVectorIOConfig
 async def get_adapter_impl(config: ChromaVectorIOConfig, deps: dict[Api, ProviderSpec]):
     from .chroma import ChromaVectorIOAdapter
 
-    impl = ChromaVectorIOAdapter(config, deps[Api.inference])
+    impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -12,25 +12,19 @@ from urllib.parse import urlparse
 import chromadb
 from numpy.typing import NDArray
 
+from llama_stack.apis.files import Files
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
     QueryChunksResponse,
-    SearchRankingOptions,
     VectorIO,
-    VectorStoreChunkingStrategy,
-    VectorStoreDeleteResponse,
-    VectorStoreFileContentsResponse,
-    VectorStoreFileObject,
-    VectorStoreFileStatus,
-    VectorStoreListFilesResponse,
-    VectorStoreListResponse,
-    VectorStoreObject,
-    VectorStoreSearchResponsePage,
 )
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
+from llama_stack.providers.utils.kvstore import kvstore_impl
+from llama_stack.providers.utils.kvstore.api import KVStore
+from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
@@ -42,6 +36,13 @@ log = logging.getLogger(__name__)
 
 ChromaClientType = chromadb.api.AsyncClientAPI | chromadb.api.ClientAPI
 
+VERSION = "v3"
+VECTOR_DBS_PREFIX = f"vector_dbs:chroma:{VERSION}::"
+VECTOR_INDEX_PREFIX = f"vector_index:chroma:{VERSION}::"
+OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:chroma:{VERSION}::"
+OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:chroma:{VERSION}::"
+OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:chroma:{VERSION}::"
+
 
 # this is a helper to allow us to use async and non-async chroma clients interchangeably
 async def maybe_await(result):
@@ -51,9 +52,10 @@ async def maybe_await(result):
 
 
 class ChromaIndex(EmbeddingIndex):
-    def __init__(self, client: ChromaClientType, collection):
+    def __init__(self, client: ChromaClientType, collection, kvstore: KVStore | None = None):
         self.client = client
         self.collection = collection
+        self.kvstore = kvstore
 
     async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray):
         assert len(chunks) == len(embeddings), (
@@ -122,24 +124,23 @@ class ChromaIndex(EmbeddingIndex):
         raise NotImplementedError("Hybrid search is not supported in Chroma")
 
 
-class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
+class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
     def __init__(
         self,
         config: RemoteChromaVectorIOConfig | InlineChromaVectorIOConfig,
         inference_api: Api.inference,
+        files_api: Files | None,
     ) -> None:
         log.info(f"Initializing ChromaVectorIOAdapter with url: {config}")
         self.config = config
         self.inference_api = inference_api
-
         self.client = None
         self.cache = {}
+        self.kvstore: KVStore | None = None
 
     async def initialize(self) -> None:
+        self.kvstore = await kvstore_impl(self.config.kvstore)
         if isinstance(self.config, RemoteChromaVectorIOConfig):
-            if not self.config.url:
-                raise ValueError("URL is a required parameter for the remote Chroma provider's config")
-
             log.info(f"Connecting to Chroma server at: {self.config.url}")
             url = self.config.url.rstrip("/")
             parsed = urlparse(url)
@@ -151,6 +152,7 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         else:
             log.info(f"Connecting to Chroma local db at: {self.config.db_path}")
             self.client = chromadb.PersistentClient(path=self.config.db_path)
+        self.openai_vector_stores = await self._load_openai_vector_stores()
 
     async def shutdown(self) -> None:
         pass
@@ -206,107 +208,3 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api)
         self.cache[vector_db_id] = index
         return index
-
-    async def openai_create_vector_store(
-        self,
-        name: str,
-        file_ids: list[str] | None = None,
-        expires_after: dict[str, Any] | None = None,
-        chunking_strategy: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-        embedding_model: str | None = None,
-        embedding_dimension: int | None = 384,
-        provider_id: str | None = None,
-    ) -> VectorStoreObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_list_vector_stores(
-        self,
-        limit: int | None = 20,
-        order: str | None = "desc",
-        after: str | None = None,
-        before: str | None = None,
-    ) -> VectorStoreListResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_retrieve_vector_store(
-        self,
-        vector_store_id: str,
-    ) -> VectorStoreObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_update_vector_store(
-        self,
-        vector_store_id: str,
-        name: str | None = None,
-        expires_after: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-    ) -> VectorStoreObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_delete_vector_store(
-        self,
-        vector_store_id: str,
-    ) -> VectorStoreDeleteResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_search_vector_store(
-        self,
-        vector_store_id: str,
-        query: str | list[str],
-        filters: dict[str, Any] | None = None,
-        max_num_results: int | None = 10,
-        ranking_options: SearchRankingOptions | None = None,
-        rewrite_query: bool | None = False,
-        search_mode: str | None = "vector",
-    ) -> VectorStoreSearchResponsePage:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_attach_file_to_vector_store(
-        self,
-        vector_store_id: str,
-        file_id: str,
-        attributes: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_list_files_in_vector_store(
-        self,
-        vector_store_id: str,
-        limit: int | None = 20,
-        order: str | None = "desc",
-        after: str | None = None,
-        before: str | None = None,
-        filter: VectorStoreFileStatus | None = None,
-    ) -> VectorStoreListFilesResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_retrieve_vector_store_file(
-        self,
-        vector_store_id: str,
-        file_id: str,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_retrieve_vector_store_file_contents(
-        self,
-        vector_store_id: str,
-        file_id: str,
-    ) -> VectorStoreFileContentsResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_update_vector_store_file(
-        self,
-        vector_store_id: str,
-        file_id: str,
-        attributes: dict[str, Any] | None = None,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
-
-    async def openai_delete_vector_store_file(
-        self,
-        vector_store_id: str,
-        file_id: str,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")

llama_stack/providers/remote/vector_io/chroma/config.py

@@ -6,12 +6,23 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+
+from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
+from llama_stack.schema_utils import json_schema_type
 
 
+@json_schema_type
 class ChromaVectorIOConfig(BaseModel):
     url: str | None
+    kvstore: KVStoreConfig = Field(description="Config for KV store backend")
 
     @classmethod
-    def sample_run_config(cls, url: str = "${env.CHROMADB_URL}", **kwargs: Any) -> dict[str, Any]:
-        return {"url": url}
+    def sample_run_config(cls, __distro_dir__: str, url: str = "${env.CHROMADB_URL}", **kwargs: Any) -> dict[str, Any]:
+        return {
+            "url": url,
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="chroma_remote_registry.db",
+            ),
+        }

llama_stack/templates/ci-tests/run.yaml

@@ -133,6 +133,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db
   - provider_id: ${env.ENABLE_PGVECTOR:=__disabled__}
     provider_type: remote::pgvector
     config:

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -122,7 +122,9 @@ def get_distribution_template() -> DistributionTemplate:
         Provider(
             provider_id="${env.ENABLE_CHROMADB:+chromadb}",
             provider_type="remote::chromadb",
-            config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:=}"),
+            config=ChromaVectorIOConfig.sample_run_config(
+                f"~/.llama/distributions/{name}", url="${env.CHROMADB_URL:=}"
+            ),
         ),
         Provider(
             provider_id="${env.ENABLE_PGVECTOR:+pgvector}",

llama_stack/templates/open-benchmark/run.yaml

@@ -46,6 +46,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/chroma_remote_registry.db
   - provider_id: ${env.ENABLE_PGVECTOR:+pgvector}
     provider_type: remote::pgvector
     config:

llama_stack/templates/postgres-demo/postgres_demo.py

@@ -52,7 +52,10 @@ def get_distribution_template() -> DistributionTemplate:
         Provider(
             provider_id="${env.ENABLE_CHROMADB:+chromadb}",
             provider_type="remote::chromadb",
-            config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:=}"),
+            config=ChromaVectorIOConfig.sample_run_config(
+                f"~/.llama/distributions/{name}",
+                url="${env.CHROMADB_URL:=}",
+            ),
         ),
     ]
     default_tool_groups = [

llama_stack/templates/postgres-demo/run.yaml

@@ -24,6 +24,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/postgres-demo}/chroma_remote_registry.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard

llama_stack/templates/starter/run.yaml

@@ -133,6 +133,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db
   - provider_id: ${env.ENABLE_PGVECTOR:=__disabled__}
     provider_type: remote::pgvector
     config:

llama_stack/templates/starter/starter.py

@@ -235,7 +235,10 @@ def get_distribution_template() -> DistributionTemplate:
         Provider(
             provider_id="${env.ENABLE_CHROMADB:=__disabled__}",
             provider_type="remote::chromadb",
-            config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:=}"),
+            config=ChromaVectorIOConfig.sample_run_config(
+                f"~/.llama/distributions/{name}/",
+                url="${env.CHROMADB_URL:=}",
+            ),
         ),
         Provider(
             provider_id="${env.ENABLE_PGVECTOR:=__disabled__}",

pyproject.toml

@@ -92,6 +92,7 @@ unit = [
     "litellm",
     "together",
     "coverage",
+    "chromadb>=1.0.15",
 ]
 # These are the core dependencies required for running integration tests. They are shared across all
 # providers. If a provider requires additional dependencies, please add them to your environment

tests/integration/vector_io/test_openai_vector_stores.py

@@ -22,7 +22,7 @@ logger = logging.getLogger(__name__)
 def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
     vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"]
     for p in vector_io_providers:
-        if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus"]:
+        if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "inline::chromadb"]:
             return
 
     pytest.skip("OpenAI vector stores are not supported by any provider")
@@ -31,7 +31,12 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
 def skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models):
     vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"]
     for p in vector_io_providers:
-        if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "remote::pgvector"]:
+        if p.provider_type in [
+            "inline::faiss",
+            "inline::sqlite-vec",
+            "inline::milvus",
+            "remote::pgvector",
+        ]:
             return
 
     pytest.skip("OpenAI vector stores are not supported by any provider")

tests/unit/providers/vector_io/conftest.py

@@ -12,11 +12,13 @@ from pymilvus import MilvusClient, connections
 
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, ChunkMetadata
+from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOConfig
 from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
 from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
 from llama_stack.providers.inline.vector_io.milvus.config import MilvusVectorIOConfig, SqliteKVStoreConfig
 from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig
 from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter
+from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter
 from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter
 
 EMBEDDING_DIMENSION = 384
@@ -236,15 +238,54 @@ async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding
     await adapter.shutdown()
 
 
+@pytest.fixture
+def chroma_vec_db_path(tmp_path_factory):
+    persist_dir = tmp_path_factory.mktemp(f"chroma_{np.random.randint(1e6)}")
+    return str(persist_dir)
+
+
+@pytest.fixture
+async def chroma_vec_index(chroma_vec_db_path, embedding_dimension):
+    index = ChromaIndex(
+        embedding_dimension=embedding_dimension,
+        persist_directory=chroma_vec_db_path,
+    )
+    await index.initialize()
+    yield index
+    await index.delete()
+
+
+@pytest.fixture
+async def chroma_vec_adapter(chroma_vec_db_path, mock_inference_api, embedding_dimension):
+    config = ChromaVectorIOConfig(persist_directory=chroma_vec_db_path)
+    adapter = ChromaVectorIOAdapter(
+        config=config,
+        inference_api=mock_inference_api,
+        files_api=None,
+    )
+    await adapter.initialize()
+    await adapter.register_vector_db(
+        VectorDB(
+            identifier=f"chroma_test_collection_{random.randint(1, 1_000_000)}",
+            provider_id="test_provider",
+            embedding_model="test_model",
+            embedding_dimension=embedding_dimension,
+        )
+    )
+    yield adapter
+    await adapter.shutdown()
+
+
 @pytest.fixture
 def vector_io_adapter(vector_provider, request):
     """Returns the appropriate vector IO adapter based on the provider parameter."""
-    if vector_provider == "milvus":
-        return request.getfixturevalue("milvus_vec_adapter")
-    elif vector_provider == "faiss":
-        return request.getfixturevalue("faiss_vec_adapter")
-    else:
-        return request.getfixturevalue("sqlite_vec_adapter")
+    vector_provider_dict = {
+        "milvus": "milvus_vec_adapter",
+        "faiss": "faiss_vec_adapter",
+        "sqlite_vec": "sqlite_vec_adapter",
+        "chroma": "chroma_vec_adapter",
+    }
+    return request.getfixturevalue(vector_provider_dict[vector_provider])
 
 
 @pytest.fixture

uv.lock

@@ -217,6 +217,65 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
 ]
 
+[[package]]
+name = "backoff"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148 },
+]
+
+[[package]]
+name = "bcrypt"
+version = "4.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bb/5d/6d7433e0f3cd46ce0b43cd65e1db465ea024dbb8216fb2404e919c2ad77b/bcrypt-4.3.0.tar.gz", hash = "sha256:3a3fd2204178b6d2adcf09cb4f6426ffef54762577a7c9b54c159008cb288c18", size = 25697 }
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