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feat: Qdrant inline provider (#1273)
# What does this PR do? Removed local execution option from the remote Qdrant provider and introduced an explicit inline provider for the embedded execution. Updated the ollama template to include this option: this part can be reverted in case we don't want to have two default `vector_io` providers. (Closes #1082) ## Test Plan Build and run an ollama distro: ```bash llama stack build --template ollama --image-type conda llama stack run --image-type conda ollama ``` Run one of the sample ingestionapplicatinos like [rag_with_vector_db.py](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py), but replace this line: ```py selected_vector_provider = vector_providers[0] ``` with the following, to use the `qdrant` provider: ```py selected_vector_provider = vector_providers[1] ``` After running the test code, verify the timestamp of the Qdrant store: ```bash % ls -ltr ~/.llama/distributions/ollama/qdrant.db/collection/test_vector_db_* total 784 -rw-r--r--@ 1 dmartino staff 401408 Feb 26 10:07 storage.sqlite ``` [//]: # (## Documentation) --------- Signed-off-by: Daniele Martinoli <dmartino@redhat.com> Co-authored-by: Francisco Arceo <farceo@redhat.com>
This commit is contained in:
Daniele Martinoli
GitHub
parent
141b3c14dd
commit
cca9bd6cc3
11 changed files with 454 additions and 48 deletions
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@ -3,21 +3,36 @@ orphan: true
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---
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# Qdrant
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[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
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[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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That means you'll get fast and efficient vector retrieval.
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> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
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> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
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>
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> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
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## Features
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- Easy to use
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- Lightweight and easy to use
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- Fully integrated with Llama Stack
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- Apache 2.0 license terms
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- Store embeddings and their metadata
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- Supports search by
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[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
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and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
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- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
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- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
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- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
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## Usage
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To use Qdrant in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use Faiss.
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2. Configure your Llama Stack project to use Qdrant.
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3. Start storing and querying vectors.
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## Installation
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19
llama_stack/providers/inline/vector_io/qdrant/__init__.py
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19
llama_stack/providers/inline/vector_io/qdrant/__init__.py
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@ -0,0 +1,19 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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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 typing import Dict
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from llama_stack.providers.datatypes import Api, ProviderSpec
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from .config import QdrantVectorIOConfig
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async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]):
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from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
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impl = QdrantVectorIOAdapter(config, deps[Api.inference])
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await impl.initialize()
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return impl
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23
llama_stack/providers/inline/vector_io/qdrant/config.py
Normal file
23
llama_stack/providers/inline/vector_io/qdrant/config.py
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@ -0,0 +1,23 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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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 typing import Any, Dict
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from pydantic import BaseModel
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from llama_stack.schema_utils import json_schema_type
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@json_schema_type
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class QdrantVectorIOConfig(BaseModel):
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path: str
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@classmethod
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def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
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return {
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"path": "${env.QDRANT_PATH:~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
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}
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@ -92,6 +92,14 @@ def available_providers() -> List[ProviderSpec]:
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),
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::qdrant",
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pip_packages=["qdrant-client"],
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module="llama_stack.providers.inline.vector_io.qdrant",
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config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.vector_io,
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AdapterSpec(
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@ -23,7 +23,6 @@ class QdrantVectorIOConfig(BaseModel):
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prefix: Optional[str] = None
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timeout: Optional[int] = None
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host: Optional[str] = None
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path: Optional[str] = None
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@classmethod
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def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:
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@ -6,7 +6,7 @@
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import logging
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import uuid
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional, Union
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from numpy.typing import NDArray
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from qdrant_client import AsyncQdrantClient, models
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@ -16,12 +16,13 @@ from llama_stack.apis.inference import InterleavedContent
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
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from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
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from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
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from llama_stack.providers.utils.memory.vector_store import (
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EmbeddingIndex,
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VectorDBWithIndex,
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)
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from .config import QdrantVectorIOConfig
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from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
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log = logging.getLogger(__name__)
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CHUNK_ID_KEY = "_chunk_id"
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@ -99,17 +100,19 @@ class QdrantIndex(EmbeddingIndex):
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class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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def __init__(self, config: QdrantVectorIOConfig, inference_api: Api.inference) -> None:
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def __init__(
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self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference
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) -> None:
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self.config = config
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self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
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self.client: AsyncQdrantClient = None
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self.cache = {}
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self.inference_api = inference_api
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async def initialize(self) -> None:
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pass
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self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
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async def shutdown(self) -> None:
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self.client.close()
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await self.client.close()
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async def register_vector_db(
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self,
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@ -123,6 +126,11 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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self.cache[vector_db.identifier] = index
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async def unregister_vector_db(self, vector_db_id: str) -> None:
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if vector_db_id in self.cache:
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await self.cache[vector_db_id].index.delete()
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del self.cache[vector_db_id]
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async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
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if vector_db_id in self.cache:
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return self.cache[vector_db_id]
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@ -56,7 +56,7 @@ dev = [
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"ruamel.yaml", # needed for openapi generator
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]
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# These are the dependencies required for running unit tests.
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unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet"]
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unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet", "qdrant-client"]
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# These are the core dependencies required for running integration tests. They are shared across all
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# providers. If a provider requires additional dependencies, please add them to your environment
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# separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra
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@ -247,6 +247,7 @@ exclude = [
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"^llama_stack/providers/inline/vector_io/chroma/",
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"^llama_stack/providers/inline/vector_io/faiss/",
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"^llama_stack/providers/inline/vector_io/milvus/",
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"^llama_stack/providers/inline/vector_io/qdrant/",
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"^llama_stack/providers/inline/vector_io/sqlite_vec/",
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"^llama_stack/providers/remote/agents/sample/",
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"^llama_stack/providers/remote/datasetio/huggingface/",
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42
tests/unit/providers/vector_io/conftest.py
Normal file
42
tests/unit/providers/vector_io/conftest.py
Normal file
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@ -0,0 +1,42 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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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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import random
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import numpy as np
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import pytest
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from llama_stack.apis.vector_io import Chunk
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EMBEDDING_DIMENSION = 384
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@pytest.fixture
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def vector_db_id() -> str:
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return f"test-vector-db-{random.randint(1, 100)}"
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@pytest.fixture(scope="session")
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def embedding_dimension() -> int:
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return EMBEDDING_DIMENSION
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@pytest.fixture(scope="session")
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def sample_chunks():
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"""Generates chunks that force multiple batches for a single document to expose ID conflicts."""
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n, k = 10, 3
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sample = [
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Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
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for j in range(k)
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for i in range(n)
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]
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return sample
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@pytest.fixture(scope="session")
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def sample_embeddings(sample_chunks):
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np.random.seed(42)
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return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
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135
tests/unit/providers/vector_io/test_qdrant.py
Normal file
135
tests/unit/providers/vector_io/test_qdrant.py
Normal file
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@ -0,0 +1,135 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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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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import asyncio
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import os
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from typing import Any
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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import pytest_asyncio
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from llama_stack.apis.inference import EmbeddingsResponse, Inference
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from llama_stack.apis.vector_io import (
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QueryChunksResponse,
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VectorDB,
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VectorDBStore,
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)
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from llama_stack.providers.inline.vector_io.qdrant.config import (
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QdrantVectorIOConfig as InlineQdrantVectorIOConfig,
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)
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from llama_stack.providers.remote.vector_io.qdrant.qdrant import (
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QdrantVectorIOAdapter,
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)
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# This test is a unit test for the QdrantVectorIOAdapter class. This should only contain
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# tests which are specific to this class. More general (API-level) tests should be placed in
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# tests/integration/vector_io/
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#
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# How to run this test:
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#
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# pytest tests/unit/providers/vector_io/test_qdrant.py \
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# -v -s --tb=short --disable-warnings --asyncio-mode=auto
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@pytest.fixture
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def qdrant_config(tmp_path) -> InlineQdrantVectorIOConfig:
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return InlineQdrantVectorIOConfig(path=os.path.join(tmp_path, "qdrant.db"))
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@pytest.fixture(scope="session")
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def loop():
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return asyncio.new_event_loop()
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@pytest.fixture
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def mock_vector_db(vector_db_id) -> MagicMock:
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mock_vector_db = MagicMock(spec=VectorDB)
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mock_vector_db.embedding_model = "embedding_model"
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mock_vector_db.identifier = vector_db_id
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return mock_vector_db
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@pytest.fixture
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def mock_vector_db_store(mock_vector_db) -> MagicMock:
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mock_store = MagicMock(spec=VectorDBStore)
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mock_store.get_vector_db = AsyncMock(return_value=mock_vector_db)
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return mock_store
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@pytest.fixture
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def mock_api_service(sample_embeddings):
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mock_api_service = MagicMock(spec=Inference)
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mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
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return mock_api_service
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@pytest_asyncio.fixture
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async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter:
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adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service)
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adapter.vector_db_store = mock_vector_db_store
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await adapter.initialize()
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yield adapter
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await adapter.shutdown()
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__QUERY = "Sample query"
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@pytest.mark.asyncio
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@pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 30)])
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async def test_qdrant_adapter_returns_expected_chunks(
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qdrant_adapter: QdrantVectorIOAdapter,
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vector_db_id,
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sample_chunks,
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sample_embeddings,
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max_query_chunks,
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expected_chunks,
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) -> None:
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assert qdrant_adapter is not None
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await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
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index = await qdrant_adapter._get_and_cache_vector_db_index(vector_db_id=vector_db_id)
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assert index is not None
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response = await qdrant_adapter.query_chunks(
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query=__QUERY,
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vector_db_id=vector_db_id,
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params={"max_chunks": max_query_chunks},
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)
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assert isinstance(response, QueryChunksResponse)
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assert len(response.chunks) == expected_chunks
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# To by-pass attempt to convert a Mock to JSON
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def _prepare_for_json(value: Any) -> str:
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return str(value)
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@patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json)
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@pytest.mark.asyncio
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async def test_qdrant_register_and_unregister_vector_db(
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qdrant_adapter: QdrantVectorIOAdapter,
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mock_vector_db,
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sample_chunks,
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) -> None:
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# Initially, no collections
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vector_db_id = mock_vector_db.identifier
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assert len((await qdrant_adapter.client.get_collections()).collections) == 0
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# Register does not create a collection
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assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
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await qdrant_adapter.register_vector_db(mock_vector_db)
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assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
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# First insert creates the collection
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await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
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assert await qdrant_adapter.client.collection_exists(vector_db_id)
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# Unregister deletes the collection
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await qdrant_adapter.unregister_vector_db(vector_db_id)
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assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
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assert len((await qdrant_adapter.client.get_collections()).collections) == 0
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@ -29,8 +29,6 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
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# -v -s --tb=short --disable-warnings --asyncio-mode=auto
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SQLITE_VEC_PROVIDER = "sqlite_vec"
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EMBEDDING_DIMENSION = 384
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EMBEDDING_MODEL = "all-MiniLM-L6-v2"
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@pytest.fixture(scope="session")
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@ -50,26 +48,8 @@ def sqlite_connection(loop):
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@pytest_asyncio.fixture(scope="session", autouse=True)
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async def sqlite_vec_index(sqlite_connection):
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return await SQLiteVecIndex.create(dimension=EMBEDDING_DIMENSION, connection=sqlite_connection, bank_id="test_bank")
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@pytest.fixture(scope="session")
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def sample_chunks():
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"""Generates chunks that force multiple batches for a single document to expose ID conflicts."""
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n, k = 10, 3
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sample = [
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Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
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for j in range(k)
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for i in range(n)
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]
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return sample
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@pytest.fixture(scope="session")
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def sample_embeddings(sample_chunks):
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np.random.seed(42)
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return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
|
||||
async def sqlite_vec_index(sqlite_connection, embedding_dimension):
|
||||
return await SQLiteVecIndex.create(dimension=embedding_dimension, connection=sqlite_connection, bank_id="test_bank")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
@ -82,21 +62,21 @@ async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
|
||||
async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension):
|
||||
await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings)
|
||||
query_embedding = np.random.rand(EMBEDDING_DIMENSION).astype(np.float32)
|
||||
query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
|
||||
response = await sqlite_vec_index.query(query_embedding, k=2, score_threshold=0.0)
|
||||
assert isinstance(response, QueryChunksResponse)
|
||||
assert len(response.chunks) == 2
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks):
|
||||
async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dimension):
|
||||
"""Test that chunk IDs do not conflict across batches when inserting chunks."""
|
||||
# Reduce batch size to force multiple batches for same document
|
||||
# since there are 10 chunks per document and batch size is 2
|
||||
batch_size = 2
|
||||
sample_embeddings = np.random.rand(len(sample_chunks), EMBEDDING_DIMENSION).astype(np.float32)
|
||||
sample_embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32)
|
||||
|
||||
await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=batch_size)
|
||||
|
||||
|
|
|
|||
198
uv.lock
generated
198
uv.lock
generated
|
|
@ -1,5 +1,4 @@
|
|||
version = 1
|
||||
revision = 1
|
||||
requires-python = ">=3.10"
|
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resolution-markers = [
|
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"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
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|
|
@ -8,9 +7,12 @@ resolution-markers = [
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"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
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"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
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"python_full_version == '3.11.*' and sys_platform == 'darwin'",
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"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
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"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
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"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
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"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
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"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
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"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
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"python_full_version >= '3.13' and sys_platform == 'darwin'",
|
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"python_full_version == '3.12.*' and sys_platform == 'darwin'",
|
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]
|
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[[package]]
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|
|
@ -793,6 +795,107 @@ wheels = [
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]
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|
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[[package]]
|
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name = "grpcio"
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version = "1.71.0"
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wheels = [
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version = "2.6.7"
|
||||
|
|
@ -1250,6 +1389,7 @@ unit = [
|
|||
{ name = "chardet" },
|
||||
{ name = "openai" },
|
||||
{ name = "pypdf" },
|
||||
{ name = "qdrant-client" },
|
||||
{ name = "sqlite-vec" },
|
||||
]
|
||||
|
||||
|
|
@ -1290,6 +1430,7 @@ requires-dist = [
|
|||
{ name = "pytest-cov", marker = "extra == 'dev'" },
|
||||
{ name = "pytest-html", marker = "extra == 'dev'" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "qdrant-client", marker = "extra == 'unit'" },
|
||||
{ name = "requests" },
|
||||
{ name = "rich" },
|
||||
{ name = "rich", marker = "extra == 'codegen'" },
|
||||
|
|
@ -1314,7 +1455,6 @@ requires-dist = [
|
|||
{ name = "types-setuptools", marker = "extra == 'dev'" },
|
||||
{ name = "uvicorn", marker = "extra == 'dev'" },
|
||||
]
|
||||
provides-extras = ["dev", "unit", "test", "docs", "codegen"]
|
||||
|
||||
[[package]]
|
||||
name = "llama-stack-client"
|
||||
|
|
@ -2062,6 +2202,18 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "portalocker"
|
||||
version = "2.10.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pywin32", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/9b/fb/a70a4214956182e0d7a9099ab17d50bfcba1056188e9b14f35b9e2b62a0d/portalocker-2.10.1-py3-none-any.whl", hash = "sha256:53a5984ebc86a025552264b459b46a2086e269b21823cb572f8f28ee759e45bf", size = 18423 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pre-commit"
|
||||
version = "4.1.0"
|
||||
|
|
@ -2668,6 +2820,24 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/e3/fe/72e7e166bda3885810bee7b23049133e142f7c80c295bae02c562caeea16/pyzmq-26.2.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:bd8fdee945b877aa3bffc6a5a8816deb048dab0544f9df3731ecd0e54d8c84c9", size = 556563 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "qdrant-client"
|
||||
version = "1.13.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "grpcio" },
|
||||
{ name = "grpcio-tools" },
|
||||
{ name = "httpx", extra = ["http2"] },
|
||||
{ name = "numpy" },
|
||||
{ name = "portalocker" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "urllib3" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/eb/58/1e4acd7ff7637ed56a66e5044699e7af6067232703d0b34f05068fc6234b/qdrant_client-1.13.3.tar.gz", hash = "sha256:61ca09e07c6d7ac0dfbdeb13dca4fe5f3e08fa430cb0d74d66ef5d023a70adfc", size = 266278 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/dd/b4/bd676f91f5234ab59282e4a110f324029684482cbe08e7a1c77b6338013b/qdrant_client-1.13.3-py3-none-any.whl", hash = "sha256:f52cacbb936e547d3fceb1aaed3e3c56be0ebfd48e8ea495ea3dbc89c671d1d2", size = 306674 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rapidfuzz"
|
||||
version = "3.12.2"
|
||||
|
|
@ -3417,7 +3587,8 @@ source = { registry = "https://download.pytorch.org/whl/cpu" }
|
|||
resolution-markers = [
|
||||
"python_full_version < '3.11' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.13' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'darwin'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "filelock", marker = "sys_platform == 'darwin'" },
|
||||
|
|
@ -3444,8 +3615,10 @@ resolution-markers = [
|
|||
"python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "filelock", marker = "sys_platform != 'darwin'" },
|
||||
|
|
@ -3482,8 +3655,10 @@ resolution-markers = [
|
|||
"python_full_version < '3.11' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.13' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'darwin'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
|
||||
|
|
@ -3509,7 +3684,8 @@ source = { registry = "https://download.pytorch.org/whl/cpu" }
|
|||
resolution-markers = [
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue