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chore: Updating Milvus to use OpenAIVectorStoreMixin
docs: Add recent releases to CHANGELOG.md (#2533) <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> Update changelog. --------- Signed-off-by: Yuan Tang <terrytangyuan@gmail.com> build: update temp. created Containerfile (#2492) <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> - conditionally created folder /.llama/providers.d if external_providers_dir is set - do not create /.cache folder, not in use anywhere - combine chmod and copy to one command <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> updated test: ``` export CONTAINER_BINARY=podman LLAMA_STACK_DIR=. uv run llama stack build --template remote-vllm --image-type container --image-name <name> ``` log: ``` Containerfile created successfully in /tmp/tmp.rPMunE39Aw/Containerfile FROM python:3.11-slim WORKDIR /app RUN apt-get update && apt-get install -y iputils-ping net-tools iproute2 dnsutils telnet curl wget telnet git procps psmisc lsof traceroute bubblewrap gcc && rm -rf /var/lib/apt/lists/* ENV UV_SYSTEM_PYTHON=1 RUN pip install uv RUN uv pip install --no-cache sentencepiece pillow pypdf transformers pythainlp faiss-cpu opentelemetry-sdk requests datasets chardet scipy nltk numpy matplotlib psycopg2-binary aiosqlite langdetect autoevals tree_sitter tqdm pandas chromadb-client opentelemetry-exporter-otlp-proto-http redis scikit-learn openai pymongo emoji sqlalchemy[asyncio] mcp aiosqlite fastapi fire httpx uvicorn opentelemetry-sdk opentelemetry-exporter-otlp-proto-http RUN uv pip install --no-cache sentence-transformers --no-deps RUN uv pip install --no-cache torch torchvision --index-url https://download.pytorch.org/whl/cpu RUN mkdir -p /.llama/providers.d /.cache RUN uv pip install --no-cache llama-stack RUN pip uninstall -y uv ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--template", "remote-vllm"] RUN chmod -R g+rw /app /.llama /.cache PWD: /tmp/llama-stack Containerfile: /tmp/tmp.rPMunE39Aw/Containerfile + podman build --progress=plain --security-opt label=disable --platform linux/amd64 -t distribution-remote-vllm:0.2.12 -f /tmp/tmp.rPMunE39Aw/Containerfile /tmp/llama-stack .... Success! Build Successful! You can find the newly-built template here: /tmp/llama-stack/llama_stack/templates/remote-vllm/run.yaml You can run the new Llama Stack distro via: llama stack run /tmp/llama-stack/llama_stack/templates/remote-vllm/run.yaml --image-type container ``` ``` podman tag localhost/distribution-remote-vllm:dev quay.io/wenzhou/distribution-remote-vllm:2492_2 podman push quay.io/wenzhou/distribution-remote-vllm:2492_2 docker run --rm -p 8321:8321 -e INFERENCE_MODEL="meta-llama/Llama-2-7b-chat-hf" -e VLLM_URL="http://localhost:8000/v1" quay.io/wenzhou/distribution-remote-vllm:2492_2 --port 8321 INFO 2025-06-26 13:47:31,813 __main__:436 server: Using template remote-vllm config file: /app/llama-stack-source/llama_stack/templates/remote-vllm/run.yaml INFO 2025-06-26 13:47:31,818 __main__:438 server: Run configuration: INFO 2025-06-26 13:47:31,826 __main__:440 server: apis: - agents - datasetio - eval - inference - safety - scoring - telemetry - tool_runtime - vector_io benchmarks: [] container_image: null .... ``` ----- previous test: local run` >llama stack build --template remote-vllm --image-type container` image stored in `quay.io/wenzhou/distribution-remote-vllm:2492` --------- Signed-off-by: Wen Zhou <wenzhou@redhat.com> fix(security): Upgrade urllib3 to v2.5.0. Fixes CVE-2025-50181 and CVE-2025-50182 (#2534) This fixes CVE-2025-50181 and CVE-2025-50182. Changes via: ``` uv sync --upgrade-package urllib3 uv export --frozen --no-hashes --no-emit-project --no-default-groups --output-file=requirements.txt ``` Signed-off-by: Yuan Tang <terrytangyuan@gmail.com> fix: dataset metadata without provider_id (#2527) Fixes an error when inferring dataset provider_id with metadata Closes #[2506](https://github.com/meta-llama/llama-stack/issues/2506) Signed-off-by: Juanma Barea <juanmabareamartinez@gmail.com> fix(security): Upgrade protobuf and aiohttp. Fixes CVE-2025-4565 (#2541) Fixes CVE-2025-4565 and the following warning: ``` warning: `aiohttp==3.11.13` is yanked (reason: "Regression: https://github.com/aio-libs/aiohttp/issues/10617") ``` Signed-off-by: Yuan Tang <terrytangyuan@gmail.com> adding milvus prefix Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updating CI Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> removing CI tests for now Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> think I got the config correct for CI Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updated build and run files Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> adding marshmallow constraint Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> removing CI changes Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Update starter.py updated starter Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo
parent
0883944bc3
commit
7273a125cd
14 changed files with 233 additions and 244 deletions
70
CHANGELOG.md
70
CHANGELOG.md
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@ -1,5 +1,28 @@
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# Changelog
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# v0.2.12
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Published on: 2025-06-20T22:52:12Z
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## Highlights
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* Filter support in file search
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* Support auth attributes in inference and response stores
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---
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# v0.2.11
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Published on: 2025-06-17T20:26:26Z
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## Highlights
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* OpenAI-compatible vector store APIs
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* Hybrid Search in Sqlite-vec
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* File search tool in Responses API
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* Pagination in inference and response stores
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* Added `suffix` to completions API for fill-in-the-middle tasks
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---
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# v0.2.10.1
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Published on: 2025-06-06T20:11:02Z
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@ -481,51 +504,4 @@ Published on: 2024-11-23T17:14:07Z
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---
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# v0.0.54
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Published on: 2024-11-22T00:36:09Z
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---
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# v0.0.53
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Published on: 2024-11-20T22:18:00Z
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🚀 Initial Release Notes for Llama Stack!
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### Added
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- Resource-oriented design for models, shields, memory banks, datasets and eval tasks
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- Persistence for registered objects with distribution
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- Ability to persist memory banks created for FAISS
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- PostgreSQL KVStore implementation
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- Environment variable placeholder support in run.yaml files
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- Comprehensive Zero-to-Hero notebooks and quickstart guides
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- Support for quantized models in Ollama
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- Vision models support for Together, Fireworks, Meta-Reference, and Ollama, and vLLM
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- Bedrock distribution with safety shields support
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- Evals API with task registration and scoring functions
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- MMLU and SimpleQA benchmark scoring functions
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- Huggingface dataset provider integration for benchmarks
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- Support for custom dataset registration from local paths
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- Benchmark evaluation CLI tools with visualization tables
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- RAG evaluation scoring functions and metrics
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- Local persistence for datasets and eval tasks
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### Changed
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- Split safety into distinct providers (llama-guard, prompt-guard, code-scanner)
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- Changed provider naming convention (`impls` → `inline`, `adapters` → `remote`)
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- Updated API signatures for dataset and eval task registration
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- Restructured folder organization for providers
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- Enhanced Docker build configuration
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- Added version prefixing for REST API routes
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- Enhanced evaluation task registration workflow
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- Improved benchmark evaluation output formatting
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- Restructured evals folder organization for better modularity
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### Removed
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- `llama stack configure` command
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---
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@ -154,12 +154,6 @@ get_python_cmd() {
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fi
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}
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# Add other required item commands generic to all containers
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add_to_container << EOF
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# Allows running as non-root user
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RUN mkdir -p /.llama/providers.d /.cache
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EOF
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if [ -n "$run_config" ]; then
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# Copy the run config to the build context since it's an absolute path
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cp "$run_config" "$BUILD_CONTEXT_DIR/run.yaml"
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@ -175,7 +169,7 @@ if [ -n "$run_config" ]; then
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echo "Copying external providers directory: $external_providers_dir"
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cp -r "$external_providers_dir" "$BUILD_CONTEXT_DIR/providers.d"
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add_to_container << EOF
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COPY providers.d /.llama/providers.d
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COPY --chmod=g+w providers.d /.llama/providers.d
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EOF
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fi
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@ -268,7 +262,7 @@ fi
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# Add other require item commands genearic to all containers
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add_to_container << EOF
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RUN chmod -R g+rw /app /.llama /.cache
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RUN mkdir -p /.llama /.cache && chmod -R g+rw /app /.llama /.cache
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EOF
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printf "Containerfile created successfully in %s/Containerfile\n\n" "$TEMP_DIR"
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@ -57,9 +57,8 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
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provider_dataset_id = dataset_id
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# infer provider from source
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if metadata:
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if metadata.get("provider_id"):
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provider_id = metadata.get("provider_id") # pass through from nvidia datasetio
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if metadata and metadata.get("provider_id"):
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provider_id = metadata.get("provider_id") # pass through from nvidia datasetio
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elif source.type == DatasetType.rows.value:
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provider_id = "localfs"
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elif source.type == DatasetType.uri.value:
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@ -14,6 +14,6 @@ from .config import MilvusVectorIOConfig
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async def get_provider_impl(config: MilvusVectorIOConfig, deps: dict[Api, Any]):
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from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusVectorIOAdapter
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impl = MilvusVectorIOAdapter(config, deps[Api.inference])
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impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files, None))
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await impl.initialize()
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return impl
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@ -8,13 +8,24 @@ from typing import Any
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from pydantic import BaseModel
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from llama_stack.providers.utils.kvstore.config import (
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KVStoreConfig,
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SqliteKVStoreConfig,
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)
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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 MilvusVectorIOConfig(BaseModel):
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db_path: str
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kvstore: KVStoreConfig
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@classmethod
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def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
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return {"db_path": "${env.MILVUS_DB_PATH}"}
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return {
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"db_path": f"${{env.MILVUS_DB_PATH:={__distro_dir__}/milvus.db}}",
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"kvstore": SqliteKVStoreConfig.sample_run_config(
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__distro_dir__=__distro_dir__,
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db_name=f"${{env.MILVUS_KVSTORE_DB_PATH:={__distro_dir__}/milvus_registry.db}}",
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),
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}
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@ -130,5 +130,6 @@ def available_providers() -> list[ProviderSpec]:
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module="llama_stack.providers.inline.vector_io.milvus",
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config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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),
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]
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@ -5,34 +5,27 @@
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# the root directory of this source tree.
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import asyncio
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import hashlib
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import json
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import logging
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import os
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import uuid
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from typing import Any
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from numpy.typing import NDArray
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from pymilvus import MilvusClient
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from llama_stack.apis.inference import InterleavedContent
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from llama_stack.apis.files.files import Files
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from llama_stack.apis.inference import Inference, InterleavedContent
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import (
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Chunk,
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QueryChunksResponse,
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SearchRankingOptions,
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VectorIO,
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VectorStoreChunkingStrategy,
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VectorStoreDeleteResponse,
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VectorStoreFileContentsResponse,
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VectorStoreFileObject,
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VectorStoreFileStatus,
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VectorStoreListFilesResponse,
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VectorStoreListResponse,
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VectorStoreObject,
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VectorStoreSearchResponsePage,
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)
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from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
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from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
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from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
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from llama_stack.providers.utils.kvstore import kvstore_impl
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from llama_stack.providers.utils.kvstore.api import KVStore
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from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
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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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@ -42,12 +35,22 @@ from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
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logger = logging.getLogger(__name__)
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VERSION = "v3"
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VECTOR_DBS_PREFIX = f"vector_dbs:milvus:{VERSION}::"
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VECTOR_INDEX_PREFIX = f"vector_index:milvus:{VERSION}::"
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OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:milvus:{VERSION}::"
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OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:milvus:{VERSION}::"
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OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:milvus:{VERSION}::"
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class MilvusIndex(EmbeddingIndex):
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def __init__(self, client: MilvusClient, collection_name: str, consistency_level="Strong"):
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def __init__(
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self, client: MilvusClient, collection_name: str, consistency_level="Strong", kvstore: KVStore | None = None
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):
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self.client = client
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self.collection_name = collection_name.replace("-", "_")
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self.consistency_level = consistency_level
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self.kvstore = kvstore
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async def delete(self):
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if await asyncio.to_thread(self.client.has_collection, self.collection_name):
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data = []
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for chunk, embedding in zip(chunks, embeddings, strict=False):
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chunk_id = generate_chunk_id(chunk.metadata["document_id"], chunk.content)
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data.append(
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{
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"chunk_id": chunk_id,
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"chunk_id": chunk.chunk_id,
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"vector": embedding,
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"chunk_content": chunk.model_dump(),
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}
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@ -120,16 +121,42 @@ class MilvusIndex(EmbeddingIndex):
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raise NotImplementedError("Hybrid search is not supported in Milvus")
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class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
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def __init__(
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self, config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig, inference_api: Api.inference
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self,
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config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig,
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inference_api: Inference,
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files_api: Files | None,
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) -> None:
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self.config = config
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self.cache = {}
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self.client = None
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self.inference_api = inference_api
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self.files_api = files_api
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self.kvstore: KVStore | None = None
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self.vector_db_store = None
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self.openai_vector_stores: dict[str, dict[str, Any]] = {}
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self.metadata_collection_name = "openai_vector_stores_metadata"
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async def initialize(self) -> None:
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self.kvstore = await kvstore_impl(self.config.kvstore)
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start_key = VECTOR_DBS_PREFIX
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end_key = f"{VECTOR_DBS_PREFIX}\xff"
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stored_vector_dbs = await self.kvstore.values_in_range(start_key, end_key)
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for vector_db_data in stored_vector_dbs:
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vector_db = VectorDB.mdel_validate_json(vector_db_data)
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index = VectorDBWithIndex(
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vector_db,
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index=await MilvusIndex(
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client=self.client,
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collection_name=vector_db.identifier,
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consistency_level=self.config.consistency_level,
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kvstore=self.kvstore,
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),
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inference_api=self.inference_api,
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)
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self.cache[vector_db.identifier] = index
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if isinstance(self.config, RemoteMilvusVectorIOConfig):
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logger.info(f"Connecting to Milvus server at {self.config.uri}")
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self.client = MilvusClient(**self.config.model_dump(exclude_none=True))
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@ -138,6 +165,8 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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uri = os.path.expanduser(self.config.db_path)
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self.client = MilvusClient(uri=uri)
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self.openai_vector_stores = await self._load_openai_vector_stores()
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async def shutdown(self) -> None:
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self.client.close()
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@ -202,116 +231,62 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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return await index.query_chunks(query, params)
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async def openai_create_vector_store(
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self,
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name: str,
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file_ids: list[str] | None = None,
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expires_after: dict[str, Any] | None = None,
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chunking_strategy: dict[str, Any] | None = None,
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metadata: dict[str, Any] | None = None,
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embedding_model: str | None = None,
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embedding_dimension: int | None = 384,
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provider_id: str | None = None,
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provider_vector_db_id: str | None = None,
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) -> VectorStoreObject:
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raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
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async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
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"""Save vector store metadata to persistent storage."""
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assert self.kvstore is not None
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key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
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await self.kvstore.set(key=key, value=json.dumps(store_info))
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self.openai_vector_stores[store_id] = store_info
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async def openai_list_vector_stores(
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self,
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limit: int | None = 20,
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order: str | None = "desc",
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after: str | None = None,
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before: str | None = None,
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) -> VectorStoreListResponse:
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raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
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async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
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"""Update vector store metadata in persistent storage."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
|
||||
await self.kvstore.set(key=key, value=json.dumps(store_info))
|
||||
self.openai_vector_stores[store_id] = store_info
|
||||
|
||||
async def openai_retrieve_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
) -> VectorStoreObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None:
|
||||
"""Delete vector store metadata from persistent storage."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
|
||||
await self.kvstore.delete(key)
|
||||
|
||||
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 Qdrant")
|
||||
async def _save_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Save vector store file metadata to Milvus database."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=key, value=json.dumps(file_info))
|
||||
content_key = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=content_key, value=json.dumps(file_contents))
|
||||
|
||||
async def openai_delete_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
) -> VectorStoreDeleteResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]:
|
||||
"""Load all vector store metadata from persistent storage."""
|
||||
assert self.kvstore is not None
|
||||
start_key = OPENAI_VECTOR_STORES_PREFIX
|
||||
end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff"
|
||||
stored = await self.kvstore.values_in_range(start_key, end_key)
|
||||
return {json.loads(s)["id"]: json.loads(s) for s in stored}
|
||||
|
||||
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 Milvus")
|
||||
async def _save_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Save vector store file metadata to Milvus database."""
|
||||
raise NotImplementedError("Files API not yet implemented for Milvus")
|
||||
|
||||
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 Milvus")
|
||||
async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
|
||||
"""Load vector store file metadata from Milvus database."""
|
||||
raise NotImplementedError("Files API not yet implemented for Milvus")
|
||||
|
||||
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 Milvus")
|
||||
async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
|
||||
"""Load vector store file contents from Milvus database."""
|
||||
raise NotImplementedError("Files API not yet implemented for Milvus")
|
||||
|
||||
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 Milvus")
|
||||
async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
|
||||
"""Update vector store file metadata in Milvus database."""
|
||||
raise NotImplementedError("Files API not yet implemented for Milvus")
|
||||
|
||||
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 Milvus")
|
||||
|
||||
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 Milvus")
|
||||
|
||||
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 Milvus")
|
||||
|
||||
|
||||
def generate_chunk_id(document_id: str, chunk_text: str) -> str:
|
||||
"""Generate a unique chunk ID using a hash of document ID and chunk text."""
|
||||
hash_input = f"{document_id}:{chunk_text}".encode()
|
||||
return str(uuid.UUID(hashlib.md5(hash_input).hexdigest()))
|
||||
|
||||
|
||||
# TODO: refactor this generate_chunk_id along with the `sqlite-vec` implementation into a separate utils file
|
||||
async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
|
||||
"""Delete vector store file metadata from Milvus database."""
|
||||
raise NotImplementedError("Files API not yet implemented for Milvus")
|
||||
|
|
|
|||
|
|
@ -42,6 +42,12 @@ logger = logging.getLogger(__name__)
|
|||
# Constants for OpenAI vector stores
|
||||
CHUNK_MULTIPLIER = 5
|
||||
|
||||
VERSION = "v3"
|
||||
VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
|
||||
OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
|
||||
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:{VERSION}::"
|
||||
OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:{VERSION}::"
|
||||
|
||||
|
||||
class OpenAIVectorStoreMixin(ABC):
|
||||
"""
|
||||
|
|
@ -141,7 +147,6 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
provider_vector_db_id: str | None = None,
|
||||
) -> VectorStoreObject:
|
||||
"""Creates a vector store."""
|
||||
# store and vector_db have the same id
|
||||
store_id = name or str(uuid.uuid4())
|
||||
created_at = int(time.time())
|
||||
|
||||
|
|
@ -315,7 +320,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
await self._delete_openai_vector_store_from_storage(vector_store_id)
|
||||
|
||||
# Delete from in-memory cache
|
||||
del self.openai_vector_stores[vector_store_id]
|
||||
self.openai_vector_stores.pop(vector_store_id, None)
|
||||
|
||||
# Also delete the underlying vector DB
|
||||
try:
|
||||
|
|
@ -574,6 +579,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
# Save vector store file to persistent storage (provider-specific)
|
||||
dict_chunks = [c.model_dump() for c in chunks]
|
||||
# This should be updated to include chunk_id
|
||||
await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
|
||||
|
||||
# Update file_ids and file_counts in vector store metadata
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ distribution_spec:
|
|||
- inline::sentence-transformers
|
||||
vector_io:
|
||||
- inline::sqlite-vec
|
||||
- inline::milvus
|
||||
- remote::chromadb
|
||||
- remote::pgvector
|
||||
files:
|
||||
|
|
|
|||
|
|
@ -72,6 +72,14 @@ providers:
|
|||
provider_type: inline::sqlite-vec
|
||||
config:
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db
|
||||
- provider_id: ${env.ENABLE_MILVUS:+milvus}
|
||||
provider_type: inline::milvus
|
||||
config:
|
||||
db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter/milvus.db}
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/${env.MILVUS_KVSTORE_DB_PATH:=~/.llama/distributions/starter/milvus_registry.db}
|
||||
- provider_id: ${env.ENABLE_CHROMADB:+chromadb}
|
||||
provider_type: remote::chromadb
|
||||
config:
|
||||
|
|
|
|||
|
|
@ -17,6 +17,9 @@ from llama_stack.providers.inline.inference.sentence_transformers import (
|
|||
SentenceTransformersInferenceConfig,
|
||||
)
|
||||
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.milvus.config import (
|
||||
MilvusVectorIOConfig,
|
||||
)
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
|
||||
SQLiteVectorIOConfig,
|
||||
)
|
||||
|
|
@ -151,7 +154,7 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
inference_providers, available_models = get_inference_providers()
|
||||
providers = {
|
||||
"inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
|
||||
"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
|
||||
"vector_io": ["inline::sqlite-vec", "inline::milvus", "remote::chromadb", "remote::pgvector"],
|
||||
"files": ["inline::localfs"],
|
||||
"safety": ["inline::llama-guard"],
|
||||
"agents": ["inline::meta-reference"],
|
||||
|
|
@ -179,6 +182,11 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
provider_type="inline::sqlite-vec",
|
||||
config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
|
||||
),
|
||||
Provider(
|
||||
provider_id="${env.ENABLE_MILVUS:+milvus}",
|
||||
provider_type="inline::milvus",
|
||||
config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
|
||||
),
|
||||
Provider(
|
||||
provider_id="${env.ENABLE_CHROMADB:+chromadb}",
|
||||
provider_type="remote::chromadb",
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
# uv export --frozen --no-hashes --no-emit-project --no-default-groups --output-file=requirements.txt
|
||||
aiohappyeyeballs==2.5.0
|
||||
# via aiohttp
|
||||
aiohttp==3.11.13
|
||||
aiohttp==3.12.13
|
||||
# via llama-stack
|
||||
aiosignal==1.3.2
|
||||
# via aiohttp
|
||||
|
|
@ -144,7 +144,7 @@ propcache==0.3.0
|
|||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
protobuf==5.29.3
|
||||
protobuf==5.29.5
|
||||
# via
|
||||
# googleapis-common-protos
|
||||
# opentelemetry-proto
|
||||
|
|
@ -238,7 +238,7 @@ typing-extensions==4.12.2
|
|||
# referencing
|
||||
tzdata==2025.1
|
||||
# via pandas
|
||||
urllib3==2.3.0
|
||||
urllib3==2.5.0
|
||||
# via requests
|
||||
uvicorn==0.34.0
|
||||
# via llama-stack
|
||||
|
|
|
|||
|
|
@ -20,6 +20,15 @@ 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"]:
|
||||
return
|
||||
|
||||
pytest.skip("OpenAI vector stores are not supported by any provider")
|
||||
|
||||
|
||||
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"]:
|
||||
|
|
@ -443,6 +452,7 @@ def test_openai_vector_store_search_with_max_num_results(
|
|||
def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store attach file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
|
||||
|
|
@ -494,6 +504,7 @@ def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client
|
|||
def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store attach files on creation."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
|
||||
|
|
@ -551,6 +562,7 @@ def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_s
|
|||
def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store list files."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
|
||||
|
|
@ -624,6 +636,7 @@ def test_openai_vector_store_list_files_invalid_vector_store(compat_client_with_
|
|||
def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store retrieve file contents."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files retrieve contents is not yet supported with LlamaStackClient")
|
||||
|
|
@ -665,6 +678,7 @@ def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_sto
|
|||
def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store delete file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
|
||||
|
|
@ -718,10 +732,11 @@ def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client
|
|||
|
||||
|
||||
# TODO: Remove this xfail once we have a way to remove embeddings from vector store
|
||||
@pytest.mark.xfail(reason="Vector Store Files delete doesn't remove embeddings from vecntor store", strict=True)
|
||||
@pytest.mark.xfail(reason="Vector Store Files delete doesn't remove embeddings from vector store", strict=True)
|
||||
def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store delete file removes from vector store."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
|
||||
|
|
@ -763,6 +778,7 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client
|
|||
def test_openai_vector_store_update_file(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store update file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files update is not yet supported with LlamaStackClient")
|
||||
|
|
|
|||
100
uv.lock
generated
100
uv.lock
generated
|
|
@ -21,7 +21,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.11.13"
|
||||
version = "3.12.13"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohappyeyeballs" },
|
||||
|
|
@ -32,40 +32,42 @@ dependencies = [
|
|||
{ name = "propcache" },
|
||||
{ name = "yarl" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b3/3f/c4a667d184c69667b8f16e0704127efc5f1e60577df429382b4d95fd381e/aiohttp-3.11.13.tar.gz", hash = "sha256:8ce789231404ca8fff7f693cdce398abf6d90fd5dae2b1847477196c243b1fbb", size = 7674284, upload-time = "2025-02-24T16:02:06.91Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/42/6e/ab88e7cb2a4058bed2f7870276454f85a7c56cd6da79349eb314fc7bbcaa/aiohttp-3.12.13.tar.gz", hash = "sha256:47e2da578528264a12e4e3dd8dd72a7289e5f812758fe086473fab037a10fcce", size = 7819160, upload-time = "2025-06-14T15:15:41.354Z" }
|
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{ url = "https://files.pythonhosted.org/packages/7e/cc/7e77861000a0691aeea8f4566e5d3aa716f2b1dece4a24439437e41d3d25/protobuf-5.29.5-py3-none-any.whl", hash = "sha256:6cf42630262c59b2d8de33954443d94b746c952b01434fc58a417fdbd2e84bd5", size = 172823, upload-time = "2025-05-28T23:51:58.157Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3614,11 +3608,11 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.3.0"
|
||||
version = "2.5.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/aa/63/e53da845320b757bf29ef6a9062f5c669fe997973f966045cb019c3f4b66/urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d", size = 307268, upload-time = "2024-12-22T07:47:30.032Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185, upload-time = "2025-06-18T14:07:41.644Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/19/4ec628951a74043532ca2cf5d97b7b14863931476d117c471e8e2b1eb39f/urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df", size = 128369, upload-time = "2024-12-22T07:47:28.074Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue