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https://github.com/meta-llama/llama-stack.git
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1: rename vector db to store, remove external APIs
This commit is contained in:
Ashwin Bharambe
parent
444f6c88f3
commit
375650c6b3
5 changed files with 25 additions and 85 deletions
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@ -13,7 +13,7 @@ from pydantic import BaseModel, Field
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class ResourceType(StrEnum):
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model = "model"
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shield = "shield"
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vector_db = "vector_db"
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vector_store = "vector_store"
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dataset = "dataset"
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scoring_function = "scoring_function"
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benchmark = "benchmark"
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@ -34,4 +34,4 @@ class Resource(BaseModel):
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provider_id: str = Field(description="ID of the provider that owns this resource")
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type: ResourceType = Field(description="Type of resource (e.g. 'model', 'shield', 'vector_db', etc.)")
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type: ResourceType = Field(description="Type of resource (e.g. 'model', 'shield', 'vector_store', etc.)")
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@ -4,90 +4,47 @@
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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 Literal, Protocol, runtime_checkable
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from typing import Literal
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from pydantic import BaseModel
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from llama_stack.apis.resource import Resource, ResourceType
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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 VectorDB(Resource):
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# Internal resource type for storing the vector store routing and other information
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class VectorStore(Resource):
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"""Vector database resource for storing and querying vector embeddings.
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:param type: Type of resource, always 'vector_db' for vector databases
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:param type: Type of resource, always 'vector_store' for vector stores
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:param embedding_model: Name of the embedding model to use for vector generation
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:param embedding_dimension: Dimension of the embedding vectors
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"""
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type: Literal[ResourceType.vector_db] = ResourceType.vector_db
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type: Literal[ResourceType.vector_store] = ResourceType.vector_store
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embedding_model: str
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embedding_dimension: int
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vector_db_name: str | None = None
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@property
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def vector_db_id(self) -> str:
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def vector_store_id(self) -> str:
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return self.identifier
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@property
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def provider_vector_db_id(self) -> str | None:
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def provider_vector_store_id(self) -> str | None:
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return self.provider_resource_id
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class VectorDBInput(BaseModel):
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class VectorStoreInput(BaseModel):
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"""Input parameters for creating or configuring a vector database.
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:param vector_db_id: Unique identifier for the vector database
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:param vector_store_id: Unique identifier for the vector store
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:param embedding_model: Name of the embedding model to use for vector generation
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:param embedding_dimension: Dimension of the embedding vectors
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:param provider_vector_db_id: (Optional) Provider-specific identifier for the vector database
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:param provider_vector_store_id: (Optional) Provider-specific identifier for the vector store
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"""
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vector_db_id: str
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vector_store_id: str
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embedding_model: str
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embedding_dimension: int
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provider_id: str | None = None
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provider_vector_db_id: str | None = None
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class ListVectorDBsResponse(BaseModel):
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"""Response from listing vector databases.
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:param data: List of vector databases
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"""
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data: list[VectorDB]
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@runtime_checkable
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class VectorDBs(Protocol):
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"""Internal protocol for vector_dbs routing - no public API endpoints."""
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async def list_vector_dbs(self) -> ListVectorDBsResponse:
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"""Internal method to list vector databases."""
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...
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async def get_vector_db(
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self,
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vector_db_id: str,
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) -> VectorDB:
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"""Internal method to get a vector database by ID."""
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...
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async def register_vector_db(
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self,
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vector_db_id: str,
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embedding_model: str,
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embedding_dimension: int | None = 384,
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provider_id: str | None = None,
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vector_db_name: str | None = None,
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provider_vector_db_id: str | None = None,
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) -> VectorDB:
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"""Internal method to register a vector database."""
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...
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async def unregister_vector_db(self, vector_db_id: str) -> None:
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"""Internal method to unregister a vector database."""
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...
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provider_vector_store_id: str | None = None
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@ -15,7 +15,7 @@ from fastapi import Body
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from pydantic import BaseModel, Field
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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_dbs import VectorStore
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from llama_stack.apis.version import LLAMA_STACK_API_V1
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from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
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from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
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@ -517,17 +517,18 @@ class OpenAICreateVectorStoreFileBatchRequestWithExtraBody(BaseModel, extra="all
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chunking_strategy: VectorStoreChunkingStrategy | None = None
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class VectorDBStore(Protocol):
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def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
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class VectorStoreTable(Protocol):
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def get_vector_store(self, vector_store_id: str) -> VectorStore | None: ...
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@runtime_checkable
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@trace_protocol
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class VectorIO(Protocol):
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vector_db_store: VectorDBStore | None = None
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vector_store_table: VectorStoreTable | None = None
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# this will just block now until chunks are inserted, but it should
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# probably return a Job instance which can be polled for completion
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# TODO: rename vector_db_id to vector_store_id once Stainless is working
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@webmethod(route="/vector-io/insert", method="POST", level=LLAMA_STACK_API_V1)
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async def insert_chunks(
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self,
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@ -546,6 +547,7 @@ class VectorIO(Protocol):
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"""
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...
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# TODO: rename vector_db_id to vector_store_id once Stainless is working
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@webmethod(route="/vector-io/query", method="POST", level=LLAMA_STACK_API_V1)
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async def query_chunks(
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self,
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@ -23,7 +23,7 @@ from llama_stack.apis.scoring import Scoring
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from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnInput
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from llama_stack.apis.shields import Shield, ShieldInput
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from llama_stack.apis.tools import ToolGroup, ToolGroupInput, ToolRuntime
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from llama_stack.apis.vector_dbs import VectorDB, VectorDBInput
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from llama_stack.apis.vector_dbs import VectorStore, VectorStoreInput
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from llama_stack.apis.vector_io import VectorIO
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from llama_stack.core.access_control.datatypes import AccessRule
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from llama_stack.core.storage.datatypes import (
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@ -71,7 +71,7 @@ class ShieldWithOwner(Shield, ResourceWithOwner):
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pass
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class VectorDBWithOwner(VectorDB, ResourceWithOwner):
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class VectorStoreWithOwner(VectorStore, ResourceWithOwner):
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pass
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@ -91,12 +91,12 @@ class ToolGroupWithOwner(ToolGroup, ResourceWithOwner):
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pass
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RoutableObject = Model | Shield | VectorDB | Dataset | ScoringFn | Benchmark | ToolGroup
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RoutableObject = Model | Shield | VectorStore | Dataset | ScoringFn | Benchmark | ToolGroup
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RoutableObjectWithProvider = Annotated[
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ModelWithOwner
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| ShieldWithOwner
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| VectorDBWithOwner
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| VectorStoreWithOwner
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| DatasetWithOwner
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| ScoringFnWithOwner
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| BenchmarkWithOwner
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@ -427,7 +427,7 @@ class RegisteredResources(BaseModel):
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models: list[ModelInput] = Field(default_factory=list)
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shields: list[ShieldInput] = Field(default_factory=list)
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vector_dbs: list[VectorDBInput] = Field(default_factory=list)
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vector_stores: list[VectorStoreInput] = Field(default_factory=list)
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datasets: list[DatasetInput] = Field(default_factory=list)
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scoring_fns: list[ScoringFnInput] = Field(default_factory=list)
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benchmarks: list[BenchmarkInput] = Field(default_factory=list)
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@ -71,25 +71,6 @@ class VectorIORouter(VectorIO):
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raise ValueError(f"Embedding model '{embedding_model_id}' not found or not an embedding model")
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async def register_vector_db(
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self,
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vector_db_id: str,
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embedding_model: str,
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embedding_dimension: int | None = 384,
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provider_id: str | None = None,
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vector_db_name: str | None = None,
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provider_vector_db_id: str | None = None,
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) -> None:
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logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
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await self.routing_table.register_vector_db(
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vector_db_id,
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embedding_model,
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embedding_dimension,
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provider_id,
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vector_db_name,
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provider_vector_db_id,
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)
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async def insert_chunks(
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self,
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vector_db_id: str,
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