Merge 059d880bc0 into sapling-pr-archive-ehhuang

.github/actions/run-and-record-tests/action.yml

@@ -82,11 +82,14 @@ runs:
           echo "No recording changes"
         fi
 
-    - name: Write inference logs to file
+    - name: Write docker logs to file
       if: ${{ always() }}
       shell: bash
       run: |
         sudo docker logs ollama > ollama-${{ inputs.inference-mode }}.log || true
+        distro_name=$(echo "${{ inputs.stack-config }}" | sed 's/^docker://' | sed 's/^server://')
+        stack_container_name="llama-stack-test-$distro_name"
+        sudo docker logs $stack_container_name > docker-${distro_name}-${{ inputs.inference-mode }}.log || true
 
     - name: Upload logs
       if: ${{ always() }}

.github/workflows/integration-auth-tests.yml

@@ -73,6 +73,24 @@ jobs:
           image_name: kube
           apis: []
           providers: {}
+          storage:
+            backends:
+              kv_default:
+                type: kv_sqlite
+                db_path: $run_dir/kvstore.db
+              sql_default:
+                type: sql_sqlite
+                db_path: $run_dir/sql_store.db
+            stores:
+              metadata:
+                namespace: registry
+                backend: kv_default
+              inference:
+                table_name: inference_store
+                backend: sql_default
+              conversations:
+                table_name: openai_conversations
+                backend: sql_default
           server:
             port: 8321
           EOF

.github/workflows/integration-vector-io-tests.yml

@@ -169,9 +169,7 @@ jobs:
         run: |
           uv run --no-sync \
             pytest -sv --stack-config="files=inline::localfs,inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \
-            tests/integration/vector_io \
-            --embedding-model inline::sentence-transformers/nomic-ai/nomic-embed-text-v1.5 \
-            --embedding-dimension 768
+            tests/integration/vector_io
 
       - name: Check Storage and Memory Available After Tests
         if: ${{ always() }}

benchmarking/k8s-benchmark/stack-configmap.yaml

@@ -98,21 +98,30 @@ data:
       - provider_id: model-context-protocol
         provider_type: remote::model-context-protocol
         config: {}
-    metadata_store:
-      type: postgres
-      host: ${env.POSTGRES_HOST:=localhost}
-      port: ${env.POSTGRES_PORT:=5432}
-      db: ${env.POSTGRES_DB:=llamastack}
-      user: ${env.POSTGRES_USER:=llamastack}
-      password: ${env.POSTGRES_PASSWORD:=llamastack}
-      table_name: llamastack_kvstore
-    inference_store:
-      type: postgres
-      host: ${env.POSTGRES_HOST:=localhost}
-      port: ${env.POSTGRES_PORT:=5432}
-      db: ${env.POSTGRES_DB:=llamastack}
-      user: ${env.POSTGRES_USER:=llamastack}
-      password: ${env.POSTGRES_PASSWORD:=llamastack}
+    storage:
+      backends:
+        kv_default:
+          type: kv_postgres
+          host: ${env.POSTGRES_HOST:=localhost}
+          port: ${env.POSTGRES_PORT:=5432}
+          db: ${env.POSTGRES_DB:=llamastack}
+          user: ${env.POSTGRES_USER:=llamastack}
+          password: ${env.POSTGRES_PASSWORD:=llamastack}
+          table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+        sql_default:
+          type: sql_postgres
+          host: ${env.POSTGRES_HOST:=localhost}
+          port: ${env.POSTGRES_PORT:=5432}
+          db: ${env.POSTGRES_DB:=llamastack}
+          user: ${env.POSTGRES_USER:=llamastack}
+          password: ${env.POSTGRES_PASSWORD:=llamastack}
+      references:
+        metadata:
+          backend: kv_default
+          namespace: registry
+        inference:
+          backend: sql_default
+          table_name: inference_store
     models:
     - metadata:
         embedding_dimension: 768
@@ -137,5 +146,4 @@ data:
       port: 8323
 kind: ConfigMap
 metadata:
-  creationTimestamp: null
   name: llama-stack-config

benchmarking/k8s-benchmark/stack_run_config.yaml

@@ -95,21 +95,30 @@ providers:
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
     config: {}
-metadata_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
-  table_name: llamastack_kvstore
-inference_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
+storage:
+  backends:
+    kv_default:
+      type: kv_postgres
+      host: ${env.POSTGRES_HOST:=localhost}
+      port: ${env.POSTGRES_PORT:=5432}
+      db: ${env.POSTGRES_DB:=llamastack}
+      user: ${env.POSTGRES_USER:=llamastack}
+      password: ${env.POSTGRES_PASSWORD:=llamastack}
+      table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+    sql_default:
+      type: sql_postgres
+      host: ${env.POSTGRES_HOST:=localhost}
+      port: ${env.POSTGRES_PORT:=5432}
+      db: ${env.POSTGRES_DB:=llamastack}
+      user: ${env.POSTGRES_USER:=llamastack}
+      password: ${env.POSTGRES_PASSWORD:=llamastack}
+  references:
+    metadata:
+      backend: kv_default
+      namespace: registry
+    inference:
+      backend: sql_default
+      table_name: inference_store
 models:
 - metadata:
     embedding_dimension: 768

client-sdks/stainless/README.md

@@ -0,0 +1,8 @@
+These are the source-of-truth configuration files used to generate the Stainless client SDKs via Stainless.
+
+- `openapi.yml`: this is the OpenAPI specification for the Llama Stack API.
+- `openapi.stainless.yml`: this is the Stainless _configuration_ which instructs Stainless how to generate the client SDKs.
+
+A small side note: notice the `.yml` suffixes since Stainless uses that suffix typically for its configuration files.
+
+These files go hand-in-hand. As of now, only the `openapi.yml` file is automatically generated using the `run_openapi_generator.sh` script.

client-sdks/stainless/openapi.stainless.yml

@@ -0,0 +1,608 @@
+# yaml-language-server: $schema=https://app.stainlessapi.com/config-internal.schema.json
+
+organization:
+  # Name of your organization or company, used to determine the name of the client
+  # and headings.
+  name: llama-stack-client
+  docs: https://llama-stack.readthedocs.io/en/latest/
+  contact: llamastack@meta.com
+security:
+  - {}
+  - BearerAuth: []
+security_schemes:
+  BearerAuth:
+    type: http
+    scheme: bearer
+# `targets` define the output targets and their customization options, such as
+# whether to emit the Node SDK and what it's package name should be.
+targets:
+  node:
+    package_name: llama-stack-client
+    production_repo: llamastack/llama-stack-client-typescript
+    publish:
+      npm: false
+  python:
+    package_name: llama_stack_client
+    production_repo: llamastack/llama-stack-client-python
+    options:
+      use_uv: true
+    publish:
+      pypi: true
+    project_name: llama_stack_client
+  kotlin:
+    reverse_domain: com.llama_stack_client.api
+    production_repo: null
+    publish:
+      maven: false
+  go:
+    package_name: llama-stack-client
+    production_repo: llamastack/llama-stack-client-go
+    options:
+      enable_v2: true
+      back_compat_use_shared_package: false
+
+# `client_settings` define settings for the API client, such as extra constructor
+# arguments (used for authentication), retry behavior, idempotency, etc.
+client_settings:
+  default_env_prefix: LLAMA_STACK_CLIENT
+  opts:
+    api_key:
+      type: string
+      read_env: LLAMA_STACK_CLIENT_API_KEY
+      auth: { security_scheme: BearerAuth }
+      nullable: true
+
+# `environments` are a map of the name of the environment (e.g. "sandbox",
+# "production") to the corresponding url to use.
+environments:
+  production: http://any-hosted-llama-stack.com
+
+# `pagination` defines [pagination schemes] which provides a template to match
+# endpoints and generate next-page and auto-pagination helpers in the SDKs.
+pagination:
+  - name: datasets_iterrows
+    type: offset
+    request:
+      dataset_id:
+        type: string
+      start_index:
+        type: integer
+        x-stainless-pagination-property:
+          purpose: offset_count_param
+      limit:
+        type: integer
+    response:
+      data:
+        type: array
+        items:
+          type: object
+      next_index:
+        type: integer
+        x-stainless-pagination-property:
+          purpose: offset_count_start_field
+  - name: openai_cursor_page
+    type: cursor
+    request:
+      limit:
+        type: integer
+      after:
+        type: string
+        x-stainless-pagination-property:
+          purpose: next_cursor_param
+    response:
+      data:
+        type: array
+        items: {}
+      has_more:
+        type: boolean
+      last_id:
+        type: string
+        x-stainless-pagination-property:
+          purpose: next_cursor_field
+# `resources` define the structure and organziation for your API, such as how
+# methods and models are grouped together and accessed. See the [configuration
+# guide] for more information.
+#
+# [configuration guide]:
+#   https://app.stainlessapi.com/docs/guides/configure#resources
+resources:
+  $shared:
+    models:
+      agent_config: AgentConfig
+      interleaved_content_item: InterleavedContentItem
+      interleaved_content: InterleavedContent
+      param_type: ParamType
+      safety_violation: SafetyViolation
+      sampling_params: SamplingParams
+      scoring_result: ScoringResult
+      message: Message
+      user_message: UserMessage
+      completion_message: CompletionMessage
+      tool_response_message: ToolResponseMessage
+      system_message: SystemMessage
+      tool_call: ToolCall
+      query_result: RAGQueryResult
+      document: RAGDocument
+      query_config: RAGQueryConfig
+      response_format: ResponseFormat
+  toolgroups:
+    models:
+      tool_group: ToolGroup
+      list_tool_groups_response: ListToolGroupsResponse
+    methods:
+      register: post /v1/toolgroups
+      get: get /v1/toolgroups/{toolgroup_id}
+      list: get /v1/toolgroups
+      unregister: delete /v1/toolgroups/{toolgroup_id}
+  tools:
+    methods:
+      get: get /v1/tools/{tool_name}
+      list:
+        endpoint: get /v1/tools
+        paginated: false
+
+  tool_runtime:
+    models:
+      tool_def: ToolDef
+      tool_invocation_result: ToolInvocationResult
+    methods:
+      list_tools:
+        endpoint: get /v1/tool-runtime/list-tools
+        paginated: false
+      invoke_tool: post /v1/tool-runtime/invoke
+    subresources:
+      rag_tool:
+        methods:
+          insert: post /v1/tool-runtime/rag-tool/insert
+          query: post /v1/tool-runtime/rag-tool/query
+
+  responses:
+    models:
+      response_object_stream: OpenAIResponseObjectStream
+      response_object: OpenAIResponseObject
+    methods:
+      create:
+        type: http
+        endpoint: post /v1/responses
+        streaming:
+          stream_event_model: responses.response_object_stream
+          param_discriminator: stream
+      retrieve: get /v1/responses/{response_id}
+      list:
+        type: http
+        endpoint: get /v1/responses
+      delete:
+        type: http
+        endpoint: delete /v1/responses/{response_id}
+    subresources:
+      input_items:
+        methods:
+          list:
+            type: http
+            endpoint: get /v1/responses/{response_id}/input_items
+
+  conversations:
+    models:
+      conversation_object: Conversation
+    methods:
+      create:
+        type: http
+        endpoint: post /v1/conversations
+      retrieve: get /v1/conversations/{conversation_id}
+      update:
+        type: http
+        endpoint: post /v1/conversations/{conversation_id}
+      delete:
+        type: http
+        endpoint: delete /v1/conversations/{conversation_id}
+    subresources:
+      items:
+        methods:
+          get:
+            type: http
+            endpoint: get /v1/conversations/{conversation_id}/items/{item_id}
+          list:
+            type: http
+            endpoint: get /v1/conversations/{conversation_id}/items
+          create:
+            type: http
+            endpoint: post /v1/conversations/{conversation_id}/items
+
+  datasets:
+    models:
+      list_datasets_response: ListDatasetsResponse
+    methods:
+      register: post /v1beta/datasets
+      retrieve: get /v1beta/datasets/{dataset_id}
+      list:
+        endpoint: get /v1beta/datasets
+        paginated: false
+      unregister: delete /v1beta/datasets/{dataset_id}
+      iterrows: get /v1beta/datasetio/iterrows/{dataset_id}
+      appendrows: post /v1beta/datasetio/append-rows/{dataset_id}
+
+  inspect:
+    models:
+      healthInfo: HealthInfo
+      providerInfo: ProviderInfo
+      routeInfo: RouteInfo
+      versionInfo: VersionInfo
+    methods:
+      health: get /v1/health
+      version: get /v1/version
+
+  embeddings:
+    models:
+      create_embeddings_response: OpenAIEmbeddingsResponse
+    methods:
+      create: post /v1/embeddings
+
+  chat:
+    models:
+      chat_completion_chunk: OpenAIChatCompletionChunk
+    subresources:
+      completions:
+        methods:
+          create:
+            type: http
+            endpoint: post /v1/chat/completions
+            streaming:
+              stream_event_model: chat.chat_completion_chunk
+              param_discriminator: stream
+          list:
+            type: http
+            endpoint: get /v1/chat/completions
+          retrieve:
+            type: http
+            endpoint: get /v1/chat/completions/{completion_id}
+  completions:
+    methods:
+      create:
+        type: http
+        endpoint: post /v1/completions
+        streaming:
+          param_discriminator: stream
+
+  vector_io:
+    models:
+      queryChunksResponse: QueryChunksResponse
+    methods:
+      insert: post /v1/vector-io/insert
+      query: post /v1/vector-io/query
+
+  vector_stores:
+    models:
+      vector_store: VectorStoreObject
+      list_vector_stores_response: VectorStoreListResponse
+      vector_store_delete_response: VectorStoreDeleteResponse
+      vector_store_search_response: VectorStoreSearchResponsePage
+    methods:
+      create: post /v1/vector_stores
+      list:
+        endpoint: get /v1/vector_stores
+      retrieve: get /v1/vector_stores/{vector_store_id}
+      update: post /v1/vector_stores/{vector_store_id}
+      delete: delete /v1/vector_stores/{vector_store_id}
+      search: post /v1/vector_stores/{vector_store_id}/search
+    subresources:
+      files:
+        models:
+          vector_store_file: VectorStoreFileObject
+        methods:
+          list: get /v1/vector_stores/{vector_store_id}/files
+          retrieve: get /v1/vector_stores/{vector_store_id}/files/{file_id}
+          update: post /v1/vector_stores/{vector_store_id}/files/{file_id}
+          delete: delete /v1/vector_stores/{vector_store_id}/files/{file_id}
+          create: post /v1/vector_stores/{vector_store_id}/files
+          content: get /v1/vector_stores/{vector_store_id}/files/{file_id}/content
+      file_batches:
+        models:
+          vector_store_file_batches: VectorStoreFileBatchObject
+          list_vector_store_files_in_batch_response: VectorStoreFilesListInBatchResponse
+        methods:
+          create: post /v1/vector_stores/{vector_store_id}/file_batches
+          retrieve: get /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}
+          list_files: get /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}/files
+          cancel: post /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel
+
+  models:
+    models:
+      model: Model
+      list_models_response: ListModelsResponse
+    methods:
+      retrieve: get /v1/models/{model_id}
+      list:
+        endpoint: get /v1/models
+        paginated: false
+      register: post /v1/models
+      unregister: delete /v1/models/{model_id}
+    subresources:
+      openai:
+        methods:
+          list:
+            endpoint: get /v1/models
+            paginated: false
+
+  providers:
+    models:
+      list_providers_response: ListProvidersResponse
+    methods:
+      list:
+        endpoint: get /v1/providers
+        paginated: false
+      retrieve: get /v1/providers/{provider_id}
+
+  routes:
+    models:
+      list_routes_response: ListRoutesResponse
+    methods:
+      list:
+        endpoint: get /v1/inspect/routes
+        paginated: false
+
+
+  moderations:
+    models:
+      create_response: ModerationObject
+    methods:
+      create: post /v1/moderations
+
+
+  safety:
+    models:
+      run_shield_response: RunShieldResponse
+    methods:
+      run_shield: post /v1/safety/run-shield
+
+
+  shields:
+    models:
+      shield: Shield
+      list_shields_response: ListShieldsResponse
+    methods:
+      retrieve: get /v1/shields/{identifier}
+      list:
+        endpoint: get /v1/shields
+        paginated: false
+      register: post /v1/shields
+      delete: delete /v1/shields/{identifier}
+
+  synthetic_data_generation:
+    models:
+      syntheticDataGenerationResponse: SyntheticDataGenerationResponse
+    methods:
+      generate: post /v1/synthetic-data-generation/generate
+
+  telemetry:
+    models:
+      span_with_status: SpanWithStatus
+      trace: Trace
+      query_spans_response: QuerySpansResponse
+      event: Event
+      query_condition: QueryCondition
+    methods:
+      query_traces:
+        endpoint: post /v1alpha/telemetry/traces
+        skip_test_reason: 'unsupported query params in java / kotlin'
+      get_span_tree: post /v1alpha/telemetry/spans/{span_id}/tree
+      query_spans:
+        endpoint: post /v1alpha/telemetry/spans
+        skip_test_reason: 'unsupported query params in java / kotlin'
+      query_metrics:
+        endpoint: post /v1alpha/telemetry/metrics/{metric_name}
+        skip_test_reason: 'unsupported query params in java / kotlin'
+      # log_event: post /v1alpha/telemetry/events
+      save_spans_to_dataset: post /v1alpha/telemetry/spans/export
+      get_span: get /v1alpha/telemetry/traces/{trace_id}/spans/{span_id}
+      get_trace: get /v1alpha/telemetry/traces/{trace_id}
+
+  scoring:
+    methods:
+      score: post /v1/scoring/score
+      score_batch: post /v1/scoring/score-batch
+  scoring_functions:
+    methods:
+      retrieve: get /v1/scoring-functions/{scoring_fn_id}
+      list:
+        endpoint: get /v1/scoring-functions
+        paginated: false
+      register: post /v1/scoring-functions
+    models:
+      scoring_fn: ScoringFn
+      scoring_fn_params: ScoringFnParams
+      list_scoring_functions_response: ListScoringFunctionsResponse
+
+  benchmarks:
+    methods:
+      retrieve: get /v1alpha/eval/benchmarks/{benchmark_id}
+      list:
+        endpoint: get /v1alpha/eval/benchmarks
+        paginated: false
+      register: post /v1alpha/eval/benchmarks
+    models:
+      benchmark: Benchmark
+      list_benchmarks_response: ListBenchmarksResponse
+
+  files:
+    methods:
+      create: post /v1/files
+      list: get /v1/files
+      retrieve: get /v1/files/{file_id}
+      delete: delete /v1/files/{file_id}
+      content: get /v1/files/{file_id}/content
+    models:
+      file: OpenAIFileObject
+      list_files_response: ListOpenAIFileResponse
+      delete_file_response: OpenAIFileDeleteResponse
+
+  alpha:
+    subresources:
+      inference:
+        methods:
+          rerank: post /v1alpha/inference/rerank
+
+      post_training:
+        models:
+          algorithm_config: AlgorithmConfig
+          post_training_job: PostTrainingJob
+          list_post_training_jobs_response: ListPostTrainingJobsResponse
+        methods:
+          preference_optimize: post /v1alpha/post-training/preference-optimize
+          supervised_fine_tune: post /v1alpha/post-training/supervised-fine-tune
+        subresources:
+          job:
+            methods:
+              artifacts: get /v1alpha/post-training/job/artifacts
+              cancel: post /v1alpha/post-training/job/cancel
+              status: get /v1alpha/post-training/job/status
+              list:
+                endpoint: get /v1alpha/post-training/jobs
+                paginated: false
+
+      eval:
+        methods:
+          evaluate_rows: post /v1alpha/eval/benchmarks/{benchmark_id}/evaluations
+          run_eval: post /v1alpha/eval/benchmarks/{benchmark_id}/jobs
+          evaluate_rows_alpha: post /v1alpha/eval/benchmarks/{benchmark_id}/evaluations
+          run_eval_alpha: post /v1alpha/eval/benchmarks/{benchmark_id}/jobs
+
+        subresources:
+          jobs:
+            methods:
+              cancel: delete /v1alpha/eval/benchmarks/{benchmark_id}/jobs/{job_id}
+              status: get /v1alpha/eval/benchmarks/{benchmark_id}/jobs/{job_id}
+              retrieve: get /v1alpha/eval/benchmarks/{benchmark_id}/jobs/{job_id}/result
+        models:
+          evaluate_response: EvaluateResponse
+          benchmark_config: BenchmarkConfig
+          job: Job
+
+      agents:
+        methods:
+          create: post /v1alpha/agents
+          list: get /v1alpha/agents
+          retrieve: get /v1alpha/agents/{agent_id}
+          delete: delete /v1alpha/agents/{agent_id}
+        models:
+          inference_step: InferenceStep
+          tool_execution_step: ToolExecutionStep
+          tool_response: ToolResponse
+          shield_call_step: ShieldCallStep
+          memory_retrieval_step: MemoryRetrievalStep
+        subresources:
+          session:
+            models:
+              session: Session
+            methods:
+              list: get /v1alpha/agents/{agent_id}/sessions
+              create: post /v1alpha/agents/{agent_id}/session
+              delete: delete /v1alpha/agents/{agent_id}/session/{session_id}
+              retrieve: get /v1alpha/agents/{agent_id}/session/{session_id}
+          steps:
+            methods:
+              retrieve: get /v1alpha/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id}
+          turn:
+            models:
+              turn: Turn
+              turn_response_event: AgentTurnResponseEvent
+              agent_turn_response_stream_chunk: AgentTurnResponseStreamChunk
+            methods:
+              create:
+                type: http
+                endpoint: post /v1alpha/agents/{agent_id}/session/{session_id}/turn
+                streaming:
+                  stream_event_model: alpha.agents.turn.agent_turn_response_stream_chunk
+                  param_discriminator: stream
+              retrieve: get /v1alpha/agents/{agent_id}/session/{session_id}/turn/{turn_id}
+              resume:
+                type: http
+                endpoint: post /v1alpha/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume
+                streaming:
+                  stream_event_model: alpha.agents.turn.agent_turn_response_stream_chunk
+                  param_discriminator: stream
+
+
+settings:
+  license: MIT
+  unwrap_response_fields: [ data ]
+
+openapi:
+  transformations:
+    - command: renameValue
+      reason: pydantic reserved name
+      args:
+        filter:
+          only:
+            - '$.components.schemas.InferenceStep.properties.model_response'
+        rename:
+          python:
+            property_name: 'inference_model_response'
+
+    # - command: renameValue
+    #   reason: pydantic reserved name
+    #   args:
+    #     filter:
+    #       only:
+    #         - '$.components.schemas.Model.properties.model_type'
+    #     rename:
+    #       python:
+    #         property_name: 'type'
+    - command: mergeObject
+      reason: Better return_type using enum
+      args:
+        target:
+          - '$.components.schemas'
+        object:
+          ReturnType:
+            additionalProperties: false
+            properties:
+              type:
+                enum:
+                  - string
+                  - number
+                  - boolean
+                  - array
+                  - object
+                  - json
+                  - union
+                  - chat_completion_input
+                  - completion_input
+                  - agent_turn_input
+            required:
+              - type
+            type: object
+    - command: replaceProperties
+      reason: Replace return type properties with better model (see above)
+      args:
+        filter:
+          only:
+            - '$.components.schemas.ScoringFn.properties.return_type'
+            - '$.components.schemas.RegisterScoringFunctionRequest.properties.return_type'
+        value:
+          $ref: '#/components/schemas/ReturnType'
+    - command: oneOfToAnyOf
+      reason: Prism (mock server) doesn't like one of our requests as it technically matches multiple variants
+    - reason: For better names
+      command: extractToRefs
+      args:
+        ref:
+          target: '$.components.schemas.ToolCallDelta.properties.tool_call'
+          name: '#/components/schemas/ToolCallOrString'
+
+# `readme` is used to configure the code snippets that will be rendered in the
+# README.md of various SDKs. In particular, you can change the `headline`
+# snippet's endpoint and the arguments to call it with.
+readme:
+  example_requests:
+    default:
+      type: request
+      endpoint: post /v1/chat/completions
+      params: &ref_0 {}
+    headline:
+      type: request
+      endpoint: post /v1/models
+      params: *ref_0
+    pagination:
+      type: request
+      endpoint: post /v1/chat/completions
+      params: {}

containers/Containerfile

@@ -60,6 +60,17 @@ ENV RUN_CONFIG_PATH=${RUN_CONFIG_PATH}
 # Copy the repository so editable installs and run configurations are available.
 COPY . /workspace
 
+# Install the client package if it is provided
+# NOTE: this is installed before llama-stack since llama-stack depends on llama-stack-client-python
+RUN set -eux; \
+    if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then \
+        if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ]; then \
+            echo "LLAMA_STACK_CLIENT_DIR is set but $LLAMA_STACK_CLIENT_DIR does not exist" >&2; \
+            exit 1; \
+        fi; \
+        uv pip install --no-cache-dir -e "$LLAMA_STACK_CLIENT_DIR"; \
+    fi;
+
 # Install llama-stack
 RUN set -eux; \
     if [ "$INSTALL_MODE" = "editable" ]; then \
@@ -83,16 +94,6 @@ RUN set -eux; \
         fi; \
     fi;
 
-# Install the client package if it is provided
-RUN set -eux; \
-    if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then \
-        if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ]; then \
-            echo "LLAMA_STACK_CLIENT_DIR is set but $LLAMA_STACK_CLIENT_DIR does not exist" >&2; \
-            exit 1; \
-        fi; \
-        uv pip install --no-cache-dir -e "$LLAMA_STACK_CLIENT_DIR"; \
-    fi;
-
 # Install the dependencies for the distribution
 RUN set -eux; \
     if [ -z "$DISTRO_NAME" ]; then \

docs/docs/building_applications/rag.mdx

@@ -88,18 +88,19 @@ Llama Stack provides OpenAI-compatible RAG capabilities through:
 To enable automatic vector store creation without specifying embedding models, configure a default embedding model in your run.yaml like so:
 
 ```yaml
-models:
-  - model_id: nomic-ai/nomic-embed-text-v1.5
-    provider_id: inline::sentence-transformers
-    metadata:
-      embedding_dimension: 768
-      default_configured: true
+vector_stores:
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5
 ```
 
 With this configuration:
-- `client.vector_stores.create()` works without requiring embedding model parameters
-- The system automatically uses the default model and its embedding dimension for any newly created vector store
-- Only one model can be marked as `default_configured: true`
+- `client.vector_stores.create()` works without requiring embedding model or provider parameters
+- The system automatically uses the default vector store provider (`faiss`) when multiple providers are available
+- The system automatically uses the default embedding model (`sentence-transformers/nomic-ai/nomic-embed-text-v1.5`) for any newly created vector store
+- The `default_provider_id` specifies which vector storage backend to use
+- The `default_embedding_model` specifies both the inference provider and model for embeddings
 
 ## Vector Store Operations
 
@@ -108,14 +109,15 @@ With this configuration:
 You can create vector stores with automatic or explicit embedding model selection:
 
 ```python
-# Automatic - uses default configured embedding model
+# Automatic - uses default configured embedding model and vector store provider
 vs = client.vector_stores.create()
 
-# Explicit - specify embedding model when you need a specific one
+# Explicit - specify embedding model and/or provider when you need specific ones
 vs = client.vector_stores.create(
     extra_body={
-        "embedding_model": "nomic-ai/nomic-embed-text-v1.5",
-        "embedding_dimension": 768
+        "provider_id": "faiss",  # Optional: specify vector store provider
+        "embedding_model": "sentence-transformers/nomic-ai/nomic-embed-text-v1.5",
+        "embedding_dimension": 768  # Optional: will be auto-detected if not provided
     }
 )
 ```

docs/docs/distributions/configuration.mdx

@@ -44,18 +44,32 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/agents_store.db
+      persistence:
+        agent_state:
+          backend: kv_default
+          namespace: agents
+        responses:
+          backend: sql_default
+          table_name: responses
   telemetry:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config: {}
-metadata_store:
-  namespace: null
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/registry.db
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/sqlstore.db
+  references:
+    metadata:
+      backend: kv_default
+      namespace: registry
+    inference:
+      backend: sql_default
+      table_name: inference_store
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

docs/docs/distributions/k8s/stack-configmap.yaml

@@ -1,56 +1,155 @@
 apiVersion: v1
 data:
-  stack_run_config.yaml: "version: '2'\nimage_name: kubernetes-demo\napis:\n- agents\n-
-    inference\n- files\n- safety\n- telemetry\n- tool_runtime\n- vector_io\nproviders:\n
-    \ inference:\n  - provider_id: vllm-inference\n    provider_type: remote::vllm\n
-    \   config:\n      url: ${env.VLLM_URL:=http://localhost:8000/v1}\n      max_tokens:
-    ${env.VLLM_MAX_TOKENS:=4096}\n      api_token: ${env.VLLM_API_TOKEN:=fake}\n      tls_verify:
-    ${env.VLLM_TLS_VERIFY:=true}\n  - provider_id: vllm-safety\n    provider_type:
-    remote::vllm\n    config:\n      url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}\n
-    \     max_tokens: ${env.VLLM_MAX_TOKENS:=4096}\n      api_token: ${env.VLLM_API_TOKEN:=fake}\n
-    \     tls_verify: ${env.VLLM_TLS_VERIFY:=true}\n  - provider_id: sentence-transformers\n
-    \   provider_type: inline::sentence-transformers\n    config: {}\n  vector_io:\n
-    \ - provider_id: ${env.ENABLE_CHROMADB:+chromadb}\n    provider_type: remote::chromadb\n
-    \   config:\n      url: ${env.CHROMADB_URL:=}\n      kvstore:\n        type: postgres\n
-    \       host: ${env.POSTGRES_HOST:=localhost}\n        port: ${env.POSTGRES_PORT:=5432}\n
-    \       db: ${env.POSTGRES_DB:=llamastack}\n        user: ${env.POSTGRES_USER:=llamastack}\n
-    \       password: ${env.POSTGRES_PASSWORD:=llamastack}\n  files:\n  - provider_id:
-    meta-reference-files\n    provider_type: inline::localfs\n    config:\n      storage_dir:
-    ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}\n      metadata_store:\n
-    \       type: sqlite\n        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db
-    \ \n  safety:\n  - provider_id: llama-guard\n    provider_type: inline::llama-guard\n
-    \   config:\n      excluded_categories: []\n  agents:\n  - provider_id: meta-reference\n
-    \   provider_type: inline::meta-reference\n    config:\n      persistence_store:\n
-    \       type: postgres\n        host: ${env.POSTGRES_HOST:=localhost}\n        port:
-    ${env.POSTGRES_PORT:=5432}\n        db: ${env.POSTGRES_DB:=llamastack}\n        user:
-    ${env.POSTGRES_USER:=llamastack}\n        password: ${env.POSTGRES_PASSWORD:=llamastack}\n
-    \     responses_store:\n        type: postgres\n        host: ${env.POSTGRES_HOST:=localhost}\n
-    \       port: ${env.POSTGRES_PORT:=5432}\n        db: ${env.POSTGRES_DB:=llamastack}\n
-    \       user: ${env.POSTGRES_USER:=llamastack}\n        password: ${env.POSTGRES_PASSWORD:=llamastack}\n
-    \ telemetry:\n  - provider_id: meta-reference\n    provider_type: inline::meta-reference\n
-    \   config:\n      service_name: \"${env.OTEL_SERVICE_NAME:=\\u200B}\"\n      sinks:
-    ${env.TELEMETRY_SINKS:=console}\n  tool_runtime:\n  - provider_id: brave-search\n
-    \   provider_type: remote::brave-search\n    config:\n      api_key: ${env.BRAVE_SEARCH_API_KEY:+}\n
-    \     max_results: 3\n  - provider_id: tavily-search\n    provider_type: remote::tavily-search\n
-    \   config:\n      api_key: ${env.TAVILY_SEARCH_API_KEY:+}\n      max_results:
-    3\n  - provider_id: rag-runtime\n    provider_type: inline::rag-runtime\n    config:
-    {}\n  - provider_id: model-context-protocol\n    provider_type: remote::model-context-protocol\n
-    \   config: {}\nmetadata_store:\n  type: postgres\n  host: ${env.POSTGRES_HOST:=localhost}\n
-    \ port: ${env.POSTGRES_PORT:=5432}\n  db: ${env.POSTGRES_DB:=llamastack}\n  user:
-    ${env.POSTGRES_USER:=llamastack}\n  password: ${env.POSTGRES_PASSWORD:=llamastack}\n
-    \ table_name: llamastack_kvstore\ninference_store:\n  type: postgres\n  host:
-    ${env.POSTGRES_HOST:=localhost}\n  port: ${env.POSTGRES_PORT:=5432}\n  db: ${env.POSTGRES_DB:=llamastack}\n
-    \ user: ${env.POSTGRES_USER:=llamastack}\n  password: ${env.POSTGRES_PASSWORD:=llamastack}\nmodels:\n-
-    metadata:\n    embedding_dimension: 384\n  model_id: all-MiniLM-L6-v2\n  provider_id:
-    sentence-transformers\n  model_type: embedding\n- metadata: {}\n  model_id: ${env.INFERENCE_MODEL}\n
-    \ provider_id: vllm-inference\n  model_type: llm\n- metadata: {}\n  model_id:
-    ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}\n  provider_id: vllm-safety\n
-    \ model_type: llm\nshields:\n- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}\nvector_dbs:
-    []\ndatasets: []\nscoring_fns: []\nbenchmarks: []\ntool_groups:\n- toolgroup_id:
-    builtin::websearch\n  provider_id: tavily-search\n- toolgroup_id: builtin::rag\n
-    \ provider_id: rag-runtime\nserver:\n  port: 8321\n  auth:\n    provider_config:\n
-    \     type: github_token\n"
+  stack_run_config.yaml: |
+    version: '2'
+    image_name: kubernetes-demo
+    apis:
+    - agents
+    - inference
+    - files
+    - safety
+    - telemetry
+    - tool_runtime
+    - vector_io
+    providers:
+      inference:
+      - provider_id: vllm-inference
+        provider_type: remote::vllm
+        config:
+          url: ${env.VLLM_URL:=http://localhost:8000/v1}
+          max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
+          api_token: ${env.VLLM_API_TOKEN:=fake}
+          tls_verify: ${env.VLLM_TLS_VERIFY:=true}
+      - provider_id: vllm-safety
+        provider_type: remote::vllm
+        config:
+          url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}
+          max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
+          api_token: ${env.VLLM_API_TOKEN:=fake}
+          tls_verify: ${env.VLLM_TLS_VERIFY:=true}
+      - provider_id: sentence-transformers
+        provider_type: inline::sentence-transformers
+        config: {}
+      vector_io:
+      - provider_id: ${env.ENABLE_CHROMADB:+chromadb}
+        provider_type: remote::chromadb
+        config:
+          url: ${env.CHROMADB_URL:=}
+          kvstore:
+            type: postgres
+            host: ${env.POSTGRES_HOST:=localhost}
+            port: ${env.POSTGRES_PORT:=5432}
+            db: ${env.POSTGRES_DB:=llamastack}
+            user: ${env.POSTGRES_USER:=llamastack}
+            password: ${env.POSTGRES_PASSWORD:=llamastack}
+      files:
+      - provider_id: meta-reference-files
+        provider_type: inline::localfs
+        config:
+          storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}
+          metadata_store:
+            type: sqlite
+            db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db
+      safety:
+      - provider_id: llama-guard
+        provider_type: inline::llama-guard
+        config:
+          excluded_categories: []
+      agents:
+      - provider_id: meta-reference
+        provider_type: inline::meta-reference
+        config:
+          persistence_store:
+            type: postgres
+            host: ${env.POSTGRES_HOST:=localhost}
+            port: ${env.POSTGRES_PORT:=5432}
+            db: ${env.POSTGRES_DB:=llamastack}
+            user: ${env.POSTGRES_USER:=llamastack}
+            password: ${env.POSTGRES_PASSWORD:=llamastack}
+          responses_store:
+            type: postgres
+            host: ${env.POSTGRES_HOST:=localhost}
+            port: ${env.POSTGRES_PORT:=5432}
+            db: ${env.POSTGRES_DB:=llamastack}
+            user: ${env.POSTGRES_USER:=llamastack}
+            password: ${env.POSTGRES_PASSWORD:=llamastack}
+      telemetry:
+      - provider_id: meta-reference
+        provider_type: inline::meta-reference
+        config:
+          service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
+          sinks: ${env.TELEMETRY_SINKS:=console}
+      tool_runtime:
+      - provider_id: brave-search
+        provider_type: remote::brave-search
+        config:
+          api_key: ${env.BRAVE_SEARCH_API_KEY:+}
+          max_results: 3
+      - provider_id: tavily-search
+        provider_type: remote::tavily-search
+        config:
+          api_key: ${env.TAVILY_SEARCH_API_KEY:+}
+          max_results: 3
+      - provider_id: rag-runtime
+        provider_type: inline::rag-runtime
+        config: {}
+      - provider_id: model-context-protocol
+        provider_type: remote::model-context-protocol
+        config: {}
+    storage:
+      backends:
+        kv_default:
+          type: kv_postgres
+          host: ${env.POSTGRES_HOST:=localhost}
+          port: ${env.POSTGRES_PORT:=5432}
+          db: ${env.POSTGRES_DB:=llamastack}
+          user: ${env.POSTGRES_USER:=llamastack}
+          password: ${env.POSTGRES_PASSWORD:=llamastack}
+          table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+        sql_default:
+          type: sql_postgres
+          host: ${env.POSTGRES_HOST:=localhost}
+          port: ${env.POSTGRES_PORT:=5432}
+          db: ${env.POSTGRES_DB:=llamastack}
+          user: ${env.POSTGRES_USER:=llamastack}
+          password: ${env.POSTGRES_PASSWORD:=llamastack}
+      references:
+        metadata:
+          backend: kv_default
+          namespace: registry
+        inference:
+          backend: sql_default
+          table_name: inference_store
+    models:
+    - metadata:
+        embedding_dimension: 768
+      model_id: nomic-embed-text-v1.5
+      provider_id: sentence-transformers
+      model_type: embedding
+    - metadata: {}
+      model_id: ${env.INFERENCE_MODEL}
+      provider_id: vllm-inference
+      model_type: llm
+    - metadata: {}
+      model_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}
+      provider_id: vllm-safety
+      model_type: llm
+    shields:
+    - shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}
+    vector_dbs: []
+    datasets: []
+    scoring_fns: []
+    benchmarks: []
+    tool_groups:
+    - toolgroup_id: builtin::websearch
+      provider_id: tavily-search
+    - toolgroup_id: builtin::rag
+      provider_id: rag-runtime
+    server:
+      port: 8321
+      auth:
+        provider_config:
+          type: github_token
 kind: ConfigMap
 metadata:
-  creationTimestamp: null
   name: llama-stack-config

docs/docs/distributions/k8s/stack_run_config.yaml

@@ -93,21 +93,30 @@ providers:
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
     config: {}
-metadata_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
-  table_name: llamastack_kvstore
-inference_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
+storage:
+  backends:
+    kv_default:
+      type: kv_postgres
+      host: ${env.POSTGRES_HOST:=localhost}
+      port: ${env.POSTGRES_PORT:=5432}
+      db: ${env.POSTGRES_DB:=llamastack}
+      user: ${env.POSTGRES_USER:=llamastack}
+      password: ${env.POSTGRES_PASSWORD:=llamastack}
+      table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+    sql_default:
+      type: sql_postgres
+      host: ${env.POSTGRES_HOST:=localhost}
+      port: ${env.POSTGRES_PORT:=5432}
+      db: ${env.POSTGRES_DB:=llamastack}
+      user: ${env.POSTGRES_USER:=llamastack}
+      password: ${env.POSTGRES_PASSWORD:=llamastack}
+  references:
+    metadata:
+      backend: kv_default
+      namespace: registry
+    inference:
+      backend: sql_default
+      table_name: inference_store
 models:
 - metadata:
     embedding_dimension: 768

docs/docs/providers/agents/inline_meta-reference.mdx

@@ -14,16 +14,18 @@ Meta's reference implementation of an agent system that can use tools, access ve
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `persistence_store` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
-| `responses_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite |  |
+| `persistence` | `<class 'inline.agents.meta_reference.config.AgentPersistenceConfig'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
-persistence_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/agents_store.db
-responses_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/responses_store.db
+persistence:
+  agent_state:
+    namespace: agents
+    backend: kv_default
+  responses:
+    table_name: responses
+    backend: sql_default
+    max_write_queue_size: 10000
+    num_writers: 4
 ```

docs/docs/providers/batches/inline_reference.mdx

@@ -14,7 +14,7 @@ Reference implementation of batches API with KVStore persistence.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Configuration for the key-value store backend. |
+| `kvstore` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Configuration for the key-value store backend. |
 | `max_concurrent_batches` | `<class 'int'>` | No | 1 | Maximum number of concurrent batches to process simultaneously. |
 | `max_concurrent_requests_per_batch` | `<class 'int'>` | No | 10 | Maximum number of concurrent requests to process per batch. |
 
@@ -22,6 +22,6 @@ Reference implementation of batches API with KVStore persistence.
 
 ```yaml
 kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/batches.db
+  namespace: batches
+  backend: kv_default
 ```

docs/docs/providers/datasetio/inline_localfs.mdx

@@ -14,12 +14,12 @@ Local filesystem-based dataset I/O provider for reading and writing datasets to
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `kvstore` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
 kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/localfs_datasetio.db
+  namespace: datasetio::localfs
+  backend: kv_default
 ```

docs/docs/providers/datasetio/remote_huggingface.mdx

@@ -14,12 +14,12 @@ HuggingFace datasets provider for accessing and managing datasets from the Huggi
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `kvstore` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
 kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/huggingface_datasetio.db
+  namespace: datasetio::huggingface
+  backend: kv_default
 ```

docs/docs/providers/eval/inline_meta-reference.mdx

@@ -14,12 +14,12 @@ Meta's reference implementation of evaluation tasks with support for multiple la
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `kvstore` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
 kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db
+  namespace: eval
+  backend: kv_default
 ```

docs/docs/providers/files/inline_localfs.mdx

@@ -15,7 +15,7 @@ Local filesystem-based file storage provider for managing files and documents lo
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `storage_dir` | `<class 'str'>` | No |  | Directory to store uploaded files |
-| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
+| `metadata_store` | `<class 'llama_stack.core.storage.datatypes.SqlStoreReference'>` | No |  | SQL store configuration for file metadata |
 | `ttl_secs` | `<class 'int'>` | No | 31536000 |  |
 
 ## Sample Configuration
@@ -23,6 +23,6 @@ Local filesystem-based file storage provider for managing files and documents lo
 ```yaml
 storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/dummy/files}
 metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/files_metadata.db
+  table_name: files_metadata
+  backend: sql_default
 ```

docs/docs/providers/files/remote_s3.mdx

@@ -20,7 +20,7 @@ AWS S3-based file storage provider for scalable cloud file management with metad
 | `aws_secret_access_key` | `str \| None` | No |  | AWS secret access key (optional if using IAM roles) |
 | `endpoint_url` | `str \| None` | No |  | Custom S3 endpoint URL (for MinIO, LocalStack, etc.) |
 | `auto_create_bucket` | `<class 'bool'>` | No | False | Automatically create the S3 bucket if it doesn't exist |
-| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
+| `metadata_store` | `<class 'llama_stack.core.storage.datatypes.SqlStoreReference'>` | No |  | SQL store configuration for file metadata |
 
 ## Sample Configuration
 
@@ -32,6 +32,6 @@ aws_secret_access_key: ${env.AWS_SECRET_ACCESS_KEY:=}
 endpoint_url: ${env.S3_ENDPOINT_URL:=}
 auto_create_bucket: ${env.S3_AUTO_CREATE_BUCKET:=false}
 metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/s3_files_metadata.db
+  table_name: s3_files_metadata
+  backend: sql_default
 ```

docs/docs/providers/vector_io/inline_chromadb.mdx

@@ -79,13 +79,13 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No |  |  |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.CHROMADB_PATH}
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db
+persistence:
+  namespace: vector_io::chroma
+  backend: kv_default
 ```

docs/docs/providers/vector_io/inline_faiss.mdx

@@ -95,12 +95,12 @@ more details about Faiss in general.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
+persistence:
+  namespace: vector_io::faiss
+  backend: kv_default
 ```

docs/docs/providers/vector_io/inline_meta-reference.mdx

@@ -14,14 +14,14 @@ Meta's reference implementation of a vector database.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
+persistence:
+  namespace: vector_io::faiss
+  backend: kv_default
 ```
 ## Deprecation Notice
 

docs/docs/providers/vector_io/inline_milvus.mdx

@@ -17,14 +17,14 @@ Please refer to the remote provider documentation.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No |  |  |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend (SQLite only for now) |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.MILVUS_DB_PATH:=~/.llama/dummy}/milvus.db
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_registry.db
+persistence:
+  namespace: vector_io::milvus
+  backend: kv_default
 ```

docs/docs/providers/vector_io/inline_qdrant.mdx

@@ -98,13 +98,13 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `path` | `<class 'str'>` | No |  |  |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
 path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db
+persistence:
+  namespace: vector_io::qdrant
+  backend: kv_default
 ```

docs/docs/providers/vector_io/inline_sqlite-vec.mdx

@@ -408,13 +408,13 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No |  | Path to the SQLite database file |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
+persistence:
+  namespace: vector_io::sqlite_vec
+  backend: kv_default
 ```

docs/docs/providers/vector_io/inline_sqlite_vec.mdx

@@ -17,15 +17,15 @@ Please refer to the sqlite-vec provider documentation.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No |  | Path to the SQLite database file |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
+persistence:
+  namespace: vector_io::sqlite_vec
+  backend: kv_default
 ```
 ## Deprecation Notice
 

docs/docs/providers/vector_io/remote_chromadb.mdx

@@ -78,13 +78,13 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `url` | `str \| None` | No |  |  |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend |
 
 ## Sample Configuration
 
 ```yaml
 url: ${env.CHROMADB_URL}
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_remote_registry.db
+persistence:
+  namespace: vector_io::chroma_remote
+  backend: kv_default
 ```

docs/docs/providers/vector_io/remote_milvus.mdx

@@ -408,7 +408,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
 | `uri` | `<class 'str'>` | No |  | The URI of the Milvus server |
 | `token` | `str \| None` | No |  | The token of the Milvus server |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend |
 | `config` | `dict` | No | `{}` | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |
 
 :::note
@@ -420,7 +420,7 @@ This configuration class accepts additional fields beyond those listed above. Yo
 ```yaml
 uri: ${env.MILVUS_ENDPOINT}
 token: ${env.MILVUS_TOKEN}
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db
+persistence:
+  namespace: vector_io::milvus_remote
+  backend: kv_default
 ```

docs/docs/providers/vector_io/remote_pgvector.mdx

@@ -218,7 +218,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
 | `db` | `str \| None` | No | postgres |  |
 | `user` | `str \| None` | No | postgres |  |
 | `password` | `str \| None` | No | mysecretpassword |  |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No |  | Config for KV store backend (SQLite only for now) |
+| `persistence` | `llama_stack.core.storage.datatypes.KVStoreReference \| None` | No |  | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration
 
@@ -228,7 +228,7 @@ port: ${env.PGVECTOR_PORT:=5432}
 db: ${env.PGVECTOR_DB}
 user: ${env.PGVECTOR_USER}
 password: ${env.PGVECTOR_PASSWORD}
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db
+persistence:
+  namespace: vector_io::pgvector
+  backend: kv_default
 ```

docs/docs/providers/vector_io/remote_qdrant.mdx

@@ -26,13 +26,13 @@ Please refer to the inline provider documentation.
 | `prefix` | `str \| None` | No |  |  |
 | `timeout` | `int \| None` | No |  |  |
 | `host` | `str \| None` | No |  |  |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
 
 ## Sample Configuration
 
 ```yaml
 api_key: ${env.QDRANT_API_KEY:=}
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db
+persistence:
+  namespace: vector_io::qdrant_remote
+  backend: kv_default
 ```

docs/docs/providers/vector_io/remote_weaviate.mdx

@@ -75,14 +75,14 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
 |-------|------|----------|---------|-------------|
 | `weaviate_api_key` | `str \| None` | No |  | The API key for the Weaviate instance |
 | `weaviate_cluster_url` | `str \| None` | No | localhost:8080 | The URL of the Weaviate cluster |
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No |  | Config for KV store backend (SQLite only for now) |
+| `persistence` | `llama_stack.core.storage.datatypes.KVStoreReference \| None` | No |  | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration
 
 ```yaml
 weaviate_api_key: null
 weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db
+persistence:
+  namespace: vector_io::weaviate
+  backend: kv_default
 ```

docs/openapi_generator/run_openapi_generator.sh

@@ -30,3 +30,5 @@ fi
 stack_dir=$(dirname $(dirname $THIS_DIR))
 PYTHONPATH=$PYTHONPATH:$stack_dir \
   python -m docs.openapi_generator.generate $(dirname $THIS_DIR)/static
+
+cp $stack_dir/docs/static/stainless-llama-stack-spec.yaml $stack_dir/client-sdks/stainless/openapi.yml

docs/static/deprecated-llama-stack-spec.html

@@ -9024,6 +9024,10 @@
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
                     },
+                    "instructions": {
+                        "type": "string",
+                        "description": "(Optional) System message inserted into the model's context"
+                    },
                     "input": {
                         "type": "array",
                         "items": {
@@ -9901,6 +9905,10 @@
                     "usage": {
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
+                    },
+                    "instructions": {
+                        "type": "string",
+                        "description": "(Optional) System message inserted into the model's context"
                     }
                 },
                 "additionalProperties": false,

docs/static/deprecated-llama-stack-spec.yaml

@@ -6734,6 +6734,10 @@ components:
           $ref: '#/components/schemas/OpenAIResponseUsage'
           description: >-
             (Optional) Token usage information for the response
+        instructions:
+          type: string
+          description: >-
+            (Optional) System message inserted into the model's context
         input:
           type: array
           items:
@@ -7403,6 +7407,10 @@ components:
           $ref: '#/components/schemas/OpenAIResponseUsage'
           description: >-
             (Optional) Token usage information for the response
+        instructions:
+          type: string
+          description: >-
+            (Optional) System message inserted into the model's context
       additionalProperties: false
       required:
         - created_at

docs/static/llama-stack-spec.html

@@ -7600,6 +7600,10 @@
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
                     },
+                    "instructions": {
+                        "type": "string",
+                        "description": "(Optional) System message inserted into the model's context"
+                    },
                     "input": {
                         "type": "array",
                         "items": {
@@ -8148,6 +8152,10 @@
                     "usage": {
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
+                    },
+                    "instructions": {
+                        "type": "string",
+                        "description": "(Optional) System message inserted into the model's context"
                     }
                 },
                 "additionalProperties": false,

docs/static/llama-stack-spec.yaml

@@ -5815,6 +5815,10 @@ components:
           $ref: '#/components/schemas/OpenAIResponseUsage'
           description: >-
             (Optional) Token usage information for the response
+        instructions:
+          type: string
+          description: >-
+            (Optional) System message inserted into the model's context
         input:
           type: array
           items:
@@ -6218,6 +6222,10 @@ components:
           $ref: '#/components/schemas/OpenAIResponseUsage'
           description: >-
             (Optional) Token usage information for the response
+        instructions:
+          type: string
+          description: >-
+            (Optional) System message inserted into the model's context
       additionalProperties: false
       required:
         - created_at

docs/static/stainless-llama-stack-spec.html

@@ -9272,6 +9272,10 @@
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
                     },
+                    "instructions": {
+                        "type": "string",
+                        "description": "(Optional) System message inserted into the model's context"
+                    },
                     "input": {
                         "type": "array",
                         "items": {
@@ -9820,6 +9824,10 @@
                     "usage": {
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
+                    },
+                    "instructions": {
+                        "type": "string",
+                        "description": "(Optional) System message inserted into the model's context"
                     }
                 },
                 "additionalProperties": false,

docs/static/stainless-llama-stack-spec.yaml

@@ -7028,6 +7028,10 @@ components:
           $ref: '#/components/schemas/OpenAIResponseUsage'
           description: >-
             (Optional) Token usage information for the response
+        instructions:
+          type: string
+          description: >-
+            (Optional) System message inserted into the model's context
         input:
           type: array
           items:
@@ -7431,6 +7435,10 @@ components:
           $ref: '#/components/schemas/OpenAIResponseUsage'
           description: >-
             (Optional) Token usage information for the response
+        instructions:
+          type: string
+          description: >-
+            (Optional) System message inserted into the model's context
       additionalProperties: false
       required:
         - created_at

llama_stack/apis/agents/openai_responses.py

@@ -545,6 +545,7 @@ class OpenAIResponseObject(BaseModel):
     :param tools: (Optional) An array of tools the model may call while generating a response.
     :param truncation: (Optional) Truncation strategy applied to the response
     :param usage: (Optional) Token usage information for the response
+    :param instructions: (Optional) System message inserted into the model's context
     """
 
     created_at: int
@@ -564,6 +565,7 @@ class OpenAIResponseObject(BaseModel):
     tools: list[OpenAIResponseTool] | None = None
     truncation: str | None = None
     usage: OpenAIResponseUsage | None = None
+    instructions: str | None = None
 
 
 @json_schema_type

llama_stack/apis/datatypes.py

@@ -121,6 +121,7 @@ class Api(Enum, metaclass=DynamicApiMeta):
 
     models = "models"
     shields = "shields"
+    vector_dbs = "vector_dbs"  # only used for routing
     datasets = "datasets"
     scoring_functions = "scoring_functions"
     benchmarks = "benchmarks"

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Literal
+from typing import Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
@@ -59,3 +59,35 @@ class ListVectorDBsResponse(BaseModel):
     """
 
     data: list[VectorDB]
+
+
+@runtime_checkable
+class VectorDBs(Protocol):
+    """Internal protocol for vector_dbs routing - no public API endpoints."""
+
+    async def list_vector_dbs(self) -> ListVectorDBsResponse:
+        """Internal method to list vector databases."""
+        ...
+
+    async def get_vector_db(
+        self,
+        vector_db_id: str,
+    ) -> VectorDB:
+        """Internal method to get a vector database by ID."""
+        ...
+
+    async def register_vector_db(
+        self,
+        vector_db_id: str,
+        embedding_model: str,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        vector_db_name: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorDB:
+        """Internal method to register a vector database."""
+        ...
+
+    async def unregister_vector_db(self, vector_db_id: str) -> None:
+        """Internal method to unregister a vector database."""
+        ...

llama_stack/cli/stack/_build.py

@@ -40,12 +40,20 @@ from llama_stack.core.distribution import get_provider_registry
 from llama_stack.core.external import load_external_apis
 from llama_stack.core.resolver import InvalidProviderError
 from llama_stack.core.stack import replace_env_vars
+from llama_stack.core.storage.datatypes import (
+    InferenceStoreReference,
+    KVStoreReference,
+    ServerStoresConfig,
+    SqliteKVStoreConfig,
+    SqliteSqlStoreConfig,
+    SqlStoreReference,
+    StorageConfig,
+)
 from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR, EXTERNAL_PROVIDERS_DIR
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.core.utils.exec import formulate_run_args, run_command
 from llama_stack.core.utils.image_types import LlamaStackImageType
 from llama_stack.providers.datatypes import Api
-from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
 
 DISTRIBS_PATH = Path(__file__).parent.parent.parent / "distributions"
 
@@ -286,21 +294,42 @@ def _generate_run_config(
     Generate a run.yaml template file for user to edit from a build.yaml file
     """
     apis = list(build_config.distribution_spec.providers.keys())
+    distro_dir = DISTRIBS_BASE_DIR / image_name
+    storage = StorageConfig(
+        backends={
+            "kv_default": SqliteKVStoreConfig(
+                db_path=f"${{env.SQLITE_STORE_DIR:={distro_dir}}}/kvstore.db",
+            ),
+            "sql_default": SqliteSqlStoreConfig(
+                db_path=f"${{env.SQLITE_STORE_DIR:={distro_dir}}}/sql_store.db",
+            ),
+        },
+        stores=ServerStoresConfig(
+            metadata=KVStoreReference(
+                backend="kv_default",
+                namespace="registry",
+            ),
+            inference=InferenceStoreReference(
+                backend="sql_default",
+                table_name="inference_store",
+            ),
+            conversations=SqlStoreReference(
+                backend="sql_default",
+                table_name="openai_conversations",
+            ),
+        ),
+    )
+
     run_config = StackRunConfig(
         container_image=(image_name if build_config.image_type == LlamaStackImageType.CONTAINER.value else None),
         image_name=image_name,
         apis=apis,
         providers={},
+        storage=storage,
         external_providers_dir=build_config.external_providers_dir
         if build_config.external_providers_dir
         else EXTERNAL_PROVIDERS_DIR,
     )
-    if not run_config.inference_store:
-        run_config.inference_store = SqliteSqlStoreConfig(
-            **SqliteSqlStoreConfig.sample_run_config(
-                __distro_dir__=(DISTRIBS_BASE_DIR / image_name).as_posix(), db_name="inference_store.db"
-            )
-        )
     # build providers dict
     provider_registry = get_provider_registry(build_config)
     for api in apis:

llama_stack/cli/stack/utils.py

@@ -17,10 +17,19 @@ from llama_stack.core.datatypes import (
     BuildConfig,
     Provider,
     StackRunConfig,
+    StorageConfig,
 )
 from llama_stack.core.distribution import get_provider_registry
 from llama_stack.core.resolver import InvalidProviderError
-from llama_stack.core.utils.config_dirs import EXTERNAL_PROVIDERS_DIR
+from llama_stack.core.storage.datatypes import (
+    InferenceStoreReference,
+    KVStoreReference,
+    ServerStoresConfig,
+    SqliteKVStoreConfig,
+    SqliteSqlStoreConfig,
+    SqlStoreReference,
+)
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR, EXTERNAL_PROVIDERS_DIR
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.core.utils.image_types import LlamaStackImageType
 from llama_stack.providers.datatypes import Api
@@ -51,11 +60,23 @@ def generate_run_config(
     Generate a run.yaml template file for user to edit from a build.yaml file
     """
     apis = list(build_config.distribution_spec.providers.keys())
+    distro_dir = DISTRIBS_BASE_DIR / image_name
     run_config = StackRunConfig(
         container_image=(image_name if build_config.image_type == LlamaStackImageType.CONTAINER.value else None),
         image_name=image_name,
         apis=apis,
         providers={},
+        storage=StorageConfig(
+            backends={
+                "kv_default": SqliteKVStoreConfig(db_path=str(distro_dir / "kvstore.db")),
+                "sql_default": SqliteSqlStoreConfig(db_path=str(distro_dir / "sql_store.db")),
+            },
+            stores=ServerStoresConfig(
+                metadata=KVStoreReference(backend="kv_default", namespace="registry"),
+                inference=InferenceStoreReference(backend="sql_default", table_name="inference_store"),
+                conversations=SqlStoreReference(backend="sql_default", table_name="openai_conversations"),
+            ),
+        ),
         external_providers_dir=build_config.external_providers_dir
         if build_config.external_providers_dir
         else EXTERNAL_PROVIDERS_DIR,

llama_stack/core/configure.py

@@ -159,6 +159,37 @@ def upgrade_from_routing_table(
     config_dict["apis"] = config_dict["apis_to_serve"]
     config_dict.pop("apis_to_serve", None)
 
+    # Add default storage config if not present
+    if "storage" not in config_dict:
+        config_dict["storage"] = {
+            "backends": {
+                "kv_default": {
+                    "type": "kv_sqlite",
+                    "db_path": "~/.llama/kvstore.db",
+                },
+                "sql_default": {
+                    "type": "sql_sqlite",
+                    "db_path": "~/.llama/sql_store.db",
+                },
+            },
+            "stores": {
+                "metadata": {
+                    "namespace": "registry",
+                    "backend": "kv_default",
+                },
+                "inference": {
+                    "table_name": "inference_store",
+                    "backend": "sql_default",
+                    "max_write_queue_size": 10000,
+                    "num_writers": 4,
+                },
+                "conversations": {
+                    "table_name": "openai_conversations",
+                    "backend": "sql_default",
+                },
+            },
+        }
+
     return config_dict
 
 

llama_stack/core/conversations/conversations.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import os
 import secrets
 import time
 from typing import Any
@@ -21,16 +20,11 @@ from llama_stack.apis.conversations.conversations import (
     Conversations,
     Metadata,
 )
-from llama_stack.core.datatypes import AccessRule
-from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
+from llama_stack.core.datatypes import AccessRule, StackRunConfig
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
 from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
-from llama_stack.providers.utils.sqlstore.sqlstore import (
-    SqliteSqlStoreConfig,
-    SqlStoreConfig,
-    sqlstore_impl,
-)
+from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl
 
 logger = get_logger(name=__name__, category="openai_conversations")
 
@@ -38,13 +32,11 @@ logger = get_logger(name=__name__, category="openai_conversations")
 class ConversationServiceConfig(BaseModel):
     """Configuration for the built-in conversation service.
 
-    :param conversations_store: SQL store configuration for conversations (defaults to SQLite)
+    :param run_config: Stack run configuration for resolving persistence
     :param policy: Access control rules
     """
 
-    conversations_store: SqlStoreConfig = SqliteSqlStoreConfig(
-        db_path=(DISTRIBS_BASE_DIR / "conversations.db").as_posix()
-    )
+    run_config: StackRunConfig
     policy: list[AccessRule] = []
 
 
@@ -63,14 +55,16 @@ class ConversationServiceImpl(Conversations):
         self.deps = deps
         self.policy = config.policy
 
-        base_sql_store = sqlstore_impl(config.conversations_store)
+        # Use conversations store reference from run config
+        conversations_ref = config.run_config.storage.stores.conversations
+        if not conversations_ref:
+            raise ValueError("storage.stores.conversations must be configured in run config")
+
+        base_sql_store = sqlstore_impl(conversations_ref)
         self.sql_store = AuthorizedSqlStore(base_sql_store, self.policy)
 
     async def initialize(self) -> None:
         """Initialize the store and create tables."""
-        if isinstance(self.config.conversations_store, SqliteSqlStoreConfig):
-            os.makedirs(os.path.dirname(self.config.conversations_store.db_path), exist_ok=True)
-
         await self.sql_store.create_table(
             "openai_conversations",
             {

llama_stack/core/datatypes.py

@@ -26,9 +26,12 @@ from llama_stack.apis.tools import ToolGroup, ToolGroupInput, ToolRuntime
 from llama_stack.apis.vector_dbs import VectorDB, VectorDBInput
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.core.access_control.datatypes import AccessRule
+from llama_stack.core.storage.datatypes import (
+    KVStoreReference,
+    StorageBackendType,
+    StorageConfig,
+)
 from llama_stack.providers.datatypes import Api, ProviderSpec
-from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
-from llama_stack.providers.utils.sqlstore.sqlstore import SqlStoreConfig
 
 LLAMA_STACK_BUILD_CONFIG_VERSION = 2
 LLAMA_STACK_RUN_CONFIG_VERSION = 2
@@ -351,12 +354,32 @@ class AuthenticationRequiredError(Exception):
     pass
 
 
+class QualifiedModel(BaseModel):
+    """A qualified model identifier, consisting of a provider ID and a model ID."""
+
+    provider_id: str
+    model_id: str
+
+
+class VectorStoresConfig(BaseModel):
+    """Configuration for vector stores in the stack."""
+
+    default_provider_id: str | None = Field(
+        default=None,
+        description="ID of the vector_io provider to use as default when multiple providers are available and none is specified.",
+    )
+    default_embedding_model: QualifiedModel | None = Field(
+        default=None,
+        description="Default embedding model configuration for vector stores.",
+    )
+
+
 class QuotaPeriod(StrEnum):
     DAY = "day"
 
 
 class QuotaConfig(BaseModel):
-    kvstore: SqliteKVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)")
+    kvstore: KVStoreReference = Field(description="Config for KV store backend (SQLite only for now)")
     anonymous_max_requests: int = Field(default=100, description="Max requests for unauthenticated clients per period")
     authenticated_max_requests: int = Field(
         default=1000, description="Max requests for authenticated clients per period"
@@ -438,18 +461,6 @@ class ServerConfig(BaseModel):
     )
 
 
-class InferenceStoreConfig(BaseModel):
-    sql_store_config: SqlStoreConfig
-    max_write_queue_size: int = Field(default=10000, description="Max queued writes for inference store")
-    num_writers: int = Field(default=4, description="Number of concurrent background writers")
-
-
-class ResponsesStoreConfig(BaseModel):
-    sql_store_config: SqlStoreConfig
-    max_write_queue_size: int = Field(default=10000, description="Max queued writes for responses store")
-    num_writers: int = Field(default=4, description="Number of concurrent background writers")
-
-
 class StackRunConfig(BaseModel):
     version: int = LLAMA_STACK_RUN_CONFIG_VERSION
 
@@ -476,26 +487,8 @@ One or more providers to use for each API. The same provider_type (e.g., meta-re
 can be instantiated multiple times (with different configs) if necessary.
 """,
     )
-    metadata_store: KVStoreConfig | None = Field(
-        default=None,
-        description="""
-Configuration for the persistence store used by the distribution registry. If not specified,
-a default SQLite store will be used.""",
-    )
-
-    inference_store: InferenceStoreConfig | SqlStoreConfig | None = Field(
-        default=None,
-        description="""
-Configuration for the persistence store used by the inference API. Can be either a
-InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (deprecated).
-If not specified, a default SQLite store will be used.""",
-    )
-
-    conversations_store: SqlStoreConfig | None = Field(
-        default=None,
-        description="""
-Configuration for the persistence store used by the conversations API.
-If not specified, a default SQLite store will be used.""",
+    storage: StorageConfig = Field(
+        description="Catalog of named storage backends and references available to the stack",
     )
 
     # registry of "resources" in the distribution
@@ -526,6 +519,11 @@ If not specified, a default SQLite store will be used.""",
         description="Path to directory containing external API implementations. The APIs code and dependencies must be installed on the system.",
     )
 
+    vector_stores: VectorStoresConfig | None = Field(
+        default=None,
+        description="Configuration for vector stores, including default embedding model",
+    )
+
     @field_validator("external_providers_dir")
     @classmethod
     def validate_external_providers_dir(cls, v):
@@ -535,6 +533,49 @@ If not specified, a default SQLite store will be used.""",
             return Path(v)
         return v
 
+    @model_validator(mode="after")
+    def validate_server_stores(self) -> "StackRunConfig":
+        backend_map = self.storage.backends
+        stores = self.storage.stores
+        kv_backends = {
+            name
+            for name, cfg in backend_map.items()
+            if cfg.type
+            in {
+                StorageBackendType.KV_REDIS,
+                StorageBackendType.KV_SQLITE,
+                StorageBackendType.KV_POSTGRES,
+                StorageBackendType.KV_MONGODB,
+            }
+        }
+        sql_backends = {
+            name
+            for name, cfg in backend_map.items()
+            if cfg.type in {StorageBackendType.SQL_SQLITE, StorageBackendType.SQL_POSTGRES}
+        }
+
+        def _ensure_backend(reference, expected_set, store_name: str) -> None:
+            if reference is None:
+                return
+            backend_name = reference.backend
+            if backend_name not in backend_map:
+                raise ValueError(
+                    f"{store_name} references unknown backend '{backend_name}'. "
+                    f"Available backends: {sorted(backend_map)}"
+                )
+            if backend_name not in expected_set:
+                raise ValueError(
+                    f"{store_name} references backend '{backend_name}' of type "
+                    f"'{backend_map[backend_name].type.value}', but a backend of type "
+                    f"{'kv_*' if expected_set is kv_backends else 'sql_*'} is required."
+                )
+
+        _ensure_backend(stores.metadata, kv_backends, "storage.stores.metadata")
+        _ensure_backend(stores.inference, sql_backends, "storage.stores.inference")
+        _ensure_backend(stores.conversations, sql_backends, "storage.stores.conversations")
+        _ensure_backend(stores.responses, sql_backends, "storage.stores.responses")
+        return self
+
 
 class BuildConfig(BaseModel):
     version: int = LLAMA_STACK_BUILD_CONFIG_VERSION

llama_stack/core/distribution.py

@@ -63,6 +63,10 @@ def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]:
             routing_table_api=Api.tool_groups,
             router_api=Api.tool_runtime,
         ),
+        AutoRoutedApiInfo(
+            routing_table_api=Api.vector_dbs,
+            router_api=Api.vector_io,
+        ),
     ]
 
 

llama_stack/core/prompts/prompts.py

@@ -11,9 +11,8 @@ from pydantic import BaseModel
 
 from llama_stack.apis.prompts import ListPromptsResponse, Prompt, Prompts
 from llama_stack.core.datatypes import StackRunConfig
-from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
+from llama_stack.core.storage.datatypes import KVStoreReference
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
-from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
 
 
 class PromptServiceConfig(BaseModel):
@@ -41,10 +40,12 @@ class PromptServiceImpl(Prompts):
         self.kvstore: KVStore
 
     async def initialize(self) -> None:
-        kvstore_config = SqliteKVStoreConfig(
-            db_path=(DISTRIBS_BASE_DIR / self.config.run_config.image_name / "prompts.db").as_posix()
-        )
-        self.kvstore = await kvstore_impl(kvstore_config)
+        # Use metadata store backend with prompts-specific namespace
+        metadata_ref = self.config.run_config.storage.stores.metadata
+        if not metadata_ref:
+            raise ValueError("storage.stores.metadata must be configured in run config")
+        prompts_ref = KVStoreReference(namespace="prompts", backend=metadata_ref.backend)
+        self.kvstore = await kvstore_impl(prompts_ref)
 
     def _get_default_key(self, prompt_id: str) -> str:
         """Get the KVStore key that stores the default version number."""

llama_stack/core/resolver.py

@@ -29,6 +29,7 @@ from llama_stack.apis.scoring_functions import ScoringFunctions
 from llama_stack.apis.shields import Shields
 from llama_stack.apis.telemetry import Telemetry
 from llama_stack.apis.tools import ToolGroups, ToolRuntime
+from llama_stack.apis.vector_dbs import VectorDBs
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA
 from llama_stack.core.client import get_client_impl
@@ -81,6 +82,7 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) ->
         Api.inspect: Inspect,
         Api.batches: Batches,
         Api.vector_io: VectorIO,
+        Api.vector_dbs: VectorDBs,
         Api.models: Models,
         Api.safety: Safety,
         Api.shields: Shields,

llama_stack/core/routers/__init__.py

@@ -6,7 +6,10 @@
 
 from typing import Any
 
-from llama_stack.core.datatypes import AccessRule, RoutedProtocol
+from llama_stack.core.datatypes import (
+    AccessRule,
+    RoutedProtocol,
+)
 from llama_stack.core.stack import StackRunConfig
 from llama_stack.core.store import DistributionRegistry
 from llama_stack.providers.datatypes import Api, RoutingTable
@@ -26,6 +29,7 @@ async def get_routing_table_impl(
     from ..routing_tables.scoring_functions import ScoringFunctionsRoutingTable
     from ..routing_tables.shields import ShieldsRoutingTable
     from ..routing_tables.toolgroups import ToolGroupsRoutingTable
+    from ..routing_tables.vector_dbs import VectorDBsRoutingTable
 
     api_to_tables = {
         "models": ModelsRoutingTable,
@@ -34,6 +38,7 @@ async def get_routing_table_impl(
         "scoring_functions": ScoringFunctionsRoutingTable,
         "benchmarks": BenchmarksRoutingTable,
         "tool_groups": ToolGroupsRoutingTable,
+        "vector_dbs": VectorDBsRoutingTable,
     }
 
     if api.value not in api_to_tables:
@@ -76,14 +81,21 @@ async def get_auto_router_impl(
                 api_to_dep_impl[dep_name] = deps[dep_api]
 
     # TODO: move pass configs to routers instead
-    if api == Api.inference and run_config.inference_store:
+    if api == Api.inference:
+        inference_ref = run_config.storage.stores.inference
+        if not inference_ref:
+            raise ValueError("storage.stores.inference must be configured in run config")
+
         inference_store = InferenceStore(
-            config=run_config.inference_store,
+            reference=inference_ref,
             policy=policy,
         )
         await inference_store.initialize()
         api_to_dep_impl["store"] = inference_store
 
+    elif api == Api.vector_io:
+        api_to_dep_impl["vector_stores_config"] = run_config.vector_stores
+
     impl = api_to_routers[api.value](routing_table, **api_to_dep_impl)
     await impl.initialize()
     return impl

llama_stack/core/routers/vector_io.py

@@ -31,6 +31,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreObject,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.core.datatypes import VectorStoresConfig
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
 
@@ -43,9 +44,11 @@ class VectorIORouter(VectorIO):
     def __init__(
         self,
         routing_table: RoutingTable,
+        vector_stores_config: VectorStoresConfig | None = None,
     ) -> None:
         logger.debug("Initializing VectorIORouter")
         self.routing_table = routing_table
+        self.vector_stores_config = vector_stores_config
 
     async def initialize(self) -> None:
         logger.debug("VectorIORouter.initialize")
@@ -122,6 +125,17 @@ class VectorIORouter(VectorIO):
         embedding_dimension = extra.get("embedding_dimension")
         provider_id = extra.get("provider_id")
 
+        # Use default embedding model if not specified
+        if (
+            embedding_model is None
+            and self.vector_stores_config
+            and self.vector_stores_config.default_embedding_model is not None
+        ):
+            # Construct the full model ID with provider prefix
+            embedding_provider_id = self.vector_stores_config.default_embedding_model.provider_id
+            model_id = self.vector_stores_config.default_embedding_model.model_id
+            embedding_model = f"{embedding_provider_id}/{model_id}"
+
         if embedding_model is not None and embedding_dimension is None:
             embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
 
@@ -132,11 +146,24 @@ class VectorIORouter(VectorIO):
                 raise ValueError("No vector_io providers available")
             if num_providers > 1:
                 available_providers = list(self.routing_table.impls_by_provider_id.keys())
-                raise ValueError(
-                    f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
-                    f"Available providers: {available_providers}"
-                )
-            provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
+                # Use default configured provider
+                if self.vector_stores_config and self.vector_stores_config.default_provider_id:
+                    default_provider = self.vector_stores_config.default_provider_id
+                    if default_provider in available_providers:
+                        provider_id = default_provider
+                        logger.debug(f"Using configured default vector store provider: {provider_id}")
+                    else:
+                        raise ValueError(
+                            f"Configured default vector store provider '{default_provider}' not found. "
+                            f"Available providers: {available_providers}"
+                        )
+                else:
+                    raise ValueError(
+                        f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
+                        f"Available providers: {available_providers}"
+                    )
+            else:
+                provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
 
         vector_db_id = f"vs_{uuid.uuid4()}"
         registered_vector_db = await self.routing_table.register_vector_db(
@@ -243,8 +270,7 @@ class VectorIORouter(VectorIO):
         vector_store_id: str,
     ) -> VectorStoreDeleteResponse:
         logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
-        provider = await self.routing_table.get_provider_impl(vector_store_id)
-        return await provider.openai_delete_vector_store(vector_store_id)
+        return await self.routing_table.openai_delete_vector_store(vector_store_id)
 
     async def openai_search_vector_store(
         self,

llama_stack/core/routing_tables/common.py

@@ -134,12 +134,15 @@ class CommonRoutingTableImpl(RoutingTable):
         from .scoring_functions import ScoringFunctionsRoutingTable
         from .shields import ShieldsRoutingTable
         from .toolgroups import ToolGroupsRoutingTable
+        from .vector_dbs import VectorDBsRoutingTable
 
         def apiname_object():
             if isinstance(self, ModelsRoutingTable):
                 return ("Inference", "model")
             elif isinstance(self, ShieldsRoutingTable):
                 return ("Safety", "shield")
+            elif isinstance(self, VectorDBsRoutingTable):
+                return ("VectorIO", "vector_db")
             elif isinstance(self, DatasetsRoutingTable):
                 return ("DatasetIO", "dataset")
             elif isinstance(self, ScoringFunctionsRoutingTable):

llama_stack/core/routing_tables/vector_dbs.py

@@ -0,0 +1,323 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from pydantic import TypeAdapter
+
+from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
+from llama_stack.apis.models import ModelType
+from llama_stack.apis.resource import ResourceType
+
+# Removed VectorDBs import to avoid exposing public API
+from llama_stack.apis.vector_io.vector_io import (
+    OpenAICreateVectorStoreRequestWithExtraBody,
+    SearchRankingOptions,
+    VectorStoreChunkingStrategy,
+    VectorStoreDeleteResponse,
+    VectorStoreFileContentsResponse,
+    VectorStoreFileDeleteResponse,
+    VectorStoreFileObject,
+    VectorStoreFileStatus,
+    VectorStoreObject,
+    VectorStoreSearchResponsePage,
+)
+from llama_stack.core.datatypes import (
+    VectorDBWithOwner,
+)
+from llama_stack.log import get_logger
+
+from .common import CommonRoutingTableImpl, lookup_model
+
+logger = get_logger(name=__name__, category="core::routing_tables")
+
+
+class VectorDBsRoutingTable(CommonRoutingTableImpl):
+    """Internal routing table for vector_db operations.
+
+    Does not inherit from VectorDBs to avoid exposing public API endpoints.
+    Only provides internal routing functionality for VectorIORouter.
+    """
+
+    # Internal methods only - no public API exposure
+
+    async def register_vector_db(
+        self,
+        vector_db_id: str,
+        embedding_model: str,
+        embedding_dimension: int | None = 384,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+        vector_db_name: str | None = None,
+    ) -> Any:
+        if provider_id is None:
+            if len(self.impls_by_provider_id) > 0:
+                provider_id = list(self.impls_by_provider_id.keys())[0]
+                if len(self.impls_by_provider_id) > 1:
+                    logger.warning(
+                        f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}."
+                    )
+            else:
+                raise ValueError("No provider available. Please configure a vector_io provider.")
+        model = await lookup_model(self, embedding_model)
+        if model is None:
+            raise ModelNotFoundError(embedding_model)
+        if model.model_type != ModelType.embedding:
+            raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
+        if "embedding_dimension" not in model.metadata:
+            raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
+
+        try:
+            provider = self.impls_by_provider_id[provider_id]
+        except KeyError:
+            available_providers = list(self.impls_by_provider_id.keys())
+            raise ValueError(
+                f"Provider '{provider_id}' not found in routing table. Available providers: {available_providers}"
+            ) from None
+        logger.warning(
+            "VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
+        )
+        request = OpenAICreateVectorStoreRequestWithExtraBody(
+            name=vector_db_name or vector_db_id,
+            embedding_model=embedding_model,
+            embedding_dimension=model.metadata["embedding_dimension"],
+            provider_id=provider_id,
+            provider_vector_db_id=provider_vector_db_id,
+        )
+        vector_store = await provider.openai_create_vector_store(request)
+
+        vector_store_id = vector_store.id
+        actual_provider_vector_db_id = provider_vector_db_id or vector_store_id
+        logger.warning(
+            f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name"
+        )
+
+        vector_db_data = {
+            "identifier": vector_store_id,
+            "type": ResourceType.vector_db.value,
+            "provider_id": provider_id,
+            "provider_resource_id": actual_provider_vector_db_id,
+            "embedding_model": embedding_model,
+            "embedding_dimension": model.metadata["embedding_dimension"],
+            "vector_db_name": vector_store.name,
+        }
+        vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
+        await self.register_object(vector_db)
+        return vector_db
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store(vector_store_id)
+
+    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:
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store(
+            vector_store_id=vector_store_id,
+            name=name,
+            expires_after=expires_after,
+            metadata=metadata,
+        )
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        await self.assert_action_allowed("delete", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        result = await provider.openai_delete_vector_store(vector_store_id)
+        await self.unregister_vector_db(vector_store_id)
+        return result
+
+    async def unregister_vector_db(self, vector_store_id: str) -> None:
+        """Remove the vector store from the routing table registry."""
+        try:
+            vector_db_obj = await self.get_object_by_identifier("vector_db", vector_store_id)
+            if vector_db_obj:
+                await self.unregister_object(vector_db_obj)
+        except Exception as e:
+            # Log the error but don't fail the operation
+            logger.warning(f"Failed to unregister vector store {vector_store_id} from routing table: {e}")
+
+    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:
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_search_vector_store(
+            vector_store_id=vector_store_id,
+            query=query,
+            filters=filters,
+            max_num_results=max_num_results,
+            ranking_options=ranking_options,
+            rewrite_query=rewrite_query,
+            search_mode=search_mode,
+        )
+
+    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:
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_attach_file_to_vector_store(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+            attributes=attributes,
+            chunking_strategy=chunking_strategy,
+        )
+
+    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,
+    ) -> list[VectorStoreFileObject]:
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store(
+            vector_store_id=vector_store_id,
+            limit=limit,
+            order=order,
+            after=after,
+            before=before,
+            filter=filter,
+        )
+
+    async def openai_retrieve_vector_store_file(
+        self,
+        vector_store_id: str,
+        file_id: str,
+    ) -> VectorStoreFileObject:
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+        )
+
+    async def openai_retrieve_vector_store_file_contents(
+        self,
+        vector_store_id: str,
+        file_id: str,
+    ) -> VectorStoreFileContentsResponse:
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_contents(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+        )
+
+    async def openai_update_vector_store_file(
+        self,
+        vector_store_id: str,
+        file_id: str,
+        attributes: dict[str, Any],
+    ) -> VectorStoreFileObject:
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store_file(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+            attributes=attributes,
+        )
+
+    async def openai_delete_vector_store_file(
+        self,
+        vector_store_id: str,
+        file_id: str,
+    ) -> VectorStoreFileDeleteResponse:
+        await self.assert_action_allowed("delete", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_delete_vector_store_file(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+        )
+
+    async def openai_create_vector_store_file_batch(
+        self,
+        vector_store_id: str,
+        file_ids: list[str],
+        attributes: dict[str, Any] | None = None,
+        chunking_strategy: Any | None = None,
+    ):
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_create_vector_store_file_batch(
+            vector_store_id=vector_store_id,
+            file_ids=file_ids,
+            attributes=attributes,
+            chunking_strategy=chunking_strategy,
+        )
+
+    async def openai_retrieve_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ):
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_batch(
+            batch_id=batch_id,
+            vector_store_id=vector_store_id,
+        )
+
+    async def openai_list_files_in_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+        after: str | None = None,
+        before: str | None = None,
+        filter: str | None = None,
+        limit: int | None = 20,
+        order: str | None = "desc",
+    ):
+        await self.assert_action_allowed("read", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store_file_batch(
+            batch_id=batch_id,
+            vector_store_id=vector_store_id,
+            after=after,
+            before=before,
+            filter=filter,
+            limit=limit,
+            order=order,
+        )
+
+    async def openai_cancel_vector_store_file_batch(
+        self,
+        batch_id: str,
+        vector_store_id: str,
+    ):
+        await self.assert_action_allowed("update", "vector_db", vector_store_id)
+        provider = await self.get_provider_impl(vector_store_id)
+        return await provider.openai_cancel_vector_store_file_batch(
+            batch_id=batch_id,
+            vector_store_id=vector_store_id,
+        )

llama_stack/core/server/auth_providers.py

@@ -72,13 +72,30 @@ class AuthProvider(ABC):
 def get_attributes_from_claims(claims: dict[str, str], mapping: dict[str, str]) -> dict[str, list[str]]:
     attributes: dict[str, list[str]] = {}
     for claim_key, attribute_key in mapping.items():
-        if claim_key not in claims:
+        # First try dot notation for nested traversal (e.g., "resource_access.llamastack.roles")
+        # Then fall back to literal key with dots (e.g., "my.dotted.key")
+        claim: object = claims
+        keys = claim_key.split(".")
+        for key in keys:
+            if isinstance(claim, dict) and key in claim:
+                claim = claim[key]
+            else:
+                claim = None
+                break
+
+        if claim is None and claim_key in claims:
+            # Fall back to checking if claim_key exists as a literal key
+            claim = claims[claim_key]
+
+        if claim is None:
             continue
-        claim = claims[claim_key]
+
         if isinstance(claim, list):
             values = claim
-        else:
+        elif isinstance(claim, str):
             values = claim.split()
+        else:
+            continue
 
         if attribute_key in attributes:
             attributes[attribute_key].extend(values)

llama_stack/core/server/quota.py

@@ -10,10 +10,10 @@ from datetime import UTC, datetime, timedelta
 
 from starlette.types import ASGIApp, Receive, Scope, Send
 
+from llama_stack.core.storage.datatypes import KVStoreReference, StorageBackendType
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore.api import KVStore
-from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
-from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl
+from llama_stack.providers.utils.kvstore.kvstore import _KVSTORE_BACKENDS, kvstore_impl
 
 logger = get_logger(name=__name__, category="core::server")
 
@@ -33,7 +33,7 @@ class QuotaMiddleware:
     def __init__(
         self,
         app: ASGIApp,
-        kv_config: KVStoreConfig,
+        kv_config: KVStoreReference,
         anonymous_max_requests: int,
         authenticated_max_requests: int,
         window_seconds: int = 86400,
@@ -45,15 +45,15 @@ class QuotaMiddleware:
         self.authenticated_max_requests = authenticated_max_requests
         self.window_seconds = window_seconds
 
-        if isinstance(self.kv_config, SqliteKVStoreConfig):
-            logger.warning(
-                "QuotaMiddleware: Using SQLite backend. Expiry/TTL is not enforced; cleanup is manual. "
-                f"window_seconds={self.window_seconds}"
-            )
-
     async def _get_kv(self) -> KVStore:
         if self.kv is None:
             self.kv = await kvstore_impl(self.kv_config)
+            backend_config = _KVSTORE_BACKENDS.get(self.kv_config.backend)
+            if backend_config and backend_config.type == StorageBackendType.KV_SQLITE:
+                logger.warning(
+                    "QuotaMiddleware: Using SQLite backend. Expiry/TTL is not enforced; cleanup is manual. "
+                    f"window_seconds={self.window_seconds}"
+                )
         return self.kv
 
     async def __call__(self, scope: Scope, receive: Receive, send: Send):

llama_stack/core/stack.py

@@ -35,13 +35,23 @@ from llama_stack.apis.telemetry import Telemetry
 from llama_stack.apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.core.conversations.conversations import ConversationServiceConfig, ConversationServiceImpl
-from llama_stack.core.datatypes import Provider, StackRunConfig
+from llama_stack.core.datatypes import Provider, StackRunConfig, VectorStoresConfig
 from llama_stack.core.distribution import get_provider_registry
 from llama_stack.core.inspect import DistributionInspectConfig, DistributionInspectImpl
 from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl
 from llama_stack.core.providers import ProviderImpl, ProviderImplConfig
 from llama_stack.core.resolver import ProviderRegistry, resolve_impls
 from llama_stack.core.routing_tables.common import CommonRoutingTableImpl
+from llama_stack.core.storage.datatypes import (
+    InferenceStoreReference,
+    KVStoreReference,
+    ServerStoresConfig,
+    SqliteKVStoreConfig,
+    SqliteSqlStoreConfig,
+    SqlStoreReference,
+    StorageBackendConfig,
+    StorageConfig,
+)
 from llama_stack.core.store.registry import create_dist_registry
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.log import get_logger
@@ -98,30 +108,6 @@ REGISTRY_REFRESH_TASK = None
 TEST_RECORDING_CONTEXT = None
 
 
-async def validate_default_embedding_model(impls: dict[Api, Any]):
-    """Validate that at most one embedding model is marked as default."""
-    if Api.models not in impls:
-        return
-
-    models_impl = impls[Api.models]
-    response = await models_impl.list_models()
-    models_list = response.data if hasattr(response, "data") else response
-
-    default_embedding_models = []
-    for model in models_list:
-        if model.model_type == "embedding" and model.metadata.get("default_configured") is True:
-            default_embedding_models.append(model.identifier)
-
-    if len(default_embedding_models) > 1:
-        raise ValueError(
-            f"Multiple embedding models marked as default_configured=True: {default_embedding_models}. "
-            "Only one embedding model can be marked as default."
-        )
-
-    if default_embedding_models:
-        logger.info(f"Default embedding model configured: {default_embedding_models[0]}")
-
-
 async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
     for rsrc, api, register_method, list_method in RESOURCES:
         objects = getattr(run_config, rsrc)
@@ -152,7 +138,41 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
                 f"{rsrc.capitalize()}: {obj.identifier} served by {obj.provider_id}",
             )
 
-    await validate_default_embedding_model(impls)
+
+async def validate_vector_stores_config(vector_stores_config: VectorStoresConfig | None, impls: dict[Api, Any]):
+    """Validate vector stores configuration."""
+    if vector_stores_config is None:
+        return
+
+    default_embedding_model = vector_stores_config.default_embedding_model
+    if default_embedding_model is None:
+        return
+
+    provider_id = default_embedding_model.provider_id
+    model_id = default_embedding_model.model_id
+    default_model_id = f"{provider_id}/{model_id}"
+
+    if Api.models not in impls:
+        raise ValueError(f"Models API is not available but vector_stores config requires model '{default_model_id}'")
+
+    models_impl = impls[Api.models]
+    response = await models_impl.list_models()
+    models_list = {m.identifier: m for m in response.data if m.model_type == "embedding"}
+
+    default_model = models_list.get(default_model_id)
+    if default_model is None:
+        raise ValueError(f"Embedding model '{default_model_id}' not found. Available embedding models: {models_list}")
+
+    embedding_dimension = default_model.metadata.get("embedding_dimension")
+    if embedding_dimension is None:
+        raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
+
+    try:
+        int(embedding_dimension)
+    except ValueError as err:
+        raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
+
+    logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
 
 
 class EnvVarError(Exception):
@@ -329,6 +349,25 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf
     impls[Api.conversations] = conversations_impl
 
 
+def _initialize_storage(run_config: StackRunConfig):
+    kv_backends: dict[str, StorageBackendConfig] = {}
+    sql_backends: dict[str, StorageBackendConfig] = {}
+    for backend_name, backend_config in run_config.storage.backends.items():
+        type = backend_config.type.value
+        if type.startswith("kv_"):
+            kv_backends[backend_name] = backend_config
+        elif type.startswith("sql_"):
+            sql_backends[backend_name] = backend_config
+        else:
+            raise ValueError(f"Unknown storage backend type: {type}")
+
+    from llama_stack.providers.utils.kvstore.kvstore import register_kvstore_backends
+    from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
+
+    register_kvstore_backends(kv_backends)
+    register_sqlstore_backends(sql_backends)
+
+
 class Stack:
     def __init__(self, run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None):
         self.run_config = run_config
@@ -347,7 +386,11 @@ class Stack:
                 TEST_RECORDING_CONTEXT.__enter__()
                 logger.info(f"API recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")
 
-        dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name)
+        _initialize_storage(self.run_config)
+        stores = self.run_config.storage.stores
+        if not stores.metadata:
+            raise ValueError("storage.stores.metadata must be configured with a kv_* backend")
+        dist_registry, _ = await create_dist_registry(stores.metadata, self.run_config.image_name)
         policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else []
 
         internal_impls = {}
@@ -367,8 +410,8 @@ class Stack:
             await impls[Api.conversations].initialize()
 
         await register_resources(self.run_config, impls)
-
         await refresh_registry_once(impls)
+        await validate_vector_stores_config(self.run_config.vector_stores, impls)
         self.impls = impls
 
     def create_registry_refresh_task(self):
@@ -488,5 +531,16 @@ def run_config_from_adhoc_config_spec(
         image_name="distro-test",
         apis=list(provider_configs_by_api.keys()),
         providers=provider_configs_by_api,
+        storage=StorageConfig(
+            backends={
+                "kv_default": SqliteKVStoreConfig(db_path=f"{distro_dir}/kvstore.db"),
+                "sql_default": SqliteSqlStoreConfig(db_path=f"{distro_dir}/sql_store.db"),
+            },
+            stores=ServerStoresConfig(
+                metadata=KVStoreReference(backend="kv_default", namespace="registry"),
+                inference=InferenceStoreReference(backend="sql_default", table_name="inference_store"),
+                conversations=SqlStoreReference(backend="sql_default", table_name="openai_conversations"),
+            ),
+        ),
     )
     return config

llama_stack/core/storage/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/core/storage/datatypes.py

@@ -0,0 +1,283 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import re
+from abc import abstractmethod
+from enum import StrEnum
+from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class StorageBackendType(StrEnum):
+    KV_REDIS = "kv_redis"
+    KV_SQLITE = "kv_sqlite"
+    KV_POSTGRES = "kv_postgres"
+    KV_MONGODB = "kv_mongodb"
+    SQL_SQLITE = "sql_sqlite"
+    SQL_POSTGRES = "sql_postgres"
+
+
+class CommonConfig(BaseModel):
+    namespace: str | None = Field(
+        default=None,
+        description="All keys will be prefixed with this namespace",
+    )
+
+
+class RedisKVStoreConfig(CommonConfig):
+    type: Literal[StorageBackendType.KV_REDIS] = StorageBackendType.KV_REDIS
+    host: str = "localhost"
+    port: int = 6379
+
+    @property
+    def url(self) -> str:
+        return f"redis://{self.host}:{self.port}"
+
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return ["redis"]
+
+    @classmethod
+    def sample_run_config(cls):
+        return {
+            "type": StorageBackendType.KV_REDIS.value,
+            "host": "${env.REDIS_HOST:=localhost}",
+            "port": "${env.REDIS_PORT:=6379}",
+        }
+
+
+class SqliteKVStoreConfig(CommonConfig):
+    type: Literal[StorageBackendType.KV_SQLITE] = StorageBackendType.KV_SQLITE
+    db_path: str = Field(
+        description="File path for the sqlite database",
+    )
+
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return ["aiosqlite"]
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str, db_name: str = "kvstore.db"):
+        return {
+            "type": StorageBackendType.KV_SQLITE.value,
+            "db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + db_name,
+        }
+
+
+class PostgresKVStoreConfig(CommonConfig):
+    type: Literal[StorageBackendType.KV_POSTGRES] = StorageBackendType.KV_POSTGRES
+    host: str = "localhost"
+    port: int | str = 5432
+    db: str = "llamastack"
+    user: str
+    password: str | None = None
+    ssl_mode: str | None = None
+    ca_cert_path: str | None = None
+    table_name: str = "llamastack_kvstore"
+
+    @classmethod
+    def sample_run_config(cls, table_name: str = "llamastack_kvstore", **kwargs):
+        return {
+            "type": StorageBackendType.KV_POSTGRES.value,
+            "host": "${env.POSTGRES_HOST:=localhost}",
+            "port": "${env.POSTGRES_PORT:=5432}",
+            "db": "${env.POSTGRES_DB:=llamastack}",
+            "user": "${env.POSTGRES_USER:=llamastack}",
+            "password": "${env.POSTGRES_PASSWORD:=llamastack}",
+            "table_name": "${env.POSTGRES_TABLE_NAME:=" + table_name + "}",
+        }
+
+    @classmethod
+    @field_validator("table_name")
+    def validate_table_name(cls, v: str) -> str:
+        # PostgreSQL identifiers rules:
+        # - Must start with a letter or underscore
+        # - Can contain letters, numbers, and underscores
+        # - Maximum length is 63 bytes
+        pattern = r"^[a-zA-Z_][a-zA-Z0-9_]*$"
+        if not re.match(pattern, v):
+            raise ValueError(
+                "Invalid table name. Must start with letter or underscore and contain only letters, numbers, and underscores"
+            )
+        if len(v) > 63:
+            raise ValueError("Table name must be less than 63 characters")
+        return v
+
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return ["psycopg2-binary"]
+
+
+class MongoDBKVStoreConfig(CommonConfig):
+    type: Literal[StorageBackendType.KV_MONGODB] = StorageBackendType.KV_MONGODB
+    host: str = "localhost"
+    port: int = 27017
+    db: str = "llamastack"
+    user: str | None = None
+    password: str | None = None
+    collection_name: str = "llamastack_kvstore"
+
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return ["pymongo"]
+
+    @classmethod
+    def sample_run_config(cls, collection_name: str = "llamastack_kvstore"):
+        return {
+            "type": StorageBackendType.KV_MONGODB.value,
+            "host": "${env.MONGODB_HOST:=localhost}",
+            "port": "${env.MONGODB_PORT:=5432}",
+            "db": "${env.MONGODB_DB}",
+            "user": "${env.MONGODB_USER}",
+            "password": "${env.MONGODB_PASSWORD}",
+            "collection_name": "${env.MONGODB_COLLECTION_NAME:=" + collection_name + "}",
+        }
+
+
+class SqlAlchemySqlStoreConfig(BaseModel):
+    @property
+    @abstractmethod
+    def engine_str(self) -> str: ...
+
+    # TODO: move this when we have a better way to specify dependencies with internal APIs
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return ["sqlalchemy[asyncio]"]
+
+
+class SqliteSqlStoreConfig(SqlAlchemySqlStoreConfig):
+    type: Literal[StorageBackendType.SQL_SQLITE] = StorageBackendType.SQL_SQLITE
+    db_path: str = Field(
+        description="Database path, e.g. ~/.llama/distributions/ollama/sqlstore.db",
+    )
+
+    @property
+    def engine_str(self) -> str:
+        return "sqlite+aiosqlite:///" + Path(self.db_path).expanduser().as_posix()
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str, db_name: str = "sqlstore.db"):
+        return {
+            "type": StorageBackendType.SQL_SQLITE.value,
+            "db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + db_name,
+        }
+
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return super().pip_packages() + ["aiosqlite"]
+
+
+class PostgresSqlStoreConfig(SqlAlchemySqlStoreConfig):
+    type: Literal[StorageBackendType.SQL_POSTGRES] = StorageBackendType.SQL_POSTGRES
+    host: str = "localhost"
+    port: int | str = 5432
+    db: str = "llamastack"
+    user: str
+    password: str | None = None
+
+    @property
+    def engine_str(self) -> str:
+        return f"postgresql+asyncpg://{self.user}:{self.password}@{self.host}:{self.port}/{self.db}"
+
+    @classmethod
+    def pip_packages(cls) -> list[str]:
+        return super().pip_packages() + ["asyncpg"]
+
+    @classmethod
+    def sample_run_config(cls, **kwargs):
+        return {
+            "type": StorageBackendType.SQL_POSTGRES.value,
+            "host": "${env.POSTGRES_HOST:=localhost}",
+            "port": "${env.POSTGRES_PORT:=5432}",
+            "db": "${env.POSTGRES_DB:=llamastack}",
+            "user": "${env.POSTGRES_USER:=llamastack}",
+            "password": "${env.POSTGRES_PASSWORD:=llamastack}",
+        }
+
+
+# reference = (backend_name, table_name)
+class SqlStoreReference(BaseModel):
+    """A reference to a 'SQL-like' persistent store. A table name must be provided."""
+
+    table_name: str = Field(
+        description="Name of the table to use for the SqlStore",
+    )
+
+    backend: str = Field(
+        description="Name of backend from storage.backends",
+    )
+
+
+# reference = (backend_name, namespace)
+class KVStoreReference(BaseModel):
+    """A reference to a 'key-value' persistent store. A namespace must be provided."""
+
+    namespace: str = Field(
+        description="Key prefix for KVStore backends",
+    )
+
+    backend: str = Field(
+        description="Name of backend from storage.backends",
+    )
+
+
+StorageBackendConfig = Annotated[
+    RedisKVStoreConfig
+    | SqliteKVStoreConfig
+    | PostgresKVStoreConfig
+    | MongoDBKVStoreConfig
+    | SqliteSqlStoreConfig
+    | PostgresSqlStoreConfig,
+    Field(discriminator="type"),
+]
+
+
+class InferenceStoreReference(SqlStoreReference):
+    """Inference store configuration with queue tuning."""
+
+    max_write_queue_size: int = Field(
+        default=10000,
+        description="Max queued writes for inference store",
+    )
+    num_writers: int = Field(
+        default=4,
+        description="Number of concurrent background writers",
+    )
+
+
+class ResponsesStoreReference(InferenceStoreReference):
+    """Responses store configuration with queue tuning."""
+
+
+class ServerStoresConfig(BaseModel):
+    metadata: KVStoreReference | None = Field(
+        default=None,
+        description="Metadata store configuration (uses KV backend)",
+    )
+    inference: InferenceStoreReference | None = Field(
+        default=None,
+        description="Inference store configuration (uses SQL backend)",
+    )
+    conversations: SqlStoreReference | None = Field(
+        default=None,
+        description="Conversations store configuration (uses SQL backend)",
+    )
+    responses: ResponsesStoreReference | None = Field(
+        default=None,
+        description="Responses store configuration (uses SQL backend)",
+    )
+
+
+class StorageConfig(BaseModel):
+    backends: dict[str, StorageBackendConfig] = Field(
+        description="Named backend configurations (e.g., 'default', 'cache')",
+    )
+    stores: ServerStoresConfig = Field(
+        default_factory=lambda: ServerStoresConfig(),
+        description="Named references to storage backends used by the stack core",
+    )

llama_stack/core/store/registry.py

@@ -11,10 +11,9 @@ from typing import Protocol
 import pydantic
 
 from llama_stack.core.datatypes import RoutableObjectWithProvider
-from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
+from llama_stack.core.storage.datatypes import KVStoreReference
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
-from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 
 logger = get_logger(__name__, category="core::registry")
 
@@ -191,16 +190,10 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry):
 
 
 async def create_dist_registry(
-    metadata_store: KVStoreConfig | None,
-    image_name: str,
+    metadata_store: KVStoreReference, image_name: str
 ) -> tuple[CachedDiskDistributionRegistry, KVStore]:
     # instantiate kvstore for storing and retrieving distribution metadata
-    if metadata_store:
-        dist_kvstore = await kvstore_impl(metadata_store)
-    else:
-        dist_kvstore = await kvstore_impl(
-            SqliteKVStoreConfig(db_path=(DISTRIBS_BASE_DIR / image_name / "kvstore.db").as_posix())
-        )
+    dist_kvstore = await kvstore_impl(metadata_store)
     dist_registry = CachedDiskDistributionRegistry(dist_kvstore)
     await dist_registry.initialize()
     return dist_registry, dist_kvstore

llama_stack/distributions/ci-tests/build.yaml

@@ -25,6 +25,8 @@ distribution_spec:
     - provider_type: inline::milvus
     - provider_type: remote::chromadb
     - provider_type: remote::pgvector
+    - provider_type: remote::qdrant
+    - provider_type: remote::weaviate
     files:
     - provider_type: inline::localfs
     safety:

llama_stack/distributions/ci-tests/run.yaml

@@ -93,30 +93,30 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec
     config:
       db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec_registry.db
+      persistence:
+        namespace: vector_io::sqlite_vec
+        backend: kv_default
   - provider_id: ${env.MILVUS_URL:+milvus}
     provider_type: inline::milvus
     config:
       db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ci-tests}/milvus.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/milvus_registry.db
+      persistence:
+        namespace: vector_io::milvus
+        backend: kv_default
   - provider_id: ${env.CHROMADB_URL:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests/}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   - provider_id: ${env.PGVECTOR_DB:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -125,17 +125,32 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/pgvector_registry.db
+      persistence:
+        namespace: vector_io::pgvector
+        backend: kv_default
+  - provider_id: ${env.QDRANT_URL:+qdrant}
+    provider_type: remote::qdrant
+    config:
+      api_key: ${env.QDRANT_API_KEY:=}
+      persistence:
+        namespace: vector_io::qdrant_remote
+        backend: kv_default
+  - provider_id: ${env.WEAVIATE_CLUSTER_URL:+weaviate}
+    provider_type: remote::weaviate
+    config:
+      weaviate_api_key: null
+      weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
+      persistence:
+        namespace: vector_io::weaviate
+        backend: kv_default
   files:
   - provider_id: meta-reference-files
     provider_type: inline::localfs
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ci-tests/files}
       metadata_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/files_metadata.db
+        table_name: files_metadata
+        backend: sql_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -147,12 +162,15 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   post_training:
   - provider_id: torchtune-cpu
     provider_type: inline::torchtune-cpu
@@ -163,21 +181,21 @@ providers:
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -207,17 +225,28 @@ providers:
     provider_type: inline::reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/batches.db
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/conversations.db
+        namespace: batches
+        backend: kv_default
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models: []
 shields:
 - shield_id: llama-guard
@@ -239,3 +268,8 @@ server:
   port: 8321
 telemetry:
   enabled: true
+vector_stores:
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5

llama_stack/distributions/dell/run-with-safety.yaml

@@ -26,9 +26,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell/}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -38,32 +38,35 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -86,15 +89,26 @@ providers:
       max_results: 3
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/conversations.db
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

llama_stack/distributions/dell/run.yaml

@@ -22,9 +22,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell/}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -34,32 +34,35 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -82,15 +85,26 @@ providers:
       max_results: 3
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/conversations.db
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml

@@ -37,9 +37,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -49,32 +49,35 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -99,15 +102,26 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/conversations.db
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

llama_stack/distributions/meta-reference-gpu/run.yaml

@@ -27,9 +27,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -39,32 +39,35 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -89,15 +92,26 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/conversations.db
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

llama_stack/distributions/nvidia/run-with-safety.yaml

@@ -28,9 +28,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   safety:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -41,12 +41,15 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -65,8 +68,8 @@ providers:
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   - provider_id: nvidia
     provider_type: remote::nvidia
     config:
@@ -86,17 +89,28 @@ providers:
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files}
       metadata_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/files_metadata.db
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/conversations.db
+        table_name: files_metadata
+        backend: sql_default
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

llama_stack/distributions/nvidia/run.yaml

@@ -23,9 +23,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   safety:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -36,12 +36,15 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -75,17 +78,28 @@ providers:
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files}
       metadata_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/files_metadata.db
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/conversations.db
+        table_name: files_metadata
+        backend: sql_default
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models: []
 shields: []
 vector_dbs: []

llama_stack/distributions/open-benchmark/run.yaml

@@ -39,16 +39,16 @@ providers:
     provider_type: inline::sqlite-vec
     config:
       db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec_registry.db
+      persistence:
+        namespace: vector_io::sqlite_vec
+        backend: kv_default
   - provider_id: ${env.ENABLE_CHROMADB:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   - provider_id: ${env.ENABLE_PGVECTOR:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -57,9 +57,9 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/pgvector_registry.db
+      persistence:
+        namespace: vector_io::pgvector
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -69,32 +69,35 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -119,15 +122,26 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/conversations.db
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: gpt-4o

llama_stack/distributions/postgres-demo/postgres_demo.py

@@ -91,7 +91,6 @@ def get_distribution_template() -> DistributionTemplate:
             "embedding_dimension": 768,
         },
     )
-    postgres_config = PostgresSqlStoreConfig.sample_run_config()
     return DistributionTemplate(
         name=name,
         distro_type="self_hosted",
@@ -105,22 +104,16 @@ def get_distribution_template() -> DistributionTemplate:
                 provider_overrides={
                     "inference": inference_providers + [embedding_provider],
                     "vector_io": vector_io_providers,
-                    "agents": [
-                        Provider(
-                            provider_id="meta-reference",
-                            provider_type="inline::meta-reference",
-                            config=dict(
-                                persistence_store=postgres_config,
-                                responses_store=postgres_config,
-                            ),
-                        )
-                    ],
                 },
                 default_models=default_models + [embedding_model],
                 default_tool_groups=default_tool_groups,
                 default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
-                metadata_store=PostgresKVStoreConfig.sample_run_config(),
-                inference_store=postgres_config,
+                storage_backends={
+                    "kv_default": PostgresKVStoreConfig.sample_run_config(
+                        table_name="llamastack_kvstore",
+                    ),
+                    "sql_default": PostgresSqlStoreConfig.sample_run_config(),
+                },
             ),
         },
         run_config_env_vars={

llama_stack/distributions/postgres-demo/run.yaml

@@ -22,9 +22,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/postgres-demo}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -34,20 +34,15 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: postgres
-        host: ${env.POSTGRES_HOST:=localhost}
-        port: ${env.POSTGRES_PORT:=5432}
-        db: ${env.POSTGRES_DB:=llamastack}
-        user: ${env.POSTGRES_USER:=llamastack}
-        password: ${env.POSTGRES_PASSWORD:=llamastack}
-      responses_store:
-        type: postgres
-        host: ${env.POSTGRES_HOST:=localhost}
-        port: ${env.POSTGRES_PORT:=5432}
-        db: ${env.POSTGRES_DB:=llamastack}
-        user: ${env.POSTGRES_USER:=llamastack}
-        password: ${env.POSTGRES_PASSWORD:=llamastack}
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   tool_runtime:
   - provider_id: brave-search
     provider_type: remote::brave-search
@@ -63,24 +58,35 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-metadata_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
-  table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
-inference_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/postgres-demo}/conversations.db
+storage:
+  backends:
+    kv_default:
+      type: kv_postgres
+      host: ${env.POSTGRES_HOST:=localhost}
+      port: ${env.POSTGRES_PORT:=5432}
+      db: ${env.POSTGRES_DB:=llamastack}
+      user: ${env.POSTGRES_USER:=llamastack}
+      password: ${env.POSTGRES_PASSWORD:=llamastack}
+      table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+    sql_default:
+      type: sql_postgres
+      host: ${env.POSTGRES_HOST:=localhost}
+      port: ${env.POSTGRES_PORT:=5432}
+      db: ${env.POSTGRES_DB:=llamastack}
+      user: ${env.POSTGRES_USER:=llamastack}
+      password: ${env.POSTGRES_PASSWORD:=llamastack}
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}

llama_stack/distributions/starter-gpu/build.yaml

@@ -26,6 +26,8 @@ distribution_spec:
     - provider_type: inline::milvus
     - provider_type: remote::chromadb
     - provider_type: remote::pgvector
+    - provider_type: remote::qdrant
+    - provider_type: remote::weaviate
     files:
     - provider_type: inline::localfs
     safety:

llama_stack/distributions/starter-gpu/run.yaml

@@ -93,30 +93,30 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec
     config:
       db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec_registry.db
+      persistence:
+        namespace: vector_io::sqlite_vec
+        backend: kv_default
   - provider_id: ${env.MILVUS_URL:+milvus}
     provider_type: inline::milvus
     config:
       db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter-gpu}/milvus.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/milvus_registry.db
+      persistence:
+        namespace: vector_io::milvus
+        backend: kv_default
   - provider_id: ${env.CHROMADB_URL:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu/}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   - provider_id: ${env.PGVECTOR_DB:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -125,17 +125,32 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/pgvector_registry.db
+      persistence:
+        namespace: vector_io::pgvector
+        backend: kv_default
+  - provider_id: ${env.QDRANT_URL:+qdrant}
+    provider_type: remote::qdrant
+    config:
+      api_key: ${env.QDRANT_API_KEY:=}
+      persistence:
+        namespace: vector_io::qdrant_remote
+        backend: kv_default
+  - provider_id: ${env.WEAVIATE_CLUSTER_URL:+weaviate}
+    provider_type: remote::weaviate
+    config:
+      weaviate_api_key: null
+      weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
+      persistence:
+        namespace: vector_io::weaviate
+        backend: kv_default
   files:
   - provider_id: meta-reference-files
     provider_type: inline::localfs
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter-gpu/files}
       metadata_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/files_metadata.db
+        table_name: files_metadata
+        backend: sql_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -147,12 +162,15 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   post_training:
   - provider_id: huggingface-gpu
     provider_type: inline::huggingface-gpu
@@ -166,21 +184,21 @@ providers:
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -210,17 +228,28 @@ providers:
     provider_type: inline::reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/batches.db
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/conversations.db
+        namespace: batches
+        backend: kv_default
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models: []
 shields:
 - shield_id: llama-guard
@@ -242,3 +271,8 @@ server:
   port: 8321
 telemetry:
   enabled: true
+vector_stores:
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5

llama_stack/distributions/starter/build.yaml

@@ -26,6 +26,8 @@ distribution_spec:
     - provider_type: inline::milvus
     - provider_type: remote::chromadb
     - provider_type: remote::pgvector
+    - provider_type: remote::qdrant
+    - provider_type: remote::weaviate
     files:
     - provider_type: inline::localfs
     safety:

llama_stack/distributions/starter/run.yaml

@@ -93,30 +93,30 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec
     config:
       db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db
+      persistence:
+        namespace: vector_io::sqlite_vec
+        backend: kv_default
   - provider_id: ${env.MILVUS_URL:+milvus}
     provider_type: inline::milvus
     config:
       db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db
+      persistence:
+        namespace: vector_io::milvus
+        backend: kv_default
   - provider_id: ${env.CHROMADB_URL:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
   - provider_id: ${env.PGVECTOR_DB:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -125,17 +125,32 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db
+      persistence:
+        namespace: vector_io::pgvector
+        backend: kv_default
+  - provider_id: ${env.QDRANT_URL:+qdrant}
+    provider_type: remote::qdrant
+    config:
+      api_key: ${env.QDRANT_API_KEY:=}
+      persistence:
+        namespace: vector_io::qdrant_remote
+        backend: kv_default
+  - provider_id: ${env.WEAVIATE_CLUSTER_URL:+weaviate}
+    provider_type: remote::weaviate
+    config:
+      weaviate_api_key: null
+      weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
+      persistence:
+        namespace: vector_io::weaviate
+        backend: kv_default
   files:
   - provider_id: meta-reference-files
     provider_type: inline::localfs
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}
       metadata_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db
+        table_name: files_metadata
+        backend: sql_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -147,12 +162,15 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   post_training:
   - provider_id: torchtune-cpu
     provider_type: inline::torchtune-cpu
@@ -163,21 +181,21 @@ providers:
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -207,17 +225,28 @@ providers:
     provider_type: inline::reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/batches.db
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/conversations.db
+        namespace: batches
+        backend: kv_default
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models: []
 shields:
 - shield_id: llama-guard
@@ -239,3 +268,8 @@ server:
   port: 8321
 telemetry:
   enabled: true
+vector_stores:
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5

llama_stack/distributions/starter/starter.py

@@ -11,8 +11,10 @@ from llama_stack.core.datatypes import (
     BuildProvider,
     Provider,
     ProviderSpec,
+    QualifiedModel,
     ShieldInput,
     ToolGroupInput,
+    VectorStoresConfig,
 )
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
@@ -31,6 +33,8 @@ from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOC
 from llama_stack.providers.remote.vector_io.pgvector.config import (
     PGVectorVectorIOConfig,
 )
+from llama_stack.providers.remote.vector_io.qdrant.config import QdrantVectorIOConfig
+from llama_stack.providers.remote.vector_io.weaviate.config import WeaviateVectorIOConfig
 from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
 
 
@@ -113,6 +117,8 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
             BuildProvider(provider_type="inline::milvus"),
             BuildProvider(provider_type="remote::chromadb"),
             BuildProvider(provider_type="remote::pgvector"),
+            BuildProvider(provider_type="remote::qdrant"),
+            BuildProvider(provider_type="remote::weaviate"),
         ],
         "files": [BuildProvider(provider_type="inline::localfs")],
         "safety": [
@@ -221,12 +227,35 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
                                 password="${env.PGVECTOR_PASSWORD:=}",
                             ),
                         ),
+                        Provider(
+                            provider_id="${env.QDRANT_URL:+qdrant}",
+                            provider_type="remote::qdrant",
+                            config=QdrantVectorIOConfig.sample_run_config(
+                                f"~/.llama/distributions/{name}",
+                                url="${env.QDRANT_URL:=}",
+                            ),
+                        ),
+                        Provider(
+                            provider_id="${env.WEAVIATE_CLUSTER_URL:+weaviate}",
+                            provider_type="remote::weaviate",
+                            config=WeaviateVectorIOConfig.sample_run_config(
+                                f"~/.llama/distributions/{name}",
+                                cluster_url="${env.WEAVIATE_CLUSTER_URL:=}",
+                            ),
+                        ),
                     ],
                     "files": [files_provider],
                 },
                 default_models=[],
                 default_tool_groups=default_tool_groups,
                 default_shields=default_shields,
+                vector_stores_config=VectorStoresConfig(
+                    default_provider_id="faiss",
+                    default_embedding_model=QualifiedModel(
+                        provider_id="sentence-transformers",
+                        model_id="nomic-ai/nomic-embed-text-v1.5",
+                    ),
+                ),
             ),
         },
         run_config_env_vars={

llama_stack/distributions/template.py

@@ -27,8 +27,15 @@ from llama_stack.core.datatypes import (
     ShieldInput,
     TelemetryConfig,
     ToolGroupInput,
+    VectorStoresConfig,
 )
 from llama_stack.core.distribution import get_provider_registry
+from llama_stack.core.storage.datatypes import (
+    InferenceStoreReference,
+    KVStoreReference,
+    SqlStoreReference,
+    StorageBackendType,
+)
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.core.utils.image_types import LlamaStackImageType
 from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
@@ -180,10 +187,10 @@ class RunConfigSettings(BaseModel):
     default_tool_groups: list[ToolGroupInput] | None = None
     default_datasets: list[DatasetInput] | None = None
     default_benchmarks: list[BenchmarkInput] | None = None
-    metadata_store: dict | None = None
-    inference_store: dict | None = None
-    conversations_store: dict | None = None
+    vector_stores_config: VectorStoresConfig | None = None
     telemetry: TelemetryConfig = Field(default_factory=lambda: TelemetryConfig(enabled=True))
+    storage_backends: dict[str, Any] | None = None
+    storage_stores: dict[str, Any] | None = None
 
     def run_config(
         self,
@@ -226,28 +233,45 @@ class RunConfigSettings(BaseModel):
         # Get unique set of APIs from providers
         apis = sorted(providers.keys())
 
+        storage_backends = self.storage_backends or {
+            "kv_default": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=f"~/.llama/distributions/{name}",
+                db_name="kvstore.db",
+            ),
+            "sql_default": SqliteSqlStoreConfig.sample_run_config(
+                __distro_dir__=f"~/.llama/distributions/{name}",
+                db_name="sql_store.db",
+            ),
+        }
+
+        storage_stores = self.storage_stores or {
+            "metadata": KVStoreReference(
+                backend="kv_default",
+                namespace="registry",
+            ).model_dump(exclude_none=True),
+            "inference": InferenceStoreReference(
+                backend="sql_default",
+                table_name="inference_store",
+            ).model_dump(exclude_none=True),
+            "conversations": SqlStoreReference(
+                backend="sql_default",
+                table_name="openai_conversations",
+            ).model_dump(exclude_none=True),
+        }
+
+        storage_config = dict(
+            backends=storage_backends,
+            stores=storage_stores,
+        )
+
         # Return a dict that matches StackRunConfig structure
-        return {
+        config = {
             "version": LLAMA_STACK_RUN_CONFIG_VERSION,
             "image_name": name,
             "container_image": container_image,
             "apis": apis,
             "providers": provider_configs,
-            "metadata_store": self.metadata_store
-            or SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=f"~/.llama/distributions/{name}",
-                db_name="registry.db",
-            ),
-            "inference_store": self.inference_store
-            or SqliteSqlStoreConfig.sample_run_config(
-                __distro_dir__=f"~/.llama/distributions/{name}",
-                db_name="inference_store.db",
-            ),
-            "conversations_store": self.conversations_store
-            or SqliteSqlStoreConfig.sample_run_config(
-                __distro_dir__=f"~/.llama/distributions/{name}",
-                db_name="conversations.db",
-            ),
+            "storage": storage_config,
             "models": [m.model_dump(exclude_none=True) for m in (self.default_models or [])],
             "shields": [s.model_dump(exclude_none=True) for s in (self.default_shields or [])],
             "vector_dbs": [],
@@ -261,6 +285,11 @@ class RunConfigSettings(BaseModel):
             "telemetry": self.telemetry.model_dump(exclude_none=True) if self.telemetry else None,
         }
 
+        if self.vector_stores_config:
+            config["vector_stores"] = self.vector_stores_config.model_dump(exclude_none=True)
+
+        return config
+
 
 class DistributionTemplate(BaseModel):
     """
@@ -297,11 +326,15 @@ class DistributionTemplate(BaseModel):
             # We should have a better way to do this by formalizing the concept of "internal" APIs
             # and providers, with a way to specify dependencies for them.
 
-            if run_config_.get("inference_store"):
-                additional_pip_packages.extend(get_sql_pip_packages(run_config_["inference_store"]))
-
-            if run_config_.get("metadata_store"):
-                additional_pip_packages.extend(get_kv_pip_packages(run_config_["metadata_store"]))
+            storage_cfg = run_config_.get("storage", {})
+            for backend_cfg in storage_cfg.get("backends", {}).values():
+                store_type = backend_cfg.get("type")
+                if not store_type:
+                    continue
+                if str(store_type).startswith("kv_"):
+                    additional_pip_packages.extend(get_kv_pip_packages(backend_cfg))
+                elif str(store_type).startswith("sql_"):
+                    additional_pip_packages.extend(get_sql_pip_packages(backend_cfg))
 
         if self.additional_pip_packages:
             additional_pip_packages.extend(self.additional_pip_packages)
@@ -387,11 +420,13 @@ class DistributionTemplate(BaseModel):
         def enum_representer(dumper, data):
             return dumper.represent_scalar("tag:yaml.org,2002:str", data.value)
 
-        # Register YAML representer for ModelType
+        # Register YAML representer for enums
         yaml.add_representer(ModelType, enum_representer)
         yaml.add_representer(DatasetPurpose, enum_representer)
+        yaml.add_representer(StorageBackendType, enum_representer)
         yaml.SafeDumper.add_representer(ModelType, enum_representer)
         yaml.SafeDumper.add_representer(DatasetPurpose, enum_representer)
+        yaml.SafeDumper.add_representer(StorageBackendType, enum_representer)
 
         for output_dir in [yaml_output_dir, doc_output_dir]:
             output_dir.mkdir(parents=True, exist_ok=True)

llama_stack/distributions/watsonx/run.yaml

@@ -22,9 +22,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/faiss_store.db
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -34,32 +34,35 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/agents_store.db
-      responses_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/responses_store.db
+      persistence:
+        agent_state:
+          namespace: agents
+          backend: kv_default
+        responses:
+          table_name: responses
+          backend: sql_default
+          max_write_queue_size: 10000
+          num_writers: 4
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/meta_reference_eval.db
+        namespace: eval
+        backend: kv_default
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/huggingface_datasetio.db
+        namespace: datasetio::huggingface
+        backend: kv_default
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/localfs_datasetio.db
+        namespace: datasetio::localfs
+        backend: kv_default
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -90,17 +93,28 @@ providers:
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/watsonx/files}
       metadata_store:
-        type: sqlite
-        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/files_metadata.db
-metadata_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/registry.db
-inference_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/inference_store.db
-conversations_store:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/conversations.db
+        table_name: files_metadata
+        backend: sql_default
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/kvstore.db
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/sql_store.db
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
 models: []
 shields: []
 vector_dbs: []

llama_stack/providers/inline/agents/meta_reference/agents.py

@@ -83,8 +83,8 @@ class MetaReferenceAgentsImpl(Agents):
         self.policy = policy
 
     async def initialize(self) -> None:
-        self.persistence_store = await kvstore_impl(self.config.persistence_store)
-        self.responses_store = ResponsesStore(self.config.responses_store, self.policy)
+        self.persistence_store = await kvstore_impl(self.config.persistence.agent_state)
+        self.responses_store = ResponsesStore(self.config.persistence.responses, self.policy)
         await self.responses_store.initialize()
         self.openai_responses_impl = OpenAIResponsesImpl(
             inference_api=self.inference_api,

llama_stack/providers/inline/agents/meta_reference/config.py

@@ -8,24 +8,30 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from llama_stack.providers.utils.kvstore import KVStoreConfig
-from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
-from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
+from llama_stack.core.storage.datatypes import KVStoreReference, ResponsesStoreReference
+
+
+class AgentPersistenceConfig(BaseModel):
+    """Nested persistence configuration for agents."""
+
+    agent_state: KVStoreReference
+    responses: ResponsesStoreReference
 
 
 class MetaReferenceAgentsImplConfig(BaseModel):
-    persistence_store: KVStoreConfig
-    responses_store: SqlStoreConfig
+    persistence: AgentPersistenceConfig
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
         return {
-            "persistence_store": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="agents_store.db",
-            ),
-            "responses_store": SqliteSqlStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="responses_store.db",
-            ),
+            "persistence": {
+                "agent_state": KVStoreReference(
+                    backend="kv_default",
+                    namespace="agents",
+                ).model_dump(exclude_none=True),
+                "responses": ResponsesStoreReference(
+                    backend="sql_default",
+                    table_name="responses",
+                ).model_dump(exclude_none=True),
+            }
         }

llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py

@@ -359,6 +359,7 @@ class OpenAIResponsesImpl:
             tool_executor=self.tool_executor,
             safety_api=self.safety_api,
             guardrail_ids=guardrail_ids,
+            instructions=instructions,
         )
 
         # Stream the response

llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -110,6 +110,7 @@ class StreamingResponseOrchestrator:
         text: OpenAIResponseText,
         max_infer_iters: int,
         tool_executor,  # Will be the tool execution logic from the main class
+        instructions: str,
         safety_api,
         guardrail_ids: list[str] | None = None,
     ):
@@ -133,6 +134,8 @@ class StreamingResponseOrchestrator:
         self.accumulated_usage: OpenAIResponseUsage | None = None
         # Track if we've sent a refusal response
         self.violation_detected = False
+        # system message that is inserted into the model's context
+        self.instructions = instructions
 
     async def _create_refusal_response(self, violation_message: str) -> OpenAIResponseObjectStream:
         """Create a refusal response to replace streaming content."""
@@ -176,6 +179,7 @@ class StreamingResponseOrchestrator:
             tools=self.ctx.available_tools(),
             error=error,
             usage=self.accumulated_usage,
+            instructions=self.instructions,
         )
 
     async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:

llama_stack/providers/inline/batches/reference/config.py

@@ -6,13 +6,13 @@
 
 from pydantic import BaseModel, Field
 
-from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
+from llama_stack.core.storage.datatypes import KVStoreReference
 
 
 class ReferenceBatchesImplConfig(BaseModel):
     """Configuration for the Reference Batches implementation."""
 
-    kvstore: KVStoreConfig = Field(
+    kvstore: KVStoreReference = Field(
         description="Configuration for the key-value store backend.",
     )
 
@@ -33,8 +33,8 @@ class ReferenceBatchesImplConfig(BaseModel):
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict:
         return {
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="batches.db",
-            ),
+            "kvstore": KVStoreReference(
+                backend="kv_default",
+                namespace="batches",
+            ).model_dump(exclude_none=True),
         }

llama_stack/providers/inline/datasetio/localfs/config.py

@@ -7,20 +7,17 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 
 
 class LocalFSDatasetIOConfig(BaseModel):
-    kvstore: KVStoreConfig
+    kvstore: KVStoreReference
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
         return {
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="localfs_datasetio.db",
-            )
+            "kvstore": KVStoreReference(
+                backend="kv_default",
+                namespace="datasetio::localfs",
+            ).model_dump(exclude_none=True)
         }

llama_stack/providers/inline/eval/meta_reference/config.py

@@ -7,20 +7,17 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 
 
 class MetaReferenceEvalConfig(BaseModel):
-    kvstore: KVStoreConfig
+    kvstore: KVStoreReference
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
         return {
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="meta_reference_eval.db",
-            )
+            "kvstore": KVStoreReference(
+                backend="kv_default",
+                namespace="eval",
+            ).model_dump(exclude_none=True)
         }

llama_stack/providers/inline/files/localfs/config.py

@@ -8,14 +8,14 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
+from llama_stack.core.storage.datatypes import SqlStoreReference
 
 
 class LocalfsFilesImplConfig(BaseModel):
     storage_dir: str = Field(
         description="Directory to store uploaded files",
     )
-    metadata_store: SqlStoreConfig = Field(
+    metadata_store: SqlStoreReference = Field(
         description="SQL store configuration for file metadata",
     )
     ttl_secs: int = 365 * 24 * 60 * 60  # 1 year
@@ -24,8 +24,8 @@ class LocalfsFilesImplConfig(BaseModel):
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
         return {
             "storage_dir": "${env.FILES_STORAGE_DIR:=" + __distro_dir__ + "/files}",
-            "metadata_store": SqliteSqlStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="files_metadata.db",
-            ),
+            "metadata_store": SqlStoreReference(
+                backend="sql_default",
+                table_name="files_metadata",
+            ).model_dump(exclude_none=True),
         }

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -59,7 +59,6 @@ class SentenceTransformersInferenceImpl(
                 provider_id=self.__provider_id__,
                 metadata={
                     "embedding_dimension": 768,
-                    "default_configured": True,
                 },
                 model_type=ModelType.embedding,
             ),

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

@@ -12,15 +12,8 @@ from .config import ChromaVectorIOConfig
 
 
 async def get_provider_impl(config: ChromaVectorIOConfig, deps: dict[Api, Any]):
-    from llama_stack.providers.remote.vector_io.chroma.chroma import (
-        ChromaVectorIOAdapter,
-    )
+    from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaVectorIOAdapter
 
-    impl = ChromaVectorIOAdapter(
-        config,
-        deps[Api.inference],
-        deps[Api.models],
-        deps.get(Api.files),
-    )
+    impl = ChromaVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -8,14 +8,14 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
+from llama_stack.core.storage.datatypes import KVStoreReference
 from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type
 class ChromaVectorIOConfig(BaseModel):
     db_path: str
-    kvstore: KVStoreConfig = Field(description="Config for KV store backend")
+    persistence: KVStoreReference = Field(description="Config for KV store backend")
 
     @classmethod
     def sample_run_config(
@@ -23,8 +23,8 @@ class ChromaVectorIOConfig(BaseModel):
     ) -> dict[str, Any]:
         return {
             "db_path": db_path,
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="chroma_inline_registry.db",
-            ),
+            "persistence": KVStoreReference(
+                backend="kv_default",
+                namespace="vector_io::chroma",
+            ).model_dump(exclude_none=True),
         }

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

@@ -16,11 +16,6 @@ async def get_provider_impl(config: FaissVectorIOConfig, deps: dict[Api, Any]):
 
     assert isinstance(config, FaissVectorIOConfig), f"Unexpected config type: {type(config)}"
 
-    impl = FaissVectorIOAdapter(
-        config,
-        deps[Api.inference],
-        deps[Api.models],
-        deps.get(Api.files),
-    )
+    impl = FaissVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -8,22 +8,19 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type
 class FaissVectorIOConfig(BaseModel):
-    kvstore: KVStoreConfig
+    persistence: KVStoreReference
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
         return {
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="faiss_store.db",
-            )
+            "persistence": KVStoreReference(
+                backend="kv_default",
+                namespace="vector_io::faiss",
+            ).model_dump(exclude_none=True)
         }

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -17,27 +17,14 @@ from numpy.typing import NDArray
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
-from llama_stack.apis.models import Models
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import (
-    Chunk,
-    QueryChunksResponse,
-    VectorIO,
-)
+from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.log import get_logger
-from llama_stack.providers.datatypes import (
-    HealthResponse,
-    HealthStatus,
-    VectorDBsProtocolPrivate,
-)
+from llama_stack.providers.datatypes import HealthResponse, HealthStatus, VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
-from llama_stack.providers.utils.memory.vector_store import (
-    ChunkForDeletion,
-    EmbeddingIndex,
-    VectorDBWithIndex,
-)
+from llama_stack.providers.utils.memory.vector_store import ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex
 
 from .config import FaissVectorIOConfig
 
@@ -155,12 +142,7 @@ class FaissIndex(EmbeddingIndex):
 
         await self._save_index()
 
-    async def query_vector(
-        self,
-        embedding: NDArray,
-        k: int,
-        score_threshold: float,
-    ) -> QueryChunksResponse:
+    async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         distances, indices = await asyncio.to_thread(self.index.search, embedding.reshape(1, -1).astype(np.float32), k)
         chunks = []
         scores = []
@@ -175,12 +157,7 @@ class FaissIndex(EmbeddingIndex):
 
         return QueryChunksResponse(chunks=chunks, scores=scores)
 
-    async def query_keyword(
-        self,
-        query_string: str,
-        k: int,
-        score_threshold: float,
-    ) -> QueryChunksResponse:
+    async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
         raise NotImplementedError(
             "Keyword search is not supported - underlying DB FAISS does not support this search mode"
         )
@@ -200,21 +177,14 @@ class FaissIndex(EmbeddingIndex):
 
 
 class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
-    def __init__(
-        self,
-        config: FaissVectorIOConfig,
-        inference_api: Inference,
-        models_api: Models,
-        files_api: Files | None,
-    ) -> None:
+    def __init__(self, config: FaissVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.models_api = models_api
         self.cache: dict[str, VectorDBWithIndex] = {}
 
     async def initialize(self) -> None:
-        self.kvstore = await kvstore_impl(self.config.kvstore)
+        self.kvstore = await kvstore_impl(self.config.persistence)
         # Load existing banks from kvstore
         start_key = VECTOR_DBS_PREFIX
         end_key = f"{VECTOR_DBS_PREFIX}\xff"
@@ -252,17 +222,11 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
         except Exception as e:
             return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
 
-    async def register_vector_db(
-        self,
-        vector_db: VectorDB,
-    ) -> None:
+    async def register_vector_db(self, vector_db: VectorDB) -> None:
         assert self.kvstore is not None
 
         key = f"{VECTOR_DBS_PREFIX}{vector_db.identifier}"
-        await self.kvstore.set(
-            key=key,
-            value=vector_db.model_dump_json(),
-        )
+        await self.kvstore.set(key=key, value=vector_db.model_dump_json())
 
         # Store in cache
         self.cache[vector_db.identifier] = VectorDBWithIndex(
@@ -285,12 +249,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
         del self.cache[vector_db_id]
         await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_db_id}")
 
-    async def insert_chunks(
-        self,
-        vector_db_id: str,
-        chunks: list[Chunk],
-        ttl_seconds: int | None = None,
-    ) -> None:
+    async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
         index = self.cache.get(vector_db_id)
         if index is None:
             raise ValueError(f"Vector DB {vector_db_id} not found. found: {self.cache.keys()}")
@@ -298,10 +257,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
         await index.insert_chunks(chunks)
 
     async def query_chunks(
-        self,
-        vector_db_id: str,
-        query: InterleavedContent,
-        params: dict[str, Any] | None = None,
+        self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
     ) -> QueryChunksResponse:
         index = self.cache.get(vector_db_id)
         if index is None:

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

@@ -14,11 +14,6 @@ from .config import MilvusVectorIOConfig
 async def get_provider_impl(config: MilvusVectorIOConfig, deps: dict[Api, Any]):
     from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusVectorIOAdapter
 
-    impl = MilvusVectorIOAdapter(
-        config,
-        deps[Api.inference],
-        deps[Api.models],
-        deps.get(Api.files),
-    )
+    impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -8,25 +8,22 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type
 class MilvusVectorIOConfig(BaseModel):
     db_path: str
-    kvstore: KVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)")
+    persistence: KVStoreReference = Field(description="Config for KV store backend (SQLite only for now)")
     consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong")
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
         return {
             "db_path": "${env.MILVUS_DB_PATH:=" + __distro_dir__ + "}/" + "milvus.db",
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="milvus_registry.db",
-            ),
+            "persistence": KVStoreReference(
+                backend="kv_default",
+                namespace="vector_io::milvus",
+            ).model_dump(exclude_none=True),
         }

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

@@ -15,11 +15,6 @@ async def get_provider_impl(config: QdrantVectorIOConfig, deps: dict[Api, Any]):
     from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
 
     assert isinstance(config, QdrantVectorIOConfig), f"Unexpected config type: {type(config)}"
-    impl = QdrantVectorIOAdapter(
-        config,
-        deps[Api.inference],
-        deps[Api.models],
-        deps.get(Api.files),
-    )
+    impl = QdrantVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -9,23 +9,21 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 from llama_stack.schema_utils import json_schema_type
 
 
 @json_schema_type
 class QdrantVectorIOConfig(BaseModel):
     path: str
-    kvstore: KVStoreConfig
+    persistence: KVStoreReference
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
         return {
             "path": "${env.QDRANT_PATH:=~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__, db_name="qdrant_registry.db"
-            ),
+            "persistence": KVStoreReference(
+                backend="kv_default",
+                namespace="vector_io::qdrant",
+            ).model_dump(exclude_none=True),
         }

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

@@ -15,11 +15,6 @@ async def get_provider_impl(config: SQLiteVectorIOConfig, deps: dict[Api, Any]):
     from .sqlite_vec import SQLiteVecVectorIOAdapter
 
     assert isinstance(config, SQLiteVectorIOConfig), f"Unexpected config type: {type(config)}"
-    impl = SQLiteVecVectorIOAdapter(
-        config,
-        deps[Api.inference],
-        deps[Api.models],
-        deps.get(Api.files),
-    )
+    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files))
     await impl.initialize()
     return impl

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

@@ -8,22 +8,19 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 
 
 class SQLiteVectorIOConfig(BaseModel):
     db_path: str = Field(description="Path to the SQLite database file")
-    kvstore: KVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)")
+    persistence: KVStoreReference = Field(description="Config for KV store backend (SQLite only for now)")
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
         return {
             "db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + "sqlite_vec.db",
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="sqlite_vec_registry.db",
-            ),
+            "persistence": KVStoreReference(
+                backend="kv_default",
+                namespace="vector_io::sqlite_vec",
+            ).model_dump(exclude_none=True),
         }

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -17,13 +17,8 @@ from numpy.typing import NDArray
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import Inference
-from llama_stack.apis.models import Models
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import (
-    Chunk,
-    QueryChunksResponse,
-    VectorIO,
-)
+from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
@@ -175,32 +170,18 @@ class SQLiteVecIndex(EmbeddingIndex):
 
                     # Insert vector embeddings
                     embedding_data = [
-                        (
-                            (
-                                chunk.chunk_id,
-                                serialize_vector(emb.tolist()),
-                            )
-                        )
+                        ((chunk.chunk_id, serialize_vector(emb.tolist())))
                         for chunk, emb in zip(batch_chunks, batch_embeddings, strict=True)
                     ]
-                    cur.executemany(
-                        f"INSERT INTO [{self.vector_table}] (id, embedding) VALUES (?, ?);",
-                        embedding_data,
-                    )
+                    cur.executemany(f"INSERT INTO [{self.vector_table}] (id, embedding) VALUES (?, ?);", embedding_data)
 
                     # Insert FTS content
                     fts_data = [(chunk.chunk_id, chunk.content) for chunk in batch_chunks]
                     # DELETE existing entries with same IDs (FTS5 doesn't support ON CONFLICT)
-                    cur.executemany(
-                        f"DELETE FROM [{self.fts_table}] WHERE id = ?;",
-                        [(row[0],) for row in fts_data],
-                    )
+                    cur.executemany(f"DELETE FROM [{self.fts_table}] WHERE id = ?;", [(row[0],) for row in fts_data])
 
                     # INSERT new entries
-                    cur.executemany(
-                        f"INSERT INTO [{self.fts_table}] (id, content) VALUES (?, ?);",
-                        fts_data,
-                    )
+                    cur.executemany(f"INSERT INTO [{self.fts_table}] (id, content) VALUES (?, ?);", fts_data)
 
                 connection.commit()
 
@@ -216,12 +197,7 @@ class SQLiteVecIndex(EmbeddingIndex):
         # Run batch insertion in a background thread
         await asyncio.to_thread(_execute_all_batch_inserts)
 
-    async def query_vector(
-        self,
-        embedding: NDArray,
-        k: int,
-        score_threshold: float,
-    ) -> QueryChunksResponse:
+    async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         """
         Performs vector-based search using a virtual table for vector similarity.
         """
@@ -261,12 +237,7 @@ class SQLiteVecIndex(EmbeddingIndex):
             scores.append(score)
         return QueryChunksResponse(chunks=chunks, scores=scores)
 
-    async def query_keyword(
-        self,
-        query_string: str,
-        k: int,
-        score_threshold: float,
-    ) -> QueryChunksResponse:
+    async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
         """
         Performs keyword-based search using SQLite FTS5 for relevance-ranked full-text search.
         """
@@ -410,22 +381,15 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
     and creates a cache of VectorDBWithIndex instances (each wrapping a SQLiteVecIndex).
     """
 
-    def __init__(
-        self,
-        config,
-        inference_api: Inference,
-        models_api: Models,
-        files_api: Files | None,
-    ) -> None:
+    def __init__(self, config, inference_api: Inference, files_api: Files | None) -> None:
         super().__init__(files_api=files_api, kvstore=None)
         self.config = config
         self.inference_api = inference_api
-        self.models_api = models_api
         self.cache: dict[str, VectorDBWithIndex] = {}
         self.vector_db_store = None
 
     async def initialize(self) -> None:
-        self.kvstore = await kvstore_impl(self.config.kvstore)
+        self.kvstore = await kvstore_impl(self.config.persistence)
 
         start_key = VECTOR_DBS_PREFIX
         end_key = f"{VECTOR_DBS_PREFIX}\xff"
@@ -433,9 +397,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
         for db_json in stored_vector_dbs:
             vector_db = VectorDB.model_validate_json(db_json)
             index = await SQLiteVecIndex.create(
-                vector_db.embedding_dimension,
-                self.config.db_path,
-                vector_db.identifier,
+                vector_db.embedding_dimension, self.config.db_path, vector_db.identifier
             )
             self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api)
 
@@ -450,11 +412,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
         return [v.vector_db for v in self.cache.values()]
 
     async def register_vector_db(self, vector_db: VectorDB) -> None:
-        index = await SQLiteVecIndex.create(
-            vector_db.embedding_dimension,
-            self.config.db_path,
-            vector_db.identifier,
-        )
+        index = await SQLiteVecIndex.create(vector_db.embedding_dimension, self.config.db_path, vector_db.identifier)
         self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api)
 
     async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex | None:

llama_stack/providers/remote/datasetio/huggingface/config.py

@@ -7,20 +7,17 @@ from typing import Any
 
 from pydantic import BaseModel
 
-from llama_stack.providers.utils.kvstore.config import (
-    KVStoreConfig,
-    SqliteKVStoreConfig,
-)
+from llama_stack.core.storage.datatypes import KVStoreReference
 
 
 class HuggingfaceDatasetIOConfig(BaseModel):
-    kvstore: KVStoreConfig
+    kvstore: KVStoreReference
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
         return {
-            "kvstore": SqliteKVStoreConfig.sample_run_config(
-                __distro_dir__=__distro_dir__,
-                db_name="huggingface_datasetio.db",
-            )
+            "kvstore": KVStoreReference(
+                backend="kv_default",
+                namespace="datasetio::huggingface",
+            ).model_dump(exclude_none=True)
         }