Release candidate 0.3.0rc2

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

@@ -82,13 +82,11 @@ runs:
           echo "No recording changes"
         fi
 
-    - name: Write docker logs to file
+    - name: Write inference logs to file
       if: ${{ always() }}
       shell: bash
       run: |
-        # Ollama logs (if ollama container exists)
-        sudo docker logs ollama > ollama-${{ inputs.inference-mode }}.log 2>&1 || true
-        # Note: distro container logs are now dumped in integration-tests.sh before container is removed
+        sudo docker logs ollama > ollama-${{ inputs.inference-mode }}.log || true
 
     - name: Upload logs
       if: ${{ always() }}

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

@@ -73,24 +73,6 @@ 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

@@ -169,7 +169,9 @@ 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
+            tests/integration/vector_io \
+            --embedding-model inline::sentence-transformers/nomic-ai/nomic-embed-text-v1.5 \
+            --embedding-dimension 768
 
       - name: Check Storage and Memory Available After Tests
         if: ${{ always() }}

.github/workflows/pre-commit.yml

@@ -37,7 +37,7 @@ jobs:
             .pre-commit-config.yaml
 
       - name: Set up Node.js
-        uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # v6.0.0
+        uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0
         with:
           node-version: '20'
           cache: 'npm'

.github/workflows/precommit-trigger.yml

@@ -99,7 +99,7 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo,
               issue_number: ${{ steps.check_author.outputs.pr_number }},
-              body: `⏳ Running [pre-commit hooks](https://github.com/${context.repo.owner}/${context.repo.repo}/actions/runs/${context.runId}) on PR #${{ steps.check_author.outputs.pr_number }}...`
+              body: `⏳ Running pre-commit hooks on PR #${{ steps.check_author.outputs.pr_number }}...`
             });
 
       - name: Checkout PR branch (same-repo)
@@ -141,7 +141,7 @@ jobs:
 
       - name: Set up Node.js
         if: steps.check_author.outputs.authorized == 'true'
-        uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # v6.0.0
+        uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0
         with:
           node-version: '20'
           cache: 'npm'

.github/workflows/providers-list-deps.yml

@@ -36,7 +36,7 @@ jobs:
       distros: ${{ steps.set-matrix.outputs.distros }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Generate Distribution List
         id: set-matrix
@@ -55,7 +55,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -79,7 +79,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -92,7 +92,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/python-build-test.yml

@@ -24,7 +24,7 @@ jobs:
       uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
     - name: Install uv
-      uses: astral-sh/setup-uv@3259c6206f993105e3a61b142c2d97bf4b9ef83d # v7.1.0
+      uses: astral-sh/setup-uv@eb1897b8dc4b5d5bfe39a428a8f2304605e0983c # v7.0.0
       with:
         python-version: ${{ matrix.python-version }}
         activate-environment: true

.github/workflows/ui-unit-tests.yml

@@ -29,7 +29,7 @@ jobs:
         uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
 
       - name: Setup Node.js
-        uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # v6.0.0
+        uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0
         with:
           node-version: ${{ matrix.node-version }}
           cache: 'npm'

benchmarking/k8s-benchmark/stack-configmap.yaml

@@ -98,30 +98,21 @@ data:
       - 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
+    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}
     models:
     - metadata:
         embedding_dimension: 768
@@ -146,4 +137,5 @@ data:
       port: 8323
 kind: ConfigMap
 metadata:
+  creationTimestamp: null
   name: llama-stack-config

benchmarking/k8s-benchmark/stack_run_config.yaml

@@ -95,30 +95,21 @@ providers:
   - 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
+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}
 models:
 - metadata:
     embedding_dimension: 768

client-sdks/stainless/README.md

@@ -1,8 +0,0 @@
-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

@@ -1,610 +0,0 @@
-# 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
-
-  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
-
-  beta:
-    subresources:
-      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}
-
-
-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

@@ -60,17 +60,6 @@ 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 \
@@ -94,6 +83,16 @@ 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

@@ -88,19 +88,18 @@ 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
-vector_stores:
-  default_provider_id: faiss
-  default_embedding_model:
-    provider_id: sentence-transformers
-    model_id: nomic-ai/nomic-embed-text-v1.5
+models:
+  - model_id: nomic-ai/nomic-embed-text-v1.5
+    provider_id: inline::sentence-transformers
+    metadata:
+      embedding_dimension: 768
+      default_configured: true
 ```
 
 With this configuration:
-- `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
+- `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`
 
 ## Vector Store Operations
 
@@ -109,15 +108,14 @@ With this configuration:
 You can create vector stores with automatic or explicit embedding model selection:
 
 ```python
-# Automatic - uses default configured embedding model and vector store provider
+# Automatic - uses default configured embedding model
 vs = client.vector_stores.create()
 
-# Explicit - specify embedding model and/or provider when you need specific ones
+# Explicit - specify embedding model when you need a specific one
 vs = client.vector_stores.create(
     extra_body={
-        "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
+        "embedding_model": "nomic-ai/nomic-embed-text-v1.5",
+        "embedding_dimension": 768
     }
 )
 ```

docs/docs/distributions/building_distro.mdx

@@ -19,7 +19,6 @@ Browse that folder to understand available providers and copy a distribution to
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
-
 <Tabs>
 <TabItem value="container" label="Building a container">
 

docs/docs/distributions/configuration.mdx

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

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

@@ -1,155 +1,56 @@
 apiVersion: v1
 data:
-  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
+  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"
 kind: ConfigMap
 metadata:
+  creationTimestamp: null
   name: llama-stack-config

docs/docs/distributions/k8s/stack_run_config.yaml

@@ -93,30 +93,21 @@ providers:
   - 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
+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}
 models:
 - metadata:
     embedding_dimension: 768

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

@@ -14,18 +14,16 @@ Meta's reference implementation of an agent system that can use tools, access ve
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `persistence` | `<class 'inline.agents.meta_reference.config.AgentPersistenceConfig'>` | No |  |  |
+| `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 |  |
 
 ## Sample Configuration
 
 ```yaml
-persistence:
-  agent_state:
-    namespace: agents
-    backend: kv_default
-  responses:
-    table_name: responses
-    backend: sql_default
-    max_write_queue_size: 10000
-    num_writers: 4
+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
 ```

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` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Configuration for the key-value store backend. |
+| `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. |
 | `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:
-  namespace: batches
-  backend: kv_default
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/batches.db
 ```

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` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration
 
 ```yaml
 kvstore:
-  namespace: datasetio::localfs
-  backend: kv_default
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/localfs_datasetio.db
 ```

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` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration
 
 ```yaml
 kvstore:
-  namespace: datasetio::huggingface
-  backend: kv_default
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/huggingface_datasetio.db
 ```

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` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration
 
 ```yaml
 kvstore:
-  namespace: eval
-  backend: kv_default
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db
 ```

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` | `<class 'llama_stack.core.storage.datatypes.SqlStoreReference'>` | No |  | SQL store configuration for file metadata |
+| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | 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:
-  table_name: files_metadata
-  backend: sql_default
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/files_metadata.db
 ```

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` | `<class 'llama_stack.core.storage.datatypes.SqlStoreReference'>` | No |  | SQL store configuration for file metadata |
+| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | 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:
-  table_name: s3_files_metadata
-  backend: sql_default
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/s3_files_metadata.db
 ```

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 |  |  |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.CHROMADB_PATH}
-persistence:
-  namespace: vector_io::chroma
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db
 ```

docs/docs/providers/vector_io/inline_faiss.mdx

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

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 |
 |-------|------|----------|---------|-------------|
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration
 
 ```yaml
-persistence:
-  namespace: vector_io::faiss
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
 ```
 ## 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 |  |  |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend (SQLite only for now) |
+| `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) |
 | `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
-persistence:
-  namespace: vector_io::milvus
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_registry.db
 ```

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 |  |  |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration
 
 ```yaml
 path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
-persistence:
-  namespace: vector_io::qdrant
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db
 ```

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 |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend (SQLite only for now) |
+| `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) |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
-persistence:
-  namespace: vector_io::sqlite_vec
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
 ```

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 |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend (SQLite only for now) |
+| `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) |
 
 ## Sample Configuration
 
 ```yaml
 db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
-persistence:
-  namespace: vector_io::sqlite_vec
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
 ```
 ## 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 |  |  |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration
 
 ```yaml
 url: ${env.CHROMADB_URL}
-persistence:
-  namespace: vector_io::chroma_remote
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_remote_registry.db
 ```

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 |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | Config for KV store backend |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | 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}
-persistence:
-  namespace: vector_io::milvus_remote
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db
 ```

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 |  |
-| `persistence` | `llama_stack.core.storage.datatypes.KVStoreReference \| None` | No |  | Config for KV store backend (SQLite only for now) |
+| `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) |
 
 ## Sample Configuration
 
@@ -228,7 +228,7 @@ port: ${env.PGVECTOR_PORT:=5432}
 db: ${env.PGVECTOR_DB}
 user: ${env.PGVECTOR_USER}
 password: ${env.PGVECTOR_PASSWORD}
-persistence:
-  namespace: vector_io::pgvector
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db
 ```

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 |  |  |
-| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  |  |
+| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration
 
 ```yaml
 api_key: ${env.QDRANT_API_KEY:=}
-persistence:
-  namespace: vector_io::qdrant_remote
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db
 ```

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 |
-| `persistence` | `llama_stack.core.storage.datatypes.KVStoreReference \| None` | No |  | Config for KV store backend (SQLite only for now) |
+| `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) |
 
 ## Sample Configuration
 
 ```yaml
 weaviate_api_key: null
 weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
-persistence:
-  namespace: vector_io::weaviate
-  backend: kv_default
+kvstore:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db
 ```

docs/docs/references/llama_stack_client_cli_reference.md

@@ -32,6 +32,7 @@ Commands:
   scoring_functions  Manage scoring functions.
   shields            Manage safety shield services.
   toolgroups         Manage available tool groups.
+  vector_dbs         Manage vector databases.
 ```
 
 ### `llama-stack-client configure`
@@ -210,6 +211,53 @@ Unregister a model from distribution endpoint
 llama-stack-client models unregister <model_id>
 ```
 
+## Vector DB Management
+Manage vector databases.
+
+
+### `llama-stack-client vector_dbs list`
+Show available vector dbs on distribution endpoint
+```bash
+llama-stack-client vector_dbs list
+```
+```
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ identifier               ┃ provider_id ┃ provider_resource_id     ┃ vector_db_type ┃ params                            ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ my_demo_vector_db        │ faiss       │ my_demo_vector_db        │                │ embedding_dimension: 768          │
+│                          │             │                          │                │ embedding_model: nomic-embed-text-v1.5 │
+│                          │             │                          │                │ type: vector_db                   │
+│                          │             │                          │                │                                   │
+└──────────────────────────┴─────────────┴──────────────────────────┴────────────────┴───────────────────────────────────┘
+```
+
+### `llama-stack-client vector_dbs register`
+Create a new vector db
+```bash
+llama-stack-client vector_dbs register <vector-db-id> [--provider-id <provider-id>] [--provider-vector-db-id <provider-vector-db-id>] [--embedding-model <embedding-model>] [--embedding-dimension <embedding-dimension>]
+```
+
+
+Required arguments:
+- `VECTOR_DB_ID`: Vector DB ID
+
+Optional arguments:
+- `--provider-id`: Provider ID for the vector db
+- `--provider-vector-db-id`: Provider's vector db ID
+- `--embedding-model`: Embedding model to use. Default: `nomic-embed-text-v1.5`
+- `--embedding-dimension`: Dimension of embeddings. Default: 768
+
+### `llama-stack-client vector_dbs unregister`
+Delete a vector db
+```bash
+llama-stack-client vector_dbs unregister <vector-db-id>
+```
+
+
+Required arguments:
+- `VECTOR_DB_ID`: Vector DB ID
+
+
 ## Shield Management
 Manage safety shield services.
 ### `llama-stack-client shields list`

docs/openapi_generator/run_openapi_generator.sh

@@ -30,5 +30,3 @@ 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/quick_start.ipynb

@@ -126,31 +126,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "J2kGed0R5PSf",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
+    "collapsed": true,
     "id": "J2kGed0R5PSf",
     "outputId": "2478ea60-8d35-48a1-b011-f233831740c5"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n",
-      "\u001b[2mAudited \u001b[1m52 packages\u001b[0m \u001b[2min 1.56s\u001b[0m\u001b[0m\n",
-      "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n",
-      "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 122ms\u001b[0m\u001b[0m\n",
-      "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n",
-      "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 197ms\u001b[0m\u001b[0m\n",
-      "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n",
-      "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 11ms\u001b[0m\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import subprocess\n",
@@ -164,7 +150,7 @@
     "def run_llama_stack_server_background():\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",
     "    process = subprocess.Popen(\n",
-    "        f\"OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter\",\n",
+    "        f\"OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter\n",
     "        shell=True,\n",
     "        stdout=log_file,\n",
     "        stderr=log_file,\n",
@@ -214,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "f779283d",
    "metadata": {},
    "outputs": [
@@ -222,8 +208,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting Llama Stack server with PID: 20778\n",
-      "Waiting for server to start........\n",
+      "Starting Llama Stack server with PID: 787100\n",
+      "Waiting for server to start\n",
       "Server is ready!\n"
      ]
     }
@@ -243,84 +229,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "7da71011",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n",
-      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/files \"HTTP/1.1 200 OK\"\n",
-      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector_stores \"HTTP/1.1 200 OK\"\n",
-      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/conversations \"HTTP/1.1 200 OK\"\n",
-      "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/responses \"HTTP/1.1 200 OK\"\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html\n",
       "prompt> How do you do great work?\n",
-      "🤔 Doing great work involves a combination of skills, habits, and mindsets. Here are some key principles:\n",
-      "\n",
-      "1. **Set Clear Goals**: Start with a clear vision of what you want to achieve. Define specific, measurable, achievable, relevant, and time-bound (SMART) goals.\n",
-      "\n",
-      "2. **Plan and Prioritize**: Break your goals into smaller, manageable tasks. Prioritize these tasks based on their importance and urgency.\n",
-      "\n",
-      "3. **Focus on Quality**: Aim for high-quality outcomes rather than just finishing tasks. Pay attention to detail, and ensure your work meets or exceeds standards.\n",
-      "\n",
-      "4. **Stay Organized**: Keep your workspace, both physical and digital, organized to help you stay focused and efficient.\n",
-      "\n",
-      "5. **Manage Your Time**: Use time management techniques such as the Pomodoro Technique, time blocking, or the Eisenhower Box to maximize productivity.\n",
-      "\n",
-      "6. **Seek Feedback and Learn**: Regularly seek feedback from peers, mentors, or supervisors. Use constructive criticism to improve continuously.\n",
-      "\n",
-      "7. **Innovate and Improve**: Look for ways to improve processes or introduce new ideas. Be open to change and willing to adapt.\n",
-      "\n",
-      "8. **Stay Motivated and Persistent**: Keep your end goals in mind to stay motivated. Overcome setbacks with resilience and persistence.\n",
-      "\n",
-      "9. **Balance and Rest**: Ensure you maintain a healthy work-life balance. Take breaks and manage stress to sustain long-term productivity.\n",
-      "\n",
-      "10. **Reflect and Adjust**: Regularly assess your progress and adjust your strategies as needed. Reflect on what works well and what doesn't.\n",
-      "\n",
-      "By incorporating these elements, you can consistently produce high-quality work and achieve excellence in your endeavors.\n"
+      "\u001b[33minference> \u001b[0m\u001b[33m[k\u001b[0m\u001b[33mnowledge\u001b[0m\u001b[33m_search\u001b[0m\u001b[33m(query\u001b[0m\u001b[33m=\"\u001b[0m\u001b[33mWhat\u001b[0m\u001b[33m is\u001b[0m\u001b[33m the\u001b[0m\u001b[33m key\u001b[0m\u001b[33m to\u001b[0m\u001b[33m doing\u001b[0m\u001b[33m great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m\")]\u001b[0m\u001b[97m\u001b[0m\n",
+      "\u001b[32mtool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'}\u001b[0m\n",
+      "\u001b[32mtool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\\nBEGIN of knowledge_search tool results.\\n', type='text'), TextContentItem(text=\"Result 1:\\nDocument_id:docum\\nContent:  work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 2:\\nDocument_id:docum\\nContent:  work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 3:\\nDocument_id:docum\\nContent:  work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 4:\\nDocument_id:docum\\nContent:  work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 5:\\nDocument_id:docum\\nContent:  work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text='END of knowledge_search tool results.\\n', type='text'), TextContentItem(text='The above results were retrieved to help answer the user\\'s query: \"What is the key to doing great work\". Use them as supporting information only in answering this query.\\n', type='text')]\u001b[0m\n",
+      "\u001b[33minference> \u001b[0m\u001b[33mDoing\u001b[0m\u001b[33m great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m means\u001b[0m\u001b[33m doing\u001b[0m\u001b[33m something\u001b[0m\u001b[33m important\u001b[0m\u001b[33m so\u001b[0m\u001b[33m well\u001b[0m\u001b[33m that\u001b[0m\u001b[33m you\u001b[0m\u001b[33m expand\u001b[0m\u001b[33m people\u001b[0m\u001b[33m's\u001b[0m\u001b[33m ideas\u001b[0m\u001b[33m of\u001b[0m\u001b[33m what\u001b[0m\u001b[33m's\u001b[0m\u001b[33m possible\u001b[0m\u001b[33m.\u001b[0m\u001b[33m However\u001b[0m\u001b[33m,\u001b[0m\u001b[33m there\u001b[0m\u001b[33m's\u001b[0m\u001b[33m no\u001b[0m\u001b[33m threshold\u001b[0m\u001b[33m for\u001b[0m\u001b[33m importance\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m it\u001b[0m\u001b[33m's\u001b[0m\u001b[33m often\u001b[0m\u001b[33m hard\u001b[0m\u001b[33m to\u001b[0m\u001b[33m judge\u001b[0m\u001b[33m at\u001b[0m\u001b[33m the\u001b[0m\u001b[33m time\u001b[0m\u001b[33m anyway\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m is\u001b[0m\u001b[33m a\u001b[0m\u001b[33m matter\u001b[0m\u001b[33m of\u001b[0m\u001b[33m degree\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m it\u001b[0m\u001b[33m can\u001b[0m\u001b[33m be\u001b[0m\u001b[33m difficult\u001b[0m\u001b[33m to\u001b[0m\u001b[33m determine\u001b[0m\u001b[33m whether\u001b[0m\u001b[33m someone\u001b[0m\u001b[33m has\u001b[0m\u001b[33m done\u001b[0m\u001b[33m great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m until\u001b[0m\u001b[33m after\u001b[0m\u001b[33m the\u001b[0m\u001b[33m fact\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n",
+      "\u001b[30m\u001b[0m"
      ]
     }
    ],
    "source": [
     "from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient\n",
-    "import requests\n",
     "\n",
-    "vector_store_id = \"my_demo_vector_db\"\n",
+    "vector_db_id = \"my_demo_vector_db\"\n",
     "client = LlamaStackClient(base_url=\"http://0.0.0.0:8321\")\n",
     "\n",
     "models = client.models.list()\n",
     "\n",
     "# Select the first ollama and first ollama's embedding model\n",
     "model_id = next(m for m in models if m.model_type == \"llm\" and m.provider_id == \"ollama\").identifier\n",
+    "embedding_model = next(m for m in models if m.model_type == \"embedding\" and m.provider_id == \"ollama\")\n",
+    "embedding_model_id = embedding_model.identifier\n",
+    "embedding_dimension = embedding_model.metadata[\"embedding_dimension\"]\n",
     "\n",
-    "\n",
+    "_ = client.vector_dbs.register(\n",
+    "    vector_db_id=vector_db_id,\n",
+    "    embedding_model=embedding_model_id,\n",
+    "    embedding_dimension=embedding_dimension,\n",
+    "    provider_id=\"faiss\",\n",
+    ")\n",
     "source = \"https://www.paulgraham.com/greatwork.html\"\n",
-    "response = requests.get(source)\n",
-    "file = client.files.create(\n",
-    "    file=response.content,\n",
-    "    purpose='assistants'\n",
+    "print(\"rag_tool> Ingesting document:\", source)\n",
+    "document = RAGDocument(\n",
+    "    document_id=\"document_1\",\n",
+    "    content=source,\n",
+    "    mime_type=\"text/html\",\n",
+    "    metadata={},\n",
     ")\n",
-    "vector_store = client.vector_stores.create(\n",
-    "    name=vector_store_id,\n",
-    "    file_ids=[file.id],\n",
+    "client.tool_runtime.rag_tool.insert(\n",
+    "    documents=[document],\n",
+    "    vector_db_id=vector_db_id,\n",
+    "    chunk_size_in_tokens=50,\n",
     ")\n",
-    "\n",
     "agent = Agent(\n",
     "    client,\n",
     "    model=model_id,\n",
     "    instructions=\"You are a helpful assistant\",\n",
     "    tools=[\n",
     "        {\n",
-    "            \"type\": \"file_search\",\n",
-    "            \"vector_store_ids\": [vector_store_id],\n",
+    "            \"name\": \"builtin::rag/knowledge_search\",\n",
+    "            \"args\": {\"vector_db_ids\": [vector_db_id]},\n",
     "        }\n",
     "    ],\n",
     ")\n",
@@ -335,7 +302,7 @@
     ")\n",
     "\n",
     "for log in AgentEventLogger().log(response):\n",
-    "    print(log, end=\"\")"
+    "    log.print()"
    ]
   },
   {
@@ -377,7 +344,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -391,7 +358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

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

@@ -5547,7 +5547,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -5798,7 +5798,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -9024,10 +9024,6 @@
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
                     },
-                    "instructions": {
-                        "type": "string",
-                        "description": "(Optional) System message inserted into the model's context"
-                    },
                     "input": {
                         "type": "array",
                         "items": {
@@ -9905,10 +9901,6 @@
                     "usage": {
                         "$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

@@ -4114,7 +4114,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -4303,7 +4303,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -6734,10 +6734,6 @@ 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:
@@ -7407,10 +7403,6 @@ 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/experimental-llama-stack-spec.html

@@ -1850,7 +1850,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -3983,7 +3983,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",

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

@@ -1320,7 +1320,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -2927,7 +2927,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark

docs/static/llama-stack-spec.html

@@ -6800,7 +6800,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -7600,10 +7600,6 @@
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
                     },
-                    "instructions": {
-                        "type": "string",
-                        "description": "(Optional) System message inserted into the model's context"
-                    },
                     "input": {
                         "type": "array",
                         "items": {
@@ -8152,10 +8148,6 @@
                     "usage": {
                         "$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,
@@ -10205,7 +10197,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -10687,7 +10679,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -11740,7 +11732,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",

docs/static/llama-stack-spec.yaml

@@ -5227,7 +5227,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -5815,10 +5815,6 @@ 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:
@@ -6222,10 +6218,6 @@ 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
@@ -7919,7 +7911,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -8227,7 +8219,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -8990,7 +8982,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark

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

@@ -8472,7 +8472,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -9272,10 +9272,6 @@
                         "$ref": "#/components/schemas/OpenAIResponseUsage",
                         "description": "(Optional) Token usage information for the response"
                     },
-                    "instructions": {
-                        "type": "string",
-                        "description": "(Optional) System message inserted into the model's context"
-                    },
                     "input": {
                         "type": "array",
                         "items": {
@@ -9824,10 +9820,6 @@
                     "usage": {
                         "$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,
@@ -11877,7 +11869,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -12359,7 +12351,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -13412,7 +13404,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -14959,7 +14951,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",
@@ -16704,7 +16696,7 @@
                         "enum": [
                             "model",
                             "shield",
-                            "vector_store",
+                            "vector_db",
                             "dataset",
                             "scoring_function",
                             "benchmark",

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

@@ -6440,7 +6440,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -7028,10 +7028,6 @@ 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:
@@ -7435,10 +7431,6 @@ 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
@@ -9132,7 +9124,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -9440,7 +9432,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -10203,7 +10195,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -11325,7 +11317,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark
@@ -12652,7 +12644,7 @@ components:
           enum:
             - model
             - shield
-            - vector_store
+            - vector_db
             - dataset
             - scoring_function
             - benchmark

llama_stack/apis/agents/openai_responses.py

@@ -545,7 +545,6 @@ 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
@@ -565,7 +564,6 @@ 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

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

llama_stack/apis/resource.py

@@ -13,7 +13,7 @@ from pydantic import BaseModel, Field
 class ResourceType(StrEnum):
     model = "model"
     shield = "shield"
-    vector_store = "vector_store"
+    vector_db = "vector_db"
     dataset = "dataset"
     scoring_function = "scoring_function"
     benchmark = "benchmark"
@@ -34,4 +34,4 @@ class Resource(BaseModel):
 
     provider_id: str = Field(description="ID of the provider that owns this resource")
 
-    type: ResourceType = Field(description="Type of resource (e.g. 'model', 'shield', 'vector_store', etc.)")
+    type: ResourceType = Field(description="Type of resource (e.g. 'model', 'shield', 'vector_db', etc.)")

llama_stack/apis/vector_dbs/__init__.py

@@ -3,3 +3,5 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+
+from .vector_dbs import *

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -9,43 +9,53 @@ from typing import Literal
 from pydantic import BaseModel
 
 from llama_stack.apis.resource import Resource, ResourceType
+from llama_stack.schema_utils import json_schema_type
 
 
-# Internal resource type for storing the vector store routing and other information
-class VectorStore(Resource):
+@json_schema_type
+class VectorDB(Resource):
     """Vector database resource for storing and querying vector embeddings.
 
-    :param type: Type of resource, always 'vector_store' for vector stores
+    :param type: Type of resource, always 'vector_db' for vector databases
     :param embedding_model: Name of the embedding model to use for vector generation
     :param embedding_dimension: Dimension of the embedding vectors
     """
 
-    type: Literal[ResourceType.vector_store] = ResourceType.vector_store
+    type: Literal[ResourceType.vector_db] = ResourceType.vector_db
 
     embedding_model: str
     embedding_dimension: int
-    vector_store_name: str | None = None
+    vector_db_name: str | None = None
 
     @property
-    def vector_store_id(self) -> str:
+    def vector_db_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_vector_store_id(self) -> str | None:
+    def provider_vector_db_id(self) -> str | None:
         return self.provider_resource_id
 
 
-class VectorStoreInput(BaseModel):
+class VectorDBInput(BaseModel):
     """Input parameters for creating or configuring a vector database.
 
-    :param vector_store_id: Unique identifier for the vector store
+    :param vector_db_id: Unique identifier for the vector database
     :param embedding_model: Name of the embedding model to use for vector generation
     :param embedding_dimension: Dimension of the embedding vectors
-    :param provider_vector_store_id: (Optional) Provider-specific identifier for the vector store
+    :param provider_vector_db_id: (Optional) Provider-specific identifier for the vector database
     """
 
-    vector_store_id: str
+    vector_db_id: str
     embedding_model: str
     embedding_dimension: int
     provider_id: str | None = None
-    provider_vector_store_id: str | None = None
+    provider_vector_db_id: str | None = None
+
+
+class ListVectorDBsResponse(BaseModel):
+    """Response from listing vector databases.
+
+    :param data: List of vector databases
+    """
+
+    data: list[VectorDB]

llama_stack/apis/vector_io/vector_io.py

@@ -15,7 +15,7 @@ from fastapi import Body
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.inference import InterleavedContent
-from llama_stack.apis.vector_stores import VectorStore
+from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.version import LLAMA_STACK_API_V1
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
 from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
@@ -140,7 +140,6 @@ class VectorStoreFileCounts(BaseModel):
     total: int
 
 
-# TODO: rename this as OpenAIVectorStore
 @json_schema_type
 class VectorStoreObject(BaseModel):
     """OpenAI Vector Store object.
@@ -518,18 +517,17 @@ class OpenAICreateVectorStoreFileBatchRequestWithExtraBody(BaseModel, extra="all
     chunking_strategy: VectorStoreChunkingStrategy | None = None
 
 
-class VectorStoreTable(Protocol):
-    def get_vector_store(self, vector_store_id: str) -> VectorStore | None: ...
+class VectorDBStore(Protocol):
+    def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
 
 
 @runtime_checkable
 @trace_protocol
 class VectorIO(Protocol):
-    vector_store_table: VectorStoreTable | None = None
+    vector_db_store: VectorDBStore | None = None
 
     # this will just block now until chunks are inserted, but it should
     # probably return a Job instance which can be polled for completion
-    # TODO: rename vector_db_id to vector_store_id once Stainless is working
     @webmethod(route="/vector-io/insert", method="POST", level=LLAMA_STACK_API_V1)
     async def insert_chunks(
         self,
@@ -548,7 +546,6 @@ class VectorIO(Protocol):
         """
         ...
 
-    # TODO: rename vector_db_id to vector_store_id once Stainless is working
     @webmethod(route="/vector-io/query", method="POST", level=LLAMA_STACK_API_V1)
     async def query_chunks(
         self,

llama_stack/apis/vector_stores/__init__.py

@@ -1,7 +0,0 @@
-# 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 .vector_stores import *

llama_stack/cli/llama.py

@@ -6,8 +6,6 @@
 
 import argparse
 
-from llama_stack.log import setup_logging
-
 from .stack import StackParser
 from .stack.utils import print_subcommand_description
 
@@ -44,9 +42,6 @@ class LlamaCLIParser:
 
 
 def main():
-    # Initialize logging from environment variables before any other operations
-    setup_logging()
-
     parser = LlamaCLIParser()
     args = parser.parse_args()
     parser.run(args)

llama_stack/cli/stack/_build.py

@@ -0,0 +1,490 @@
+# 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 argparse
+import importlib.resources
+import json
+import os
+import shutil
+import sys
+import textwrap
+from functools import lru_cache
+from importlib.abc import Traversable
+from pathlib import Path
+
+import yaml
+from prompt_toolkit import prompt
+from prompt_toolkit.completion import WordCompleter
+from prompt_toolkit.validation import Validator
+from termcolor import colored, cprint
+
+from llama_stack.cli.stack.utils import ImageType
+from llama_stack.cli.table import print_table
+from llama_stack.core.build import (
+    SERVER_DEPENDENCIES,
+    build_image,
+    get_provider_dependencies,
+)
+from llama_stack.core.configure import parse_and_maybe_upgrade_config
+from llama_stack.core.datatypes import (
+    BuildConfig,
+    BuildProvider,
+    DistributionSpec,
+    Provider,
+    StackRunConfig,
+)
+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.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"
+
+
+@lru_cache
+def available_distros_specs() -> dict[str, BuildConfig]:
+    import yaml
+
+    distro_specs = {}
+    for p in DISTRIBS_PATH.rglob("*build.yaml"):
+        distro_name = p.parent.name
+        with open(p) as f:
+            build_config = BuildConfig(**yaml.safe_load(f))
+            distro_specs[distro_name] = build_config
+    return distro_specs
+
+
+def run_stack_build_command(args: argparse.Namespace) -> None:
+    if args.list_distros:
+        return _run_distro_list_cmd()
+
+    if args.image_type == ImageType.VENV.value:
+        current_venv = os.environ.get("VIRTUAL_ENV")
+        image_name = args.image_name or current_venv
+    else:
+        image_name = args.image_name
+
+    if args.template:
+        cprint(
+            "The --template argument is deprecated. Please use --distro instead.",
+            color="red",
+            file=sys.stderr,
+        )
+        distro_name = args.template
+    else:
+        distro_name = args.distribution
+
+    if distro_name:
+        available_distros = available_distros_specs()
+        if distro_name not in available_distros:
+            cprint(
+                f"Could not find distribution {distro_name}. Please run `llama stack build --list-distros` to check out the available distributions",
+                color="red",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+        build_config = available_distros[distro_name]
+        if args.image_type:
+            build_config.image_type = args.image_type
+        else:
+            cprint(
+                f"Please specify a image-type ({' | '.join(e.value for e in ImageType)}) for {distro_name}",
+                color="red",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+    elif args.providers:
+        provider_list: dict[str, list[BuildProvider]] = dict()
+        for api_provider in args.providers.split(","):
+            if "=" not in api_provider:
+                cprint(
+                    "Could not parse `--providers`. Please ensure the list is in the format api1=provider1,api2=provider2",
+                    color="red",
+                    file=sys.stderr,
+                )
+                sys.exit(1)
+            api, provider_type = api_provider.split("=")
+            providers_for_api = get_provider_registry().get(Api(api), None)
+            if providers_for_api is None:
+                cprint(
+                    f"{api} is not a valid API.",
+                    color="red",
+                    file=sys.stderr,
+                )
+                sys.exit(1)
+            if provider_type in providers_for_api:
+                provider = BuildProvider(
+                    provider_type=provider_type,
+                    module=None,
+                )
+                provider_list.setdefault(api, []).append(provider)
+            else:
+                cprint(
+                    f"{provider} is not a valid provider for the {api} API.",
+                    color="red",
+                    file=sys.stderr,
+                )
+                sys.exit(1)
+        distribution_spec = DistributionSpec(
+            providers=provider_list,
+            description=",".join(args.providers),
+        )
+        if not args.image_type:
+            cprint(
+                f"Please specify a image-type (container | venv) for {args.template}",
+                color="red",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+
+        build_config = BuildConfig(image_type=args.image_type, distribution_spec=distribution_spec)
+    elif not args.config and not distro_name:
+        name = prompt(
+            "> Enter a name for your Llama Stack (e.g. my-local-stack): ",
+            validator=Validator.from_callable(
+                lambda x: len(x) > 0,
+                error_message="Name cannot be empty, please enter a name",
+            ),
+        )
+
+        image_type = prompt(
+            "> Enter the image type you want your Llama Stack to be built as (use <TAB> to see options): ",
+            completer=WordCompleter([e.value for e in ImageType]),
+            complete_while_typing=True,
+            validator=Validator.from_callable(
+                lambda x: x in [e.value for e in ImageType],
+                error_message="Invalid image type. Use <TAB> to see options",
+            ),
+        )
+
+        image_name = f"llamastack-{name}"
+
+        cprint(
+            textwrap.dedent(
+                """
+            Llama Stack is composed of several APIs working together. Let's select
+            the provider types (implementations) you want to use for these APIs.
+            """,
+            ),
+            color="green",
+            file=sys.stderr,
+        )
+
+        cprint("Tip: use <TAB> to see options for the providers.\n", color="green", file=sys.stderr)
+
+        providers: dict[str, list[BuildProvider]] = dict()
+        for api, providers_for_api in get_provider_registry().items():
+            available_providers = [x for x in providers_for_api.keys() if x not in ("remote", "remote::sample")]
+            if not available_providers:
+                continue
+            api_provider = prompt(
+                f"> Enter provider for API {api.value}: ",
+                completer=WordCompleter(available_providers),
+                complete_while_typing=True,
+                validator=Validator.from_callable(
+                    lambda x: x in available_providers,  # noqa: B023 - see https://github.com/astral-sh/ruff/issues/7847
+                    error_message="Invalid provider, use <TAB> to see options",
+                ),
+            )
+
+            string_providers = api_provider.split(" ")
+
+            for provider in string_providers:
+                providers.setdefault(api.value, []).append(BuildProvider(provider_type=provider))
+
+        description = prompt(
+            "\n > (Optional) Enter a short description for your Llama Stack: ",
+            default="",
+        )
+
+        distribution_spec = DistributionSpec(
+            providers=providers,
+            description=description,
+        )
+
+        build_config = BuildConfig(image_type=image_type, distribution_spec=distribution_spec)
+    else:
+        with open(args.config) as f:
+            try:
+                contents = yaml.safe_load(f)
+                contents = replace_env_vars(contents)
+                build_config = BuildConfig(**contents)
+                if args.image_type:
+                    build_config.image_type = args.image_type
+            except Exception as e:
+                cprint(
+                    f"Could not parse config file {args.config}: {e}",
+                    color="red",
+                    file=sys.stderr,
+                )
+                sys.exit(1)
+
+    if args.print_deps_only:
+        print(f"# Dependencies for {distro_name or args.config or image_name}")
+        normal_deps, special_deps, external_provider_dependencies = get_provider_dependencies(build_config)
+        normal_deps += SERVER_DEPENDENCIES
+        print(f"uv pip install {' '.join(normal_deps)}")
+        for special_dep in special_deps:
+            print(f"uv pip install {special_dep}")
+        for external_dep in external_provider_dependencies:
+            print(f"uv pip install {external_dep}")
+        return
+
+    try:
+        run_config = _run_stack_build_command_from_build_config(
+            build_config,
+            image_name=image_name,
+            config_path=args.config,
+            distro_name=distro_name,
+        )
+
+    except (Exception, RuntimeError) as exc:
+        import traceback
+
+        cprint(
+            f"Error building stack: {exc}",
+            color="red",
+            file=sys.stderr,
+        )
+        cprint("Stack trace:", color="red", file=sys.stderr)
+        traceback.print_exc()
+        sys.exit(1)
+
+    if run_config is None:
+        cprint(
+            "Run config path is empty",
+            color="red",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
+    if args.run:
+        config_dict = yaml.safe_load(run_config.read_text())
+        config = parse_and_maybe_upgrade_config(config_dict)
+        if config.external_providers_dir and not config.external_providers_dir.exists():
+            config.external_providers_dir.mkdir(exist_ok=True)
+        run_args = formulate_run_args(args.image_type, image_name or config.image_name)
+        run_args.extend([str(os.getenv("LLAMA_STACK_PORT", 8321)), "--config", str(run_config)])
+        run_command(run_args)
+
+
+def _generate_run_config(
+    build_config: BuildConfig,
+    build_dir: Path,
+    image_name: str,
+) -> Path:
+    """
+    Generate a run.yaml template file for user to edit from a build.yaml file
+    """
+    apis = list(build_config.distribution_spec.providers.keys())
+    run_config = StackRunConfig(
+        container_image=(image_name if build_config.image_type == LlamaStackImageType.CONTAINER.value else None),
+        image_name=image_name,
+        apis=apis,
+        providers={},
+        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:
+        run_config.providers[api] = []
+        providers = build_config.distribution_spec.providers[api]
+
+        for provider in providers:
+            pid = provider.provider_type.split("::")[-1]
+
+            p = provider_registry[Api(api)][provider.provider_type]
+            if p.deprecation_error:
+                raise InvalidProviderError(p.deprecation_error)
+
+            try:
+                config_type = instantiate_class_type(provider_registry[Api(api)][provider.provider_type].config_class)
+            except (ModuleNotFoundError, ValueError) as exc:
+                # HACK ALERT:
+                # This code executes after building is done, the import cannot work since the
+                # package is either available in the venv or container - not available on the host.
+                # TODO: use a "is_external" flag in ProviderSpec to check if the provider is
+                # external
+                cprint(
+                    f"Failed to import provider {provider.provider_type} for API {api} - assuming it's external, skipping: {exc}",
+                    color="yellow",
+                    file=sys.stderr,
+                )
+                # Set config_type to None to avoid UnboundLocalError
+                config_type = None
+
+            if config_type is not None and hasattr(config_type, "sample_run_config"):
+                config = config_type.sample_run_config(__distro_dir__=f"~/.llama/distributions/{image_name}")
+            else:
+                config = {}
+
+            p_spec = Provider(
+                provider_id=pid,
+                provider_type=provider.provider_type,
+                config=config,
+                module=provider.module,
+            )
+            run_config.providers[api].append(p_spec)
+
+    run_config_file = build_dir / f"{image_name}-run.yaml"
+
+    with open(run_config_file, "w") as f:
+        to_write = json.loads(run_config.model_dump_json())
+        f.write(yaml.dump(to_write, sort_keys=False))
+
+    # Only print this message for non-container builds since it will be displayed before the
+    # container is built
+    # For non-container builds, the run.yaml is generated at the very end of the build process so it
+    # makes sense to display this message
+    if build_config.image_type != LlamaStackImageType.CONTAINER.value:
+        cprint(f"You can now run your stack with `llama stack run {run_config_file}`", color="green", file=sys.stderr)
+    return run_config_file
+
+
+def _run_stack_build_command_from_build_config(
+    build_config: BuildConfig,
+    image_name: str | None = None,
+    distro_name: str | None = None,
+    config_path: str | None = None,
+) -> Path | Traversable:
+    image_name = image_name or build_config.image_name
+    if build_config.image_type == LlamaStackImageType.CONTAINER.value:
+        if distro_name:
+            image_name = f"distribution-{distro_name}"
+        else:
+            if not image_name:
+                raise ValueError("Please specify an image name when building a container image without a template")
+    else:
+        if not image_name and os.environ.get("UV_SYSTEM_PYTHON"):
+            image_name = "__system__"
+        if not image_name:
+            raise ValueError("Please specify an image name when building a venv image")
+
+    # At this point, image_name should be guaranteed to be a string
+    if image_name is None:
+        raise ValueError("image_name should not be None after validation")
+
+    if distro_name:
+        build_dir = DISTRIBS_BASE_DIR / distro_name
+        build_file_path = build_dir / f"{distro_name}-build.yaml"
+    else:
+        if image_name is None:
+            raise ValueError("image_name cannot be None")
+        build_dir = DISTRIBS_BASE_DIR / image_name
+        build_file_path = build_dir / f"{image_name}-build.yaml"
+
+    os.makedirs(build_dir, exist_ok=True)
+    run_config_file = None
+    # Generate the run.yaml so it can be included in the container image with the proper entrypoint
+    # Only do this if we're building a container image and we're not using a template
+    if build_config.image_type == LlamaStackImageType.CONTAINER.value and not distro_name and config_path:
+        cprint("Generating run.yaml file", color="yellow", file=sys.stderr)
+        run_config_file = _generate_run_config(build_config, build_dir, image_name)
+
+    with open(build_file_path, "w") as f:
+        to_write = json.loads(build_config.model_dump_json(exclude_none=True))
+        f.write(yaml.dump(to_write, sort_keys=False))
+
+    # We first install the external APIs so that the build process can use them and discover the
+    # providers dependencies
+    if build_config.external_apis_dir:
+        cprint("Installing external APIs", color="yellow", file=sys.stderr)
+        external_apis = load_external_apis(build_config)
+        if external_apis:
+            # install the external APIs
+            packages = []
+            for _, api_spec in external_apis.items():
+                if api_spec.pip_packages:
+                    packages.extend(api_spec.pip_packages)
+                    cprint(
+                        f"Installing {api_spec.name} with pip packages {api_spec.pip_packages}",
+                        color="yellow",
+                        file=sys.stderr,
+                    )
+            return_code = run_command(["uv", "pip", "install", *packages])
+            if return_code != 0:
+                packages_str = ", ".join(packages)
+                raise RuntimeError(
+                    f"Failed to install external APIs packages: {packages_str} (return code: {return_code})"
+                )
+
+    return_code = build_image(
+        build_config,
+        image_name,
+        distro_or_config=distro_name or config_path or str(build_file_path),
+        run_config=run_config_file.as_posix() if run_config_file else None,
+    )
+    if return_code != 0:
+        raise RuntimeError(f"Failed to build image {image_name}")
+
+    if distro_name:
+        # copy run.yaml from distribution to build_dir instead of generating it again
+        distro_path = importlib.resources.files("llama_stack") / f"distributions/{distro_name}/run.yaml"
+        run_config_file = build_dir / f"{distro_name}-run.yaml"
+
+        with importlib.resources.as_file(distro_path) as path:
+            shutil.copy(path, run_config_file)
+
+        cprint("Build Successful!", color="green", file=sys.stderr)
+        cprint(f"You can find the newly-built distribution here: {run_config_file}", color="blue", file=sys.stderr)
+        if build_config.image_type == LlamaStackImageType.VENV:
+            cprint(
+                "You can run the new Llama Stack distro (after activating "
+                + colored(image_name, "cyan")
+                + ") via: "
+                + colored(f"llama stack run {run_config_file}", "blue"),
+                color="green",
+                file=sys.stderr,
+            )
+        elif build_config.image_type == LlamaStackImageType.CONTAINER:
+            cprint(
+                "You can run the container with: "
+                + colored(
+                    f"docker run -p 8321:8321 -v ~/.llama:/root/.llama localhost/{image_name} --port 8321", "blue"
+                ),
+                color="green",
+                file=sys.stderr,
+            )
+        return distro_path
+    else:
+        return _generate_run_config(build_config, build_dir, image_name)
+
+
+def _run_distro_list_cmd() -> None:
+    headers = [
+        "Distribution Name",
+        # "Providers",
+        "Description",
+    ]
+
+    rows = []
+    for distro_name, spec in available_distros_specs().items():
+        rows.append(
+            [
+                distro_name,
+                # json.dumps(spec.distribution_spec.providers, indent=2),
+                spec.distribution_spec.description,
+            ]
+        )
+    print_table(
+        rows,
+        headers,
+        separate_rows=True,
+    )

llama_stack/cli/stack/build.py

@@ -0,0 +1,106 @@
+# 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 argparse
+import textwrap
+
+from llama_stack.cli.stack.utils import ImageType
+from llama_stack.cli.subcommand import Subcommand
+from llama_stack.log import get_logger
+
+logger = get_logger(__name__, category="cli")
+
+
+class StackBuild(Subcommand):
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "build",
+            prog="llama stack build",
+            description="[DEPRECATED] Build a Llama stack container. This command is deprecated and will be removed in a future release. Use `llama stack list-deps <distro>' instead.",
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_stack_build_command)
+
+    def _add_arguments(self):
+        self.parser.add_argument(
+            "--config",
+            type=str,
+            default=None,
+            help="Path to a config file to use for the build. You can find example configs in llama_stack.cores/**/build.yaml. If this argument is not provided, you will be prompted to enter information interactively",
+        )
+
+        self.parser.add_argument(
+            "--template",
+            type=str,
+            default=None,
+            help="""(deprecated) Name of the example template config to use for build. You may use `llama stack build --list-distros` to check out the available distributions""",
+        )
+        self.parser.add_argument(
+            "--distro",
+            "--distribution",
+            dest="distribution",
+            type=str,
+            default=None,
+            help="""Name of the distribution to use for build. You may use `llama stack build --list-distros` to check out the available distributions""",
+        )
+
+        self.parser.add_argument(
+            "--list-distros",
+            "--list-distributions",
+            action="store_true",
+            dest="list_distros",
+            default=False,
+            help="Show the available distributions for building a Llama Stack distribution",
+        )
+
+        self.parser.add_argument(
+            "--image-type",
+            type=str,
+            help="Image Type to use for the build. If not specified, will use the image type from the template config.",
+            choices=[e.value for e in ImageType],
+            default=None,  # no default so we can detect if a user specified --image-type and override image_type in the config
+        )
+
+        self.parser.add_argument(
+            "--image-name",
+            type=str,
+            help=textwrap.dedent(
+                f"""[for image-type={"|".join(e.value for e in ImageType)}] Name of the virtual environment to use for
+the build. If not specified, currently active environment will be used if found.
+            """
+            ),
+            default=None,
+        )
+        self.parser.add_argument(
+            "--print-deps-only",
+            default=False,
+            action="store_true",
+            help="Print the dependencies for the stack only, without building the stack",
+        )
+
+        self.parser.add_argument(
+            "--run",
+            action="store_true",
+            default=False,
+            help="Run the stack after building using the same image type, name, and other applicable arguments",
+        )
+        self.parser.add_argument(
+            "--providers",
+            type=str,
+            default=None,
+            help="Build a config for a list of providers and only those providers. This list is formatted like: api1=provider1,api2=provider2. Where there can be multiple providers per API.",
+        )
+
+    def _run_stack_build_command(self, args: argparse.Namespace) -> None:
+        logger.warning(
+            "The 'llama stack build' command is deprecated and will be removed in a future release. Please use 'llama stack list-deps'"
+        )
+        # always keep implementation completely silo-ed away from CLI so CLI
+        # can be fast to load and reduces dependencies
+        from ._build import run_stack_build_command
+
+        return run_stack_build_command(args)

llama_stack/cli/stack/stack.py

@@ -11,6 +11,7 @@ from llama_stack.cli.stack.list_stacks import StackListBuilds
 from llama_stack.cli.stack.utils import print_subcommand_description
 from llama_stack.cli.subcommand import Subcommand
 
+from .build import StackBuild
 from .list_apis import StackListApis
 from .list_deps import StackListDeps
 from .list_providers import StackListProviders
@@ -40,6 +41,7 @@ class StackParser(Subcommand):
 
         # Add sub-commands
         StackListDeps.create(subparsers)
+        StackBuild.create(subparsers)
         StackListApis.create(subparsers)
         StackListProviders.create(subparsers)
         StackRun.create(subparsers)

llama_stack/cli/stack/utils.py

@@ -17,19 +17,10 @@ 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.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.config_dirs import 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
@@ -60,23 +51,11 @@ 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/access_control/datatypes.py

@@ -41,7 +41,7 @@ class AccessRule(BaseModel):
     A rule defines a list of action either to permit or to forbid. It may specify a
     principal or a resource that must match for the rule to take effect. The resource
     to match should be specified in the form of a type qualified identifier, e.g.
-    model::my-model or vector_store::some-db, or a wildcard for all resources of a type,
+    model::my-model or vector_db::some-db, or a wildcard for all resources of a type,
     e.g. model::*. If the principal or resource are not specified, they will match all
     requests.
 
@@ -79,9 +79,9 @@ class AccessRule(BaseModel):
       description: any user has read access to any resource created by a member of their team
     - forbid:
         actions: [create, read, delete]
-        resource: vector_store::*
+        resource: vector_db::*
       unless: user with admin in roles
-      description: only user with admin role can use vector_store resources
+      description: only user with admin role can use vector_db resources
 
     """
 

llama_stack/core/build_container.sh

@@ -0,0 +1,410 @@
+#!/usr/bin/env bash
+
+# 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_DIR=${LLAMA_STACK_DIR:-}
+LLAMA_STACK_CLIENT_DIR=${LLAMA_STACK_CLIENT_DIR:-}
+
+TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
+PYPI_VERSION=${PYPI_VERSION:-}
+BUILD_PLATFORM=${BUILD_PLATFORM:-}
+# This timeout (in seconds) is necessary when installing PyTorch via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
+
+# mounting is not supported by docker buildx, so we use COPY instead
+USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-}
+# Path to the run.yaml file in the container
+RUN_CONFIG_PATH=/app/run.yaml
+
+BUILD_CONTEXT_DIR=$(pwd)
+
+set -euo pipefail
+
+# Define color codes
+RED='\033[0;31m'
+NC='\033[0m' # No Color
+
+# Usage function
+usage() {
+  echo "Usage: $0 --image-name <image_name> --container-base <container_base> --normal-deps <pip_dependencies> [--run-config <run_config>] [--external-provider-deps <external_provider_deps>] [--optional-deps <special_pip_deps>]"
+  echo "Example: $0 --image-name llama-stack-img --container-base python:3.12-slim --normal-deps 'numpy pandas' --run-config ./run.yaml --external-provider-deps 'foo' --optional-deps 'bar'"
+  exit 1
+}
+
+# Parse arguments
+image_name=""
+container_base=""
+normal_deps=""
+external_provider_deps=""
+optional_deps=""
+run_config=""
+distro_or_config=""
+
+while [[ $# -gt 0 ]]; do
+  key="$1"
+  case "$key" in
+    --image-name)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --image-name requires a string value" >&2
+        usage
+      fi
+      image_name="$2"
+      shift 2
+      ;;
+    --container-base)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --container-base requires a string value" >&2
+        usage
+      fi
+      container_base="$2"
+      shift 2
+      ;;
+    --normal-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --normal-deps requires a string value" >&2
+        usage
+      fi
+      normal_deps="$2"
+      shift 2
+      ;;
+    --external-provider-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --external-provider-deps requires a string value" >&2
+        usage
+      fi
+      external_provider_deps="$2"
+      shift 2
+      ;;
+    --optional-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --optional-deps requires a string value" >&2
+        usage
+      fi
+      optional_deps="$2"
+      shift 2
+      ;;
+    --run-config)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --run-config requires a string value" >&2
+        usage
+      fi
+      run_config="$2"
+      shift 2
+      ;;
+    --distro-or-config)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --distro-or-config requires a string value" >&2
+        usage
+      fi
+      distro_or_config="$2"
+      shift 2
+      ;;
+    *)
+      echo "Unknown option: $1" >&2
+      usage
+      ;;
+  esac
+done
+
+# Check required arguments
+if [[ -z "$image_name" || -z "$container_base" || -z "$normal_deps" ]]; then
+  echo "Error: --image-name, --container-base, and --normal-deps are required." >&2
+  usage
+fi
+
+CONTAINER_BINARY=${CONTAINER_BINARY:-docker}
+CONTAINER_OPTS=${CONTAINER_OPTS:---progress=plain}
+TEMP_DIR=$(mktemp -d)
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+source "$SCRIPT_DIR/common.sh"
+
+add_to_container() {
+  output_file="$TEMP_DIR/Containerfile"
+  if [ -t 0 ]; then
+    printf '%s\n' "$1" >>"$output_file"
+  else
+    cat >>"$output_file"
+  fi
+}
+
+if ! is_command_available "$CONTAINER_BINARY"; then
+  printf "${RED}Error: ${CONTAINER_BINARY} command not found. Is ${CONTAINER_BINARY} installed and in your PATH?${NC}" >&2
+  exit 1
+fi
+
+if [[ $container_base == *"registry.access.redhat.com/ubi9"* ]]; then
+  add_to_container << EOF
+FROM $container_base
+WORKDIR /app
+
+# We install the Python 3.12 dev headers and build tools so that any
+# C-extension wheels (e.g. polyleven, faiss-cpu) can compile successfully.
+
+RUN dnf -y update && dnf install -y iputils git net-tools wget \
+    vim-minimal python3.12 python3.12-pip python3.12-wheel \
+    python3.12-setuptools python3.12-devel gcc gcc-c++ make && \
+    ln -s /bin/pip3.12 /bin/pip && ln -s /bin/python3.12 /bin/python && dnf clean all
+
+ENV UV_SYSTEM_PYTHON=1
+RUN pip install uv
+EOF
+else
+  add_to_container << EOF
+FROM $container_base
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+       iputils-ping net-tools iproute2 dnsutils telnet \
+       curl wget telnet git\
+       procps psmisc lsof \
+       traceroute \
+       bubblewrap \
+       gcc g++ \
+       && rm -rf /var/lib/apt/lists/*
+
+ENV UV_SYSTEM_PYTHON=1
+RUN pip install uv
+EOF
+fi
+
+# Add pip dependencies first since llama-stack is what will change most often
+# so we can reuse layers.
+if [ -n "$normal_deps" ]; then
+  read -ra pip_args <<<  "$normal_deps"
+  quoted_deps=$(printf " %q" "${pip_args[@]}")
+  add_to_container << EOF
+RUN uv pip install --no-cache $quoted_deps
+EOF
+fi
+
+if [ -n "$optional_deps" ]; then
+  IFS='#' read -ra parts <<<"$optional_deps"
+  for part in "${parts[@]}"; do
+    read -ra pip_args <<< "$part"
+    quoted_deps=$(printf " %q" "${pip_args[@]}")
+    add_to_container <<EOF
+RUN uv pip install --no-cache $quoted_deps
+EOF
+  done
+fi
+
+if [ -n "$external_provider_deps" ]; then
+  IFS='#' read -ra parts <<<"$external_provider_deps"
+  for part in "${parts[@]}"; do
+    read -ra pip_args <<< "$part"
+    quoted_deps=$(printf " %q" "${pip_args[@]}")
+    add_to_container <<EOF
+RUN uv pip install --no-cache $quoted_deps
+EOF
+    add_to_container <<EOF
+RUN python3 - <<PYTHON | uv pip install --no-cache -r -
+import importlib
+import sys
+
+try:
+    package_name = '$part'.split('==')[0].split('>=')[0].split('<=')[0].split('!=')[0].split('<')[0].split('>')[0]
+    module = importlib.import_module(f'{package_name}.provider')
+    spec = module.get_provider_spec()
+    if hasattr(spec, 'pip_packages') and spec.pip_packages:
+        if isinstance(spec.pip_packages, (list, tuple)):
+            print('\n'.join(spec.pip_packages))
+except Exception as e:
+    print(f'Error getting provider spec for {package_name}: {e}', file=sys.stderr)
+PYTHON
+EOF
+  done
+fi
+
+get_python_cmd() {
+    if is_command_available python; then
+        echo "python"
+    elif is_command_available python3; then
+        echo "python3"
+    else
+        echo "Error: Neither python nor python3 is installed. Please install Python to continue." >&2
+        exit 1
+    fi
+}
+
+if [ -n "$run_config" ]; then
+  # Copy the run config to the build context since it's an absolute path
+  cp "$run_config" "$BUILD_CONTEXT_DIR/run.yaml"
+
+  # Parse the run.yaml configuration to identify external provider directories
+  # If external providers are specified, copy their directory to the container
+  # and update the configuration to reference the new container path
+  python_cmd=$(get_python_cmd)
+  external_providers_dir=$($python_cmd -c "import yaml; config = yaml.safe_load(open('$run_config')); print(config.get('external_providers_dir') or '')")
+  external_providers_dir=$(eval echo "$external_providers_dir")
+  if [ -n "$external_providers_dir" ]; then
+    if [ -d "$external_providers_dir" ]; then
+    echo "Copying external providers directory: $external_providers_dir"
+    cp -r "$external_providers_dir" "$BUILD_CONTEXT_DIR/providers.d"
+    add_to_container << EOF
+COPY providers.d /.llama/providers.d
+EOF
+    fi
+
+    # Edit the run.yaml file to change the external_providers_dir to /.llama/providers.d
+    if [ "$(uname)" = "Darwin" ]; then
+      sed -i.bak -e 's|external_providers_dir:.*|external_providers_dir: /.llama/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
+      rm -f "$BUILD_CONTEXT_DIR/run.yaml.bak"
+    else
+      sed -i 's|external_providers_dir:.*|external_providers_dir: /.llama/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
+    fi
+  fi
+
+  # Copy run config into docker image
+  add_to_container << EOF
+COPY run.yaml $RUN_CONFIG_PATH
+EOF
+fi
+
+stack_mount="/app/llama-stack-source"
+client_mount="/app/llama-stack-client-source"
+
+install_local_package() {
+  local dir="$1"
+  local mount_point="$2"
+  local name="$3"
+
+  if [ ! -d "$dir" ]; then
+    echo "${RED}Warning: $name is set but directory does not exist: $dir${NC}" >&2
+    exit 1
+  fi
+
+  if [ "$USE_COPY_NOT_MOUNT" = "true" ]; then
+    add_to_container << EOF
+COPY $dir $mount_point
+EOF
+  fi
+  add_to_container << EOF
+RUN uv pip install --no-cache -e $mount_point
+EOF
+}
+
+
+if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
+  install_local_package "$LLAMA_STACK_CLIENT_DIR" "$client_mount" "LLAMA_STACK_CLIENT_DIR"
+fi
+
+if [ -n "$LLAMA_STACK_DIR" ]; then
+  install_local_package "$LLAMA_STACK_DIR" "$stack_mount" "LLAMA_STACK_DIR"
+else
+  if [ -n "$TEST_PYPI_VERSION" ]; then
+    # these packages are damaged in test-pypi, so install them first
+    add_to_container << EOF
+RUN uv pip install --no-cache fastapi libcst
+EOF
+    add_to_container << EOF
+RUN uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ \
+  --index-strategy unsafe-best-match \
+  llama-stack==$TEST_PYPI_VERSION
+
+EOF
+  else
+    if [ -n "$PYPI_VERSION" ]; then
+      SPEC_VERSION="llama-stack==${PYPI_VERSION}"
+    else
+      SPEC_VERSION="llama-stack"
+    fi
+    add_to_container << EOF
+RUN uv pip install --no-cache $SPEC_VERSION
+EOF
+  fi
+fi
+
+# remove uv after installation
+  add_to_container << EOF
+RUN pip uninstall -y uv
+EOF
+
+# If a run config is provided, we use the llama stack CLI
+if [[ -n "$run_config" ]]; then
+  add_to_container << EOF
+ENTRYPOINT ["llama", "stack", "run", "$RUN_CONFIG_PATH"]
+EOF
+elif [[ "$distro_or_config" != *.yaml ]]; then
+  add_to_container << EOF
+ENTRYPOINT ["llama", "stack", "run", "$distro_or_config"]
+EOF
+fi
+
+# Add other require item commands genearic to all containers
+add_to_container << EOF
+
+RUN mkdir -p /.llama /.cache && chmod -R g+rw /.llama /.cache && (chmod -R g+rw /app 2>/dev/null || true)
+EOF
+
+printf "Containerfile created successfully in %s/Containerfile\n\n" "$TEMP_DIR"
+cat "$TEMP_DIR"/Containerfile
+printf "\n"
+
+# Start building the CLI arguments
+CLI_ARGS=()
+
+# Read CONTAINER_OPTS and put it in an array
+read -ra CLI_ARGS <<< "$CONTAINER_OPTS"
+
+if [ "$USE_COPY_NOT_MOUNT" != "true" ]; then
+  if [ -n "$LLAMA_STACK_DIR" ]; then
+    CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_STACK_DIR"):$stack_mount")
+  fi
+  if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
+    CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_STACK_CLIENT_DIR"):$client_mount")
+  fi
+fi
+
+if is_command_available selinuxenabled && selinuxenabled; then
+  # Disable SELinux labels -- we don't want to relabel the llama-stack source dir
+  CLI_ARGS+=("--security-opt" "label=disable")
+fi
+
+# Set version tag based on PyPI version
+if [ -n "$PYPI_VERSION" ]; then
+  version_tag="$PYPI_VERSION"
+elif [ -n "$TEST_PYPI_VERSION" ]; then
+  version_tag="test-$TEST_PYPI_VERSION"
+elif [[ -n "$LLAMA_STACK_DIR" || -n "$LLAMA_STACK_CLIENT_DIR" ]]; then
+  version_tag="dev"
+else
+  URL="https://pypi.org/pypi/llama-stack/json"
+  version_tag=$(curl -s $URL | jq -r '.info.version')
+fi
+
+# Add version tag to image name
+image_tag="$image_name:$version_tag"
+
+# Detect platform architecture
+ARCH=$(uname -m)
+if [ -n "$BUILD_PLATFORM" ]; then
+  CLI_ARGS+=("--platform" "$BUILD_PLATFORM")
+elif [ "$ARCH" = "arm64" ] || [ "$ARCH" = "aarch64" ]; then
+  CLI_ARGS+=("--platform" "linux/arm64")
+elif [ "$ARCH" = "x86_64" ]; then
+  CLI_ARGS+=("--platform" "linux/amd64")
+else
+  echo "Unsupported architecture: $ARCH"
+  exit 1
+fi
+
+echo "PWD: $(pwd)"
+echo "Containerfile: $TEMP_DIR/Containerfile"
+set -x
+
+$CONTAINER_BINARY build \
+  "${CLI_ARGS[@]}" \
+  -t "$image_tag" \
+  -f "$TEMP_DIR/Containerfile" \
+  "$BUILD_CONTEXT_DIR"
+
+# clean up tmp/configs
+rm -rf "$BUILD_CONTEXT_DIR/run.yaml" "$TEMP_DIR"
+set +x
+
+echo "Success!"

llama_stack/core/build_venv.sh

@@ -0,0 +1,220 @@
+#!/bin/bash
+
+# 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_DIR=${LLAMA_STACK_DIR:-}
+LLAMA_STACK_CLIENT_DIR=${LLAMA_STACK_CLIENT_DIR:-}
+TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
+# This timeout (in seconds) is necessary when installing PyTorch via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
+UV_SYSTEM_PYTHON=${UV_SYSTEM_PYTHON:-}
+VIRTUAL_ENV=${VIRTUAL_ENV:-}
+
+set -euo pipefail
+
+# Define color codes
+RED='\033[0;31m'
+NC='\033[0m' # No Color
+
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+source "$SCRIPT_DIR/common.sh"
+
+# Usage function
+usage() {
+  echo "Usage: $0 --env-name <env_name> --normal-deps <pip_dependencies> [--external-provider-deps <external_provider_deps>] [--optional-deps <special_pip_deps>]"
+  echo "Example: $0 --env-name mybuild --normal-deps 'numpy pandas scipy' --external-provider-deps 'foo' --optional-deps 'bar'"
+  exit 1
+}
+
+# Parse arguments
+env_name=""
+normal_deps=""
+external_provider_deps=""
+optional_deps=""
+
+while [[ $# -gt 0 ]]; do
+  key="$1"
+  case "$key" in
+    --env-name)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --env-name requires a string value" >&2
+        usage
+      fi
+      env_name="$2"
+      shift 2
+      ;;
+    --normal-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --normal-deps requires a string value" >&2
+        usage
+      fi
+      normal_deps="$2"
+      shift 2
+      ;;
+    --external-provider-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --external-provider-deps requires a string value" >&2
+        usage
+      fi
+      external_provider_deps="$2"
+      shift 2
+      ;;
+    --optional-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --optional-deps requires a string value" >&2
+        usage
+      fi
+      optional_deps="$2"
+      shift 2
+      ;;
+    *)
+      echo "Unknown option: $1" >&2
+      usage
+      ;;
+  esac
+done
+
+# Check required arguments
+if [[ -z "$env_name" || -z "$normal_deps" ]]; then
+  echo "Error: --env-name and --normal-deps are required." >&2
+  usage
+fi
+
+if [ -n "$LLAMA_STACK_DIR" ]; then
+  echo "Using llama-stack-dir=$LLAMA_STACK_DIR"
+fi
+if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
+  echo "Using llama-stack-client-dir=$LLAMA_STACK_CLIENT_DIR"
+fi
+
+ENVNAME=""
+
+# pre-run checks to make sure we can proceed with the installation
+pre_run_checks() {
+  local env_name="$1"
+
+  if ! is_command_available uv; then
+    echo "uv is not installed, trying to install it."
+    if ! is_command_available pip; then
+      echo "pip is not installed, cannot automatically install 'uv'."
+      echo "Follow this link to install it:"
+      echo "https://docs.astral.sh/uv/getting-started/installation/"
+      exit 1
+    else
+      pip install uv
+    fi
+  fi
+
+  # checking if an environment with the same name already exists
+  if [ -d "$env_name" ]; then
+    echo "Environment '$env_name' already exists, re-using it."
+  fi
+}
+
+run() {
+  # Use only global variables set by flag parser
+  if [ -n "$UV_SYSTEM_PYTHON" ] || [ "$env_name" == "__system__" ]; then
+    echo "Installing dependencies in system Python environment"
+    export UV_SYSTEM_PYTHON=1
+  elif [ "$VIRTUAL_ENV" == "$env_name" ]; then
+    echo "Virtual environment $env_name is already active"
+  else
+    echo "Using virtual environment $env_name"
+    uv venv "$env_name"
+    source "$env_name/bin/activate"
+  fi
+
+  if [ -n "$TEST_PYPI_VERSION" ]; then
+    uv pip install fastapi libcst
+    uv pip install --extra-index-url https://test.pypi.org/simple/ \
+      --index-strategy unsafe-best-match \
+      llama-stack=="$TEST_PYPI_VERSION" \
+      $normal_deps
+    if [ -n "$optional_deps" ]; then
+      IFS='#' read -ra parts <<<"$optional_deps"
+      for part in "${parts[@]}"; do
+        echo "$part"
+        uv pip install $part
+      done
+    fi
+    if [ -n "$external_provider_deps" ]; then
+      IFS='#' read -ra parts <<<"$external_provider_deps"
+      for part in "${parts[@]}"; do
+        echo "$part"
+        uv pip install "$part"
+      done
+    fi
+  else
+    if [ -n "$LLAMA_STACK_DIR" ]; then
+      # only warn if DIR does not start with "git+"
+      if [ ! -d "$LLAMA_STACK_DIR" ] && [[ "$LLAMA_STACK_DIR" != git+* ]]; then
+        printf "${RED}Warning: LLAMA_STACK_DIR is set but directory does not exist: %s${NC}\n" "$LLAMA_STACK_DIR" >&2
+        exit 1
+      fi
+      printf "Installing from LLAMA_STACK_DIR: %s\n"  "$LLAMA_STACK_DIR"
+      # editable only if LLAMA_STACK_DIR does not start with "git+"
+      if [[ "$LLAMA_STACK_DIR" != git+* ]]; then
+        EDITABLE="-e"
+      else
+        EDITABLE=""
+      fi
+      uv pip install --no-cache-dir $EDITABLE "$LLAMA_STACK_DIR"
+    else
+      uv pip install --no-cache-dir llama-stack
+    fi
+
+    if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
+      # only warn if DIR does not start with "git+"
+      if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ] && [[ "$LLAMA_STACK_CLIENT_DIR" != git+* ]]; then
+        printf "${RED}Warning: LLAMA_STACK_CLIENT_DIR is set but directory does not exist: %s${NC}\n" "$LLAMA_STACK_CLIENT_DIR" >&2
+        exit 1
+      fi
+      printf "Installing from LLAMA_STACK_CLIENT_DIR: %s\n" "$LLAMA_STACK_CLIENT_DIR"
+      # editable only if LLAMA_STACK_CLIENT_DIR does not start with "git+"
+      if [[ "$LLAMA_STACK_CLIENT_DIR" != git+* ]]; then
+        EDITABLE="-e"
+      else
+        EDITABLE=""
+      fi
+      uv pip install --no-cache-dir $EDITABLE "$LLAMA_STACK_CLIENT_DIR"
+    fi
+
+    printf "Installing pip dependencies\n"
+    uv pip install $normal_deps
+    if [ -n "$optional_deps" ]; then
+      IFS='#' read -ra parts <<<"$optional_deps"
+      for part in "${parts[@]}"; do
+        echo "Installing special provider module: $part"
+        uv pip install $part
+      done
+    fi
+    if [ -n "$external_provider_deps" ]; then
+      IFS='#' read -ra parts <<<"$external_provider_deps"
+      for part in "${parts[@]}"; do
+        echo "Installing external provider module: $part"
+        uv pip install "$part"
+        echo "Getting provider spec for module: $part and installing dependencies"
+        package_name=$(echo "$part" | sed 's/[<>=!].*//')
+        python3 -c "
+import importlib
+import sys
+try:
+    module = importlib.import_module(f'$package_name.provider')
+    spec = module.get_provider_spec()
+    if hasattr(spec, 'pip_packages') and spec.pip_packages:
+        print('\\n'.join(spec.pip_packages))
+except Exception as e:
+    print(f'Error getting provider spec for $package_name: {e}', file=sys.stderr)
+" | uv pip install -r -
+      done
+    fi
+  fi
+}
+
+pre_run_checks "$env_name"
+run

llama_stack/core/configure.py

@@ -159,37 +159,6 @@ 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

@@ -4,6 +4,7 @@
 # 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
@@ -20,11 +21,16 @@ from llama_stack.apis.conversations.conversations import (
     Conversations,
     Metadata,
 )
-from llama_stack.core.datatypes import AccessRule, StackRunConfig
+from llama_stack.core.datatypes import AccessRule
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 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 sqlstore_impl
+from llama_stack.providers.utils.sqlstore.sqlstore import (
+    SqliteSqlStoreConfig,
+    SqlStoreConfig,
+    sqlstore_impl,
+)
 
 logger = get_logger(name=__name__, category="openai_conversations")
 
@@ -32,11 +38,13 @@ logger = get_logger(name=__name__, category="openai_conversations")
 class ConversationServiceConfig(BaseModel):
     """Configuration for the built-in conversation service.
 
-    :param run_config: Stack run configuration for resolving persistence
+    :param conversations_store: SQL store configuration for conversations (defaults to SQLite)
     :param policy: Access control rules
     """
 
-    run_config: StackRunConfig
+    conversations_store: SqlStoreConfig = SqliteSqlStoreConfig(
+        db_path=(DISTRIBS_BASE_DIR / "conversations.db").as_posix()
+    )
     policy: list[AccessRule] = []
 
 
@@ -55,16 +63,14 @@ class ConversationServiceImpl(Conversations):
         self.deps = deps
         self.policy = config.policy
 
-        # 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)
+        base_sql_store = sqlstore_impl(config.conversations_store)
         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

@@ -23,15 +23,12 @@ from llama_stack.apis.scoring import Scoring
 from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnInput
 from llama_stack.apis.shields import Shield, ShieldInput
 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.apis.vector_stores import VectorStore, VectorStoreInput
 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
@@ -71,7 +68,7 @@ class ShieldWithOwner(Shield, ResourceWithOwner):
     pass
 
 
-class VectorStoreWithOwner(VectorStore, ResourceWithOwner):
+class VectorDBWithOwner(VectorDB, ResourceWithOwner):
     pass
 
 
@@ -91,12 +88,12 @@ class ToolGroupWithOwner(ToolGroup, ResourceWithOwner):
     pass
 
 
-RoutableObject = Model | Shield | VectorStore | Dataset | ScoringFn | Benchmark | ToolGroup
+RoutableObject = Model | Shield | VectorDB | Dataset | ScoringFn | Benchmark | ToolGroup
 
 RoutableObjectWithProvider = Annotated[
     ModelWithOwner
     | ShieldWithOwner
-    | VectorStoreWithOwner
+    | VectorDBWithOwner
     | DatasetWithOwner
     | ScoringFnWithOwner
     | BenchmarkWithOwner
@@ -354,32 +351,12 @@ 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: KVStoreReference = Field(description="Config for KV store backend (SQLite only for now)")
+    kvstore: SqliteKVStoreConfig = 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"
@@ -422,18 +399,6 @@ def process_cors_config(cors_config: bool | CORSConfig | None) -> CORSConfig | N
     raise ValueError(f"Expected bool or CORSConfig, got {type(cors_config).__name__}")
 
 
-class RegisteredResources(BaseModel):
-    """Registry of resources available in the distribution."""
-
-    models: list[ModelInput] = Field(default_factory=list)
-    shields: list[ShieldInput] = Field(default_factory=list)
-    vector_stores: list[VectorStoreInput] = Field(default_factory=list)
-    datasets: list[DatasetInput] = Field(default_factory=list)
-    scoring_fns: list[ScoringFnInput] = Field(default_factory=list)
-    benchmarks: list[BenchmarkInput] = Field(default_factory=list)
-    tool_groups: list[ToolGroupInput] = Field(default_factory=list)
-
-
 class ServerConfig(BaseModel):
     port: int = Field(
         default=8321,
@@ -473,6 +438,18 @@ 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
 
@@ -499,15 +476,37 @@ 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.
 """,
     )
-    storage: StorageConfig = Field(
-        description="Catalog of named storage backends and references available to the stack",
+    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.""",
     )
 
-    registered_resources: RegisteredResources = Field(
-        default_factory=RegisteredResources,
-        description="Registry of resources available in the distribution",
+    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.""",
+    )
+
+    # registry of "resources" in the distribution
+    models: list[ModelInput] = Field(default_factory=list)
+    shields: list[ShieldInput] = Field(default_factory=list)
+    vector_dbs: list[VectorDBInput] = Field(default_factory=list)
+    datasets: list[DatasetInput] = Field(default_factory=list)
+    scoring_fns: list[ScoringFnInput] = Field(default_factory=list)
+    benchmarks: list[BenchmarkInput] = Field(default_factory=list)
+    tool_groups: list[ToolGroupInput] = Field(default_factory=list)
+
     logging: LoggingConfig | None = Field(default=None, description="Configuration for Llama Stack Logging")
 
     telemetry: TelemetryConfig = Field(default_factory=TelemetryConfig, description="Configuration for telemetry")
@@ -527,11 +526,6 @@ can be instantiated multiple times (with different configs) if necessary.
         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):
@@ -541,49 +535,6 @@ can be instantiated multiple times (with different configs) if necessary.
             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

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

llama_stack/core/library_client.py

@@ -47,7 +47,7 @@ from llama_stack.core.stack import (
 from llama_stack.core.utils.config import redact_sensitive_fields
 from llama_stack.core.utils.context import preserve_contexts_async_generator
 from llama_stack.core.utils.exec import in_notebook
-from llama_stack.log import get_logger, setup_logging
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.telemetry.tracing import CURRENT_TRACE_CONTEXT, end_trace, setup_logger, start_trace
 from llama_stack.strong_typing.inspection import is_unwrapped_body_param
 
@@ -200,9 +200,6 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         skip_logger_removal: bool = False,
     ):
         super().__init__()
-        # Initialize logging from environment variables first
-        setup_logging()
-
         # when using the library client, we should not log to console since many
         # of our logs are intended for server-side usage
         if sinks_from_env := os.environ.get("TELEMETRY_SINKS", None):
@@ -281,7 +278,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
             else:
                 prefix = "!" if in_notebook() else ""
                 cprint(
-                    f"Please run:\n\n{prefix}llama stack list-deps {self.config_path_or_distro_name} | xargs -L1 uv pip install\n\n",
+                    f"Please run:\n\n{prefix}llama stack build --distro {self.config_path_or_distro_name} --image-type venv\n\n",
                     "yellow",
                     file=sys.stderr,
                 )

llama_stack/core/prompts/prompts.py

@@ -11,8 +11,9 @@ from pydantic import BaseModel
 
 from llama_stack.apis.prompts import ListPromptsResponse, Prompt, Prompts
 from llama_stack.core.datatypes import StackRunConfig
-from llama_stack.core.storage.datatypes import KVStoreReference
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
+from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
 
 
 class PromptServiceConfig(BaseModel):
@@ -40,12 +41,10 @@ class PromptServiceImpl(Prompts):
         self.kvstore: KVStore
 
     async def initialize(self) -> None:
-        # 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)
+        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)
 
     def _get_default_key(self, prompt_id: str) -> str:
         """Get the KVStore key that stores the default version number."""

llama_stack/core/resolver.py

@@ -30,7 +30,6 @@ 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_io import VectorIO
-from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA
 from llama_stack.core.client import get_client_impl
 from llama_stack.core.datatypes import (
@@ -82,7 +81,6 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) ->
         Api.inspect: Inspect,
         Api.batches: Batches,
         Api.vector_io: VectorIO,
-        Api.vector_stores: VectorStore,
         Api.models: Models,
         Api.safety: Safety,
         Api.shields: Shields,

llama_stack/core/routers/__init__.py

@@ -6,10 +6,7 @@
 
 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
@@ -29,7 +26,6 @@ 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_stores import VectorStoresRoutingTable
 
     api_to_tables = {
         "models": ModelsRoutingTable,
@@ -38,7 +34,6 @@ async def get_routing_table_impl(
         "scoring_functions": ScoringFunctionsRoutingTable,
         "benchmarks": BenchmarksRoutingTable,
         "tool_groups": ToolGroupsRoutingTable,
-        "vector_stores": VectorStoresRoutingTable,
     }
 
     if api.value not in api_to_tables:
@@ -81,21 +76,14 @@ 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:
-        inference_ref = run_config.storage.stores.inference
-        if not inference_ref:
-            raise ValueError("storage.stores.inference must be configured in run config")
-
+    if api == Api.inference and run_config.inference_store:
         inference_store = InferenceStore(
-            reference=inference_ref,
+            config=run_config.inference_store,
             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/tool_runtime.py

@@ -37,24 +37,24 @@ class ToolRuntimeRouter(ToolRuntime):
         async def query(
             self,
             content: InterleavedContent,
-            vector_store_ids: list[str],
+            vector_db_ids: list[str],
             query_config: RAGQueryConfig | None = None,
         ) -> RAGQueryResult:
-            logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_store_ids}")
+            logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}")
             provider = await self.routing_table.get_provider_impl("knowledge_search")
-            return await provider.query(content, vector_store_ids, query_config)
+            return await provider.query(content, vector_db_ids, query_config)
 
         async def insert(
             self,
             documents: list[RAGDocument],
-            vector_store_id: str,
+            vector_db_id: str,
             chunk_size_in_tokens: int = 512,
         ) -> None:
             logger.debug(
-                f"ToolRuntimeRouter.RagToolImpl.insert: {vector_store_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
+                f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
             )
             provider = await self.routing_table.get_provider_impl("insert_into_memory")
-            return await provider.insert(documents, vector_store_id, chunk_size_in_tokens)
+            return await provider.insert(documents, vector_db_id, chunk_size_in_tokens)
 
     def __init__(
         self,

llama_stack/core/routers/vector_io.py

@@ -31,7 +31,6 @@ 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
 
@@ -44,11 +43,9 @@ 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")
@@ -71,6 +68,25 @@ class VectorIORouter(VectorIO):
 
         raise ValueError(f"Embedding model '{embedding_model_id}' not found or not an embedding model")
 
+    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,
+    ) -> None:
+        logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
+        await self.routing_table.register_vector_db(
+            vector_db_id,
+            embedding_model,
+            embedding_dimension,
+            provider_id,
+            vector_db_name,
+            provider_vector_db_id,
+        )
+
     async def insert_chunks(
         self,
         vector_db_id: str,
@@ -106,17 +122,6 @@ 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)
 
@@ -127,41 +132,28 @@ 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())
-                # 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]
+                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]
 
-        vector_store_id = f"vs_{uuid.uuid4()}"
-        registered_vector_store = await self.routing_table.register_vector_store(
-            vector_store_id=vector_store_id,
+        vector_db_id = f"vs_{uuid.uuid4()}"
+        registered_vector_db = await self.routing_table.register_vector_db(
+            vector_db_id=vector_db_id,
             embedding_model=embedding_model,
             embedding_dimension=embedding_dimension,
             provider_id=provider_id,
-            provider_vector_store_id=vector_store_id,
-            vector_store_name=params.name,
+            provider_vector_db_id=vector_db_id,
+            vector_db_name=params.name,
         )
-        provider = await self.routing_table.get_provider_impl(registered_vector_store.identifier)
+        provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier)
 
-        # Update model_extra with registered values so provider uses the already-registered vector_store
+        # Update model_extra with registered values so provider uses the already-registered vector_db
         if params.model_extra is None:
             params.model_extra = {}
-        params.model_extra["provider_vector_store_id"] = registered_vector_store.provider_resource_id
-        params.model_extra["provider_id"] = registered_vector_store.provider_id
+        params.model_extra["provider_vector_db_id"] = registered_vector_db.provider_resource_id
+        params.model_extra["provider_id"] = registered_vector_db.provider_id
         if embedding_model is not None:
             params.model_extra["embedding_model"] = embedding_model
         if embedding_dimension is not None:
@@ -179,15 +171,15 @@ class VectorIORouter(VectorIO):
         logger.debug(f"VectorIORouter.openai_list_vector_stores: limit={limit}")
         # Route to default provider for now - could aggregate from all providers in the future
         # call retrieve on each vector dbs to get list of vector stores
-        vector_stores = await self.routing_table.get_all_with_type("vector_store")
+        vector_dbs = await self.routing_table.get_all_with_type("vector_db")
         all_stores = []
-        for vector_store in vector_stores:
+        for vector_db in vector_dbs:
             try:
-                provider = await self.routing_table.get_provider_impl(vector_store.identifier)
-                vector_store = await provider.openai_retrieve_vector_store(vector_store.identifier)
+                provider = await self.routing_table.get_provider_impl(vector_db.identifier)
+                vector_store = await provider.openai_retrieve_vector_store(vector_db.identifier)
                 all_stores.append(vector_store)
             except Exception as e:
-                logger.error(f"Error retrieving vector store {vector_store.identifier}: {e}")
+                logger.error(f"Error retrieving vector store {vector_db.identifier}: {e}")
                 continue
 
         # Sort by created_at
@@ -251,7 +243,8 @@ class VectorIORouter(VectorIO):
         vector_store_id: str,
     ) -> VectorStoreDeleteResponse:
         logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
-        return await self.routing_table.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)
 
     async def openai_search_vector_store(
         self,

llama_stack/core/routing_tables/common.py

@@ -41,7 +41,7 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> Routable
     elif api == Api.safety:
         return await p.register_shield(obj)
     elif api == Api.vector_io:
-        return await p.register_vector_store(obj)
+        return await p.register_vector_db(obj)
     elif api == Api.datasetio:
         return await p.register_dataset(obj)
     elif api == Api.scoring:
@@ -57,7 +57,7 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> Routable
 async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None:
     api = get_impl_api(p)
     if api == Api.vector_io:
-        return await p.unregister_vector_store(obj.identifier)
+        return await p.unregister_vector_db(obj.identifier)
     elif api == Api.inference:
         return await p.unregister_model(obj.identifier)
     elif api == Api.safety:
@@ -108,7 +108,7 @@ class CommonRoutingTableImpl(RoutingTable):
             elif api == Api.safety:
                 p.shield_store = self
             elif api == Api.vector_io:
-                p.vector_store_store = self
+                p.vector_db_store = self
             elif api == Api.datasetio:
                 p.dataset_store = self
             elif api == Api.scoring:
@@ -134,15 +134,12 @@ class CommonRoutingTableImpl(RoutingTable):
         from .scoring_functions import ScoringFunctionsRoutingTable
         from .shields import ShieldsRoutingTable
         from .toolgroups import ToolGroupsRoutingTable
-        from .vector_stores import VectorStoresRoutingTable
 
         def apiname_object():
             if isinstance(self, ModelsRoutingTable):
                 return ("Inference", "model")
             elif isinstance(self, ShieldsRoutingTable):
                 return ("Safety", "shield")
-            elif isinstance(self, VectorStoresRoutingTable):
-                return ("VectorIO", "vector_store")
             elif isinstance(self, DatasetsRoutingTable):
                 return ("DatasetIO", "dataset")
             elif isinstance(self, ScoringFunctionsRoutingTable):

llama_stack/core/routing_tables/vector_stores.py

@@ -1,292 +0,0 @@
-# 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 llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
-from llama_stack.apis.models import ModelType
-from llama_stack.apis.resource import ResourceType
-
-# Removed VectorStores import to avoid exposing public API
-from llama_stack.apis.vector_io.vector_io import (
-    SearchRankingOptions,
-    VectorStoreChunkingStrategy,
-    VectorStoreDeleteResponse,
-    VectorStoreFileContentsResponse,
-    VectorStoreFileDeleteResponse,
-    VectorStoreFileObject,
-    VectorStoreFileStatus,
-    VectorStoreObject,
-    VectorStoreSearchResponsePage,
-)
-from llama_stack.core.datatypes import (
-    VectorStoreWithOwner,
-)
-from llama_stack.log import get_logger
-
-from .common import CommonRoutingTableImpl, lookup_model
-
-logger = get_logger(name=__name__, category="core::routing_tables")
-
-
-class VectorStoresRoutingTable(CommonRoutingTableImpl):
-    """Internal routing table for vector_store operations.
-
-    Does not inherit from VectorStores to avoid exposing public API endpoints.
-    Only provides internal routing functionality for VectorIORouter.
-    """
-
-    # Internal methods only - no public API exposure
-
-    async def register_vector_store(
-        self,
-        vector_store_id: str,
-        embedding_model: str,
-        embedding_dimension: int | None = 384,
-        provider_id: str | None = None,
-        provider_vector_store_id: str | None = None,
-        vector_store_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)
-
-        vector_store = VectorStoreWithOwner(
-            identifier=vector_store_id,
-            type=ResourceType.vector_store.value,
-            provider_id=provider_id,
-            provider_resource_id=provider_vector_store_id,
-            embedding_model=embedding_model,
-            embedding_dimension=embedding_dimension,
-            vector_store_name=vector_store_name,
-        )
-        await self.register_object(vector_store)
-        return vector_store
-
-    async def openai_retrieve_vector_store(
-        self,
-        vector_store_id: str,
-    ) -> VectorStoreObject:
-        await self.assert_action_allowed("read", "vector_store", 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_store", 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_store", 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_store(vector_store_id)
-        return result
-
-    async def unregister_vector_store(self, vector_store_id: str) -> None:
-        """Remove the vector store from the routing table registry."""
-        try:
-            vector_store_obj = await self.get_object_by_identifier("vector_store", vector_store_id)
-            if vector_store_obj:
-                await self.unregister_object(vector_store_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_store", 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_store", 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_store", 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_store", 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_store", 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_store", 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_store", 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_store", 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_store", 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_store", 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_store", 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

@@ -72,30 +72,13 @@ 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():
-        # 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:
+        if claim_key not in claims:
             continue
-
+        claim = claims[claim_key]
         if isinstance(claim, list):
             values = claim
-        elif isinstance(claim, str):
-            values = claim.split()
         else:
-            continue
+            values = claim.split()
 
         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.kvstore import _KVSTORE_BACKENDS, kvstore_impl
+from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
+from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl
 
 logger = get_logger(name=__name__, category="core::server")
 
@@ -33,7 +33,7 @@ class QuotaMiddleware:
     def __init__(
         self,
         app: ASGIApp,
-        kv_config: KVStoreReference,
+        kv_config: KVStoreConfig,
         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/server/server.py

@@ -56,7 +56,7 @@ from llama_stack.core.stack import (
 from llama_stack.core.utils.config import redact_sensitive_fields
 from llama_stack.core.utils.config_resolution import Mode, resolve_config_or_distro
 from llama_stack.core.utils.context import preserve_contexts_async_generator
-from llama_stack.log import get_logger, setup_logging
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api
 from llama_stack.providers.inline.telemetry.meta_reference.config import TelemetryConfig
 from llama_stack.providers.inline.telemetry.meta_reference.telemetry import (
@@ -374,9 +374,6 @@ def create_app() -> StackApp:
     Returns:
         Configured StackApp instance.
     """
-    # Initialize logging from environment variables first
-    setup_logging()
-
     config_file = os.getenv("LLAMA_STACK_CONFIG")
     if config_file is None:
         raise ValueError("LLAMA_STACK_CONFIG environment variable is required")

llama_stack/core/stack.py

@@ -35,23 +35,13 @@ 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, VectorStoresConfig
+from llama_stack.core.datatypes import Provider, StackRunConfig
 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
@@ -108,9 +98,33 @@ 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.registered_resources, rsrc)
+        objects = getattr(run_config, rsrc)
         if api not in impls:
             continue
 
@@ -138,41 +152,7 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
                 f"{rsrc.capitalize()}: {obj.identifier} served by {obj.provider_id}",
             )
 
-
-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})")
+    await validate_default_embedding_model(impls)
 
 
 class EnvVarError(Exception):
@@ -349,25 +329,6 @@ 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
@@ -386,11 +347,7 @@ class Stack:
                 TEST_RECORDING_CONTEXT.__enter__()
                 logger.info(f"API recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")
 
-        _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)
+        dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name)
         policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else []
 
         internal_impls = {}
@@ -410,8 +367,8 @@ 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):
@@ -531,16 +488,5 @@ 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/datatypes.py

@@ -1,283 +0,0 @@
-# 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

@@ -11,9 +11,10 @@ from typing import Protocol
 import pydantic
 
 from llama_stack.core.datatypes import RoutableObjectWithProvider
-from llama_stack.core.storage.datatypes import KVStoreReference
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 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")
 
@@ -190,10 +191,16 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry):
 
 
 async def create_dist_registry(
-    metadata_store: KVStoreReference, image_name: str
+    metadata_store: KVStoreConfig | None,
+    image_name: str,
 ) -> tuple[CachedDiskDistributionRegistry, KVStore]:
     # instantiate kvstore for storing and retrieving distribution metadata
-    dist_kvstore = await kvstore_impl(metadata_store)
+    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_registry = CachedDiskDistributionRegistry(dist_kvstore)
     await dist_registry.initialize()
     return dist_registry, dist_kvstore

llama_stack/core/ui/README.md

@@ -9,7 +9,7 @@
 1. Start up Llama Stack API server. More details [here](https://llamastack.github.io/latest/getting_started/index.htmll).
 
 ```
-llama stack list-deps together | xargs -L1 uv pip install
+llama stack build --distro together --image-type venv
 
 llama stack run together
 ```

llama_stack/core/ui/page/playground/tools.py

@@ -32,7 +32,7 @@ def tool_chat_page():
     tool_groups_list = [tool_group.identifier for tool_group in tool_groups]
     mcp_tools_list = [tool for tool in tool_groups_list if tool.startswith("mcp::")]
     builtin_tools_list = [tool for tool in tool_groups_list if not tool.startswith("mcp::")]
-    selected_vector_stores = []
+    selected_vector_dbs = []
 
     def reset_agent():
         st.session_state.clear()
@@ -55,13 +55,13 @@ def tool_chat_page():
         )
 
         if "builtin::rag" in toolgroup_selection:
-            vector_stores = llama_stack_api.client.vector_stores.list() or []
-            if not vector_stores:
+            vector_dbs = llama_stack_api.client.vector_dbs.list() or []
+            if not vector_dbs:
                 st.info("No vector databases available for selection.")
-            vector_stores = [vector_store.identifier for vector_store in vector_stores]
-            selected_vector_stores = st.multiselect(
+            vector_dbs = [vector_db.identifier for vector_db in vector_dbs]
+            selected_vector_dbs = st.multiselect(
                 label="Select Document Collections to use in RAG queries",
-                options=vector_stores,
+                options=vector_dbs,
                 on_change=reset_agent,
             )
 
@@ -119,7 +119,7 @@ def tool_chat_page():
             tool_dict = dict(
                 name="builtin::rag",
                 args={
-                    "vector_store_ids": list(selected_vector_stores),
+                    "vector_db_ids": list(selected_vector_dbs),
                 },
             )
             toolgroup_selection[i] = tool_dict

llama_stack/distributions/ci-tests/build.yaml

@@ -25,8 +25,6 @@ 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:
-      persistence:
-        namespace: vector_io::faiss
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/faiss_store.db
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec
     config:
       db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db
-      persistence:
-        namespace: vector_io::sqlite_vec
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec_registry.db
   - provider_id: ${env.MILVUS_URL:+milvus}
     provider_type: inline::milvus
     config:
       db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ci-tests}/milvus.db
-      persistence:
-        namespace: vector_io::milvus
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/milvus_registry.db
   - provider_id: ${env.CHROMADB_URL:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      persistence:
-        namespace: vector_io::chroma_remote
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests/}/chroma_remote_registry.db
   - provider_id: ${env.PGVECTOR_DB:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -125,32 +125,17 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      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
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/pgvector_registry.db
   files:
   - provider_id: meta-reference-files
     provider_type: inline::localfs
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ci-tests/files}
       metadata_store:
-        table_name: files_metadata
-        backend: sql_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/files_metadata.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -162,15 +147,12 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   post_training:
   - provider_id: torchtune-cpu
     provider_type: inline::torchtune-cpu
@@ -181,21 +163,21 @@ providers:
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -225,52 +207,35 @@ providers:
     provider_type: inline::reference
     config:
       kvstore:
-        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
-registered_resources:
-  models: []
-  shields:
-  - shield_id: llama-guard
-    provider_id: ${env.SAFETY_MODEL:+llama-guard}
-    provider_shield_id: ${env.SAFETY_MODEL:=}
-  - shield_id: code-scanner
-    provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner}
-    provider_shield_id: ${env.CODE_SCANNER_MODEL:=}
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: tavily-search
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+        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
+models: []
+shields:
+- shield_id: llama-guard
+  provider_id: ${env.SAFETY_MODEL:+llama-guard}
+  provider_shield_id: ${env.SAFETY_MODEL:=}
+- shield_id: code-scanner
+  provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner}
+  provider_shield_id: ${env.CODE_SCANNER_MODEL:=}
+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
 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/doc_template.md

@@ -157,7 +157,7 @@ docker run \
 Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
 
 ```bash
-llama stack list-deps {{ name }} | xargs -L1 pip install
+llama stack build --distro {{ name }} --image-type conda
 INFERENCE_MODEL=$INFERENCE_MODEL \
 DEH_URL=$DEH_URL \
 CHROMA_URL=$CHROMA_URL \

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

@@ -26,9 +26,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      persistence:
-        namespace: vector_io::chroma_remote
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell/}/chroma_remote_registry.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -38,35 +38,32 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -89,52 +86,40 @@ providers:
       max_results: 3
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
-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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: tgi0
-    model_type: llm
-  - metadata: {}
-    model_id: ${env.SAFETY_MODEL}
-    provider_id: tgi1
-    model_type: llm
-  - metadata:
-      embedding_dimension: 768
-    model_id: nomic-embed-text-v1.5
-    provider_id: sentence-transformers
-    model_type: embedding
-  shields:
-  - shield_id: ${env.SAFETY_MODEL}
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: brave-search
-  - toolgroup_id: builtin::rag
-    provider_id: 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
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: tgi0
+  model_type: llm
+- metadata: {}
+  model_id: ${env.SAFETY_MODEL}
+  provider_id: tgi1
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+  model_id: nomic-embed-text-v1.5
+  provider_id: sentence-transformers
+  model_type: embedding
+shields:
+- shield_id: ${env.SAFETY_MODEL}
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: brave-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
 server:
   port: 8321
 telemetry:

llama_stack/distributions/dell/run.yaml

@@ -22,9 +22,9 @@ providers:
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      persistence:
-        namespace: vector_io::chroma_remote
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell/}/chroma_remote_registry.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -34,35 +34,32 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -85,47 +82,35 @@ providers:
       max_results: 3
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
-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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: tgi0
-    model_type: llm
-  - metadata:
-      embedding_dimension: 768
-    model_id: nomic-embed-text-v1.5
-    provider_id: sentence-transformers
-    model_type: embedding
-  shields: []
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: brave-search
-  - toolgroup_id: builtin::rag
-    provider_id: 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
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: tgi0
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+  model_id: nomic-embed-text-v1.5
+  provider_id: sentence-transformers
+  model_type: embedding
+shields: []
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: brave-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
 server:
   port: 8321
 telemetry:

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

@@ -37,9 +37,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      persistence:
-        namespace: vector_io::faiss
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/faiss_store.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -49,35 +49,32 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -102,52 +99,40 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: meta-reference-inference
-    model_type: llm
-  - metadata: {}
-    model_id: ${env.SAFETY_MODEL}
-    provider_id: meta-reference-safety
-    model_type: llm
-  - metadata:
-      embedding_dimension: 768
-    model_id: nomic-embed-text-v1.5
-    provider_id: sentence-transformers
-    model_type: embedding
-  shields:
-  - shield_id: ${env.SAFETY_MODEL}
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: tavily-search
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+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
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: meta-reference-inference
+  model_type: llm
+- metadata: {}
+  model_id: ${env.SAFETY_MODEL}
+  provider_id: meta-reference-safety
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+  model_id: nomic-embed-text-v1.5
+  provider_id: sentence-transformers
+  model_type: embedding
+shields:
+- shield_id: ${env.SAFETY_MODEL}
+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
 telemetry:

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

@@ -27,9 +27,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      persistence:
-        namespace: vector_io::faiss
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/faiss_store.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -39,35 +39,32 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -92,47 +89,35 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: meta-reference-inference
-    model_type: llm
-  - metadata:
-      embedding_dimension: 768
-    model_id: nomic-embed-text-v1.5
-    provider_id: sentence-transformers
-    model_type: embedding
-  shields: []
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: tavily-search
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+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
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: meta-reference-inference
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+  model_id: nomic-embed-text-v1.5
+  provider_id: sentence-transformers
+  model_type: embedding
+shields: []
+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
 telemetry:

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

@@ -28,9 +28,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      persistence:
-        namespace: vector_io::faiss
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
   safety:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -41,15 +41,12 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -68,8 +65,8 @@ providers:
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/localfs_datasetio.db
   - provider_id: nvidia
     provider_type: remote::nvidia
     config:
@@ -89,48 +86,36 @@ providers:
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files}
       metadata_store:
-        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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: nvidia
-    model_type: llm
-  - metadata: {}
-    model_id: ${env.SAFETY_MODEL}
-    provider_id: nvidia
-    model_type: llm
-  shields:
-  - shield_id: ${env.SAFETY_MODEL}
-    provider_id: nvidia
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+        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
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: nvidia
+  model_type: llm
+- metadata: {}
+  model_id: ${env.SAFETY_MODEL}
+  provider_id: nvidia
+  model_type: llm
+shields:
+- shield_id: ${env.SAFETY_MODEL}
+  provider_id: nvidia
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
 server:
   port: 8321
 telemetry:

llama_stack/distributions/nvidia/run.yaml

@@ -23,9 +23,9 @@ providers:
   - provider_id: faiss
     provider_type: inline::faiss
     config:
-      persistence:
-        namespace: vector_io::faiss
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
   safety:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -36,15 +36,12 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: nvidia
     provider_type: remote::nvidia
@@ -78,38 +75,26 @@ providers:
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files}
       metadata_store:
-        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
-registered_resources:
-  models: []
-  shields: []
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+        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
+models: []
+shields: []
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
 server:
   port: 8321
 telemetry:

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
-      persistence:
-        namespace: vector_io::sqlite_vec
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec_registry.db
   - provider_id: ${env.ENABLE_CHROMADB:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      persistence:
-        namespace: vector_io::chroma_remote
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/chroma_remote_registry.db
   - provider_id: ${env.ENABLE_PGVECTOR:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -57,9 +57,9 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      persistence:
-        namespace: vector_io::pgvector
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/pgvector_registry.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -69,35 +69,32 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -122,130 +119,118 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: gpt-4o
-    provider_id: openai
-    provider_model_id: gpt-4o
-    model_type: llm
-  - metadata: {}
-    model_id: claude-3-5-sonnet-latest
-    provider_id: anthropic
-    provider_model_id: claude-3-5-sonnet-latest
-    model_type: llm
-  - metadata: {}
-    model_id: gemini/gemini-1.5-flash
-    provider_id: gemini
-    provider_model_id: gemini/gemini-1.5-flash
-    model_type: llm
-  - metadata: {}
-    model_id: meta-llama/Llama-3.3-70B-Instruct
-    provider_id: groq
-    provider_model_id: groq/llama-3.3-70b-versatile
-    model_type: llm
-  - metadata: {}
-    model_id: meta-llama/Llama-3.1-405B-Instruct
-    provider_id: together
-    provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
-    model_type: llm
-  shields:
-  - shield_id: meta-llama/Llama-Guard-3-8B
-  vector_dbs: []
-  datasets:
-  - purpose: eval/messages-answer
-    source:
-      type: uri
-      uri: huggingface://datasets/llamastack/simpleqa?split=train
-    metadata: {}
-    dataset_id: simpleqa
-  - purpose: eval/messages-answer
-    source:
-      type: uri
-      uri: huggingface://datasets/llamastack/mmlu_cot?split=test&name=all
-    metadata: {}
-    dataset_id: mmlu_cot
-  - purpose: eval/messages-answer
-    source:
-      type: uri
-      uri: huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main
-    metadata: {}
-    dataset_id: gpqa_cot
-  - purpose: eval/messages-answer
-    source:
-      type: uri
-      uri: huggingface://datasets/llamastack/math_500?split=test
-    metadata: {}
-    dataset_id: math_500
-  - purpose: eval/messages-answer
-    source:
-      type: uri
-      uri: huggingface://datasets/llamastack/IfEval?split=train
-    metadata: {}
-    dataset_id: ifeval
-  - purpose: eval/messages-answer
-    source:
-      type: uri
-      uri: huggingface://datasets/llamastack/docvqa?split=val
-    metadata: {}
-    dataset_id: docvqa
-  scoring_fns: []
-  benchmarks:
-  - dataset_id: simpleqa
-    scoring_functions:
-    - llm-as-judge::405b-simpleqa
-    metadata: {}
-    benchmark_id: meta-reference-simpleqa
-  - dataset_id: mmlu_cot
-    scoring_functions:
-    - basic::regex_parser_multiple_choice_answer
-    metadata: {}
-    benchmark_id: meta-reference-mmlu-cot
-  - dataset_id: gpqa_cot
-    scoring_functions:
-    - basic::regex_parser_multiple_choice_answer
-    metadata: {}
-    benchmark_id: meta-reference-gpqa-cot
-  - dataset_id: math_500
-    scoring_functions:
-    - basic::regex_parser_math_response
-    metadata: {}
-    benchmark_id: meta-reference-math-500
-  - dataset_id: ifeval
-    scoring_functions:
-    - basic::ifeval
-    metadata: {}
-    benchmark_id: meta-reference-ifeval
-  - dataset_id: docvqa
-    scoring_functions:
-    - basic::docvqa
-    metadata: {}
-    benchmark_id: meta-reference-docvqa
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: tavily-search
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+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
+models:
+- metadata: {}
+  model_id: gpt-4o
+  provider_id: openai
+  provider_model_id: gpt-4o
+  model_type: llm
+- metadata: {}
+  model_id: claude-3-5-sonnet-latest
+  provider_id: anthropic
+  provider_model_id: claude-3-5-sonnet-latest
+  model_type: llm
+- metadata: {}
+  model_id: gemini/gemini-1.5-flash
+  provider_id: gemini
+  provider_model_id: gemini/gemini-1.5-flash
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.3-70B-Instruct
+  provider_id: groq
+  provider_model_id: groq/llama-3.3-70b-versatile
+  model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct
+  provider_id: together
+  provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
+  model_type: llm
+shields:
+- shield_id: meta-llama/Llama-Guard-3-8B
+vector_dbs: []
+datasets:
+- purpose: eval/messages-answer
+  source:
+    type: uri
+    uri: huggingface://datasets/llamastack/simpleqa?split=train
+  metadata: {}
+  dataset_id: simpleqa
+- purpose: eval/messages-answer
+  source:
+    type: uri
+    uri: huggingface://datasets/llamastack/mmlu_cot?split=test&name=all
+  metadata: {}
+  dataset_id: mmlu_cot
+- purpose: eval/messages-answer
+  source:
+    type: uri
+    uri: huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main
+  metadata: {}
+  dataset_id: gpqa_cot
+- purpose: eval/messages-answer
+  source:
+    type: uri
+    uri: huggingface://datasets/llamastack/math_500?split=test
+  metadata: {}
+  dataset_id: math_500
+- purpose: eval/messages-answer
+  source:
+    type: uri
+    uri: huggingface://datasets/llamastack/IfEval?split=train
+  metadata: {}
+  dataset_id: ifeval
+- purpose: eval/messages-answer
+  source:
+    type: uri
+    uri: huggingface://datasets/llamastack/docvqa?split=val
+  metadata: {}
+  dataset_id: docvqa
+scoring_fns: []
+benchmarks:
+- dataset_id: simpleqa
+  scoring_functions:
+  - llm-as-judge::405b-simpleqa
+  metadata: {}
+  benchmark_id: meta-reference-simpleqa
+- dataset_id: mmlu_cot
+  scoring_functions:
+  - basic::regex_parser_multiple_choice_answer
+  metadata: {}
+  benchmark_id: meta-reference-mmlu-cot
+- dataset_id: gpqa_cot
+  scoring_functions:
+  - basic::regex_parser_multiple_choice_answer
+  metadata: {}
+  benchmark_id: meta-reference-gpqa-cot
+- dataset_id: math_500
+  scoring_functions:
+  - basic::regex_parser_math_response
+  metadata: {}
+  benchmark_id: meta-reference-math-500
+- dataset_id: ifeval
+  scoring_functions:
+  - basic::ifeval
+  metadata: {}
+  benchmark_id: meta-reference-ifeval
+- dataset_id: docvqa
+  scoring_functions:
+  - basic::docvqa
+  metadata: {}
+  benchmark_id: meta-reference-docvqa
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: tavily-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
 server:
   port: 8321
 telemetry:

llama_stack/distributions/postgres-demo/postgres_demo.py

@@ -91,6 +91,7 @@ def get_distribution_template() -> DistributionTemplate:
             "embedding_dimension": 768,
         },
     )
+    postgres_config = PostgresSqlStoreConfig.sample_run_config()
     return DistributionTemplate(
         name=name,
         distro_type="self_hosted",
@@ -104,16 +105,22 @@ 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")],
-                storage_backends={
-                    "kv_default": PostgresKVStoreConfig.sample_run_config(
-                        table_name="llamastack_kvstore",
-                    ),
-                    "sql_default": PostgresSqlStoreConfig.sample_run_config(),
-                },
+                metadata_store=PostgresKVStoreConfig.sample_run_config(),
+                inference_store=postgres_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:=}
-      persistence:
-        namespace: vector_io::chroma_remote
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/postgres-demo}/chroma_remote_registry.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -34,15 +34,20 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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}
   tool_runtime:
   - provider_id: brave-search
     provider_type: remote::brave-search
@@ -58,57 +63,45 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
-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
-registered_resources:
-  models:
-  - metadata: {}
-    model_id: ${env.INFERENCE_MODEL}
-    provider_id: vllm-inference
-    model_type: llm
-  - metadata:
-      embedding_dimension: 768
-    model_id: nomic-embed-text-v1.5
-    provider_id: sentence-transformers
-    model_type: embedding
-  shields:
-  - shield_id: meta-llama/Llama-Guard-3-8B
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: tavily-search
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+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
+models:
+- metadata: {}
+  model_id: ${env.INFERENCE_MODEL}
+  provider_id: vllm-inference
+  model_type: llm
+- metadata:
+    embedding_dimension: 768
+  model_id: nomic-embed-text-v1.5
+  provider_id: sentence-transformers
+  model_type: embedding
+shields:
+- shield_id: meta-llama/Llama-Guard-3-8B
+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
 telemetry:

llama_stack/distributions/starter-gpu/build.yaml

@@ -26,8 +26,6 @@ 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:
-      persistence:
-        namespace: vector_io::faiss
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/faiss_store.db
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec
     config:
       db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec.db
-      persistence:
-        namespace: vector_io::sqlite_vec
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec_registry.db
   - provider_id: ${env.MILVUS_URL:+milvus}
     provider_type: inline::milvus
     config:
       db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter-gpu}/milvus.db
-      persistence:
-        namespace: vector_io::milvus
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/milvus_registry.db
   - provider_id: ${env.CHROMADB_URL:+chromadb}
     provider_type: remote::chromadb
     config:
       url: ${env.CHROMADB_URL:=}
-      persistence:
-        namespace: vector_io::chroma_remote
-        backend: kv_default
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu/}/chroma_remote_registry.db
   - provider_id: ${env.PGVECTOR_DB:+pgvector}
     provider_type: remote::pgvector
     config:
@@ -125,32 +125,17 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
-      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
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/pgvector_registry.db
   files:
   - provider_id: meta-reference-files
     provider_type: inline::localfs
     config:
       storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter-gpu/files}
       metadata_store:
-        table_name: files_metadata
-        backend: sql_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/files_metadata.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -162,15 +147,12 @@ providers:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
-      persistence:
-        agent_state:
-          namespace: agents
-          backend: kv_default
-        responses:
-          table_name: responses
-          backend: sql_default
-          max_write_queue_size: 10000
-          num_writers: 4
+      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
   post_training:
   - provider_id: huggingface-gpu
     provider_type: inline::huggingface-gpu
@@ -184,21 +166,21 @@ providers:
     provider_type: inline::meta-reference
     config:
       kvstore:
-        namespace: eval
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/meta_reference_eval.db
   datasetio:
   - provider_id: huggingface
     provider_type: remote::huggingface
     config:
       kvstore:
-        namespace: datasetio::huggingface
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/huggingface_datasetio.db
   - provider_id: localfs
     provider_type: inline::localfs
     config:
       kvstore:
-        namespace: datasetio::localfs
-        backend: kv_default
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/localfs_datasetio.db
   scoring:
   - provider_id: basic
     provider_type: inline::basic
@@ -228,52 +210,35 @@ providers:
     provider_type: inline::reference
     config:
       kvstore:
-        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
-registered_resources:
-  models: []
-  shields:
-  - shield_id: llama-guard
-    provider_id: ${env.SAFETY_MODEL:+llama-guard}
-    provider_shield_id: ${env.SAFETY_MODEL:=}
-  - shield_id: code-scanner
-    provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner}
-    provider_shield_id: ${env.CODE_SCANNER_MODEL:=}
-  vector_dbs: []
-  datasets: []
-  scoring_fns: []
-  benchmarks: []
-  tool_groups:
-  - toolgroup_id: builtin::websearch
-    provider_id: tavily-search
-  - toolgroup_id: builtin::rag
-    provider_id: rag-runtime
+        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
+models: []
+shields:
+- shield_id: llama-guard
+  provider_id: ${env.SAFETY_MODEL:+llama-guard}
+  provider_shield_id: ${env.SAFETY_MODEL:=}
+- shield_id: code-scanner
+  provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner}
+  provider_shield_id: ${env.CODE_SCANNER_MODEL:=}
+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
 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

@@ -26,8 +26,6 @@ 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: