mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 01:48:05 +00:00
Merge remote-tracking branch 'upstream/main' into api-pkg
Signed-off-by: Charlie Doern <cdoern@redhat.com>
This commit is contained in:
Charlie Doern
commit
d6b915ce0a
48 changed files with 1990 additions and 425 deletions
|
|
@ -39,6 +39,32 @@ runs:
|
|||
if: ${{ inputs.setup == 'vllm' && inputs.inference-mode == 'record' }}
|
||||
uses: ./.github/actions/setup-vllm
|
||||
|
||||
- name: Start Postgres service
|
||||
if: ${{ contains(inputs.setup, 'postgres') }}
|
||||
shell: bash
|
||||
run: |
|
||||
sudo docker rm -f postgres-ci || true
|
||||
sudo docker run -d --name postgres-ci \
|
||||
-e POSTGRES_USER=llamastack \
|
||||
-e POSTGRES_PASSWORD=llamastack \
|
||||
-e POSTGRES_DB=llamastack \
|
||||
-p 5432:5432 \
|
||||
postgres:16
|
||||
|
||||
echo "Waiting for Postgres to become ready..."
|
||||
for i in {1..30}; do
|
||||
if sudo docker exec postgres-ci pg_isready -U llamastack -d llamastack >/dev/null 2>&1; then
|
||||
echo "Postgres is ready"
|
||||
break
|
||||
fi
|
||||
if [ "$i" -eq 30 ]; then
|
||||
echo "Postgres failed to start in time"
|
||||
sudo docker logs postgres-ci || true
|
||||
exit 1
|
||||
fi
|
||||
sleep 2
|
||||
done
|
||||
|
||||
- name: Build Llama Stack
|
||||
shell: bash
|
||||
run: |
|
||||
|
|
|
|||
12
.github/workflows/integration-tests.yml
vendored
12
.github/workflows/integration-tests.yml
vendored
|
|
@ -66,12 +66,12 @@ jobs:
|
|||
run-replay-mode-tests:
|
||||
needs: generate-matrix
|
||||
runs-on: ubuntu-latest
|
||||
name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.config.setup, matrix.python-version, matrix.client-version, matrix.config.suite) }}
|
||||
name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client, matrix.config.setup, matrix.python-version, matrix.client-version, matrix.config.suite) }}
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
client-type: [library, docker, server]
|
||||
client: [library, docker, server]
|
||||
# Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12
|
||||
python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }}
|
||||
client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }}
|
||||
|
|
@ -84,6 +84,7 @@ jobs:
|
|||
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
|
||||
|
||||
- name: Setup test environment
|
||||
if: ${{ matrix.config.allowed_clients == null || contains(matrix.config.allowed_clients, matrix.client) }}
|
||||
uses: ./.github/actions/setup-test-environment
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
|
|
@ -93,11 +94,16 @@ jobs:
|
|||
inference-mode: 'replay'
|
||||
|
||||
- name: Run tests
|
||||
if: ${{ matrix.config.allowed_clients == null || contains(matrix.config.allowed_clients, matrix.client) }}
|
||||
uses: ./.github/actions/run-and-record-tests
|
||||
env:
|
||||
OPENAI_API_KEY: dummy
|
||||
with:
|
||||
stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || matrix.client-type == 'server' && 'server:ci-tests' || 'docker:ci-tests' }}
|
||||
stack-config: >-
|
||||
${{ matrix.config.stack_config
|
||||
|| (matrix.client == 'library' && 'ci-tests')
|
||||
|| (matrix.client == 'server' && 'server:ci-tests')
|
||||
|| 'docker:ci-tests' }}
|
||||
setup: ${{ matrix.config.setup }}
|
||||
inference-mode: 'replay'
|
||||
suite: ${{ matrix.config.suite }}
|
||||
|
|
|
|||
|
|
@ -463,6 +463,12 @@ resources:
|
|||
settings:
|
||||
license: MIT
|
||||
unwrap_response_fields: [data]
|
||||
file_header: |
|
||||
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.
|
||||
|
||||
openapi:
|
||||
transformations:
|
||||
|
|
|
|||
|
|
@ -2691,7 +2691,8 @@ paths:
|
|||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreFileContentResponse representing the file contents.
|
||||
File contents, optionally with embeddings and metadata based on query
|
||||
parameters.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
|
|
@ -2726,6 +2727,20 @@ paths:
|
|||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: include_embeddings
|
||||
in: query
|
||||
description: >-
|
||||
Whether to include embedding vectors in the response.
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/bool'
|
||||
- name: include_metadata
|
||||
in: query
|
||||
description: >-
|
||||
Whether to include chunk metadata in the response.
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/bool'
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}/search:
|
||||
post:
|
||||
|
|
@ -10091,6 +10106,8 @@ components:
|
|||
title: VectorStoreFileDeleteResponse
|
||||
description: >-
|
||||
Response from deleting a vector store file.
|
||||
bool:
|
||||
type: boolean
|
||||
VectorStoreContent:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -10102,6 +10119,26 @@ components:
|
|||
text:
|
||||
type: string
|
||||
description: The actual text content
|
||||
embedding:
|
||||
type: array
|
||||
items:
|
||||
type: number
|
||||
description: >-
|
||||
Optional embedding vector for this content chunk
|
||||
chunk_metadata:
|
||||
$ref: '#/components/schemas/ChunkMetadata'
|
||||
description: Optional chunk metadata
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: 'null'
|
||||
- type: boolean
|
||||
- type: number
|
||||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
description: Optional user-defined metadata
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
|
|
@ -10125,6 +10162,7 @@ components:
|
|||
description: Parsed content of the file
|
||||
has_more:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
Indicates if there are more content pages to fetch
|
||||
next_page:
|
||||
|
|
|
|||
|
|
@ -221,7 +221,15 @@ models:
|
|||
```
|
||||
A Model is an instance of a "Resource" (see [Concepts](../concepts/)) and is associated with a specific inference provider (in this case, the provider with identifier `ollama`). This is an instance of a "pre-registered" model. While we always encourage the clients to register models before using them, some Stack servers may come up a list of "already known and available" models.
|
||||
|
||||
What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. Contrast it with `model_id` which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set `provider_model_id` to be the same as `model_id`.
|
||||
What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. The `model_id` field is provided for configuration purposes but is not used as part of the model identifier.
|
||||
|
||||
**Important:** Models are identified as `provider_id/provider_model_id` in the system and when making API calls. When `provider_model_id` is omitted, the server will set it to be the same as `model_id`.
|
||||
|
||||
Examples:
|
||||
- Config: `model_id: llama3.2`, `provider_id: ollama`, `provider_model_id: null`
|
||||
→ Access as: `ollama/llama3.2`
|
||||
- Config: `model_id: my-llama`, `provider_id: vllm-inference`, `provider_model_id: llama-3-2-3b`
|
||||
→ Access as: `vllm-inference/llama-3-2-3b` (the `model_id` is not used in the identifier)
|
||||
|
||||
If you need to conditionally register a model in the configuration, such as only when specific environment variable(s) are set, this can be accomplished by utilizing a special `__disabled__` string as the default value of an environment variable substitution, as shown below:
|
||||
|
||||
|
|
|
|||
|
|
@ -19,3 +19,4 @@ This section provides an overview of the distributions available in Llama Stack.
|
|||
- **[Starting Llama Stack Server](./starting_llama_stack_server.mdx)** - How to run distributions
|
||||
- **[Importing as Library](./importing_as_library.mdx)** - Use distributions in your code
|
||||
- **[Configuration Reference](./configuration.mdx)** - Configuration file format details
|
||||
- **[Llama Stack UI](./llama_stack_ui.mdx)** - Web-based user interface for interacting with Llama Stack servers
|
||||
|
|
|
|||
109
docs/docs/distributions/llama_stack_ui.mdx
Normal file
109
docs/docs/distributions/llama_stack_ui.mdx
Normal file
|
|
@ -0,0 +1,109 @@
|
|||
---
|
||||
title: Llama Stack UI
|
||||
description: Web-based user interface for interacting with Llama Stack servers
|
||||
sidebar_label: Llama Stack UI
|
||||
sidebar_position: 8
|
||||
---
|
||||
|
||||
# Llama Stack UI
|
||||
|
||||
The Llama Stack UI is a web-based interface for interacting with Llama Stack servers. Built with Next.js and React, it provides a visual way to work with agents, manage resources, and view logs.
|
||||
|
||||
## Features
|
||||
|
||||
- **Logs & Monitoring**: View chat completions, agent responses, and vector store activity
|
||||
- **Vector Stores**: Create and manage vector databases for RAG (Retrieval-Augmented Generation) workflows
|
||||
- **Prompt Management**: Create and manage reusable prompts
|
||||
|
||||
## Prerequisites
|
||||
|
||||
You need a running Llama Stack server. The UI is a client that connects to the Llama Stack backend.
|
||||
|
||||
If you don't have a Llama Stack server running yet, see the [Starting Llama Stack Server](../getting_started/starting_llama_stack_server.mdx) guide.
|
||||
|
||||
## Running the UI
|
||||
|
||||
### Option 1: Using npx (Recommended for Quick Start)
|
||||
|
||||
The fastest way to get started is using `npx`:
|
||||
|
||||
```bash
|
||||
npx llama-stack-ui
|
||||
```
|
||||
|
||||
This will start the UI server on `http://localhost:8322` (default port).
|
||||
|
||||
### Option 2: Using Docker
|
||||
|
||||
Run the UI in a container:
|
||||
|
||||
```bash
|
||||
docker run -p 8322:8322 llamastack/ui
|
||||
```
|
||||
|
||||
Access the UI at `http://localhost:8322`.
|
||||
|
||||
## Environment Variables
|
||||
|
||||
The UI can be configured using the following environment variables:
|
||||
|
||||
| Variable | Description | Default |
|
||||
|----------|-------------|---------|
|
||||
| `LLAMA_STACK_BACKEND_URL` | URL of your Llama Stack server | `http://localhost:8321` |
|
||||
| `LLAMA_STACK_UI_PORT` | Port for the UI server | `8322` |
|
||||
|
||||
If the Llama Stack server is running with authentication enabled, you can configure the UI to use it by setting the following environment variables:
|
||||
|
||||
| Variable | Description | Default |
|
||||
|----------|-------------|---------|
|
||||
| `NEXTAUTH_URL` | NextAuth URL for authentication | `http://localhost:8322` |
|
||||
| `GITHUB_CLIENT_ID` | GitHub OAuth client ID (optional, for authentication) | - |
|
||||
| `GITHUB_CLIENT_SECRET` | GitHub OAuth client secret (optional, for authentication) | - |
|
||||
|
||||
### Setting Environment Variables
|
||||
|
||||
#### For npx:
|
||||
|
||||
```bash
|
||||
LLAMA_STACK_BACKEND_URL=http://localhost:8321 \
|
||||
LLAMA_STACK_UI_PORT=8080 \
|
||||
npx llama-stack-ui
|
||||
```
|
||||
|
||||
#### For Docker:
|
||||
|
||||
```bash
|
||||
docker run -p 8080:8080 \
|
||||
-e LLAMA_STACK_BACKEND_URL=http://localhost:8321 \
|
||||
-e LLAMA_STACK_UI_PORT=8080 \
|
||||
llamastack/ui
|
||||
```
|
||||
|
||||
## Using the UI
|
||||
|
||||
### Managing Resources
|
||||
|
||||
- **Vector Stores**: Create vector databases for RAG workflows, view stored documents and embeddings
|
||||
- **Prompts**: Create and manage reusable prompt templates
|
||||
- **Chat Completions**: View history of chat interactions
|
||||
- **Responses**: Browse detailed agent responses and tool calls
|
||||
|
||||
## Development
|
||||
|
||||
If you want to run the UI from source for development:
|
||||
|
||||
```bash
|
||||
# From the project root
|
||||
cd src/llama_stack_ui
|
||||
|
||||
# Install dependencies
|
||||
npm install
|
||||
|
||||
# Set environment variables
|
||||
export LLAMA_STACK_BACKEND_URL=http://localhost:8321
|
||||
|
||||
# Start the development server
|
||||
npm run dev
|
||||
```
|
||||
|
||||
The development server will start on `http://localhost:8322` with hot reloading enabled.
|
||||
|
|
@ -57,6 +57,7 @@ const sidebars: SidebarsConfig = {
|
|||
'distributions/importing_as_library',
|
||||
'distributions/configuration',
|
||||
'distributions/starting_llama_stack_server',
|
||||
'distributions/llama_stack_ui',
|
||||
{
|
||||
type: 'category',
|
||||
label: 'Self-Hosted Distributions',
|
||||
|
|
|
|||
40
docs/static/llama-stack-spec.yaml
vendored
40
docs/static/llama-stack-spec.yaml
vendored
|
|
@ -2688,7 +2688,8 @@ paths:
|
|||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreFileContentResponse representing the file contents.
|
||||
File contents, optionally with embeddings and metadata based on query
|
||||
parameters.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
|
|
@ -2723,6 +2724,20 @@ paths:
|
|||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: include_embeddings
|
||||
in: query
|
||||
description: >-
|
||||
Whether to include embedding vectors in the response.
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/bool'
|
||||
- name: include_metadata
|
||||
in: query
|
||||
description: >-
|
||||
Whether to include chunk metadata in the response.
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/bool'
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}/search:
|
||||
post:
|
||||
|
|
@ -9375,6 +9390,8 @@ components:
|
|||
title: VectorStoreFileDeleteResponse
|
||||
description: >-
|
||||
Response from deleting a vector store file.
|
||||
bool:
|
||||
type: boolean
|
||||
VectorStoreContent:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9386,6 +9403,26 @@ components:
|
|||
text:
|
||||
type: string
|
||||
description: The actual text content
|
||||
embedding:
|
||||
type: array
|
||||
items:
|
||||
type: number
|
||||
description: >-
|
||||
Optional embedding vector for this content chunk
|
||||
chunk_metadata:
|
||||
$ref: '#/components/schemas/ChunkMetadata'
|
||||
description: Optional chunk metadata
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: 'null'
|
||||
- type: boolean
|
||||
- type: number
|
||||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
description: Optional user-defined metadata
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
|
|
@ -9409,6 +9446,7 @@ components:
|
|||
description: Parsed content of the file
|
||||
has_more:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
Indicates if there are more content pages to fetch
|
||||
next_page:
|
||||
|
|
|
|||
40
docs/static/stainless-llama-stack-spec.yaml
vendored
40
docs/static/stainless-llama-stack-spec.yaml
vendored
|
|
@ -2691,7 +2691,8 @@ paths:
|
|||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreFileContentResponse representing the file contents.
|
||||
File contents, optionally with embeddings and metadata based on query
|
||||
parameters.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
|
|
@ -2726,6 +2727,20 @@ paths:
|
|||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: include_embeddings
|
||||
in: query
|
||||
description: >-
|
||||
Whether to include embedding vectors in the response.
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/bool'
|
||||
- name: include_metadata
|
||||
in: query
|
||||
description: >-
|
||||
Whether to include chunk metadata in the response.
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/bool'
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}/search:
|
||||
post:
|
||||
|
|
@ -10091,6 +10106,8 @@ components:
|
|||
title: VectorStoreFileDeleteResponse
|
||||
description: >-
|
||||
Response from deleting a vector store file.
|
||||
bool:
|
||||
type: boolean
|
||||
VectorStoreContent:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -10102,6 +10119,26 @@ components:
|
|||
text:
|
||||
type: string
|
||||
description: The actual text content
|
||||
embedding:
|
||||
type: array
|
||||
items:
|
||||
type: number
|
||||
description: >-
|
||||
Optional embedding vector for this content chunk
|
||||
chunk_metadata:
|
||||
$ref: '#/components/schemas/ChunkMetadata'
|
||||
description: Optional chunk metadata
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: 'null'
|
||||
- type: boolean
|
||||
- type: number
|
||||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
description: Optional user-defined metadata
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
|
|
@ -10125,6 +10162,7 @@ components:
|
|||
description: Parsed content of the file
|
||||
has_more:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
Indicates if there are more content pages to fetch
|
||||
next_page:
|
||||
|
|
|
|||
|
|
@ -113,7 +113,7 @@ unit = [
|
|||
"aiosqlite",
|
||||
"aiohttp",
|
||||
"psycopg2-binary>=2.9.0",
|
||||
"pypdf",
|
||||
"pypdf>=6.1.3",
|
||||
"mcp",
|
||||
"chardet",
|
||||
"sqlalchemy",
|
||||
|
|
@ -136,7 +136,7 @@ test = [
|
|||
"torchvision>=0.21.0",
|
||||
"chardet",
|
||||
"psycopg2-binary>=2.9.0",
|
||||
"pypdf",
|
||||
"pypdf>=6.1.3",
|
||||
"mcp",
|
||||
"datasets>=4.0.0",
|
||||
"autoevals",
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@
|
|||
# the root directory of this source tree.
|
||||
from typing import Annotated, Any, Literal, Protocol, runtime_checkable
|
||||
|
||||
from fastapi import Body
|
||||
from fastapi import Body, Query
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack_api.apis.common.tracing import telemetry_traceable
|
||||
|
|
@ -224,10 +224,16 @@ class VectorStoreContent(BaseModel):
|
|||
|
||||
:param type: Content type, currently only "text" is supported
|
||||
:param text: The actual text content
|
||||
:param embedding: Optional embedding vector for this content chunk
|
||||
:param chunk_metadata: Optional chunk metadata
|
||||
:param metadata: Optional user-defined metadata
|
||||
"""
|
||||
|
||||
type: Literal["text"]
|
||||
text: str
|
||||
embedding: list[float] | None = None
|
||||
chunk_metadata: ChunkMetadata | None = None
|
||||
metadata: dict[str, Any] | None = None
|
||||
|
||||
|
||||
@json_schema_type
|
||||
|
|
@ -280,6 +286,22 @@ class VectorStoreDeleteResponse(BaseModel):
|
|||
deleted: bool = True
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileContentResponse(BaseModel):
|
||||
"""Represents the parsed content of a vector store file.
|
||||
|
||||
:param object: The object type, which is always `vector_store.file_content.page`
|
||||
:param data: Parsed content of the file
|
||||
:param has_more: Indicates if there are more content pages to fetch
|
||||
:param next_page: The token for the next page, if any
|
||||
"""
|
||||
|
||||
object: Literal["vector_store.file_content.page"] = "vector_store.file_content.page"
|
||||
data: list[VectorStoreContent]
|
||||
has_more: bool = False
|
||||
next_page: str | None = None
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreChunkingStrategyAuto(BaseModel):
|
||||
"""Automatic chunking strategy for vector store files.
|
||||
|
|
@ -395,22 +417,6 @@ class VectorStoreListFilesResponse(BaseModel):
|
|||
has_more: bool = False
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileContentResponse(BaseModel):
|
||||
"""Represents the parsed content of a vector store file.
|
||||
|
||||
:param object: The object type, which is always `vector_store.file_content.page`
|
||||
:param data: Parsed content of the file
|
||||
:param has_more: Indicates if there are more content pages to fetch
|
||||
:param next_page: The token for the next page, if any
|
||||
"""
|
||||
|
||||
object: Literal["vector_store.file_content.page"] = "vector_store.file_content.page"
|
||||
data: list[VectorStoreContent]
|
||||
has_more: bool
|
||||
next_page: str | None = None
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileDeleteResponse(BaseModel):
|
||||
"""Response from deleting a vector store file.
|
||||
|
|
@ -732,12 +738,16 @@ class VectorIO(Protocol):
|
|||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
include_embeddings: Annotated[bool | None, Query(default=False)] = False,
|
||||
include_metadata: Annotated[bool | None, Query(default=False)] = False,
|
||||
) -> VectorStoreFileContentResponse:
|
||||
"""Retrieves the contents of a vector store file.
|
||||
|
||||
:param vector_store_id: The ID of the vector store containing the file to retrieve.
|
||||
:param file_id: The ID of the file to retrieve.
|
||||
:returns: A VectorStoreFileContentResponse representing the file contents.
|
||||
:param include_embeddings: Whether to include embedding vectors in the response.
|
||||
:param include_metadata: Whether to include chunk metadata in the response.
|
||||
:returns: File contents, optionally with embeddings and metadata based on query parameters.
|
||||
"""
|
||||
...
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,6 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import importlib.resources
|
||||
import sys
|
||||
|
||||
from llama_stack_api.providers.datatypes import Api
|
||||
|
|
@ -13,9 +12,6 @@ from termcolor import cprint
|
|||
|
||||
from llama_stack.core.datatypes import BuildConfig
|
||||
from llama_stack.core.distribution import get_provider_registry
|
||||
from llama_stack.core.external import load_external_apis
|
||||
from llama_stack.core.utils.exec import run_command
|
||||
from llama_stack.core.utils.image_types import LlamaStackImageType
|
||||
from llama_stack.distributions.template import DistributionTemplate
|
||||
from llama_stack.log import get_logger
|
||||
|
||||
|
|
@ -101,64 +97,3 @@ def print_pip_install_help(config: BuildConfig):
|
|||
for special_dep in special_deps:
|
||||
cprint(f"uv pip install {special_dep}", color="yellow", file=sys.stderr)
|
||||
print()
|
||||
|
||||
|
||||
def build_image(
|
||||
build_config: BuildConfig,
|
||||
image_name: str,
|
||||
distro_or_config: str,
|
||||
run_config: str | None = None,
|
||||
):
|
||||
container_base = build_config.distribution_spec.container_image or "python:3.12-slim"
|
||||
|
||||
normal_deps, special_deps, external_provider_deps = get_provider_dependencies(build_config)
|
||||
normal_deps += SERVER_DEPENDENCIES
|
||||
if build_config.external_apis_dir:
|
||||
external_apis = load_external_apis(build_config)
|
||||
if external_apis:
|
||||
for _, api_spec in external_apis.items():
|
||||
normal_deps.extend(api_spec.pip_packages)
|
||||
|
||||
if build_config.image_type == LlamaStackImageType.CONTAINER.value:
|
||||
script = str(importlib.resources.files("llama_stack") / "core/build_container.sh")
|
||||
args = [
|
||||
script,
|
||||
"--distro-or-config",
|
||||
distro_or_config,
|
||||
"--image-name",
|
||||
image_name,
|
||||
"--container-base",
|
||||
container_base,
|
||||
"--normal-deps",
|
||||
" ".join(normal_deps),
|
||||
]
|
||||
# When building from a config file (not a template), include the run config path in the
|
||||
# build arguments
|
||||
if run_config is not None:
|
||||
args.extend(["--run-config", run_config])
|
||||
else:
|
||||
script = str(importlib.resources.files("llama_stack") / "core/build_venv.sh")
|
||||
args = [
|
||||
script,
|
||||
"--env-name",
|
||||
str(image_name),
|
||||
"--normal-deps",
|
||||
" ".join(normal_deps),
|
||||
]
|
||||
|
||||
# Always pass both arguments, even if empty, to maintain consistent positional arguments
|
||||
if special_deps:
|
||||
args.extend(["--optional-deps", "#".join(special_deps)])
|
||||
if external_provider_deps:
|
||||
args.extend(
|
||||
["--external-provider-deps", "#".join(external_provider_deps)]
|
||||
) # the script will install external provider module, get its deps, and install those too.
|
||||
|
||||
return_code = run_command(args)
|
||||
|
||||
if return_code != 0:
|
||||
log.error(
|
||||
f"Failed to build target {image_name} with return code {return_code}",
|
||||
)
|
||||
|
||||
return return_code
|
||||
|
|
|
|||
|
|
@ -389,6 +389,12 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
|
|||
matched_func, path_params, route_path, webmethod = find_matching_route(options.method, path, self.route_impls)
|
||||
body |= path_params
|
||||
|
||||
# Pass through params that aren't already handled as path params
|
||||
if options.params:
|
||||
extra_query_params = {k: v for k, v in options.params.items() if k not in path_params}
|
||||
if extra_query_params:
|
||||
body["extra_query"] = extra_query_params
|
||||
|
||||
body, field_names = self._handle_file_uploads(options, body)
|
||||
|
||||
body = self._convert_body(matched_func, body, exclude_params=set(field_names))
|
||||
|
|
|
|||
|
|
@ -247,6 +247,13 @@ class VectorIORouter(VectorIO):
|
|||
metadata: dict[str, Any] | None = None,
|
||||
) -> VectorStoreObject:
|
||||
logger.debug(f"VectorIORouter.openai_update_vector_store: {vector_store_id}")
|
||||
|
||||
# Check if provider_id is being changed (not supported)
|
||||
if metadata and "provider_id" in metadata:
|
||||
current_store = await self.routing_table.get_object_by_identifier("vector_store", vector_store_id)
|
||||
if current_store and current_store.provider_id != metadata["provider_id"]:
|
||||
raise ValueError("provider_id cannot be changed after vector store creation")
|
||||
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_update_vector_store(
|
||||
vector_store_id=vector_store_id,
|
||||
|
|
@ -338,12 +345,19 @@ class VectorIORouter(VectorIO):
|
|||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
include_embeddings: bool | None = False,
|
||||
include_metadata: bool | None = False,
|
||||
) -> VectorStoreFileContentResponse:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}")
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_retrieve_vector_store_file_contents(
|
||||
logger.debug(
|
||||
f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}, "
|
||||
f"include_embeddings={include_embeddings}, include_metadata={include_metadata}"
|
||||
)
|
||||
|
||||
return await self.routing_table.openai_retrieve_vector_store_file_contents(
|
||||
vector_store_id=vector_store_id,
|
||||
file_id=file_id,
|
||||
include_embeddings=include_embeddings,
|
||||
include_metadata=include_metadata,
|
||||
)
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
|
|
|
|||
|
|
@ -196,12 +196,17 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
|
|||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
include_embeddings: bool | None = False,
|
||||
include_metadata: bool | None = False,
|
||||
) -> VectorStoreFileContentResponse:
|
||||
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,
|
||||
include_embeddings=include_embeddings,
|
||||
include_metadata=include_metadata,
|
||||
)
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
|
|
|
|||
|
|
@ -13,6 +13,5 @@ from ..starter.starter import get_distribution_template as get_starter_distribut
|
|||
def get_distribution_template() -> DistributionTemplate:
|
||||
template = get_starter_distribution_template(name="ci-tests")
|
||||
template.description = "CI tests for Llama Stack"
|
||||
template.run_configs.pop("run-with-postgres-store.yaml", None)
|
||||
|
||||
return template
|
||||
|
|
|
|||
|
|
@ -0,0 +1,293 @@
|
|||
version: 2
|
||||
image_name: ci-tests
|
||||
apis:
|
||||
- agents
|
||||
- batches
|
||||
- datasetio
|
||||
- eval
|
||||
- files
|
||||
- inference
|
||||
- post_training
|
||||
- safety
|
||||
- scoring
|
||||
- tool_runtime
|
||||
- vector_io
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: ${env.CEREBRAS_API_KEY:+cerebras}
|
||||
provider_type: remote::cerebras
|
||||
config:
|
||||
base_url: https://api.cerebras.ai
|
||||
api_key: ${env.CEREBRAS_API_KEY:=}
|
||||
- provider_id: ${env.OLLAMA_URL:+ollama}
|
||||
provider_type: remote::ollama
|
||||
config:
|
||||
url: ${env.OLLAMA_URL:=http://localhost:11434}
|
||||
- provider_id: ${env.VLLM_URL:+vllm}
|
||||
provider_type: remote::vllm
|
||||
config:
|
||||
url: ${env.VLLM_URL:=}
|
||||
max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
|
||||
api_token: ${env.VLLM_API_TOKEN:=fake}
|
||||
tls_verify: ${env.VLLM_TLS_VERIFY:=true}
|
||||
- provider_id: ${env.TGI_URL:+tgi}
|
||||
provider_type: remote::tgi
|
||||
config:
|
||||
url: ${env.TGI_URL:=}
|
||||
- provider_id: fireworks
|
||||
provider_type: remote::fireworks
|
||||
config:
|
||||
url: https://api.fireworks.ai/inference/v1
|
||||
api_key: ${env.FIREWORKS_API_KEY:=}
|
||||
- provider_id: together
|
||||
provider_type: remote::together
|
||||
config:
|
||||
url: https://api.together.xyz/v1
|
||||
api_key: ${env.TOGETHER_API_KEY:=}
|
||||
- provider_id: bedrock
|
||||
provider_type: remote::bedrock
|
||||
config:
|
||||
api_key: ${env.AWS_BEDROCK_API_KEY:=}
|
||||
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
|
||||
- provider_id: ${env.NVIDIA_API_KEY:+nvidia}
|
||||
provider_type: remote::nvidia
|
||||
config:
|
||||
url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
|
||||
api_key: ${env.NVIDIA_API_KEY:=}
|
||||
append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
|
||||
- provider_id: openai
|
||||
provider_type: remote::openai
|
||||
config:
|
||||
api_key: ${env.OPENAI_API_KEY:=}
|
||||
base_url: ${env.OPENAI_BASE_URL:=https://api.openai.com/v1}
|
||||
- provider_id: anthropic
|
||||
provider_type: remote::anthropic
|
||||
config:
|
||||
api_key: ${env.ANTHROPIC_API_KEY:=}
|
||||
- provider_id: gemini
|
||||
provider_type: remote::gemini
|
||||
config:
|
||||
api_key: ${env.GEMINI_API_KEY:=}
|
||||
- provider_id: ${env.VERTEX_AI_PROJECT:+vertexai}
|
||||
provider_type: remote::vertexai
|
||||
config:
|
||||
project: ${env.VERTEX_AI_PROJECT:=}
|
||||
location: ${env.VERTEX_AI_LOCATION:=us-central1}
|
||||
- provider_id: groq
|
||||
provider_type: remote::groq
|
||||
config:
|
||||
url: https://api.groq.com
|
||||
api_key: ${env.GROQ_API_KEY:=}
|
||||
- provider_id: sambanova
|
||||
provider_type: remote::sambanova
|
||||
config:
|
||||
url: https://api.sambanova.ai/v1
|
||||
api_key: ${env.SAMBANOVA_API_KEY:=}
|
||||
- provider_id: ${env.AZURE_API_KEY:+azure}
|
||||
provider_type: remote::azure
|
||||
config:
|
||||
api_key: ${env.AZURE_API_KEY:=}
|
||||
api_base: ${env.AZURE_API_BASE:=}
|
||||
api_version: ${env.AZURE_API_VERSION:=}
|
||||
api_type: ${env.AZURE_API_TYPE:=}
|
||||
- provider_id: sentence-transformers
|
||||
provider_type: inline::sentence-transformers
|
||||
vector_io:
|
||||
- provider_id: faiss
|
||||
provider_type: inline::faiss
|
||||
config:
|
||||
persistence:
|
||||
namespace: vector_io::faiss
|
||||
backend: kv_default
|
||||
- provider_id: sqlite-vec
|
||||
provider_type: inline::sqlite-vec
|
||||
config:
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db
|
||||
persistence:
|
||||
namespace: vector_io::sqlite_vec
|
||||
backend: kv_default
|
||||
- provider_id: ${env.MILVUS_URL:+milvus}
|
||||
provider_type: inline::milvus
|
||||
config:
|
||||
db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ci-tests}/milvus.db
|
||||
persistence:
|
||||
namespace: vector_io::milvus
|
||||
backend: kv_default
|
||||
- provider_id: ${env.CHROMADB_URL:+chromadb}
|
||||
provider_type: remote::chromadb
|
||||
config:
|
||||
url: ${env.CHROMADB_URL:=}
|
||||
persistence:
|
||||
namespace: vector_io::chroma_remote
|
||||
backend: kv_default
|
||||
- provider_id: ${env.PGVECTOR_DB:+pgvector}
|
||||
provider_type: remote::pgvector
|
||||
config:
|
||||
host: ${env.PGVECTOR_HOST:=localhost}
|
||||
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
|
||||
- provider_id: ${env.QDRANT_URL:+qdrant}
|
||||
provider_type: remote::qdrant
|
||||
config:
|
||||
api_key: ${env.QDRANT_API_KEY:=}
|
||||
persistence:
|
||||
namespace: vector_io::qdrant_remote
|
||||
backend: kv_default
|
||||
- provider_id: ${env.WEAVIATE_CLUSTER_URL:+weaviate}
|
||||
provider_type: remote::weaviate
|
||||
config:
|
||||
weaviate_api_key: null
|
||||
weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
|
||||
persistence:
|
||||
namespace: vector_io::weaviate
|
||||
backend: kv_default
|
||||
files:
|
||||
- provider_id: meta-reference-files
|
||||
provider_type: inline::localfs
|
||||
config:
|
||||
storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ci-tests/files}
|
||||
metadata_store:
|
||||
table_name: files_metadata
|
||||
backend: sql_default
|
||||
safety:
|
||||
- provider_id: llama-guard
|
||||
provider_type: inline::llama-guard
|
||||
config:
|
||||
excluded_categories: []
|
||||
- provider_id: code-scanner
|
||||
provider_type: inline::code-scanner
|
||||
agents:
|
||||
- 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
|
||||
post_training:
|
||||
- provider_id: torchtune-cpu
|
||||
provider_type: inline::torchtune-cpu
|
||||
config:
|
||||
checkpoint_format: meta
|
||||
eval:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
kvstore:
|
||||
namespace: eval
|
||||
backend: kv_default
|
||||
datasetio:
|
||||
- provider_id: huggingface
|
||||
provider_type: remote::huggingface
|
||||
config:
|
||||
kvstore:
|
||||
namespace: datasetio::huggingface
|
||||
backend: kv_default
|
||||
- provider_id: localfs
|
||||
provider_type: inline::localfs
|
||||
config:
|
||||
kvstore:
|
||||
namespace: datasetio::localfs
|
||||
backend: kv_default
|
||||
scoring:
|
||||
- provider_id: basic
|
||||
provider_type: inline::basic
|
||||
- provider_id: llm-as-judge
|
||||
provider_type: inline::llm-as-judge
|
||||
- provider_id: braintrust
|
||||
provider_type: inline::braintrust
|
||||
config:
|
||||
openai_api_key: ${env.OPENAI_API_KEY:=}
|
||||
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
|
||||
- provider_id: model-context-protocol
|
||||
provider_type: remote::model-context-protocol
|
||||
batches:
|
||||
- provider_id: reference
|
||||
provider_type: inline::reference
|
||||
config:
|
||||
kvstore:
|
||||
namespace: batches
|
||||
backend: kv_default
|
||||
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
|
||||
prompts:
|
||||
namespace: prompts
|
||||
backend: kv_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
|
||||
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
|
||||
safety:
|
||||
default_shield_id: llama-guard
|
||||
|
|
@ -165,20 +165,15 @@ providers:
|
|||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
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}
|
||||
responses_store:
|
||||
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}
|
||||
persistence:
|
||||
agent_state:
|
||||
namespace: agents
|
||||
backend: kv_default
|
||||
responses:
|
||||
table_name: responses
|
||||
backend: sql_default
|
||||
max_write_queue_size: 10000
|
||||
num_writers: 4
|
||||
post_training:
|
||||
- provider_id: huggingface-gpu
|
||||
provider_type: inline::huggingface-gpu
|
||||
|
|
@ -237,10 +232,10 @@ providers:
|
|||
config:
|
||||
kvstore:
|
||||
namespace: batches
|
||||
backend: kv_postgres
|
||||
backend: kv_default
|
||||
storage:
|
||||
backends:
|
||||
kv_postgres:
|
||||
kv_default:
|
||||
type: kv_postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
|
|
@ -248,7 +243,7 @@ storage:
|
|||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
|
||||
sql_postgres:
|
||||
sql_default:
|
||||
type: sql_postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
|
|
@ -258,27 +253,44 @@ storage:
|
|||
stores:
|
||||
metadata:
|
||||
namespace: registry
|
||||
backend: kv_postgres
|
||||
backend: kv_default
|
||||
inference:
|
||||
table_name: inference_store
|
||||
backend: sql_postgres
|
||||
backend: sql_default
|
||||
max_write_queue_size: 10000
|
||||
num_writers: 4
|
||||
conversations:
|
||||
table_name: openai_conversations
|
||||
backend: sql_postgres
|
||||
backend: sql_default
|
||||
prompts:
|
||||
namespace: prompts
|
||||
backend: kv_postgres
|
||||
backend: kv_default
|
||||
registered_resources:
|
||||
models: []
|
||||
shields: []
|
||||
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: []
|
||||
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
|
||||
safety:
|
||||
default_shield_id: llama-guard
|
||||
|
|
|
|||
|
|
@ -165,20 +165,15 @@ providers:
|
|||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
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}
|
||||
responses_store:
|
||||
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}
|
||||
persistence:
|
||||
agent_state:
|
||||
namespace: agents
|
||||
backend: kv_default
|
||||
responses:
|
||||
table_name: responses
|
||||
backend: sql_default
|
||||
max_write_queue_size: 10000
|
||||
num_writers: 4
|
||||
post_training:
|
||||
- provider_id: torchtune-cpu
|
||||
provider_type: inline::torchtune-cpu
|
||||
|
|
@ -234,10 +229,10 @@ providers:
|
|||
config:
|
||||
kvstore:
|
||||
namespace: batches
|
||||
backend: kv_postgres
|
||||
backend: kv_default
|
||||
storage:
|
||||
backends:
|
||||
kv_postgres:
|
||||
kv_default:
|
||||
type: kv_postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
|
|
@ -245,7 +240,7 @@ storage:
|
|||
user: ${env.POSTGRES_USER:=llamastack}
|
||||
password: ${env.POSTGRES_PASSWORD:=llamastack}
|
||||
table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
|
||||
sql_postgres:
|
||||
sql_default:
|
||||
type: sql_postgres
|
||||
host: ${env.POSTGRES_HOST:=localhost}
|
||||
port: ${env.POSTGRES_PORT:=5432}
|
||||
|
|
@ -255,27 +250,44 @@ storage:
|
|||
stores:
|
||||
metadata:
|
||||
namespace: registry
|
||||
backend: kv_postgres
|
||||
backend: kv_default
|
||||
inference:
|
||||
table_name: inference_store
|
||||
backend: sql_postgres
|
||||
backend: sql_default
|
||||
max_write_queue_size: 10000
|
||||
num_writers: 4
|
||||
conversations:
|
||||
table_name: openai_conversations
|
||||
backend: sql_postgres
|
||||
backend: sql_default
|
||||
prompts:
|
||||
namespace: prompts
|
||||
backend: kv_postgres
|
||||
backend: kv_default
|
||||
registered_resources:
|
||||
models: []
|
||||
shields: []
|
||||
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: ${env.CODE_SCANNER_MODEL:=}
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
scoring_fns: []
|
||||
benchmarks: []
|
||||
tool_groups: []
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
server:
|
||||
port: 8321
|
||||
telemetry:
|
||||
enabled: true
|
||||
vector_stores:
|
||||
default_provider_id: faiss
|
||||
default_embedding_model:
|
||||
provider_id: sentence-transformers
|
||||
model_id: nomic-ai/nomic-embed-text-v1.5
|
||||
safety:
|
||||
default_shield_id: llama-guard
|
||||
|
|
|
|||
|
|
@ -19,11 +19,6 @@ from llama_stack.core.datatypes import (
|
|||
ToolGroupInput,
|
||||
VectorStoresConfig,
|
||||
)
|
||||
from llama_stack.core.storage.datatypes import (
|
||||
InferenceStoreReference,
|
||||
KVStoreReference,
|
||||
SqlStoreReference,
|
||||
)
|
||||
from llama_stack.core.utils.dynamic import instantiate_class_type
|
||||
from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
|
||||
from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
|
||||
|
|
@ -155,10 +150,11 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
|
|||
BuildProvider(provider_type="inline::reference"),
|
||||
],
|
||||
}
|
||||
files_config = LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}")
|
||||
files_provider = Provider(
|
||||
provider_id="meta-reference-files",
|
||||
provider_type="inline::localfs",
|
||||
config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
|
||||
config=files_config,
|
||||
)
|
||||
embedding_provider = Provider(
|
||||
provider_id="sentence-transformers",
|
||||
|
|
@ -188,7 +184,8 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
|
|||
provider_shield_id="${env.CODE_SCANNER_MODEL:=}",
|
||||
),
|
||||
]
|
||||
postgres_config = PostgresSqlStoreConfig.sample_run_config()
|
||||
postgres_sql_config = PostgresSqlStoreConfig.sample_run_config()
|
||||
postgres_kv_config = PostgresKVStoreConfig.sample_run_config()
|
||||
default_overrides = {
|
||||
"inference": remote_inference_providers + [embedding_provider],
|
||||
"vector_io": [
|
||||
|
|
@ -245,6 +242,33 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
|
|||
"files": [files_provider],
|
||||
}
|
||||
|
||||
base_run_settings = RunConfigSettings(
|
||||
provider_overrides=default_overrides,
|
||||
default_models=[],
|
||||
default_tool_groups=default_tool_groups,
|
||||
default_shields=default_shields,
|
||||
vector_stores_config=VectorStoresConfig(
|
||||
default_provider_id="faiss",
|
||||
default_embedding_model=QualifiedModel(
|
||||
provider_id="sentence-transformers",
|
||||
model_id="nomic-ai/nomic-embed-text-v1.5",
|
||||
),
|
||||
),
|
||||
safety_config=SafetyConfig(
|
||||
default_shield_id="llama-guard",
|
||||
),
|
||||
)
|
||||
|
||||
postgres_run_settings = base_run_settings.model_copy(
|
||||
update={
|
||||
"storage_backends": {
|
||||
"kv_default": postgres_kv_config,
|
||||
"sql_default": postgres_sql_config,
|
||||
}
|
||||
},
|
||||
deep=True,
|
||||
)
|
||||
|
||||
return DistributionTemplate(
|
||||
name=name,
|
||||
distro_type="self_hosted",
|
||||
|
|
@ -254,71 +278,8 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
|
|||
providers=providers,
|
||||
additional_pip_packages=list(set(PostgresSqlStoreConfig.pip_packages() + PostgresKVStoreConfig.pip_packages())),
|
||||
run_configs={
|
||||
"run.yaml": RunConfigSettings(
|
||||
provider_overrides=default_overrides,
|
||||
default_models=[],
|
||||
default_tool_groups=default_tool_groups,
|
||||
default_shields=default_shields,
|
||||
vector_stores_config=VectorStoresConfig(
|
||||
default_provider_id="faiss",
|
||||
default_embedding_model=QualifiedModel(
|
||||
provider_id="sentence-transformers",
|
||||
model_id="nomic-ai/nomic-embed-text-v1.5",
|
||||
),
|
||||
),
|
||||
safety_config=SafetyConfig(
|
||||
default_shield_id="llama-guard",
|
||||
),
|
||||
),
|
||||
"run-with-postgres-store.yaml": RunConfigSettings(
|
||||
provider_overrides={
|
||||
**default_overrides,
|
||||
"agents": [
|
||||
Provider(
|
||||
provider_id="meta-reference",
|
||||
provider_type="inline::meta-reference",
|
||||
config=dict(
|
||||
persistence_store=postgres_config,
|
||||
responses_store=postgres_config,
|
||||
),
|
||||
)
|
||||
],
|
||||
"batches": [
|
||||
Provider(
|
||||
provider_id="reference",
|
||||
provider_type="inline::reference",
|
||||
config=dict(
|
||||
kvstore=KVStoreReference(
|
||||
backend="kv_postgres",
|
||||
namespace="batches",
|
||||
).model_dump(exclude_none=True),
|
||||
),
|
||||
)
|
||||
],
|
||||
},
|
||||
storage_backends={
|
||||
"kv_postgres": PostgresKVStoreConfig.sample_run_config(),
|
||||
"sql_postgres": postgres_config,
|
||||
},
|
||||
storage_stores={
|
||||
"metadata": KVStoreReference(
|
||||
backend="kv_postgres",
|
||||
namespace="registry",
|
||||
).model_dump(exclude_none=True),
|
||||
"inference": InferenceStoreReference(
|
||||
backend="sql_postgres",
|
||||
table_name="inference_store",
|
||||
).model_dump(exclude_none=True),
|
||||
"conversations": SqlStoreReference(
|
||||
backend="sql_postgres",
|
||||
table_name="openai_conversations",
|
||||
).model_dump(exclude_none=True),
|
||||
"prompts": KVStoreReference(
|
||||
backend="kv_postgres",
|
||||
namespace="prompts",
|
||||
).model_dump(exclude_none=True),
|
||||
},
|
||||
),
|
||||
"run.yaml": base_run_settings,
|
||||
"run-with-postgres-store.yaml": postgres_run_settings,
|
||||
},
|
||||
run_config_env_vars={
|
||||
"LLAMA_STACK_PORT": (
|
||||
|
|
|
|||
|
|
@ -66,14 +66,6 @@ class InferenceStore:
|
|||
},
|
||||
)
|
||||
|
||||
if self.enable_write_queue:
|
||||
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
|
||||
for _ in range(self._num_writers):
|
||||
self._worker_tasks.append(asyncio.create_task(self._worker_loop()))
|
||||
logger.debug(
|
||||
f"Inference store write queue enabled with {self._num_writers} writers, max queue size {self._max_write_queue_size}"
|
||||
)
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
if not self._worker_tasks:
|
||||
return
|
||||
|
|
@ -94,10 +86,29 @@ class InferenceStore:
|
|||
if self.enable_write_queue and self._queue is not None:
|
||||
await self._queue.join()
|
||||
|
||||
async def _ensure_workers_started(self) -> None:
|
||||
"""Ensure the async write queue workers run on the current loop."""
|
||||
if not self.enable_write_queue:
|
||||
return
|
||||
|
||||
if self._queue is None:
|
||||
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
|
||||
logger.debug(
|
||||
f"Inference store write queue created with max size {self._max_write_queue_size} "
|
||||
f"and {self._num_writers} writers"
|
||||
)
|
||||
|
||||
if not self._worker_tasks:
|
||||
loop = asyncio.get_running_loop()
|
||||
for _ in range(self._num_writers):
|
||||
task = loop.create_task(self._worker_loop())
|
||||
self._worker_tasks.append(task)
|
||||
|
||||
async def store_chat_completion(
|
||||
self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam]
|
||||
) -> None:
|
||||
if self.enable_write_queue:
|
||||
await self._ensure_workers_started()
|
||||
if self._queue is None:
|
||||
raise ValueError("Inference store is not initialized")
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -704,34 +704,35 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
# Unknown filter type, default to no match
|
||||
raise ValueError(f"Unsupported filter type: {filter_type}")
|
||||
|
||||
def _chunk_to_vector_store_content(self, chunk: Chunk) -> list[VectorStoreContent]:
|
||||
# content is InterleavedContent
|
||||
def _chunk_to_vector_store_content(
|
||||
self, chunk: Chunk, include_embeddings: bool = False, include_metadata: bool = False
|
||||
) -> list[VectorStoreContent]:
|
||||
def extract_fields() -> dict:
|
||||
"""Extract embedding and metadata fields from chunk based on include flags."""
|
||||
return {
|
||||
"embedding": chunk.embedding if include_embeddings else None,
|
||||
"chunk_metadata": chunk.chunk_metadata if include_metadata else None,
|
||||
"metadata": chunk.metadata if include_metadata else None,
|
||||
}
|
||||
|
||||
fields = extract_fields()
|
||||
|
||||
if isinstance(chunk.content, str):
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=chunk.content,
|
||||
)
|
||||
]
|
||||
content_item = VectorStoreContent(type="text", text=chunk.content, **fields)
|
||||
content = [content_item]
|
||||
elif isinstance(chunk.content, list):
|
||||
# TODO: Add support for other types of content
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=item.text,
|
||||
)
|
||||
for item in chunk.content
|
||||
if item.type == "text"
|
||||
]
|
||||
content = []
|
||||
for item in chunk.content:
|
||||
if item.type == "text":
|
||||
content_item = VectorStoreContent(type="text", text=item.text, **fields)
|
||||
content.append(content_item)
|
||||
else:
|
||||
if chunk.content.type != "text":
|
||||
raise ValueError(f"Unsupported content type: {chunk.content.type}")
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=chunk.content.text,
|
||||
)
|
||||
]
|
||||
|
||||
content_item = VectorStoreContent(type="text", text=chunk.content.text, **fields)
|
||||
content = [content_item]
|
||||
return content
|
||||
|
||||
async def openai_attach_file_to_vector_store(
|
||||
|
|
@ -820,13 +821,12 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
message=str(e),
|
||||
)
|
||||
|
||||
# Create OpenAI vector store file metadata
|
||||
# Save vector store file to persistent storage AFTER insert_chunks
|
||||
# so that chunks include the embeddings that were generated
|
||||
file_info = vector_store_file_object.model_dump(exclude={"last_error"})
|
||||
file_info["filename"] = file_response.filename if file_response else ""
|
||||
|
||||
# Save vector store file to persistent storage (provider-specific)
|
||||
dict_chunks = [c.model_dump() for c in chunks]
|
||||
# This should be updated to include chunk_id
|
||||
await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
|
||||
|
||||
# Update file_ids and file_counts in vector store metadata
|
||||
|
|
@ -921,21 +921,27 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
include_embeddings: bool | None = False,
|
||||
include_metadata: bool | None = False,
|
||||
) -> VectorStoreFileContentResponse:
|
||||
"""Retrieves the contents of a vector store file."""
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise VectorStoreNotFoundError(vector_store_id)
|
||||
|
||||
# Parameters are already provided directly
|
||||
# include_embeddings and include_metadata are now function parameters
|
||||
|
||||
dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
|
||||
chunks = [Chunk.model_validate(c) for c in dict_chunks]
|
||||
content = []
|
||||
for chunk in chunks:
|
||||
content.extend(self._chunk_to_vector_store_content(chunk))
|
||||
content.extend(
|
||||
self._chunk_to_vector_store_content(
|
||||
chunk, include_embeddings=include_embeddings or False, include_metadata=include_metadata or False
|
||||
)
|
||||
)
|
||||
return VectorStoreFileContentResponse(
|
||||
object="vector_store.file_content.page",
|
||||
data=content,
|
||||
has_more=False,
|
||||
next_page=None,
|
||||
)
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
|
|
|
|||
|
|
@ -3,8 +3,6 @@
|
|||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
import asyncio
|
||||
from typing import Any
|
||||
|
||||
from llama_stack_api.apis.agents import (
|
||||
Order,
|
||||
|
|
@ -20,12 +18,12 @@ from llama_stack_api.apis.agents.openai_responses import (
|
|||
from llama_stack_api.apis.inference import OpenAIMessageParam
|
||||
|
||||
from llama_stack.core.datatypes import AccessRule
|
||||
from llama_stack.core.storage.datatypes import ResponsesStoreReference, SqlStoreReference, StorageBackendType
|
||||
from llama_stack.core.storage.datatypes import ResponsesStoreReference, SqlStoreReference
|
||||
from llama_stack.log import get_logger
|
||||
|
||||
from ..sqlstore.api import ColumnDefinition, ColumnType
|
||||
from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore
|
||||
from ..sqlstore.sqlstore import _SQLSTORE_BACKENDS, sqlstore_impl
|
||||
from ..sqlstore.sqlstore import sqlstore_impl
|
||||
|
||||
logger = get_logger(name=__name__, category="openai_responses")
|
||||
|
||||
|
|
@ -56,28 +54,12 @@ class ResponsesStore:
|
|||
|
||||
self.policy = policy
|
||||
self.sql_store = None
|
||||
self.enable_write_queue = True
|
||||
|
||||
# Async write queue and worker control
|
||||
self._queue: (
|
||||
asyncio.Queue[tuple[OpenAIResponseObject, list[OpenAIResponseInput], list[OpenAIMessageParam]]] | None
|
||||
) = None
|
||||
self._worker_tasks: list[asyncio.Task[Any]] = []
|
||||
self._max_write_queue_size: int = self.reference.max_write_queue_size
|
||||
self._num_writers: int = max(1, self.reference.num_writers)
|
||||
|
||||
async def initialize(self):
|
||||
"""Create the necessary tables if they don't exist."""
|
||||
base_store = sqlstore_impl(self.reference)
|
||||
self.sql_store = AuthorizedSqlStore(base_store, self.policy)
|
||||
|
||||
# Disable write queue for SQLite since WAL mode handles concurrency
|
||||
# Keep it enabled for other backends (like Postgres) for performance
|
||||
backend_config = _SQLSTORE_BACKENDS.get(self.reference.backend)
|
||||
if backend_config and backend_config.type == StorageBackendType.SQL_SQLITE:
|
||||
self.enable_write_queue = False
|
||||
logger.debug("Write queue disabled for SQLite (WAL mode handles concurrency)")
|
||||
|
||||
await self.sql_store.create_table(
|
||||
"openai_responses",
|
||||
{
|
||||
|
|
@ -96,33 +78,12 @@ class ResponsesStore:
|
|||
},
|
||||
)
|
||||
|
||||
if self.enable_write_queue:
|
||||
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
|
||||
for _ in range(self._num_writers):
|
||||
self._worker_tasks.append(asyncio.create_task(self._worker_loop()))
|
||||
logger.debug(
|
||||
f"Responses store write queue enabled with {self._num_writers} writers, max queue size {self._max_write_queue_size}"
|
||||
)
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
if not self._worker_tasks:
|
||||
return
|
||||
if self._queue is not None:
|
||||
await self._queue.join()
|
||||
for t in self._worker_tasks:
|
||||
if not t.done():
|
||||
t.cancel()
|
||||
for t in self._worker_tasks:
|
||||
try:
|
||||
await t
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
self._worker_tasks.clear()
|
||||
return
|
||||
|
||||
async def flush(self) -> None:
|
||||
"""Wait for all queued writes to complete. Useful for testing."""
|
||||
if self.enable_write_queue and self._queue is not None:
|
||||
await self._queue.join()
|
||||
"""Maintained for compatibility; no-op now that writes are synchronous."""
|
||||
return
|
||||
|
||||
async def store_response_object(
|
||||
self,
|
||||
|
|
@ -130,31 +91,7 @@ class ResponsesStore:
|
|||
input: list[OpenAIResponseInput],
|
||||
messages: list[OpenAIMessageParam],
|
||||
) -> None:
|
||||
if self.enable_write_queue:
|
||||
if self._queue is None:
|
||||
raise ValueError("Responses store is not initialized")
|
||||
try:
|
||||
self._queue.put_nowait((response_object, input, messages))
|
||||
except asyncio.QueueFull:
|
||||
logger.warning(f"Write queue full; adding response id={getattr(response_object, 'id', '<unknown>')}")
|
||||
await self._queue.put((response_object, input, messages))
|
||||
else:
|
||||
await self._write_response_object(response_object, input, messages)
|
||||
|
||||
async def _worker_loop(self) -> None:
|
||||
assert self._queue is not None
|
||||
while True:
|
||||
try:
|
||||
item = await self._queue.get()
|
||||
except asyncio.CancelledError:
|
||||
break
|
||||
response_object, input, messages = item
|
||||
try:
|
||||
await self._write_response_object(response_object, input, messages)
|
||||
except Exception as e: # noqa: BLE001
|
||||
logger.error(f"Error writing response object: {e}")
|
||||
finally:
|
||||
self._queue.task_done()
|
||||
await self._write_response_object(response_object, input, messages)
|
||||
|
||||
async def _write_response_object(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -45,8 +45,13 @@ def _enhance_item_with_access_control(item: Mapping[str, Any], current_user: Use
|
|||
enhanced["owner_principal"] = current_user.principal
|
||||
enhanced["access_attributes"] = current_user.attributes
|
||||
else:
|
||||
enhanced["owner_principal"] = None
|
||||
enhanced["access_attributes"] = None
|
||||
# IMPORTANT: Use empty string and null value (not None) to match public access filter
|
||||
# The public access filter in _get_public_access_conditions() expects:
|
||||
# - owner_principal = '' (empty string)
|
||||
# - access_attributes = null (JSON null, which serializes to the string 'null')
|
||||
# Setting them to None (SQL NULL) will cause rows to be filtered out on read.
|
||||
enhanced["owner_principal"] = ""
|
||||
enhanced["access_attributes"] = None # Pydantic/JSON will serialize this as JSON null
|
||||
return enhanced
|
||||
|
||||
|
||||
|
|
@ -188,8 +193,9 @@ class AuthorizedSqlStore:
|
|||
enhanced_data["owner_principal"] = current_user.principal
|
||||
enhanced_data["access_attributes"] = current_user.attributes
|
||||
else:
|
||||
enhanced_data["owner_principal"] = None
|
||||
enhanced_data["access_attributes"] = None
|
||||
# IMPORTANT: Use empty string for owner_principal to match public access filter
|
||||
enhanced_data["owner_principal"] = ""
|
||||
enhanced_data["access_attributes"] = None # Will serialize as JSON null
|
||||
|
||||
await self.sql_store.update(table, enhanced_data, where)
|
||||
|
||||
|
|
@ -245,14 +251,24 @@ class AuthorizedSqlStore:
|
|||
raise ValueError(f"Unsupported database type: {self.database_type}")
|
||||
|
||||
def _get_public_access_conditions(self) -> list[str]:
|
||||
"""Get the SQL conditions for public access."""
|
||||
# Public records are records that have no owner_principal or access_attributes
|
||||
"""Get the SQL conditions for public access.
|
||||
|
||||
Public records are those with:
|
||||
- owner_principal = '' (empty string)
|
||||
- access_attributes is either SQL NULL or JSON null
|
||||
|
||||
Note: Different databases serialize None differently:
|
||||
- SQLite: None → JSON null (text = 'null')
|
||||
- Postgres: None → SQL NULL (IS NULL)
|
||||
"""
|
||||
conditions = ["owner_principal = ''"]
|
||||
if self.database_type == StorageBackendType.SQL_POSTGRES.value:
|
||||
# Postgres stores JSON null as 'null'
|
||||
conditions.append("access_attributes::text = 'null'")
|
||||
# Accept both SQL NULL and JSON null for Postgres compatibility
|
||||
# This handles both old rows (SQL NULL) and new rows (JSON null)
|
||||
conditions.append("(access_attributes IS NULL OR access_attributes::text = 'null')")
|
||||
elif self.database_type == StorageBackendType.SQL_SQLITE.value:
|
||||
conditions.append("access_attributes = 'null'")
|
||||
# SQLite serializes None as JSON null
|
||||
conditions.append("(access_attributes IS NULL OR access_attributes = 'null')")
|
||||
else:
|
||||
raise ValueError(f"Unsupported database type: {self.database_type}")
|
||||
return conditions
|
||||
|
|
|
|||
20
src/llama_stack_ui/.dockerignore
Normal file
20
src/llama_stack_ui/.dockerignore
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
.git
|
||||
.gitignore
|
||||
.env.local
|
||||
.env.*.local
|
||||
.next
|
||||
node_modules
|
||||
npm-debug.log
|
||||
*.md
|
||||
.DS_Store
|
||||
.vscode
|
||||
.idea
|
||||
playwright-report
|
||||
e2e
|
||||
jest.config.ts
|
||||
jest.setup.ts
|
||||
eslint.config.mjs
|
||||
.prettierrc
|
||||
.prettierignore
|
||||
.nvmrc
|
||||
playwright.config.ts
|
||||
18
src/llama_stack_ui/Containerfile
Normal file
18
src/llama_stack_ui/Containerfile
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
FROM node:22.5.1-alpine
|
||||
|
||||
ENV NODE_ENV=production
|
||||
|
||||
# Install dumb-init for proper signal handling
|
||||
RUN apk add --no-cache dumb-init
|
||||
|
||||
# Create non-root user for security
|
||||
RUN addgroup --system --gid 1001 nodejs
|
||||
RUN adduser --system --uid 1001 nextjs
|
||||
|
||||
# Install llama-stack-ui from npm
|
||||
RUN npm install -g llama-stack-ui
|
||||
|
||||
USER nextjs
|
||||
|
||||
ENTRYPOINT ["dumb-init", "--"]
|
||||
CMD ["llama-stack-ui"]
|
||||
|
|
@ -8,6 +8,9 @@ import type {
|
|||
import { useRouter } from "next/navigation";
|
||||
import { usePagination } from "@/hooks/use-pagination";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { Plus, Trash2, Search, Edit, X } from "lucide-react";
|
||||
import { useState } from "react";
|
||||
import { Input } from "@/components/ui/input";
|
||||
import {
|
||||
Table,
|
||||
TableBody,
|
||||
|
|
@ -17,9 +20,21 @@ import {
|
|||
TableRow,
|
||||
} from "@/components/ui/table";
|
||||
import { Skeleton } from "@/components/ui/skeleton";
|
||||
import { useAuthClient } from "@/hooks/use-auth-client";
|
||||
import {
|
||||
VectorStoreEditor,
|
||||
VectorStoreFormData,
|
||||
} from "@/components/vector-stores/vector-store-editor";
|
||||
|
||||
export default function VectorStoresPage() {
|
||||
const router = useRouter();
|
||||
const client = useAuthClient();
|
||||
const [deletingStores, setDeletingStores] = useState<Set<string>>(new Set());
|
||||
const [searchTerm, setSearchTerm] = useState("");
|
||||
const [showVectorStoreModal, setShowVectorStoreModal] = useState(false);
|
||||
const [editingStore, setEditingStore] = useState<VectorStore | null>(null);
|
||||
const [modalError, setModalError] = useState<string | null>(null);
|
||||
const [showSuccessState, setShowSuccessState] = useState(false);
|
||||
const {
|
||||
data: stores,
|
||||
status,
|
||||
|
|
@ -47,6 +62,142 @@ export default function VectorStoresPage() {
|
|||
}
|
||||
}, [status, hasMore, loadMore]);
|
||||
|
||||
// Handle ESC key to close modal
|
||||
React.useEffect(() => {
|
||||
const handleEscape = (event: KeyboardEvent) => {
|
||||
if (event.key === "Escape" && showVectorStoreModal) {
|
||||
handleCancel();
|
||||
}
|
||||
};
|
||||
|
||||
document.addEventListener("keydown", handleEscape);
|
||||
return () => document.removeEventListener("keydown", handleEscape);
|
||||
}, [showVectorStoreModal]);
|
||||
|
||||
const handleDeleteVectorStore = async (storeId: string) => {
|
||||
if (
|
||||
!confirm(
|
||||
"Are you sure you want to delete this vector store? This action cannot be undone."
|
||||
)
|
||||
) {
|
||||
return;
|
||||
}
|
||||
|
||||
setDeletingStores(prev => new Set([...prev, storeId]));
|
||||
|
||||
try {
|
||||
await client.vectorStores.delete(storeId);
|
||||
// Reload the data to reflect the deletion
|
||||
window.location.reload();
|
||||
} catch (err: unknown) {
|
||||
console.error("Failed to delete vector store:", err);
|
||||
const errorMessage = err instanceof Error ? err.message : "Unknown error";
|
||||
alert(`Failed to delete vector store: ${errorMessage}`);
|
||||
} finally {
|
||||
setDeletingStores(prev => {
|
||||
const newSet = new Set(prev);
|
||||
newSet.delete(storeId);
|
||||
return newSet;
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
const handleSaveVectorStore = async (formData: VectorStoreFormData) => {
|
||||
try {
|
||||
setModalError(null);
|
||||
|
||||
if (editingStore) {
|
||||
// Update existing vector store
|
||||
const updateParams: {
|
||||
name?: string;
|
||||
extra_body?: Record<string, unknown>;
|
||||
} = {};
|
||||
|
||||
// Only include fields that have changed or are provided
|
||||
if (formData.name && formData.name !== editingStore.name) {
|
||||
updateParams.name = formData.name;
|
||||
}
|
||||
|
||||
// Add all parameters to extra_body (except provider_id which can't be changed)
|
||||
const extraBody: Record<string, unknown> = {};
|
||||
if (formData.embedding_model) {
|
||||
extraBody.embedding_model = formData.embedding_model;
|
||||
}
|
||||
if (formData.embedding_dimension) {
|
||||
extraBody.embedding_dimension = formData.embedding_dimension;
|
||||
}
|
||||
|
||||
if (Object.keys(extraBody).length > 0) {
|
||||
updateParams.extra_body = extraBody;
|
||||
}
|
||||
|
||||
await client.vectorStores.update(editingStore.id, updateParams);
|
||||
|
||||
// Show success state with close button
|
||||
setShowSuccessState(true);
|
||||
setModalError(
|
||||
"✅ Vector store updated successfully! You can close this modal and refresh the page to see changes."
|
||||
);
|
||||
return;
|
||||
}
|
||||
|
||||
const createParams: {
|
||||
name?: string;
|
||||
provider_id?: string;
|
||||
extra_body?: Record<string, unknown>;
|
||||
} = {
|
||||
name: formData.name || undefined,
|
||||
};
|
||||
|
||||
// Extract provider_id to top-level (like Python client does)
|
||||
if (formData.provider_id) {
|
||||
createParams.provider_id = formData.provider_id;
|
||||
}
|
||||
|
||||
// Add remaining parameters to extra_body
|
||||
const extraBody: Record<string, unknown> = {};
|
||||
if (formData.provider_id) {
|
||||
extraBody.provider_id = formData.provider_id;
|
||||
}
|
||||
if (formData.embedding_model) {
|
||||
extraBody.embedding_model = formData.embedding_model;
|
||||
}
|
||||
if (formData.embedding_dimension) {
|
||||
extraBody.embedding_dimension = formData.embedding_dimension;
|
||||
}
|
||||
|
||||
if (Object.keys(extraBody).length > 0) {
|
||||
createParams.extra_body = extraBody;
|
||||
}
|
||||
|
||||
await client.vectorStores.create(createParams);
|
||||
|
||||
// Show success state with close button
|
||||
setShowSuccessState(true);
|
||||
setModalError(
|
||||
"✅ Vector store created successfully! You can close this modal and refresh the page to see changes."
|
||||
);
|
||||
} catch (err: unknown) {
|
||||
console.error("Failed to create vector store:", err);
|
||||
const errorMessage =
|
||||
err instanceof Error ? err.message : "Failed to create vector store";
|
||||
setModalError(errorMessage);
|
||||
}
|
||||
};
|
||||
|
||||
const handleEditVectorStore = (store: VectorStore) => {
|
||||
setEditingStore(store);
|
||||
setShowVectorStoreModal(true);
|
||||
setModalError(null);
|
||||
};
|
||||
|
||||
const handleCancel = () => {
|
||||
setShowVectorStoreModal(false);
|
||||
setEditingStore(null);
|
||||
setModalError(null);
|
||||
setShowSuccessState(false);
|
||||
};
|
||||
|
||||
const renderContent = () => {
|
||||
if (status === "loading") {
|
||||
return (
|
||||
|
|
@ -66,73 +217,190 @@ export default function VectorStoresPage() {
|
|||
return <p>No vector stores found.</p>;
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="overflow-auto flex-1 min-h-0">
|
||||
<Table>
|
||||
<TableHeader>
|
||||
<TableRow>
|
||||
<TableHead>ID</TableHead>
|
||||
<TableHead>Name</TableHead>
|
||||
<TableHead>Created</TableHead>
|
||||
<TableHead>Completed</TableHead>
|
||||
<TableHead>Cancelled</TableHead>
|
||||
<TableHead>Failed</TableHead>
|
||||
<TableHead>In Progress</TableHead>
|
||||
<TableHead>Total</TableHead>
|
||||
<TableHead>Usage Bytes</TableHead>
|
||||
<TableHead>Provider ID</TableHead>
|
||||
<TableHead>Provider Vector DB ID</TableHead>
|
||||
</TableRow>
|
||||
</TableHeader>
|
||||
<TableBody>
|
||||
{stores.map(store => {
|
||||
const fileCounts = store.file_counts;
|
||||
const metadata = store.metadata || {};
|
||||
const providerId = metadata.provider_id ?? "";
|
||||
const providerDbId = metadata.provider_vector_db_id ?? "";
|
||||
// Filter stores based on search term
|
||||
const filteredStores = stores.filter(store => {
|
||||
if (!searchTerm) return true;
|
||||
|
||||
return (
|
||||
<TableRow
|
||||
key={store.id}
|
||||
onClick={() => router.push(`/logs/vector-stores/${store.id}`)}
|
||||
className="cursor-pointer hover:bg-muted/50"
|
||||
>
|
||||
<TableCell>
|
||||
<Button
|
||||
variant="link"
|
||||
className="p-0 h-auto font-mono text-blue-600 hover:text-blue-800 dark:text-blue-400 dark:hover:text-blue-300"
|
||||
onClick={() =>
|
||||
router.push(`/logs/vector-stores/${store.id}`)
|
||||
}
|
||||
>
|
||||
{store.id}
|
||||
</Button>
|
||||
</TableCell>
|
||||
<TableCell>{store.name}</TableCell>
|
||||
<TableCell>
|
||||
{new Date(store.created_at * 1000).toLocaleString()}
|
||||
</TableCell>
|
||||
<TableCell>{fileCounts.completed}</TableCell>
|
||||
<TableCell>{fileCounts.cancelled}</TableCell>
|
||||
<TableCell>{fileCounts.failed}</TableCell>
|
||||
<TableCell>{fileCounts.in_progress}</TableCell>
|
||||
<TableCell>{fileCounts.total}</TableCell>
|
||||
<TableCell>{store.usage_bytes}</TableCell>
|
||||
<TableCell>{providerId}</TableCell>
|
||||
<TableCell>{providerDbId}</TableCell>
|
||||
</TableRow>
|
||||
);
|
||||
})}
|
||||
</TableBody>
|
||||
</Table>
|
||||
const searchLower = searchTerm.toLowerCase();
|
||||
return (
|
||||
store.id.toLowerCase().includes(searchLower) ||
|
||||
(store.name && store.name.toLowerCase().includes(searchLower)) ||
|
||||
(store.metadata?.provider_id &&
|
||||
String(store.metadata.provider_id)
|
||||
.toLowerCase()
|
||||
.includes(searchLower)) ||
|
||||
(store.metadata?.provider_vector_db_id &&
|
||||
String(store.metadata.provider_vector_db_id)
|
||||
.toLowerCase()
|
||||
.includes(searchLower))
|
||||
);
|
||||
});
|
||||
|
||||
return (
|
||||
<div className="space-y-4">
|
||||
{/* Search Bar */}
|
||||
<div className="relative flex-1 max-w-md">
|
||||
<Search className="absolute left-3 top-1/2 transform -translate-y-1/2 text-muted-foreground h-4 w-4" />
|
||||
<Input
|
||||
placeholder="Search vector stores..."
|
||||
value={searchTerm}
|
||||
onChange={e => setSearchTerm(e.target.value)}
|
||||
className="pl-10"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="overflow-auto flex-1 min-h-0">
|
||||
<Table>
|
||||
<TableHeader>
|
||||
<TableRow>
|
||||
<TableHead>ID</TableHead>
|
||||
<TableHead>Name</TableHead>
|
||||
<TableHead>Created</TableHead>
|
||||
<TableHead>Completed</TableHead>
|
||||
<TableHead>Cancelled</TableHead>
|
||||
<TableHead>Failed</TableHead>
|
||||
<TableHead>In Progress</TableHead>
|
||||
<TableHead>Total</TableHead>
|
||||
<TableHead>Usage Bytes</TableHead>
|
||||
<TableHead>Provider ID</TableHead>
|
||||
<TableHead>Provider Vector DB ID</TableHead>
|
||||
<TableHead>Actions</TableHead>
|
||||
</TableRow>
|
||||
</TableHeader>
|
||||
<TableBody>
|
||||
{filteredStores.map(store => {
|
||||
const fileCounts = store.file_counts;
|
||||
const metadata = store.metadata || {};
|
||||
const providerId = metadata.provider_id ?? "";
|
||||
const providerDbId = metadata.provider_vector_db_id ?? "";
|
||||
|
||||
return (
|
||||
<TableRow
|
||||
key={store.id}
|
||||
onClick={() =>
|
||||
router.push(`/logs/vector-stores/${store.id}`)
|
||||
}
|
||||
className="cursor-pointer hover:bg-muted/50"
|
||||
>
|
||||
<TableCell>
|
||||
<Button
|
||||
variant="link"
|
||||
className="p-0 h-auto font-mono text-blue-600 hover:text-blue-800 dark:text-blue-400 dark:hover:text-blue-300"
|
||||
onClick={() =>
|
||||
router.push(`/logs/vector-stores/${store.id}`)
|
||||
}
|
||||
>
|
||||
{store.id}
|
||||
</Button>
|
||||
</TableCell>
|
||||
<TableCell>{store.name}</TableCell>
|
||||
<TableCell>
|
||||
{new Date(store.created_at * 1000).toLocaleString()}
|
||||
</TableCell>
|
||||
<TableCell>{fileCounts.completed}</TableCell>
|
||||
<TableCell>{fileCounts.cancelled}</TableCell>
|
||||
<TableCell>{fileCounts.failed}</TableCell>
|
||||
<TableCell>{fileCounts.in_progress}</TableCell>
|
||||
<TableCell>{fileCounts.total}</TableCell>
|
||||
<TableCell>{store.usage_bytes}</TableCell>
|
||||
<TableCell>{providerId}</TableCell>
|
||||
<TableCell>{providerDbId}</TableCell>
|
||||
<TableCell>
|
||||
<div className="flex gap-2">
|
||||
<Button
|
||||
variant="outline"
|
||||
size="sm"
|
||||
onClick={e => {
|
||||
e.stopPropagation();
|
||||
handleEditVectorStore(store);
|
||||
}}
|
||||
>
|
||||
<Edit className="h-4 w-4" />
|
||||
</Button>
|
||||
<Button
|
||||
variant="outline"
|
||||
size="sm"
|
||||
onClick={e => {
|
||||
e.stopPropagation();
|
||||
handleDeleteVectorStore(store.id);
|
||||
}}
|
||||
disabled={deletingStores.has(store.id)}
|
||||
>
|
||||
{deletingStores.has(store.id) ? (
|
||||
"Deleting..."
|
||||
) : (
|
||||
<>
|
||||
<Trash2 className="h-4 w-4" />
|
||||
</>
|
||||
)}
|
||||
</Button>
|
||||
</div>
|
||||
</TableCell>
|
||||
</TableRow>
|
||||
);
|
||||
})}
|
||||
</TableBody>
|
||||
</Table>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="space-y-4">
|
||||
<h1 className="text-2xl font-semibold">Vector Stores</h1>
|
||||
<div className="flex items-center justify-between">
|
||||
<h1 className="text-2xl font-semibold">Vector Stores</h1>
|
||||
<Button
|
||||
onClick={() => setShowVectorStoreModal(true)}
|
||||
disabled={status === "loading"}
|
||||
>
|
||||
<Plus className="h-4 w-4 mr-2" />
|
||||
New Vector Store
|
||||
</Button>
|
||||
</div>
|
||||
{renderContent()}
|
||||
|
||||
{/* Create Vector Store Modal */}
|
||||
{showVectorStoreModal && (
|
||||
<div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
|
||||
<div className="bg-background border rounded-lg shadow-lg max-w-2xl w-full mx-4 max-h-[90vh] overflow-hidden">
|
||||
<div className="p-6 border-b flex items-center justify-between">
|
||||
<h2 className="text-2xl font-bold">
|
||||
{editingStore ? "Edit Vector Store" : "Create New Vector Store"}
|
||||
</h2>
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="sm"
|
||||
onClick={handleCancel}
|
||||
className="p-1 h-auto"
|
||||
>
|
||||
<X className="h-4 w-4" />
|
||||
</Button>
|
||||
</div>
|
||||
<div className="p-6 overflow-y-auto max-h-[calc(90vh-120px)]">
|
||||
<VectorStoreEditor
|
||||
onSave={handleSaveVectorStore}
|
||||
onCancel={handleCancel}
|
||||
error={modalError}
|
||||
showSuccessState={showSuccessState}
|
||||
isEditing={!!editingStore}
|
||||
initialData={
|
||||
editingStore
|
||||
? {
|
||||
name: editingStore.name || "",
|
||||
embedding_model:
|
||||
editingStore.metadata?.embedding_model || "",
|
||||
embedding_dimension:
|
||||
editingStore.metadata?.embedding_dimension || 768,
|
||||
provider_id: editingStore.metadata?.provider_id || "",
|
||||
}
|
||||
: undefined
|
||||
}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
34
src/llama_stack_ui/bin/cli.js
Executable file
34
src/llama_stack_ui/bin/cli.js
Executable file
|
|
@ -0,0 +1,34 @@
|
|||
#!/usr/bin/env node
|
||||
|
||||
const { spawn } = require('child_process');
|
||||
const path = require('path');
|
||||
|
||||
const port = process.env.LLAMA_STACK_UI_PORT || 8322;
|
||||
const uiDir = path.resolve(__dirname, '..');
|
||||
const serverPath = path.join(uiDir, '.next', 'standalone', 'ui', 'src', 'llama_stack_ui', 'server.js');
|
||||
const serverDir = path.dirname(serverPath);
|
||||
|
||||
console.log(`Starting Llama Stack UI on http://localhost:${port}`);
|
||||
|
||||
const child = spawn(process.execPath, [serverPath], {
|
||||
cwd: serverDir,
|
||||
stdio: 'inherit',
|
||||
env: {
|
||||
...process.env,
|
||||
PORT: port,
|
||||
},
|
||||
});
|
||||
|
||||
process.on('SIGINT', () => {
|
||||
child.kill('SIGINT');
|
||||
process.exit(0);
|
||||
});
|
||||
|
||||
process.on('SIGTERM', () => {
|
||||
child.kill('SIGTERM');
|
||||
process.exit(0);
|
||||
});
|
||||
|
||||
child.on('exit', (code) => {
|
||||
process.exit(code);
|
||||
});
|
||||
|
|
@ -2,7 +2,7 @@ import React from "react";
|
|||
import { render, screen, fireEvent } from "@testing-library/react";
|
||||
import "@testing-library/jest-dom";
|
||||
import { PromptEditor } from "./prompt-editor";
|
||||
import type { Prompt, PromptFormData } from "./types";
|
||||
import type { Prompt } from "./types";
|
||||
|
||||
describe("PromptEditor", () => {
|
||||
const mockOnSave = jest.fn();
|
||||
|
|
|
|||
|
|
@ -12,6 +12,20 @@ jest.mock("next/navigation", () => ({
|
|||
}),
|
||||
}));
|
||||
|
||||
// Mock NextAuth
|
||||
jest.mock("next-auth/react", () => ({
|
||||
useSession: () => ({
|
||||
data: {
|
||||
accessToken: "mock-access-token",
|
||||
user: {
|
||||
id: "mock-user-id",
|
||||
email: "test@example.com",
|
||||
},
|
||||
},
|
||||
status: "authenticated",
|
||||
}),
|
||||
}));
|
||||
|
||||
describe("VectorStoreDetailView", () => {
|
||||
const defaultProps = {
|
||||
store: null,
|
||||
|
|
|
|||
|
|
@ -1,16 +1,18 @@
|
|||
"use client";
|
||||
|
||||
import { useRouter } from "next/navigation";
|
||||
import { useState, useEffect } from "react";
|
||||
import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
|
||||
import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
|
||||
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
|
||||
import { Skeleton } from "@/components/ui/skeleton";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { useAuthClient } from "@/hooks/use-auth-client";
|
||||
import { Edit2, Trash2, X } from "lucide-react";
|
||||
import {
|
||||
DetailLoadingView,
|
||||
DetailErrorView,
|
||||
DetailNotFoundView,
|
||||
DetailLayout,
|
||||
PropertiesCard,
|
||||
PropertyItem,
|
||||
} from "@/components/layout/detail-layout";
|
||||
|
|
@ -23,6 +25,7 @@ import {
|
|||
TableHeader,
|
||||
TableRow,
|
||||
} from "@/components/ui/table";
|
||||
import { VectorStoreEditor, VectorStoreFormData } from "./vector-store-editor";
|
||||
|
||||
interface VectorStoreDetailViewProps {
|
||||
store: VectorStore | null;
|
||||
|
|
@ -43,21 +46,122 @@ export function VectorStoreDetailView({
|
|||
errorFiles,
|
||||
id,
|
||||
}: VectorStoreDetailViewProps) {
|
||||
const title = "Vector Store Details";
|
||||
const router = useRouter();
|
||||
const client = useAuthClient();
|
||||
const [isDeleting, setIsDeleting] = useState(false);
|
||||
const [showEditModal, setShowEditModal] = useState(false);
|
||||
const [modalError, setModalError] = useState<string | null>(null);
|
||||
const [showSuccessState, setShowSuccessState] = useState(false);
|
||||
|
||||
// Handle ESC key to close modal
|
||||
useEffect(() => {
|
||||
const handleEscape = (event: KeyboardEvent) => {
|
||||
if (event.key === "Escape" && showEditModal) {
|
||||
handleCancel();
|
||||
}
|
||||
};
|
||||
|
||||
document.addEventListener("keydown", handleEscape);
|
||||
return () => document.removeEventListener("keydown", handleEscape);
|
||||
}, [showEditModal]);
|
||||
|
||||
const handleFileClick = (fileId: string) => {
|
||||
router.push(`/logs/vector-stores/${id}/files/${fileId}`);
|
||||
};
|
||||
|
||||
const handleEditVectorStore = () => {
|
||||
setShowEditModal(true);
|
||||
setModalError(null);
|
||||
setShowSuccessState(false);
|
||||
};
|
||||
|
||||
const handleCancel = () => {
|
||||
setShowEditModal(false);
|
||||
setModalError(null);
|
||||
setShowSuccessState(false);
|
||||
};
|
||||
|
||||
const handleSaveVectorStore = async (formData: VectorStoreFormData) => {
|
||||
try {
|
||||
setModalError(null);
|
||||
|
||||
// Update existing vector store (same logic as list page)
|
||||
const updateParams: {
|
||||
name?: string;
|
||||
extra_body?: Record<string, unknown>;
|
||||
} = {};
|
||||
|
||||
// Only include fields that have changed or are provided
|
||||
if (formData.name && formData.name !== store?.name) {
|
||||
updateParams.name = formData.name;
|
||||
}
|
||||
|
||||
// Add all parameters to extra_body (except provider_id which can't be changed)
|
||||
const extraBody: Record<string, unknown> = {};
|
||||
if (formData.embedding_model) {
|
||||
extraBody.embedding_model = formData.embedding_model;
|
||||
}
|
||||
if (formData.embedding_dimension) {
|
||||
extraBody.embedding_dimension = formData.embedding_dimension;
|
||||
}
|
||||
|
||||
if (Object.keys(extraBody).length > 0) {
|
||||
updateParams.extra_body = extraBody;
|
||||
}
|
||||
|
||||
await client.vectorStores.update(id, updateParams);
|
||||
|
||||
// Show success state
|
||||
setShowSuccessState(true);
|
||||
setModalError(
|
||||
"✅ Vector store updated successfully! You can close this modal and refresh the page to see changes."
|
||||
);
|
||||
} catch (err: unknown) {
|
||||
console.error("Failed to update vector store:", err);
|
||||
const errorMessage =
|
||||
err instanceof Error ? err.message : "Failed to update vector store";
|
||||
setModalError(errorMessage);
|
||||
}
|
||||
};
|
||||
|
||||
const handleDeleteVectorStore = async () => {
|
||||
if (
|
||||
!confirm(
|
||||
"Are you sure you want to delete this vector store? This action cannot be undone."
|
||||
)
|
||||
) {
|
||||
return;
|
||||
}
|
||||
|
||||
setIsDeleting(true);
|
||||
|
||||
try {
|
||||
await client.vectorStores.delete(id);
|
||||
// Redirect to the vector stores list after successful deletion
|
||||
router.push("/logs/vector-stores");
|
||||
} catch (err: unknown) {
|
||||
console.error("Failed to delete vector store:", err);
|
||||
const errorMessage = err instanceof Error ? err.message : "Unknown error";
|
||||
alert(`Failed to delete vector store: ${errorMessage}`);
|
||||
} finally {
|
||||
setIsDeleting(false);
|
||||
}
|
||||
};
|
||||
|
||||
if (errorStore) {
|
||||
return <DetailErrorView title={title} id={id} error={errorStore} />;
|
||||
return (
|
||||
<DetailErrorView
|
||||
title="Vector Store Details"
|
||||
id={id}
|
||||
error={errorStore}
|
||||
/>
|
||||
);
|
||||
}
|
||||
if (isLoadingStore) {
|
||||
return <DetailLoadingView title={title} />;
|
||||
return <DetailLoadingView />;
|
||||
}
|
||||
if (!store) {
|
||||
return <DetailNotFoundView title={title} id={id} />;
|
||||
return <DetailNotFoundView title="Vector Store Details" id={id} />;
|
||||
}
|
||||
|
||||
const mainContent = (
|
||||
|
|
@ -138,6 +242,73 @@ export function VectorStoreDetailView({
|
|||
);
|
||||
|
||||
return (
|
||||
<DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
|
||||
<>
|
||||
<div className="flex items-center justify-between mb-6">
|
||||
<h1 className="text-2xl font-bold">Vector Store Details</h1>
|
||||
<div className="flex gap-2">
|
||||
<Button
|
||||
variant="outline"
|
||||
onClick={handleEditVectorStore}
|
||||
disabled={isDeleting}
|
||||
>
|
||||
<Edit2 className="h-4 w-4 mr-2" />
|
||||
Edit
|
||||
</Button>
|
||||
<Button
|
||||
variant="destructive"
|
||||
onClick={handleDeleteVectorStore}
|
||||
disabled={isDeleting}
|
||||
>
|
||||
{isDeleting ? (
|
||||
"Deleting..."
|
||||
) : (
|
||||
<>
|
||||
<Trash2 className="h-4 w-4 mr-2" />
|
||||
Delete
|
||||
</>
|
||||
)}
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex flex-col md:flex-row gap-6">
|
||||
<div className="flex-grow md:w-2/3 space-y-6">{mainContent}</div>
|
||||
<div className="md:w-1/3">{sidebar}</div>
|
||||
</div>
|
||||
|
||||
{/* Edit Vector Store Modal */}
|
||||
{showEditModal && (
|
||||
<div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
|
||||
<div className="bg-background border rounded-lg shadow-lg max-w-2xl w-full mx-4 max-h-[90vh] overflow-hidden">
|
||||
<div className="p-6 border-b flex items-center justify-between">
|
||||
<h2 className="text-2xl font-bold">Edit Vector Store</h2>
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="sm"
|
||||
onClick={handleCancel}
|
||||
className="p-1 h-auto"
|
||||
>
|
||||
<X className="h-4 w-4" />
|
||||
</Button>
|
||||
</div>
|
||||
<div className="p-6 overflow-y-auto max-h-[calc(90vh-120px)]">
|
||||
<VectorStoreEditor
|
||||
onSave={handleSaveVectorStore}
|
||||
onCancel={handleCancel}
|
||||
error={modalError}
|
||||
showSuccessState={showSuccessState}
|
||||
isEditing={true}
|
||||
initialData={{
|
||||
name: store?.name || "",
|
||||
embedding_model: store?.metadata?.embedding_model || "",
|
||||
embedding_dimension:
|
||||
store?.metadata?.embedding_dimension || 768,
|
||||
provider_id: store?.metadata?.provider_id || "",
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,235 @@
|
|||
"use client";
|
||||
|
||||
import { useState, useEffect } from "react";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { Input } from "@/components/ui/input";
|
||||
import { Label } from "@/components/ui/label";
|
||||
import { Card, CardContent } from "@/components/ui/card";
|
||||
import {
|
||||
Select,
|
||||
SelectContent,
|
||||
SelectItem,
|
||||
SelectTrigger,
|
||||
SelectValue,
|
||||
} from "@/components/ui/select";
|
||||
import { useAuthClient } from "@/hooks/use-auth-client";
|
||||
import type { Model } from "llama-stack-client/resources/models";
|
||||
|
||||
export interface VectorStoreFormData {
|
||||
name: string;
|
||||
embedding_model?: string;
|
||||
embedding_dimension?: number;
|
||||
provider_id?: string;
|
||||
}
|
||||
|
||||
interface VectorStoreEditorProps {
|
||||
onSave: (formData: VectorStoreFormData) => Promise<void>;
|
||||
onCancel: () => void;
|
||||
error?: string | null;
|
||||
initialData?: VectorStoreFormData;
|
||||
showSuccessState?: boolean;
|
||||
isEditing?: boolean;
|
||||
}
|
||||
|
||||
export function VectorStoreEditor({
|
||||
onSave,
|
||||
onCancel,
|
||||
error,
|
||||
initialData,
|
||||
showSuccessState,
|
||||
isEditing = false,
|
||||
}: VectorStoreEditorProps) {
|
||||
const client = useAuthClient();
|
||||
const [formData, setFormData] = useState<VectorStoreFormData>(
|
||||
initialData || {
|
||||
name: "",
|
||||
embedding_model: "",
|
||||
embedding_dimension: 768,
|
||||
provider_id: "",
|
||||
}
|
||||
);
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [models, setModels] = useState<Model[]>([]);
|
||||
const [modelsLoading, setModelsLoading] = useState(true);
|
||||
const [modelsError, setModelsError] = useState<string | null>(null);
|
||||
|
||||
const embeddingModels = models.filter(
|
||||
model => model.custom_metadata?.model_type === "embedding"
|
||||
);
|
||||
|
||||
useEffect(() => {
|
||||
const fetchModels = async () => {
|
||||
try {
|
||||
setModelsLoading(true);
|
||||
setModelsError(null);
|
||||
const modelList = await client.models.list();
|
||||
setModels(modelList);
|
||||
|
||||
// Set default embedding model if available
|
||||
const embeddingModelsList = modelList.filter(model => {
|
||||
return model.custom_metadata?.model_type === "embedding";
|
||||
});
|
||||
if (embeddingModelsList.length > 0 && !formData.embedding_model) {
|
||||
setFormData(prev => ({
|
||||
...prev,
|
||||
embedding_model: embeddingModelsList[0].id,
|
||||
}));
|
||||
}
|
||||
} catch (err) {
|
||||
console.error("Failed to load models:", err);
|
||||
setModelsError(
|
||||
err instanceof Error ? err.message : "Failed to load models"
|
||||
);
|
||||
} finally {
|
||||
setModelsLoading(false);
|
||||
}
|
||||
};
|
||||
|
||||
fetchModels();
|
||||
}, [client]);
|
||||
|
||||
const handleSubmit = async (e: React.FormEvent) => {
|
||||
e.preventDefault();
|
||||
setLoading(true);
|
||||
|
||||
try {
|
||||
await onSave(formData);
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<Card>
|
||||
<CardContent className="pt-6">
|
||||
<form onSubmit={handleSubmit} className="space-y-4">
|
||||
<div className="space-y-2">
|
||||
<Label htmlFor="name">Name</Label>
|
||||
<Input
|
||||
id="name"
|
||||
value={formData.name}
|
||||
onChange={e => setFormData({ ...formData, name: e.target.value })}
|
||||
placeholder="Enter vector store name"
|
||||
required
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="space-y-2">
|
||||
<Label htmlFor="embedding_model">Embedding Model (Optional)</Label>
|
||||
{modelsLoading ? (
|
||||
<div className="text-sm text-muted-foreground">
|
||||
Loading models... ({models.length} loaded)
|
||||
</div>
|
||||
) : modelsError ? (
|
||||
<div className="text-sm text-destructive">
|
||||
Error: {modelsError}
|
||||
</div>
|
||||
) : embeddingModels.length === 0 ? (
|
||||
<div className="text-sm text-muted-foreground">
|
||||
No embedding models available ({models.length} total models)
|
||||
</div>
|
||||
) : (
|
||||
<Select
|
||||
value={formData.embedding_model}
|
||||
onValueChange={value =>
|
||||
setFormData({ ...formData, embedding_model: value })
|
||||
}
|
||||
>
|
||||
<SelectTrigger>
|
||||
<SelectValue placeholder="Select Embedding Model" />
|
||||
</SelectTrigger>
|
||||
<SelectContent>
|
||||
{embeddingModels.map((model, index) => (
|
||||
<SelectItem key={model.id} value={model.id}>
|
||||
{model.id}
|
||||
</SelectItem>
|
||||
))}
|
||||
</SelectContent>
|
||||
</Select>
|
||||
)}
|
||||
{formData.embedding_model && (
|
||||
<p className="text-xs text-muted-foreground mt-1">
|
||||
Dimension:{" "}
|
||||
{embeddingModels.find(m => m.id === formData.embedding_model)
|
||||
?.custom_metadata?.embedding_dimension || "Unknown"}
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<div className="space-y-2">
|
||||
<Label htmlFor="embedding_dimension">
|
||||
Embedding Dimension (Optional)
|
||||
</Label>
|
||||
<Input
|
||||
id="embedding_dimension"
|
||||
type="number"
|
||||
value={formData.embedding_dimension}
|
||||
onChange={e =>
|
||||
setFormData({
|
||||
...formData,
|
||||
embedding_dimension: parseInt(e.target.value) || 768,
|
||||
})
|
||||
}
|
||||
placeholder="768"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="space-y-2">
|
||||
<Label htmlFor="provider_id">
|
||||
Provider ID {isEditing ? "(Read-only)" : "(Optional)"}
|
||||
</Label>
|
||||
<Input
|
||||
id="provider_id"
|
||||
value={formData.provider_id}
|
||||
onChange={e =>
|
||||
setFormData({ ...formData, provider_id: e.target.value })
|
||||
}
|
||||
placeholder="e.g., faiss, chroma, sqlite"
|
||||
disabled={isEditing}
|
||||
/>
|
||||
{isEditing && (
|
||||
<p className="text-xs text-muted-foreground">
|
||||
Provider ID cannot be changed after vector store creation
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{error && (
|
||||
<div
|
||||
className={`text-sm p-3 rounded ${
|
||||
error.startsWith("✅")
|
||||
? "text-green-700 bg-green-50 border border-green-200"
|
||||
: "text-destructive bg-destructive/10"
|
||||
}`}
|
||||
>
|
||||
{error}
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="flex gap-2 pt-4">
|
||||
{showSuccessState ? (
|
||||
<Button type="button" onClick={onCancel}>
|
||||
Close
|
||||
</Button>
|
||||
) : (
|
||||
<>
|
||||
<Button type="submit" disabled={loading}>
|
||||
{loading
|
||||
? initialData
|
||||
? "Updating..."
|
||||
: "Creating..."
|
||||
: initialData
|
||||
? "Update Vector Store"
|
||||
: "Create Vector Store"}
|
||||
</Button>
|
||||
<Button type="button" variant="outline" onClick={onCancel}>
|
||||
Cancel
|
||||
</Button>
|
||||
</>
|
||||
)}
|
||||
</div>
|
||||
</form>
|
||||
</CardContent>
|
||||
</Card>
|
||||
);
|
||||
}
|
||||
|
|
@ -34,9 +34,35 @@ export class ContentsAPI {
|
|||
|
||||
async getFileContents(
|
||||
vectorStoreId: string,
|
||||
fileId: string
|
||||
fileId: string,
|
||||
includeEmbeddings: boolean = true,
|
||||
includeMetadata: boolean = true
|
||||
): Promise<VectorStoreContentsResponse> {
|
||||
return this.client.vectorStores.files.content(vectorStoreId, fileId);
|
||||
try {
|
||||
// Use query parameters to pass embeddings and metadata flags (OpenAI-compatible pattern)
|
||||
const extraQuery: Record<string, boolean> = {};
|
||||
if (includeEmbeddings) {
|
||||
extraQuery.include_embeddings = true;
|
||||
}
|
||||
if (includeMetadata) {
|
||||
extraQuery.include_metadata = true;
|
||||
}
|
||||
|
||||
const result = await this.client.vectorStores.files.content(
|
||||
vectorStoreId,
|
||||
fileId,
|
||||
{
|
||||
query: {
|
||||
include_embeddings: includeEmbeddings,
|
||||
include_metadata: includeMetadata,
|
||||
},
|
||||
}
|
||||
);
|
||||
return result;
|
||||
} catch (error) {
|
||||
console.error("ContentsAPI.getFileContents error:", error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
async getContent(
|
||||
|
|
@ -70,11 +96,15 @@ export class ContentsAPI {
|
|||
order?: string;
|
||||
after?: string;
|
||||
before?: string;
|
||||
includeEmbeddings?: boolean;
|
||||
includeMetadata?: boolean;
|
||||
}
|
||||
): Promise<VectorStoreListContentsResponse> {
|
||||
const fileContents = await this.client.vectorStores.files.content(
|
||||
const fileContents = await this.getFileContents(
|
||||
vectorStoreId,
|
||||
fileId
|
||||
fileId,
|
||||
options?.includeEmbeddings ?? true,
|
||||
options?.includeMetadata ?? true
|
||||
);
|
||||
const contentItems: VectorStoreContentItem[] = [];
|
||||
|
||||
|
|
@ -82,7 +112,7 @@ export class ContentsAPI {
|
|||
const rawContent = content as Record<string, unknown>;
|
||||
|
||||
// Extract actual fields from the API response
|
||||
const embedding = rawContent.embedding || undefined;
|
||||
const embedding = rawContent.embedding as number[] | undefined;
|
||||
const created_timestamp =
|
||||
rawContent.created_timestamp ||
|
||||
rawContent.created_at ||
|
||||
|
|
|
|||
|
|
@ -1,7 +1,13 @@
|
|||
import type { NextConfig } from "next";
|
||||
|
||||
const nextConfig: NextConfig = {
|
||||
/* config options here */
|
||||
typescript: {
|
||||
ignoreBuildErrors: true,
|
||||
},
|
||||
output: "standalone",
|
||||
images: {
|
||||
unoptimized: true,
|
||||
},
|
||||
};
|
||||
|
||||
export default nextConfig;
|
||||
|
|
|
|||
16
src/llama_stack_ui/package-lock.json
generated
16
src/llama_stack_ui/package-lock.json
generated
|
|
@ -1,12 +1,13 @@
|
|||
{
|
||||
"name": "ui",
|
||||
"version": "0.1.0",
|
||||
"name": "llama-stack-ui",
|
||||
"version": "0.4.0-alpha.1",
|
||||
"lockfileVersion": 3,
|
||||
"requires": true,
|
||||
"packages": {
|
||||
"": {
|
||||
"name": "ui",
|
||||
"version": "0.1.0",
|
||||
"name": "llama-stack-ui",
|
||||
"version": "0.4.0-alpha.1",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"@radix-ui/react-collapsible": "^1.1.12",
|
||||
"@radix-ui/react-dialog": "^1.1.15",
|
||||
|
|
@ -20,7 +21,7 @@
|
|||
"class-variance-authority": "^0.7.1",
|
||||
"clsx": "^2.1.1",
|
||||
"framer-motion": "^12.23.24",
|
||||
"llama-stack-client": "github:llamastack/llama-stack-client-typescript",
|
||||
"llama-stack-client": "^0.3.1",
|
||||
"lucide-react": "^0.545.0",
|
||||
"next": "15.5.4",
|
||||
"next-auth": "^4.24.11",
|
||||
|
|
@ -9684,8 +9685,9 @@
|
|||
"license": "MIT"
|
||||
},
|
||||
"node_modules/llama-stack-client": {
|
||||
"version": "0.4.0-alpha.1",
|
||||
"resolved": "git+ssh://git@github.com/llamastack/llama-stack-client-typescript.git#78de4862c4b7d77939ac210fa9f9bde77a2c5c5f",
|
||||
"version": "0.3.1",
|
||||
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.3.1.tgz",
|
||||
"integrity": "sha512-4aYoF2aAQiBSfxyZEtczeQmJn8q9T22ePDqGhR+ej5RG6a8wvl5B3v7ZoKuFkft+vcP/kbJ58GQZEPLekxekZA==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"@types/node": "^18.11.18",
|
||||
|
|
|
|||
|
|
@ -1,11 +1,31 @@
|
|||
{
|
||||
"name": "ui",
|
||||
"version": "0.1.0",
|
||||
"private": true,
|
||||
"name": "llama-stack-ui",
|
||||
"version": "0.4.0-alpha.4",
|
||||
"description": "Web UI for Llama Stack",
|
||||
"license": "MIT",
|
||||
"author": "Llama Stack <llamastack@meta.com>",
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "https://github.com/llamastack/llama-stack.git",
|
||||
"directory": "llama_stack_ui"
|
||||
},
|
||||
"bin": {
|
||||
"llama-stack-ui": "bin/cli.js"
|
||||
},
|
||||
"files": [
|
||||
"bin",
|
||||
".next",
|
||||
"public",
|
||||
"next.config.ts",
|
||||
"instrumentation.ts",
|
||||
"tsconfig.json",
|
||||
"package.json"
|
||||
],
|
||||
"scripts": {
|
||||
"dev": "next dev --turbopack --port ${LLAMA_STACK_UI_PORT:-8322}",
|
||||
"build": "next build",
|
||||
"build": "next build && node scripts/postbuild.js",
|
||||
"start": "next start",
|
||||
"prepublishOnly": "npm run build",
|
||||
"lint": "next lint",
|
||||
"format": "prettier --write \"./**/*.{ts,tsx}\"",
|
||||
"format:check": "prettier --check \"./**/*.{ts,tsx}\"",
|
||||
|
|
@ -25,7 +45,7 @@
|
|||
"class-variance-authority": "^0.7.1",
|
||||
"clsx": "^2.1.1",
|
||||
"framer-motion": "^12.23.24",
|
||||
"llama-stack-client": "github:llamastack/llama-stack-client-typescript",
|
||||
"llama-stack-client": "^0.3.1",
|
||||
"lucide-react": "^0.545.0",
|
||||
"next": "15.5.4",
|
||||
"next-auth": "^4.24.11",
|
||||
|
|
|
|||
40
src/llama_stack_ui/scripts/postbuild.js
Normal file
40
src/llama_stack_ui/scripts/postbuild.js
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
const fs = require('fs');
|
||||
const path = require('path');
|
||||
|
||||
// Copy public directory to standalone
|
||||
const publicSrc = path.join(__dirname, '..', 'public');
|
||||
const publicDest = path.join(__dirname, '..', '.next', 'standalone', 'ui', 'src', 'llama_stack_ui', 'public');
|
||||
|
||||
if (fs.existsSync(publicSrc) && !fs.existsSync(publicDest)) {
|
||||
console.log('Copying public directory to standalone...');
|
||||
copyDir(publicSrc, publicDest);
|
||||
}
|
||||
|
||||
// Copy .next/static to standalone
|
||||
const staticSrc = path.join(__dirname, '..', '.next', 'static');
|
||||
const staticDest = path.join(__dirname, '..', '.next', 'standalone', 'ui', 'src', 'llama_stack_ui', '.next', 'static');
|
||||
|
||||
if (fs.existsSync(staticSrc) && !fs.existsSync(staticDest)) {
|
||||
console.log('Copying .next/static to standalone...');
|
||||
copyDir(staticSrc, staticDest);
|
||||
}
|
||||
|
||||
function copyDir(src, dest) {
|
||||
if (!fs.existsSync(dest)) {
|
||||
fs.mkdirSync(dest, { recursive: true });
|
||||
}
|
||||
|
||||
const files = fs.readdirSync(src);
|
||||
files.forEach((file) => {
|
||||
const srcFile = path.join(src, file);
|
||||
const destFile = path.join(dest, file);
|
||||
|
||||
if (fs.statSync(srcFile).isDirectory()) {
|
||||
copyDir(srcFile, destFile);
|
||||
} else {
|
||||
fs.copyFileSync(srcFile, destFile);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
console.log('Postbuild complete!');
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
{
|
||||
"default": [
|
||||
{"suite": "base", "setup": "ollama"},
|
||||
{"suite": "base", "setup": "ollama-postgres", "allowed_clients": ["server"], "stack_config": "server:ci-tests::run-with-postgres-store.yaml"},
|
||||
{"suite": "vision", "setup": "ollama-vision"},
|
||||
{"suite": "responses", "setup": "gpt"},
|
||||
{"suite": "base-vllm-subset", "setup": "vllm"}
|
||||
|
|
|
|||
|
|
@ -233,10 +233,21 @@ def instantiate_llama_stack_client(session):
|
|||
raise ValueError("You must specify either --stack-config or LLAMA_STACK_CONFIG")
|
||||
|
||||
# Handle server:<config_name> format or server:<config_name>:<port>
|
||||
# Also handles server:<distro>::<run_file.yaml> format
|
||||
if config.startswith("server:"):
|
||||
parts = config.split(":")
|
||||
config_name = parts[1]
|
||||
port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
|
||||
# Strip the "server:" prefix first
|
||||
config_part = config[7:] # len("server:") == 7
|
||||
|
||||
# Check for :: (distro::runfile format)
|
||||
if "::" in config_part:
|
||||
config_name = config_part
|
||||
port = int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
|
||||
else:
|
||||
# Single colon format: either <name> or <name>:<port>
|
||||
parts = config_part.split(":")
|
||||
config_name = parts[0]
|
||||
port = int(parts[1]) if len(parts) > 1 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
|
||||
|
||||
base_url = f"http://localhost:{port}"
|
||||
|
||||
force_restart = os.environ.get("LLAMA_STACK_TEST_FORCE_SERVER_RESTART") == "1"
|
||||
|
|
@ -323,7 +334,13 @@ def require_server(llama_stack_client):
|
|||
@pytest.fixture(scope="session")
|
||||
def openai_client(llama_stack_client, require_server):
|
||||
base_url = f"{llama_stack_client.base_url}/v1"
|
||||
return OpenAI(base_url=base_url, api_key="fake")
|
||||
client = OpenAI(base_url=base_url, api_key="fake", max_retries=0, timeout=30.0)
|
||||
yield client
|
||||
# Cleanup: close HTTP connections
|
||||
try:
|
||||
client.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
@pytest.fixture(params=["openai_client", "client_with_models"])
|
||||
|
|
|
|||
4
tests/integration/recordings/README.md
generated
4
tests/integration/recordings/README.md
generated
|
|
@ -2,6 +2,10 @@
|
|||
|
||||
This directory contains recorded inference API responses used for deterministic testing without requiring live API access.
|
||||
|
||||
For more information, see the
|
||||
[docs](https://llamastack.github.io/docs/contributing/testing/record-replay).
|
||||
This README provides more technical information.
|
||||
|
||||
## Structure
|
||||
|
||||
- `responses/` - JSON files containing request/response pairs for inference operations
|
||||
|
|
|
|||
|
|
@ -115,7 +115,15 @@ def openai_client(base_url, api_key, provider):
|
|||
client = LlamaStackAsLibraryClient(config, skip_logger_removal=True)
|
||||
return client
|
||||
|
||||
return OpenAI(
|
||||
client = OpenAI(
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
max_retries=0,
|
||||
timeout=30.0,
|
||||
)
|
||||
yield client
|
||||
# Cleanup: close HTTP connections
|
||||
try:
|
||||
client.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -65,8 +65,14 @@ class TestConversationResponses:
|
|||
conversation_items = openai_client.conversations.items.list(conversation.id)
|
||||
assert len(conversation_items.data) >= 4 # 2 user + 2 assistant messages
|
||||
|
||||
@pytest.mark.timeout(60, method="thread")
|
||||
def test_conversation_context_loading(self, openai_client, text_model_id):
|
||||
"""Test that conversation context is properly loaded for responses."""
|
||||
"""Test that conversation context is properly loaded for responses.
|
||||
|
||||
Note: 60s timeout added due to CI-specific deadlock in pytest/OpenAI client/httpx
|
||||
after running 25+ tests. Hangs before first HTTP request is made. Works fine locally.
|
||||
Investigation needed: connection pool exhaustion or event loop state issue.
|
||||
"""
|
||||
conversation = openai_client.conversations.create(
|
||||
items=[
|
||||
{"type": "message", "role": "user", "content": "My name is Alice. I like to eat apples."},
|
||||
|
|
|
|||
|
|
@ -71,6 +71,26 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
|
|||
"embedding_model": "ollama/nomic-embed-text:v1.5",
|
||||
},
|
||||
),
|
||||
"ollama-postgres": Setup(
|
||||
name="ollama-postgres",
|
||||
description="Server-mode tests with Postgres-backed persistence",
|
||||
env={
|
||||
"OLLAMA_URL": "http://0.0.0.0:11434",
|
||||
"SAFETY_MODEL": "ollama/llama-guard3:1b",
|
||||
"POSTGRES_HOST": "127.0.0.1",
|
||||
"POSTGRES_PORT": "5432",
|
||||
"POSTGRES_DB": "llamastack",
|
||||
"POSTGRES_USER": "llamastack",
|
||||
"POSTGRES_PASSWORD": "llamastack",
|
||||
"LLAMA_STACK_LOGGING": "openai_responses=info",
|
||||
},
|
||||
defaults={
|
||||
"text_model": "ollama/llama3.2:3b-instruct-fp16",
|
||||
"embedding_model": "sentence-transformers/nomic-embed-text-v1.5",
|
||||
"safety_model": "ollama/llama-guard3:1b",
|
||||
"safety_shield": "llama-guard",
|
||||
},
|
||||
),
|
||||
"vllm": Setup(
|
||||
name="vllm",
|
||||
description="vLLM provider with a text model",
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ import time
|
|||
from io import BytesIO
|
||||
|
||||
import pytest
|
||||
from llama_stack_api.apis.files import ExpiresAfter
|
||||
from llama_stack_api.apis.vector_io import Chunk
|
||||
from llama_stack_client import BadRequestError
|
||||
from openai import BadRequestError as OpenAIBadRequestError
|
||||
|
|
@ -1604,3 +1605,97 @@ def test_openai_vector_store_embedding_config_from_metadata(
|
|||
|
||||
assert "metadata_config_store" in store_names
|
||||
assert "consistent_config_store" in store_names
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_file_contents_with_extra_query(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test that vector store file contents endpoint supports extra_query parameter."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
name="test_extra_query_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
# Create and attach a file
|
||||
test_content = b"This is test content for extra_query validation."
|
||||
with BytesIO(test_content) as file_buffer:
|
||||
file_buffer.name = "test_extra_query.txt"
|
||||
file = compat_client.files.create(
|
||||
file=file_buffer,
|
||||
purpose="assistants",
|
||||
expires_after=ExpiresAfter(anchor="created_at", seconds=86400),
|
||||
)
|
||||
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
extra_body={"embedding_model": embedding_model_id},
|
||||
)
|
||||
assert file_attach_response.status == "completed"
|
||||
|
||||
# Wait for processing
|
||||
time.sleep(2)
|
||||
|
||||
# Test that extra_query parameter is accepted and processed
|
||||
content_with_extra_query = compat_client.vector_stores.files.content(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
extra_query={"include_embeddings": True, "include_metadata": True},
|
||||
)
|
||||
|
||||
# Test without extra_query for comparison
|
||||
content_without_extra_query = compat_client.vector_stores.files.content(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
|
||||
# Validate that both calls succeed
|
||||
assert content_with_extra_query is not None
|
||||
assert content_without_extra_query is not None
|
||||
assert len(content_with_extra_query.data) > 0
|
||||
assert len(content_without_extra_query.data) > 0
|
||||
|
||||
# Validate that extra_query parameter is processed correctly
|
||||
# Both should have the embedding/metadata fields available (may be None based on flags)
|
||||
first_chunk_with_flags = content_with_extra_query.data[0]
|
||||
first_chunk_without_flags = content_without_extra_query.data[0]
|
||||
|
||||
# The key validation: extra_query fields are present in the response
|
||||
# Handle both dict and object responses (different clients may return different formats)
|
||||
def has_field(obj, field):
|
||||
if isinstance(obj, dict):
|
||||
return field in obj
|
||||
else:
|
||||
return hasattr(obj, field)
|
||||
|
||||
# Validate that all expected fields are present in both responses
|
||||
expected_fields = ["embedding", "chunk_metadata", "metadata", "text"]
|
||||
for field in expected_fields:
|
||||
assert has_field(first_chunk_with_flags, field), f"Field '{field}' missing from response with extra_query"
|
||||
assert has_field(first_chunk_without_flags, field), f"Field '{field}' missing from response without extra_query"
|
||||
|
||||
# Validate content is the same
|
||||
def get_field(obj, field):
|
||||
if isinstance(obj, dict):
|
||||
return obj[field]
|
||||
else:
|
||||
return getattr(obj, field)
|
||||
|
||||
assert get_field(first_chunk_with_flags, "text") == test_content.decode("utf-8")
|
||||
assert get_field(first_chunk_without_flags, "text") == test_content.decode("utf-8")
|
||||
|
||||
with_flags_embedding = get_field(first_chunk_with_flags, "embedding")
|
||||
without_flags_embedding = get_field(first_chunk_without_flags, "embedding")
|
||||
|
||||
# Validate that embeddings are included when requested and excluded when not requested
|
||||
assert with_flags_embedding is not None, "Embeddings should be included when include_embeddings=True"
|
||||
assert len(with_flags_embedding) > 0, "Embedding should be a non-empty list"
|
||||
assert without_flags_embedding is None, "Embeddings should not be included when include_embeddings=False"
|
||||
|
|
|
|||
|
|
@ -55,3 +55,65 @@ async def test_create_vector_stores_multiple_providers_missing_provider_id_error
|
|||
|
||||
with pytest.raises(ValueError, match="Multiple vector_io providers available"):
|
||||
await router.openai_create_vector_store(request)
|
||||
|
||||
|
||||
async def test_update_vector_store_provider_id_change_fails():
|
||||
"""Test that updating a vector store with a different provider_id fails with clear error."""
|
||||
mock_routing_table = Mock()
|
||||
|
||||
# Mock an existing vector store with provider_id "faiss"
|
||||
mock_existing_store = Mock()
|
||||
mock_existing_store.provider_id = "inline::faiss"
|
||||
mock_existing_store.identifier = "vs_123"
|
||||
|
||||
mock_routing_table.get_object_by_identifier = AsyncMock(return_value=mock_existing_store)
|
||||
mock_routing_table.get_provider_impl = AsyncMock(
|
||||
return_value=Mock(openai_update_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
|
||||
)
|
||||
|
||||
router = VectorIORouter(mock_routing_table)
|
||||
|
||||
# Try to update with different provider_id in metadata - this should fail
|
||||
with pytest.raises(ValueError, match="provider_id cannot be changed after vector store creation"):
|
||||
await router.openai_update_vector_store(
|
||||
vector_store_id="vs_123",
|
||||
name="updated_name",
|
||||
metadata={"provider_id": "inline::sqlite"}, # Different provider_id
|
||||
)
|
||||
|
||||
# Verify the existing store was looked up to check provider_id
|
||||
mock_routing_table.get_object_by_identifier.assert_called_once_with("vector_store", "vs_123")
|
||||
|
||||
# Provider should not be called since validation failed
|
||||
mock_routing_table.get_provider_impl.assert_not_called()
|
||||
|
||||
|
||||
async def test_update_vector_store_same_provider_id_succeeds():
|
||||
"""Test that updating a vector store with the same provider_id succeeds."""
|
||||
mock_routing_table = Mock()
|
||||
|
||||
# Mock an existing vector store with provider_id "faiss"
|
||||
mock_existing_store = Mock()
|
||||
mock_existing_store.provider_id = "inline::faiss"
|
||||
mock_existing_store.identifier = "vs_123"
|
||||
|
||||
mock_routing_table.get_object_by_identifier = AsyncMock(return_value=mock_existing_store)
|
||||
mock_routing_table.get_provider_impl = AsyncMock(
|
||||
return_value=Mock(openai_update_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
|
||||
)
|
||||
|
||||
router = VectorIORouter(mock_routing_table)
|
||||
|
||||
# Update with same provider_id should succeed
|
||||
await router.openai_update_vector_store(
|
||||
vector_store_id="vs_123",
|
||||
name="updated_name",
|
||||
metadata={"provider_id": "inline::faiss"}, # Same provider_id
|
||||
)
|
||||
|
||||
# Verify the provider update method was called
|
||||
mock_routing_table.get_provider_impl.assert_called_once_with("vs_123")
|
||||
provider = await mock_routing_table.get_provider_impl("vs_123")
|
||||
provider.openai_update_vector_store.assert_called_once_with(
|
||||
vector_store_id="vs_123", name="updated_name", expires_after=None, metadata={"provider_id": "inline::faiss"}
|
||||
)
|
||||
|
|
|
|||
|
|
@ -104,12 +104,18 @@ async def test_paginated_response_url_setting():
|
|||
|
||||
route_handler = create_dynamic_typed_route(mock_api_method, "get", "/test/route")
|
||||
|
||||
# Mock minimal request
|
||||
# Mock minimal request with proper state object
|
||||
request = MagicMock()
|
||||
request.scope = {"user_attributes": {}, "principal": ""}
|
||||
request.headers = {}
|
||||
request.body = AsyncMock(return_value=b"")
|
||||
|
||||
# Create a simple state object without auto-generating attributes
|
||||
class MockState:
|
||||
pass
|
||||
|
||||
request.state = MockState()
|
||||
|
||||
result = await route_handler(request)
|
||||
|
||||
assert isinstance(result, PaginatedResponse)
|
||||
|
|
|
|||
10
uv.lock
generated
10
uv.lock
generated
|
|
@ -2168,7 +2168,7 @@ test = [
|
|||
{ name = "milvus-lite", specifier = ">=2.5.0" },
|
||||
{ name = "psycopg2-binary", specifier = ">=2.9.0" },
|
||||
{ name = "pymilvus", specifier = ">=2.6.1" },
|
||||
{ name = "pypdf" },
|
||||
{ name = "pypdf", specifier = ">=6.1.3" },
|
||||
{ name = "qdrant-client" },
|
||||
{ name = "requests" },
|
||||
{ name = "sqlalchemy" },
|
||||
|
|
@ -2221,7 +2221,7 @@ unit = [
|
|||
{ name = "moto", extras = ["s3"], specifier = ">=5.1.10" },
|
||||
{ name = "ollama" },
|
||||
{ name = "psycopg2-binary", specifier = ">=2.9.0" },
|
||||
{ name = "pypdf" },
|
||||
{ name = "pypdf", specifier = ">=6.1.3" },
|
||||
{ name = "sqlalchemy" },
|
||||
{ name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0.41" },
|
||||
{ name = "sqlite-vec" },
|
||||
|
|
@ -3994,11 +3994,11 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "pypdf"
|
||||
version = "5.9.0"
|
||||
version = "6.2.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/89/3a/584b97a228950ed85aec97c811c68473d9b8d149e6a8c155668287cf1a28/pypdf-5.9.0.tar.gz", hash = "sha256:30f67a614d558e495e1fbb157ba58c1de91ffc1718f5e0dfeb82a029233890a1", size = 5035118, upload-time = "2025-07-27T14:04:52.364Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/4e/2b/8795ec0378384000b0a37a2b5e6d67fa3d84802945aa2c612a78a784d7d4/pypdf-6.2.0.tar.gz", hash = "sha256:46b4d8495d68ae9c818e7964853cd9984e6a04c19fe7112760195395992dce48", size = 5272001, upload-time = "2025-11-09T11:10:41.911Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/48/d9/6cff57c80a6963e7dd183bf09e9f21604a77716644b1e580e97b259f7612/pypdf-5.9.0-py3-none-any.whl", hash = "sha256:be10a4c54202f46d9daceaa8788be07aa8cd5ea8c25c529c50dd509206382c35", size = 313193, upload-time = "2025-07-27T14:04:50.53Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/de/ba/743ddcaf1a8fb439342399645921e2cf2c600464cba5531a11f1cc0822b6/pypdf-6.2.0-py3-none-any.whl", hash = "sha256:4c0f3e62677217a777ab79abe22bf1285442d70efabf552f61c7a03b6f5c569f", size = 326592, upload-time = "2025-11-09T11:10:39.941Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue