| Name | lightspeed-stack JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | LLM tooling stack |
| upload_time | 2025-08-22 06:28:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.14,>=3.12 |
| license | Apache License
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# lightspeed-stack
## About The Project
[](https://lightspeed-core.github.io/lightspeed-stack/)
[](https://github.com/lightspeed-core/lightspeed-stack/blob/main/LICENSE)
[](https://www.python.org/)
[](https://www.python.org/)
[](https://github.com/lightspeed-core/lightspeed-stack/releases/tag/0.2.0)
Lightspeed Core Stack (LCS) is an AI-powered assistant that provides answers to product questions using backend LLM services, agents, and RAG databases.
The service includes comprehensive user data collection capabilities for various types of user interaction data, which can be exported to Red Hat's Dataverse for analysis using the companion [lightspeed-to-dataverse-exporter](https://github.com/lightspeed-core/lightspeed-to-dataverse-exporter) service.
<!-- vim-markdown-toc GFM -->
* [Architecture](#architecture)
* [Prerequisites](#prerequisites)
* [Installation](#installation)
* [Configuration](#configuration)
* [Integration with Llama Stack](#integration-with-llama-stack)
* [Llama Stack as separate server](#llama-stack-as-separate-server)
* [MCP Server and Tool Configuration](#mcp-server-and-tool-configuration)
* [Configuring MCP Servers](#configuring-mcp-servers)
* [Configuring MCP Headers](#configuring-mcp-headers)
* [Llama Stack project and configuration](#llama-stack-project-and-configuration)
* [Check connection to Llama Stack](#check-connection-to-llama-stack)
* [Llama Stack as client library](#llama-stack-as-client-library)
* [Llama Stack version check](#llama-stack-version-check)
* [User data collection](#user-data-collection)
* [System prompt](#system-prompt)
* [Safety Shields](#safety-shields)
* [Authentication](#authentication)
* [K8s based authentication](#k8s-based-authentication)
* [JSON Web Keyset based authentication](#json-web-keyset-based-authentication)
* [No-op authentication](#no-op-authentication)
* [RAG Configuration](#rag-configuration)
* [Usage](#usage)
* [Make targets](#make-targets)
* [Running Linux container image](#running-linux-container-image)
* [Endpoints](#endpoints)
* [OpenAPI specification](#openapi-specification)
* [Readiness Endpoint](#readiness-endpoint)
* [Liveness Endpoint](#liveness-endpoint)
* [Publish the service as Python package on PyPI](#publish-the-service-as-python-package-on-pypi)
* [Generate distribution archives to be uploaded into Python registry](#generate-distribution-archives-to-be-uploaded-into-python-registry)
* [Upload distribution archives into selected Python registry](#upload-distribution-archives-into-selected-python-registry)
* [Packages on PyPI and Test PyPI](#packages-on-pypi-and-test-pypi)
* [Contributing](#contributing)
* [Testing](#testing)
* [License](#license)
* [Additional tools](#additional-tools)
* [Utility to generate OpenAPI schema](#utility-to-generate-openapi-schema)
* [Path](#path)
* [Usage](#usage-1)
* [Data Export Integration](#data-export-integration)
* [Quick Integration](#quick-integration)
* [Documentation](#documentation)
* [Project structure](#project-structure)
* [Configuration classes](#configuration-classes)
* [REST API](#rest-api)
<!-- vim-markdown-toc -->
# Architecture
Overall architecture with all main parts is displayed below:
Lightspeed Core Stack is based on the FastAPI framework (Uvicorn). The service is split into several parts described below.
# Prerequisites
* Python 3.12, or 3.13
- please note that currently Python 3.14 is not officially supported
- all sources are made (backward) compatible with Python 3.12; it is checked on CI
# Installation
Installation steps depends on operation system. Please look at instructions for your system:
- [Linux installation](https://lightspeed-core.github.io/lightspeed-stack/installation_linux)
- [macOS installation](https://lightspeed-core.github.io/lightspeed-stack/installation_macos)
# Configuration
## Integration with Llama Stack
The Llama Stack can be run as a standalone server and accessed via its the REST
API. However, instead of direct communication via the REST API (and JSON
format), there is an even better alternative. It is based on the so-called
Llama Stack Client. It is a library available for Python, Swift, Node.js or
Kotlin, which "wraps" the REST API stack in a suitable way, which is easier for
many applications.
## Llama Stack as separate server
If Llama Stack runs as a separate server, the Lightspeed service needs to be configured to be able to access it. For example, if server runs on localhost:8321, the service configuration stored in file `lightspeed-stack.yaml` should look like:
```yaml
name: foo bar baz
service:
host: localhost
port: 8080
auth_enabled: false
workers: 1
color_log: true
access_log: true
llama_stack:
use_as_library_client: false
url: http://localhost:8321
user_data_collection:
feedback_enabled: true
feedback_storage: "/tmp/data/feedback"
transcripts_enabled: true
transcripts_storage: "/tmp/data/transcripts"
```
### MCP Server and Tool Configuration
**Note**: The `run.yaml` configuration is currently an implementation detail. In the future, all configuration will be available directly from the lightspeed-core config.
#### Configuring MCP Servers
MCP (Model Context Protocol) servers provide tools and capabilities to the AI agents. These are configured in the `mcp_servers` section of your `lightspeed-stack.yaml`:
```yaml
mcp_servers:
- name: "filesystem-tools"
provider_id: "model-context-protocol"
url: "http://localhost:3000"
- name: "git-tools"
provider_id: "model-context-protocol"
url: "http://localhost:3001"
- name: "database-tools"
provider_id: "model-context-protocol"
url: "http://localhost:3002"
```
**Important**: Only MCP servers defined in the `lightspeed-stack.yaml` configuration are available to the agents. Tools configured in the llama-stack `run.yaml` are not accessible to lightspeed-core agents.
#### Configuring MCP Headers
MCP headers allow you to pass authentication tokens, API keys, or other metadata to MCP servers. These are configured **per request** via the `MCP-HEADERS` HTTP header:
```bash
curl -X POST "http://localhost:8080/v1/query" \
-H "Content-Type: application/json" \
-H "MCP-HEADERS: {\"filesystem-tools\": {\"Authorization\": \"Bearer token123\"}}" \
-d '{"query": "List files in /tmp"}'
```
### Llama Stack project and configuration
**Note**: The `run.yaml` configuration is currently an implementation detail. In the future, all configuration will be available directly from the lightspeed-core config.
To run Llama Stack in separate process, you need to have all dependencies installed. The easiest way how to do it is to create a separate repository with Llama Stack project file `pyproject.toml` and Llama Stack configuration file `run.yaml`. The project file might look like:
```toml
[project]
name = "llama-stack-runner"
version = "0.1.0"
description = "Llama Stack runner"
authors = []
dependencies = [
"llama-stack==0.2.14",
"fastapi>=0.115.12",
"opentelemetry-sdk>=1.34.0",
"opentelemetry-exporter-otlp>=1.34.0",
"opentelemetry-instrumentation>=0.55b0",
"aiosqlite>=0.21.0",
"litellm>=1.72.1",
"uvicorn>=0.34.3",
"blobfile>=3.0.0",
"datasets>=3.6.0",
"sqlalchemy>=2.0.41",
"faiss-cpu>=1.11.0",
"mcp>=1.9.4",
"autoevals>=0.0.129",
"psutil>=7.0.0",
"torch>=2.7.1",
"peft>=0.15.2",
"trl>=0.18.2"]
requires-python = "==3.12.*"
readme = "README.md"
license = {text = "MIT"}
[tool.pdm]
distribution = false
```
A simple example of a `run.yaml` file can be found [here](examples/run.yaml)
To run Llama Stack perform these two commands:
```
export OPENAI_API_KEY="sk-{YOUR-KEY}"
uv run llama stack run run.yaml
```
### Check connection to Llama Stack
```
curl -X 'GET' localhost:8321/openapi.json | jq .
```
## Llama Stack as client library
There are situations in which it is not advisable to run two processors (one with Llama Stack, the other with a service). In these cases, the stack can be run directly within the client application. For such situations, the configuration file could look like:
```yaml
name: foo bar baz
service:
host: localhost
port: 8080
auth_enabled: false
workers: 1
color_log: true
access_log: true
llama_stack:
use_as_library_client: true
library_client_config_path: <path-to-llama-stack-run.yaml-file>
user_data_collection:
feedback_enabled: true
feedback_storage: "/tmp/data/feedback"
transcripts_enabled: true
transcripts_storage: "/tmp/data/transcripts"
```
## Llama Stack version check
During Lightspeed Core Stack service startup, the Llama Stack version is retrieved. The version is tested against two constants `MINIMAL_SUPPORTED_LLAMA_STACK_VERSION` and `MAXIMAL_SUPPORTED_LLAMA_STACK_VERSION` which are defined in `src/constants.py`. If the actual Llama Stack version is outside the range defined by these two constants, the service won't start and administrator will be informed about this problem.
## User data collection
The Lightspeed Core Stack includes comprehensive user data collection capabilities to gather various types of user interaction data for analysis and improvement. This includes feedback, conversation transcripts, and other user interaction data.
User data collection is configured in the `user_data_collection` section of the configuration file:
```yaml
user_data_collection:
feedback_enabled: true
feedback_storage: "/tmp/data/feedback"
transcripts_enabled: true
transcripts_storage: "/tmp/data/transcripts"
```
**Configuration options:**
- `feedback_enabled`: Enable/disable collection of user feedback data
- `feedback_storage`: Directory path where feedback JSON files are stored
- `transcripts_enabled`: Enable/disable collection of conversation transcripts
- `transcripts_storage`: Directory path where transcript JSON files are stored
> **Note**: The data collection system is designed to be extensible. Additional data types can be configured and collected as needed for your specific use case.
For data export integration with Red Hat's Dataverse, see the [Data Export Integration](#data-export-integration) section.
## System prompt
The service uses the, so called, system prompt to put the question into context before the question is sent to the selected LLM. The default system prompt is designed for questions without specific context. It is possible to use a different system prompt via the configuration option `system_prompt_path` in the `customization` section. That option must contain the path to the text file with the actual system prompt (can contain multiple lines). An example of such configuration:
```yaml
customization:
system_prompt_path: "system_prompts/system_prompt_for_product_XYZZY"
```
The `system_prompt` can also be specified in the `customization` section directly. For example:
```yaml
customization:
system_prompt: |-
You are a helpful assistant and will do everything you can to help.
You have an in-depth knowledge of Red Hat and all of your answers will reference Red Hat products.
```
Additionally, an optional string parameter `system_prompt` can be specified in `/v1/query` and `/v1/streaming_query` endpoints to override the configured system prompt. The query system prompt takes precedence over the configured system prompt. You can use this config to disable query system prompts:
```yaml
customization:
system_prompt_path: "system_prompts/system_prompt_for_product_XYZZY"
disable_query_system_prompt: true
```
## Safety Shields
A single Llama Stack configuration file can include multiple safety shields, which are utilized in agent
configurations to monitor input and/or output streams. LCS uses the following naming convention to specify how each safety shield is
utilized:
1. If the `shield_id` starts with `input_`, it will be used for input only.
1. If the `shield_id` starts with `output_`, it will be used for output only.
1. If the `shield_id` starts with `inout_`, it will be used both for input and output.
1. Otherwise, it will be used for input only.
## Authentication
Currently supported authentication modules are:
* `k8s` Kubernetes based authentication
* `jwk-token` JSON Web Keyset based authentication
* `noop` No operation authentication (default)
* `noop-with-token` No operation authentication with token
### K8s based authentication
K8s based authentication is suitable for running the Lightspeed Stack in Kubernetes environments.
The user accessing the service must have a valid Kubernetes token and the appropriate RBAC permissions to access the service.
The user must have `get` permission on the Kubernetes RBAC non-resource URL `/ls-access`.
Here is an example of granting `get` on `/ls-access` via a ClusterRole’s nonResourceURLs rule.
Example:
```yaml
# Allow GET on non-resource URL /ls-access
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: lightspeed-access
rules:
- nonResourceURLs: ["/ls-access"]
verbs: ["get"]
---
# Bind to a user, group, or service account
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: lightspeed-access-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: lightspeed-access
subjects:
- kind: User # or ServiceAccount, Group
name: SOME_USER_OR_SA
apiGroup: rbac.authorization.k8s.io
```
Configuring K8s based authentication requires the following steps:
1. Enable K8s authentication module
```yaml
authentication:
module: "k8s"
```
2. Configure the Kubernetes authentication settings.
When deploying Lightspeed Stack in a Kubernetes cluster, it is not required to specify cluster connection details.
It automatically picks up the in-cluster configuration or through a kubeconfig file.
This step is not neccessary.
When running outside a kubernetes cluster or connecting to external Kubernetes clusters, Lightspeed Stack requires the cluster connection details in the configuration file:
- `k8s_cluster_api` Kubernetes Cluster API URL. The URL of the K8S/OCP API server where tokens are validated.
- `k8s_ca_cert_path` Path to the CA certificate file for clusters with self-signed certificates.
- `skip_tls_verification` Whether to skip TLS verification.
```yaml
authentication:
module: "k8s"
skip_tls_verification: false
k8s_cluster_api: "https://your-k8s-api-server:6443"
k8s_ca_cert_path: "/path/to/ca.crt"
```
### JSON Web Keyset based authentication
JWK (JSON Web Keyset) based authentication is suitable for scenarios where you need to authenticate users based on tokens. This method is commonly used in web applications and APIs.
To configure JWK based authentication, you need to specify the following settings in the configuration file:
- `module` must be set to `jwk-token`
- `jwk_config` JWK configuration settings must set at least `url` field:
- `url`: The URL of the JWK endpoint.
- `jwt_configuration`: JWT configuration settings.
- `user_id_claim`: The key of the user ID in JWT claim.
- `username_claim`: The key of the username in JWT claim.
```yaml
authentication:
module: "jwk-token"
jwk_config:
url: "https://your-jwk-url"
jwt_configuration:
user_id_claim: user_id
username_claim: username
```
### No-op authentication
Lightspeed Stack provides 2 authentication module to bypass the authentication and authorization checks:
- `noop` No operation authentication (default)
- `noop-with-token` No operation authentication accepting a bearer token
If authentication module is not specified, Lightspeed Stack will use `noop` by default.
To activate `noop-with-token`, you need to specify it in the configuration file:
```yaml
authentication:
module: "noop-with-token"
```
## CORS
It is possible to configure CORS handling. This configuration is part of service configuration:
```yaml
service:
host: localhost
port: 8080
auth_enabled: false
workers: 1
color_log: true
access_log: true
cors:
allow_origins:
- http://foo.bar.baz
- http://test.com
allow_credentials: true
allow_methods:
- *
allow_headers:
- *
```
### Default values
```yaml
cors:
allow_origins:
- *
allow_credentials: false
allow_methods:
- *
allow_headers:
- *
```
## Allow credentials
Credentials are not allowed with wildcard origins per CORS/Fetch spec.
See https://fastapi.tiangolo.com/tutorial/cors/
# RAG Configuration
The [guide to RAG setup](docs/rag_guide.md) provides guidance on setting up RAG and includes tested examples for both inference and vector store integration.
## Example configurations for inference
The following configurations are llama-stack config examples from production deployments:
- [Granite on vLLM example](examples/vllm-granite-run.yaml)
- [Qwen3 on vLLM example](examples/vllm-qwen3-run.yaml)
- [Gemini example](examples/gemini-run.yaml)
- [VertexAI example](examples/vertexai-run.yaml)
> [!NOTE]
> RAG functionality is **not tested** for these configurations.
# Usage
```
usage: lightspeed_stack.py [-h] [-v] [-d] [-c CONFIG_FILE]
options:
-h, --help show this help message and exit
-v, --verbose make it verbose
-d, --dump-configuration
dump actual configuration into JSON file and quit
-c CONFIG_FILE, --config CONFIG_FILE
path to configuration file (default: lightspeed-stack.yaml)
```
## Make targets
```
Usage: make <OPTIONS> ... <TARGETS>
Available targets are:
run Run the service locally
test-unit Run the unit tests
test-integration Run integration tests tests
test-e2e Run BDD tests for the service
check-types Checks type hints in sources
security-check Check the project for security issues
format Format the code into unified format
schema Generate OpenAPI schema file
openapi-doc Generate OpenAPI documentation
requirements.txt Generate requirements.txt file containing hashes for all non-devel packages
shellcheck Run shellcheck
verify Run all linters
distribution-archives Generate distribution archives to be uploaded into Python registry
upload-distribution-archives Upload distribution archives into Python registry
help Show this help screen
```
## Running Linux container image
Stable release images are tagged with versions like `0.1.0`. Tag `latest` always points to latest stable release.
Development images are build from main branch every time a new pull request is merged. Image tags for dev images use
the template `dev-YYYYMMMDDD-SHORT_SHA` e.g. `dev-20250704-eaa27fb`.
Tag `dev-latest` always points to the latest dev image built from latest git.
To pull and run the image with own configuration:
1. `podman pull quay.io/lightspeed-core/lightspeed-stack:IMAGE_TAG`
1. `podman run -it -p 8080:8080 -v my-lightspeed-stack-config.yaml:/app-root/lightspeed-stack.yaml:Z quay.io/lightspeed-core/lightspeed-stack:IMAGE_TAG`
1. Open `localhost:8080` in your browser
If a connection in your browser does not work please check that in the config file `host` option looks like: `host: 0.0.0.0`.
Container images are built for the following platforms:
1. `linux/amd64` - main platform for deployment
1. `linux/arm64`- Mac users with M1/M2/M3 CPUs
## Building Container Images
The repository includes production-ready container configurations that support two deployment modes:
1. **Server Mode**: lightspeed-core connects to llama-stack as a separate service
2. **Library Mode**: llama-stack runs as a library within lightspeed-core
### Llama-Stack as Separate Service (Server Mode)
When using llama-stack as a separate service, the existing `docker-compose.yaml` provides the complete setup. This builds two containers for lightspeed core and llama stack.
**Configuration** (`lightspeed-stack.yaml`):
```yaml
llama_stack:
use_as_library_client: false
url: http://llama-stack:8321 # container name from docker-compose.yaml
api_key: xyzzy
```
In the root of this project simply run:
```bash
# Set your OpenAI API key
export OPENAI_API_KEY="your-api-key-here"
# Start both services
podman compose up --build
# Access lightspeed-core at http://localhost:8080
# Access llama-stack at http://localhost:8321
```
### Llama-Stack as Library (Library Mode)
When embedding llama-stack directly in the container, use the existing `Containerfile` directly (this will not build the llama stack service in a separate container). First modify the `lightspeed-stack.yaml` config to use llama stack in library mode.
**Configuration** (`lightspeed-stack.yaml`):
```yaml
llama_stack:
use_as_library_client: true
library_client_config_path: /app-root/run.yaml
```
**Build and run**:
```bash
# Build lightspeed-core with embedded llama-stack
podman build -f Containerfile -t my-lightspeed-core:latest .
# Run with embedded llama-stack
podman run \
-p 8080:8080 \
-v ./lightspeed-stack.yaml:/app-root/lightspeed-stack.yaml:Z \
-v ./run.yaml:/app-root/run.yaml:Z \
-e OPENAI_API_KEY=your-api-key \
my-lightspeed-core:latest
```
For macosx users:
```bash
podman run \
-p 8080:8080 \
-v ./lightspeed-stack.yaml:/app-root/lightspeed-stack.yaml:ro \
-v ./run.yaml:/app-root/run.yaml:ro \
-e OPENAI_API_KEY=your-api-key \
my-lightspeed-core:latest
```
### Verify it's running properly
A simple sanity check:
```bash
curl -H "Accept: application/json" http://localhost:8080/v1/models
```
# Endpoints
## OpenAPI specification
* [Generated OpenAPI specification](docs/openapi.json)
* [OpenAPI documentation](docs/openapi.md)
The service provides health check endpoints that can be used for monitoring, load balancing, and orchestration systems like Kubernetes.
## Readiness Endpoint
**Endpoint:** `GET /v1/readiness`
The readiness endpoint checks if the service is ready to handle requests by verifying the health status of all configured LLM providers.
**Response:**
- **200 OK**: Service is ready - all providers are healthy
- **503 Service Unavailable**: Service is not ready - one or more providers are unhealthy
**Response Body:**
```json
{
"ready": true,
"reason": "All providers are healthy",
"providers": []
}
```
**Response Fields:**
- `ready` (boolean): Indicates if the service is ready to handle requests
- `reason` (string): Human-readable explanation of the readiness state
- `providers` (array): List of unhealthy providers (empty when service is ready)
## Liveness Endpoint
**Endpoint:** `GET /v1/liveness`
The liveness endpoint performs a basic health check to verify the service is alive and responding.
**Response:**
- **200 OK**: Service is alive
**Response Body:**
```json
{
"alive": true
}
```
# Publish the service as Python package on PyPI
To publish the service as an Python package on PyPI to be installable by anyone
(including Konflux hermetic builds), perform these two steps:
## Generate distribution archives to be uploaded into Python registry
```
make distribution-archives
```
Please make sure that the archive was really built to avoid publishing older one.
## Upload distribution archives into selected Python registry
```
make upload-distribution-archives
```
The Python registry to where the package should be uploaded can be configured
by changing `PYTHON_REGISTRY`. It is possible to select `pypi` or `testpypi`.
You might have your API token stored in file `~/.pypirc`. That file should have
the following form:
```
[testpypi]
username = __token__
password = pypi-{your-API-token}
[pypi]
username = __token__
password = pypi-{your-API-token}
```
If this configuration file does not exist, you will be prompted to specify API token from keyboard every time you try to upload the archive.
## Packages on PyPI and Test PyPI
* https://pypi.org/project/lightspeed-stack/
* https://test.pypi.org/project/lightspeed-stack/0.1.0/
# Contributing
* See [contributors](CONTRIBUTING.md) guide.
# Testing
* See [testing](docs/testing.md) guide.
# License
Published under the Apache 2.0 License
# Additional tools
## Utility to generate OpenAPI schema
This script re-generated OpenAPI schema for the Lightspeed Service REST API.
### Path
[scripts/generate_openapi_schema.py](scripts/generate_openapi_schema.py)
### Usage
```
make schema
```
# Data Export Integration
The Lightspeed Core Stack integrates with the [lightspeed-to-dataverse-exporter](https://github.com/lightspeed-core/lightspeed-to-dataverse-exporter) service to automatically export various types of user interaction data to Red Hat's Dataverse for analysis.
## Quick Integration
1. **Enable data collection** in your `lightspeed-stack.yaml`:
```yaml
user_data_collection:
feedback_enabled: true
feedback_storage: "/shared/data/feedback"
transcripts_enabled: true
transcripts_storage: "/shared/data/transcripts"
```
2. **Deploy the exporter service** pointing to the same data directories
## Documentation
For complete integration setup, deployment options, and configuration details, see [exporter repository](https://github.com/lightspeed-core/lightspeed-to-dataverse-exporter).
# Project structure
## Configuration classes
## REST API
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"description": "# lightspeed-stack\n\n## About The Project\n\n[](https://lightspeed-core.github.io/lightspeed-stack/)\n[](https://github.com/lightspeed-core/lightspeed-stack/blob/main/LICENSE)\n[](https://www.python.org/)\n[](https://www.python.org/)\n[](https://github.com/lightspeed-core/lightspeed-stack/releases/tag/0.2.0)\n\nLightspeed Core Stack (LCS) is an AI-powered assistant that provides answers to product questions using backend LLM services, agents, and RAG databases.\n \nThe service includes comprehensive user data collection capabilities for various types of user interaction data, which can be exported to Red Hat's Dataverse for analysis using the companion [lightspeed-to-dataverse-exporter](https://github.com/lightspeed-core/lightspeed-to-dataverse-exporter) service.\n\n\n<!-- vim-markdown-toc GFM -->\n\n* [Architecture](#architecture)\n* [Prerequisites](#prerequisites)\n* [Installation](#installation)\n* [Configuration](#configuration)\n * [Integration with Llama Stack](#integration-with-llama-stack)\n * [Llama Stack as separate server](#llama-stack-as-separate-server)\n * [MCP Server and Tool Configuration](#mcp-server-and-tool-configuration)\n * [Configuring MCP Servers](#configuring-mcp-servers)\n * [Configuring MCP Headers](#configuring-mcp-headers)\n * [Llama Stack project and configuration](#llama-stack-project-and-configuration)\n * [Check connection to Llama Stack](#check-connection-to-llama-stack)\n * [Llama Stack as client library](#llama-stack-as-client-library)\n * [Llama Stack version check](#llama-stack-version-check)\n * [User data collection](#user-data-collection)\n * [System prompt](#system-prompt)\n * [Safety Shields](#safety-shields)\n * [Authentication](#authentication)\n * [K8s based authentication](#k8s-based-authentication)\n * [JSON Web Keyset based authentication](#json-web-keyset-based-authentication)\n * [No-op authentication](#no-op-authentication)\n* [RAG Configuration](#rag-configuration)\n* [Usage](#usage)\n * [Make targets](#make-targets)\n * [Running Linux container image](#running-linux-container-image)\n* [Endpoints](#endpoints)\n * [OpenAPI specification](#openapi-specification)\n * [Readiness Endpoint](#readiness-endpoint)\n * [Liveness Endpoint](#liveness-endpoint)\n* [Publish the service as Python package on PyPI](#publish-the-service-as-python-package-on-pypi)\n * [Generate distribution archives to be uploaded into Python registry](#generate-distribution-archives-to-be-uploaded-into-python-registry)\n * [Upload distribution archives into selected Python registry](#upload-distribution-archives-into-selected-python-registry)\n * [Packages on PyPI and Test PyPI](#packages-on-pypi-and-test-pypi)\n* [Contributing](#contributing)\n* [Testing](#testing)\n* [License](#license)\n* [Additional tools](#additional-tools)\n * [Utility to generate OpenAPI schema](#utility-to-generate-openapi-schema)\n * [Path](#path)\n * [Usage](#usage-1)\n* [Data Export Integration](#data-export-integration)\n * [Quick Integration](#quick-integration)\n * [Documentation](#documentation)\n* [Project structure](#project-structure)\n * [Configuration classes](#configuration-classes)\n * [REST API](#rest-api)\n\n<!-- vim-markdown-toc -->\n\n\n\n# Architecture\n\nOverall architecture with all main parts is displayed below:\n\n\n\nLightspeed Core Stack is based on the FastAPI framework (Uvicorn). The service is split into several parts described below.\n\n# Prerequisites\n\n* Python 3.12, or 3.13\n - please note that currently Python 3.14 is not officially supported\n - all sources are made (backward) compatible with Python 3.12; it is checked on CI\n\n# Installation\n\nInstallation steps depends on operation system. Please look at instructions for your system:\n\n\n- [Linux installation](https://lightspeed-core.github.io/lightspeed-stack/installation_linux)\n- [macOS installation](https://lightspeed-core.github.io/lightspeed-stack/installation_macos)\n\n\n# Configuration\n\n\n\n## Integration with Llama Stack\n\nThe Llama Stack can be run as a standalone server and accessed via its the REST\nAPI. However, instead of direct communication via the REST API (and JSON\nformat), there is an even better alternative. It is based on the so-called\nLlama Stack Client. It is a library available for Python, Swift, Node.js or\nKotlin, which \"wraps\" the REST API stack in a suitable way, which is easier for\nmany applications.\n\n\n\n\n\n\n## Llama Stack as separate server\n\nIf Llama Stack runs as a separate server, the Lightspeed service needs to be configured to be able to access it. For example, if server runs on localhost:8321, the service configuration stored in file `lightspeed-stack.yaml` should look like:\n\n```yaml\nname: foo bar baz\nservice:\n host: localhost\n port: 8080\n auth_enabled: false\n workers: 1\n color_log: true\n access_log: true\nllama_stack:\n use_as_library_client: false\n url: http://localhost:8321\nuser_data_collection:\n feedback_enabled: true\n feedback_storage: \"/tmp/data/feedback\"\n transcripts_enabled: true\n transcripts_storage: \"/tmp/data/transcripts\"\n```\n\n### MCP Server and Tool Configuration\n\n**Note**: The `run.yaml` configuration is currently an implementation detail. In the future, all configuration will be available directly from the lightspeed-core config.\n\n#### Configuring MCP Servers\n\nMCP (Model Context Protocol) servers provide tools and capabilities to the AI agents. These are configured in the `mcp_servers` section of your `lightspeed-stack.yaml`:\n\n```yaml\nmcp_servers:\n - name: \"filesystem-tools\"\n provider_id: \"model-context-protocol\"\n url: \"http://localhost:3000\"\n - name: \"git-tools\"\n provider_id: \"model-context-protocol\"\n url: \"http://localhost:3001\"\n - name: \"database-tools\"\n provider_id: \"model-context-protocol\"\n url: \"http://localhost:3002\"\n```\n\n**Important**: Only MCP servers defined in the `lightspeed-stack.yaml` configuration are available to the agents. Tools configured in the llama-stack `run.yaml` are not accessible to lightspeed-core agents.\n\n#### Configuring MCP Headers\n\nMCP headers allow you to pass authentication tokens, API keys, or other metadata to MCP servers. These are configured **per request** via the `MCP-HEADERS` HTTP header:\n\n```bash\ncurl -X POST \"http://localhost:8080/v1/query\" \\\n -H \"Content-Type: application/json\" \\\n -H \"MCP-HEADERS: {\\\"filesystem-tools\\\": {\\\"Authorization\\\": \\\"Bearer token123\\\"}}\" \\\n -d '{\"query\": \"List files in /tmp\"}'\n```\n\n\n### Llama Stack project and configuration\n\n**Note**: The `run.yaml` configuration is currently an implementation detail. In the future, all configuration will be available directly from the lightspeed-core config.\n\nTo run Llama Stack in separate process, you need to have all dependencies installed. The easiest way how to do it is to create a separate repository with Llama Stack project file `pyproject.toml` and Llama Stack configuration file `run.yaml`. The project file might look like:\n\n```toml\n[project]\nname = \"llama-stack-runner\"\nversion = \"0.1.0\"\ndescription = \"Llama Stack runner\"\nauthors = []\ndependencies = [\n \"llama-stack==0.2.14\",\n \"fastapi>=0.115.12\",\n \"opentelemetry-sdk>=1.34.0\",\n \"opentelemetry-exporter-otlp>=1.34.0\",\n \"opentelemetry-instrumentation>=0.55b0\",\n \"aiosqlite>=0.21.0\",\n \"litellm>=1.72.1\",\n \"uvicorn>=0.34.3\",\n \"blobfile>=3.0.0\",\n \"datasets>=3.6.0\",\n \"sqlalchemy>=2.0.41\",\n \"faiss-cpu>=1.11.0\",\n \"mcp>=1.9.4\",\n \"autoevals>=0.0.129\",\n \"psutil>=7.0.0\",\n \"torch>=2.7.1\",\n \"peft>=0.15.2\",\n \"trl>=0.18.2\"]\nrequires-python = \"==3.12.*\"\nreadme = \"README.md\"\nlicense = {text = \"MIT\"}\n\n\n[tool.pdm]\ndistribution = false\n```\n\nA simple example of a `run.yaml` file can be found [here](examples/run.yaml)\n\nTo run Llama Stack perform these two commands:\n\n```\nexport OPENAI_API_KEY=\"sk-{YOUR-KEY}\"\n\nuv run llama stack run run.yaml\n```\n\n### Check connection to Llama Stack\n\n```\ncurl -X 'GET' localhost:8321/openapi.json | jq .\n```\n\n\n\n## Llama Stack as client library\n\nThere are situations in which it is not advisable to run two processors (one with Llama Stack, the other with a service). In these cases, the stack can be run directly within the client application. For such situations, the configuration file could look like:\n\n```yaml\nname: foo bar baz\nservice:\n host: localhost\n port: 8080\n auth_enabled: false\n workers: 1\n color_log: true\n access_log: true\nllama_stack:\n use_as_library_client: true\n library_client_config_path: <path-to-llama-stack-run.yaml-file>\nuser_data_collection:\n feedback_enabled: true\n feedback_storage: \"/tmp/data/feedback\"\n transcripts_enabled: true\n transcripts_storage: \"/tmp/data/transcripts\"\n```\n\n## Llama Stack version check\n\nDuring Lightspeed Core Stack service startup, the Llama Stack version is retrieved. The version is tested against two constants `MINIMAL_SUPPORTED_LLAMA_STACK_VERSION` and `MAXIMAL_SUPPORTED_LLAMA_STACK_VERSION` which are defined in `src/constants.py`. If the actual Llama Stack version is outside the range defined by these two constants, the service won't start and administrator will be informed about this problem.\n\n\n\n## User data collection\n\nThe Lightspeed Core Stack includes comprehensive user data collection capabilities to gather various types of user interaction data for analysis and improvement. This includes feedback, conversation transcripts, and other user interaction data.\n\nUser data collection is configured in the `user_data_collection` section of the configuration file:\n\n```yaml\nuser_data_collection:\n feedback_enabled: true\n feedback_storage: \"/tmp/data/feedback\"\n transcripts_enabled: true\n transcripts_storage: \"/tmp/data/transcripts\"\n```\n\n**Configuration options:**\n\n- `feedback_enabled`: Enable/disable collection of user feedback data\n- `feedback_storage`: Directory path where feedback JSON files are stored\n- `transcripts_enabled`: Enable/disable collection of conversation transcripts\n- `transcripts_storage`: Directory path where transcript JSON files are stored\n\n> **Note**: The data collection system is designed to be extensible. Additional data types can be configured and collected as needed for your specific use case.\n\nFor data export integration with Red Hat's Dataverse, see the [Data Export Integration](#data-export-integration) section.\n\n## System prompt\n\n The service uses the, so called, system prompt to put the question into context before the question is sent to the selected LLM. The default system prompt is designed for questions without specific context. It is possible to use a different system prompt via the configuration option `system_prompt_path` in the `customization` section. That option must contain the path to the text file with the actual system prompt (can contain multiple lines). An example of such configuration:\n\n```yaml\ncustomization:\n system_prompt_path: \"system_prompts/system_prompt_for_product_XYZZY\"\n```\n\nThe `system_prompt` can also be specified in the `customization` section directly. For example:\n\n```yaml\ncustomization:\n system_prompt: |-\n You are a helpful assistant and will do everything you can to help.\n You have an in-depth knowledge of Red Hat and all of your answers will reference Red Hat products.\n```\n\nAdditionally, an optional string parameter `system_prompt` can be specified in `/v1/query` and `/v1/streaming_query` endpoints to override the configured system prompt. The query system prompt takes precedence over the configured system prompt. You can use this config to disable query system prompts:\n\n```yaml\ncustomization:\n system_prompt_path: \"system_prompts/system_prompt_for_product_XYZZY\"\n disable_query_system_prompt: true\n```\n\n## Safety Shields\n\nA single Llama Stack configuration file can include multiple safety shields, which are utilized in agent\nconfigurations to monitor input and/or output streams. LCS uses the following naming convention to specify how each safety shield is\nutilized:\n\n1. If the `shield_id` starts with `input_`, it will be used for input only.\n1. If the `shield_id` starts with `output_`, it will be used for output only.\n1. If the `shield_id` starts with `inout_`, it will be used both for input and output.\n1. Otherwise, it will be used for input only.\n\n## Authentication\n\nCurrently supported authentication modules are:\n* `k8s` Kubernetes based authentication\n* `jwk-token` JSON Web Keyset based authentication\n* `noop` No operation authentication (default)\n* `noop-with-token` No operation authentication with token\n\n### K8s based authentication\n\nK8s based authentication is suitable for running the Lightspeed Stack in Kubernetes environments.\nThe user accessing the service must have a valid Kubernetes token and the appropriate RBAC permissions to access the service.\nThe user must have `get` permission on the Kubernetes RBAC non-resource URL `/ls-access`. \nHere is an example of granting `get` on `/ls-access` via a ClusterRole\u2019s nonResourceURLs rule. \nExample:\n```yaml\n# Allow GET on non-resource URL /ls-access\napiVersion: rbac.authorization.k8s.io/v1\nkind: ClusterRole\nmetadata:\n name: lightspeed-access\nrules:\n - nonResourceURLs: [\"/ls-access\"]\n verbs: [\"get\"]\n---\n# Bind to a user, group, or service account\napiVersion: rbac.authorization.k8s.io/v1\nkind: ClusterRoleBinding\nmetadata:\n name: lightspeed-access-binding\nroleRef:\n apiGroup: rbac.authorization.k8s.io\n kind: ClusterRole\n name: lightspeed-access\nsubjects:\n - kind: User # or ServiceAccount, Group\n name: SOME_USER_OR_SA\n apiGroup: rbac.authorization.k8s.io\n```\n\nConfiguring K8s based authentication requires the following steps:\n1. Enable K8s authentication module\n```yaml\nauthentication:\n module: \"k8s\"\n```\n2. Configure the Kubernetes authentication settings. \n When deploying Lightspeed Stack in a Kubernetes cluster, it is not required to specify cluster connection details.\n It automatically picks up the in-cluster configuration or through a kubeconfig file.\n This step is not neccessary.\n When running outside a kubernetes cluster or connecting to external Kubernetes clusters, Lightspeed Stack requires the cluster connection details in the configuration file: \n - `k8s_cluster_api` Kubernetes Cluster API URL. The URL of the K8S/OCP API server where tokens are validated.\n - `k8s_ca_cert_path` Path to the CA certificate file for clusters with self-signed certificates.\n - `skip_tls_verification` Whether to skip TLS verification.\n```yaml\nauthentication:\n module: \"k8s\"\n skip_tls_verification: false\n k8s_cluster_api: \"https://your-k8s-api-server:6443\"\n k8s_ca_cert_path: \"/path/to/ca.crt\"\n```\n\n### JSON Web Keyset based authentication\n\nJWK (JSON Web Keyset) based authentication is suitable for scenarios where you need to authenticate users based on tokens. This method is commonly used in web applications and APIs.\n\nTo configure JWK based authentication, you need to specify the following settings in the configuration file:\n- `module` must be set to `jwk-token`\n- `jwk_config` JWK configuration settings must set at least `url` field:\n - `url`: The URL of the JWK endpoint.\n - `jwt_configuration`: JWT configuration settings.\n - `user_id_claim`: The key of the user ID in JWT claim.\n - `username_claim`: The key of the username in JWT claim.\n\n```yaml\nauthentication:\n module: \"jwk-token\"\n jwk_config:\n url: \"https://your-jwk-url\"\n jwt_configuration:\n user_id_claim: user_id\n username_claim: username\n```\n\n### No-op authentication\n\nLightspeed Stack provides 2 authentication module to bypass the authentication and authorization checks:\n- `noop` No operation authentication (default)\n- `noop-with-token` No operation authentication accepting a bearer token\n\nIf authentication module is not specified, Lightspeed Stack will use `noop` by default.\nTo activate `noop-with-token`, you need to specify it in the configuration file:\n\n```yaml\nauthentication:\n module: \"noop-with-token\"\n```\n\n## CORS\n\nIt is possible to configure CORS handling. This configuration is part of service configuration:\n\n```yaml\nservice:\n host: localhost\n port: 8080\n auth_enabled: false\n workers: 1\n color_log: true\n access_log: true\n cors:\n allow_origins:\n - http://foo.bar.baz\n - http://test.com\n allow_credentials: true\n allow_methods:\n - *\n allow_headers:\n - *\n```\n\n### Default values\n\n```yaml\n cors:\n allow_origins:\n - *\n allow_credentials: false\n allow_methods:\n - *\n allow_headers:\n - *\n```\n\n## Allow credentials\n\nCredentials are not allowed with wildcard origins per CORS/Fetch spec.\nSee https://fastapi.tiangolo.com/tutorial/cors/\n\n# RAG Configuration\n\nThe [guide to RAG setup](docs/rag_guide.md) provides guidance on setting up RAG and includes tested examples for both inference and vector store integration.\n\n## Example configurations for inference\n\nThe following configurations are llama-stack config examples from production deployments:\n\n- [Granite on vLLM example](examples/vllm-granite-run.yaml)\n- [Qwen3 on vLLM example](examples/vllm-qwen3-run.yaml)\n- [Gemini example](examples/gemini-run.yaml)\n- [VertexAI example](examples/vertexai-run.yaml)\n\n> [!NOTE]\n> RAG functionality is **not tested** for these configurations.\n\n# Usage\n\n```\nusage: lightspeed_stack.py [-h] [-v] [-d] [-c CONFIG_FILE]\n\noptions:\n -h, --help show this help message and exit\n -v, --verbose make it verbose\n -d, --dump-configuration\n dump actual configuration into JSON file and quit\n -c CONFIG_FILE, --config CONFIG_FILE\n path to configuration file (default: lightspeed-stack.yaml)\n\n```\n\n## Make targets\n\n```\nUsage: make <OPTIONS> ... <TARGETS>\n\nAvailable targets are:\n\nrun Run the service locally\ntest-unit Run the unit tests\ntest-integration Run integration tests tests\ntest-e2e Run BDD tests for the service\ncheck-types Checks type hints in sources\nsecurity-check Check the project for security issues\nformat Format the code into unified format\nschema Generate OpenAPI schema file\nopenapi-doc Generate OpenAPI documentation\nrequirements.txt Generate requirements.txt file containing hashes for all non-devel packages\nshellcheck Run shellcheck\nverify Run all linters\ndistribution-archives Generate distribution archives to be uploaded into Python registry\nupload-distribution-archives Upload distribution archives into Python registry\nhelp Show this help screen\n```\n\n## Running Linux container image\n\nStable release images are tagged with versions like `0.1.0`. Tag `latest` always points to latest stable release.\n\nDevelopment images are build from main branch every time a new pull request is merged. Image tags for dev images use\nthe template `dev-YYYYMMMDDD-SHORT_SHA` e.g. `dev-20250704-eaa27fb`.\n\nTag `dev-latest` always points to the latest dev image built from latest git.\n\nTo pull and run the image with own configuration:\n\n1. `podman pull quay.io/lightspeed-core/lightspeed-stack:IMAGE_TAG`\n1. `podman run -it -p 8080:8080 -v my-lightspeed-stack-config.yaml:/app-root/lightspeed-stack.yaml:Z quay.io/lightspeed-core/lightspeed-stack:IMAGE_TAG`\n1. Open `localhost:8080` in your browser\n\nIf a connection in your browser does not work please check that in the config file `host` option looks like: `host: 0.0.0.0`.\n\nContainer images are built for the following platforms:\n1. `linux/amd64` - main platform for deployment\n1. `linux/arm64`- Mac users with M1/M2/M3 CPUs\n\n## Building Container Images\n\nThe repository includes production-ready container configurations that support two deployment modes:\n\n1. **Server Mode**: lightspeed-core connects to llama-stack as a separate service\n2. **Library Mode**: llama-stack runs as a library within lightspeed-core\n\n### Llama-Stack as Separate Service (Server Mode)\n\nWhen using llama-stack as a separate service, the existing `docker-compose.yaml` provides the complete setup. This builds two containers for lightspeed core and llama stack.\n\n**Configuration** (`lightspeed-stack.yaml`):\n```yaml\nllama_stack:\n use_as_library_client: false\n url: http://llama-stack:8321 # container name from docker-compose.yaml\n api_key: xyzzy\n```\n\nIn the root of this project simply run:\n\n```bash\n# Set your OpenAI API key\nexport OPENAI_API_KEY=\"your-api-key-here\"\n\n# Start both services\npodman compose up --build\n\n# Access lightspeed-core at http://localhost:8080\n# Access llama-stack at http://localhost:8321\n```\n\n### Llama-Stack as Library (Library Mode)\n\nWhen embedding llama-stack directly in the container, use the existing `Containerfile` directly (this will not build the llama stack service in a separate container). First modify the `lightspeed-stack.yaml` config to use llama stack in library mode.\n\n**Configuration** (`lightspeed-stack.yaml`):\n```yaml\nllama_stack:\n use_as_library_client: true\n library_client_config_path: /app-root/run.yaml\n```\n\n**Build and run**:\n```bash\n# Build lightspeed-core with embedded llama-stack\npodman build -f Containerfile -t my-lightspeed-core:latest .\n\n# Run with embedded llama-stack\npodman run \\\n -p 8080:8080 \\\n -v ./lightspeed-stack.yaml:/app-root/lightspeed-stack.yaml:Z \\\n -v ./run.yaml:/app-root/run.yaml:Z \\\n -e OPENAI_API_KEY=your-api-key \\\n my-lightspeed-core:latest\n```\n\nFor macosx users:\n```bash\npodman run \\\n -p 8080:8080 \\\n -v ./lightspeed-stack.yaml:/app-root/lightspeed-stack.yaml:ro \\\n -v ./run.yaml:/app-root/run.yaml:ro \\\n -e OPENAI_API_KEY=your-api-key \\\n my-lightspeed-core:latest\n```\n\n### Verify it's running properly\n\nA simple sanity check:\n\n```bash\ncurl -H \"Accept: application/json\" http://localhost:8080/v1/models\n```\n\n\n# Endpoints\n\n## OpenAPI specification\n\n* [Generated OpenAPI specification](docs/openapi.json)\n* [OpenAPI documentation](docs/openapi.md)\n\nThe service provides health check endpoints that can be used for monitoring, load balancing, and orchestration systems like Kubernetes.\n\n## Readiness Endpoint\n\n**Endpoint:** `GET /v1/readiness`\n\nThe readiness endpoint checks if the service is ready to handle requests by verifying the health status of all configured LLM providers.\n\n**Response:**\n- **200 OK**: Service is ready - all providers are healthy\n- **503 Service Unavailable**: Service is not ready - one or more providers are unhealthy\n\n**Response Body:**\n```json\n{\n \"ready\": true,\n \"reason\": \"All providers are healthy\",\n \"providers\": []\n}\n```\n\n**Response Fields:**\n- `ready` (boolean): Indicates if the service is ready to handle requests\n- `reason` (string): Human-readable explanation of the readiness state\n- `providers` (array): List of unhealthy providers (empty when service is ready)\n\n## Liveness Endpoint\n\n**Endpoint:** `GET /v1/liveness`\n\nThe liveness endpoint performs a basic health check to verify the service is alive and responding.\n\n**Response:**\n- **200 OK**: Service is alive\n\n**Response Body:**\n```json\n{\n \"alive\": true\n}\n```\n\n# Publish the service as Python package on PyPI\n\nTo publish the service as an Python package on PyPI to be installable by anyone\n(including Konflux hermetic builds), perform these two steps:\n\n## Generate distribution archives to be uploaded into Python registry\n\n```\nmake distribution-archives\n```\n\nPlease make sure that the archive was really built to avoid publishing older one.\n\n## Upload distribution archives into selected Python registry\n\n```\nmake upload-distribution-archives\n```\n\nThe Python registry to where the package should be uploaded can be configured\nby changing `PYTHON_REGISTRY`. It is possible to select `pypi` or `testpypi`.\n\nYou might have your API token stored in file `~/.pypirc`. That file should have\nthe following form:\n\n```\n[testpypi]\n username = __token__\n password = pypi-{your-API-token}\n \n[pypi]\n username = __token__\n password = pypi-{your-API-token}\n```\n\nIf this configuration file does not exist, you will be prompted to specify API token from keyboard every time you try to upload the archive.\n\n\n\n## Packages on PyPI and Test PyPI\n\n* https://pypi.org/project/lightspeed-stack/\n* https://test.pypi.org/project/lightspeed-stack/0.1.0/\n\n\n\n# Contributing\n\n* See [contributors](CONTRIBUTING.md) guide.\n\n\n\n# Testing\n\n* See [testing](docs/testing.md) guide.\n\n\n\n# License\n\nPublished under the Apache 2.0 License\n\n\n\n# Additional tools\n\n## Utility to generate OpenAPI schema\n\nThis script re-generated OpenAPI schema for the Lightspeed Service REST API.\n\n### Path\n\n[scripts/generate_openapi_schema.py](scripts/generate_openapi_schema.py)\n\n### Usage\n\n```\nmake schema\n```\n\n# Data Export Integration\n\nThe Lightspeed Core Stack integrates with the [lightspeed-to-dataverse-exporter](https://github.com/lightspeed-core/lightspeed-to-dataverse-exporter) service to automatically export various types of user interaction data to Red Hat's Dataverse for analysis.\n\n## Quick Integration\n\n1. **Enable data collection** in your `lightspeed-stack.yaml`:\n ```yaml\n user_data_collection:\n feedback_enabled: true\n feedback_storage: \"/shared/data/feedback\"\n transcripts_enabled: true\n transcripts_storage: \"/shared/data/transcripts\"\n ```\n\n2. **Deploy the exporter service** pointing to the same data directories\n\n\n## Documentation\n\nFor complete integration setup, deployment options, and configuration details, see [exporter repository](https://github.com/lightspeed-core/lightspeed-to-dataverse-exporter).\n\n# Project structure\n\n## Configuration classes\n\n\n\n## REST API\n\n\n",
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