mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-07-27 06:28:50 +00:00
feat: use XDG directory standards
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
This commit is contained in:
parent
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172
docs/source/distributions/self_hosted_distro/ollama.md
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docs/source/distributions/self_hosted_distro/ollama.md
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@ -0,0 +1,172 @@
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<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# Ollama Distribution
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The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| files | `inline::localfs` |
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| inference | `remote::ollama` |
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| post_training | `inline::huggingface` |
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| safety | `inline::llama-guard` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
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- `OLLAMA_URL`: URL of the Ollama server (default: `http://127.0.0.1:11434`)
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- `INFERENCE_MODEL`: Inference model loaded into the Ollama server (default: `meta-llama/Llama-3.2-3B-Instruct`)
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- `SAFETY_MODEL`: Safety model loaded into the Ollama server (default: `meta-llama/Llama-Guard-3-1B`)
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## Prerequisites
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### Ollama Server
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This distribution requires an external Ollama server to be running. You can install and run Ollama by following these steps:
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1. **Install Ollama**: Download and install Ollama from [https://ollama.ai/](https://ollama.ai/)
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2. **Start the Ollama server**:
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```bash
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ollama serve
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```
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By default, Ollama serves on `http://127.0.0.1:11434`
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3. **Pull the required models**:
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```bash
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# Pull the inference model
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ollama pull meta-llama/Llama-3.2-3B-Instruct
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# Pull the embedding model
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ollama pull all-minilm:latest
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# (Optional) Pull the safety model for run-with-safety.yaml
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ollama pull meta-llama/Llama-Guard-3-1B
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```
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## Supported Services
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### Inference: Ollama
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Uses an external Ollama server for running LLM inference. The server should be accessible at the URL specified in the `OLLAMA_URL` environment variable.
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### Vector IO: FAISS
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Provides vector storage capabilities using FAISS for embeddings and similarity search operations.
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### Safety: Llama Guard (Optional)
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When using the `run-with-safety.yaml` configuration, provides safety checks using Llama Guard models running on the Ollama server.
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### Agents: Meta Reference
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Provides agent execution capabilities using the meta-reference implementation.
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### Post-Training: Hugging Face
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Supports model fine-tuning using Hugging Face integration.
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### Tool Runtime
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Supports various external tools including:
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- Brave Search
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- Tavily Search
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- RAG Runtime
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- Model Context Protocol
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- Wolfram Alpha
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## Running Llama Stack with Ollama
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You can do this via Conda or venv (build code), or Docker which has a pre-built image.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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LLAMA_STACK_PORT=8321
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docker run \
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-it \
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--pull always \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ./run.yaml:/root/my-run.yaml \
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llamastack/distribution-ollama \
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--config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env OLLAMA_URL=$OLLAMA_URL \
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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### Via Conda
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```bash
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llama stack build --template ollama --image-type conda
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llama stack run ./run.yaml \
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--port 8321 \
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--env OLLAMA_URL=$OLLAMA_URL \
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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### Via venv
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If you've set up your local development environment, you can also build the image using your local virtual environment.
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```bash
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llama stack build --template ollama --image-type venv
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llama stack run ./run.yaml \
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--port 8321 \
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--env OLLAMA_URL=$OLLAMA_URL \
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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### Running with Safety
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To enable safety checks, use the `run-with-safety.yaml` configuration:
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```bash
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llama stack run ./run-with-safety.yaml \
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--port 8321 \
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--env OLLAMA_URL=$OLLAMA_URL \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env SAFETY_MODEL=$SAFETY_MODEL
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```
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## Example Usage
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Once your Llama Stack server is running with Ollama, you can interact with it using the Llama Stack client:
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```python
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from llama_stack_client import LlamaStackClient
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client = LlamaStackClient(base_url="http://localhost:8321")
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# Run inference
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response = client.inference.chat_completion(
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model_id="meta-llama/Llama-3.2-3B-Instruct",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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)
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print(response.completion_message.content)
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```
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## Troubleshooting
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### Common Issues
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1. **Connection refused errors**: Ensure your Ollama server is running and accessible at the configured URL.
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2. **Model not found errors**: Make sure you've pulled the required models using `ollama pull <model-name>`.
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3. **Performance issues**: Consider using more powerful models or adjusting the Ollama server configuration for better performance.
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### Logs
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Check the Ollama server logs for any issues:
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```bash
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# Ollama logs are typically available in:
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# - macOS: ~/Library/Logs/Ollama/
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# - Linux: ~/.ollama/logs/
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```
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191
docs/source/getting_started/xdg_compliance.md
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docs/source/getting_started/xdg_compliance.md
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# XDG Base Directory Specification Compliance
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Starting with version 0.2.14, Llama Stack follows the [XDG Base Directory Specification](https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html) for organizing configuration and data files. This provides better integration with modern desktop environments and allows for more flexible customization of where files are stored.
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## Overview
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The XDG Base Directory Specification defines standard locations for different types of application data:
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- **Configuration files** (`XDG_CONFIG_HOME`): User-specific configuration files
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- **Data files** (`XDG_DATA_HOME`): User-specific data files that should persist
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- **Cache files** (`XDG_CACHE_HOME`): User-specific cache files
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- **State files** (`XDG_STATE_HOME`): User-specific state files
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## Directory Mapping
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Llama Stack now uses the following XDG-compliant directory structure:
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| Data Type | XDG Directory | Default Location | Description |
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|-----------|---------------|------------------|-------------|
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| Configuration | `XDG_CONFIG_HOME` | `~/.config/llama-stack` | Distribution configs, provider configs |
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| Data | `XDG_DATA_HOME` | `~/.local/share/llama-stack` | Model checkpoints, persistent files |
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| Cache | `XDG_CACHE_HOME` | `~/.cache/llama-stack` | Temporary cache files |
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| State | `XDG_STATE_HOME` | `~/.local/state/llama-stack` | Runtime state, databases |
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## Environment Variables
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You can customize the locations by setting these environment variables:
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```bash
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# Override the base directories
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export XDG_CONFIG_HOME="/custom/config/path"
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export XDG_DATA_HOME="/custom/data/path"
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export XDG_CACHE_HOME="/custom/cache/path"
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export XDG_STATE_HOME="/custom/state/path"
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# Or override specific Llama Stack directories
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export SQLITE_STORE_DIR="/custom/database/path"
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export FILES_STORAGE_DIR="/custom/files/path"
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```
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## Backwards Compatibility
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Llama Stack maintains full backwards compatibility with existing installations:
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1. **Legacy Environment Variable**: If `LLAMA_STACK_CONFIG_DIR` is set, it will be used for all directories
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2. **Legacy Directory Detection**: If `~/.llama` exists and contains data, it will continue to be used
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3. **Gradual Migration**: New installations use XDG paths, existing installations continue to work
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## Migration Guide
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### Automatic Migration
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Use the built-in migration command to move from legacy `~/.llama` to XDG-compliant directories:
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```bash
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# Preview what would be migrated
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llama migrate-xdg --dry-run
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# Perform the migration
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llama migrate-xdg
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```
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### Manual Migration
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If you prefer to migrate manually, here's the mapping:
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```bash
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# Create XDG directories
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mkdir -p ~/.config/llama-stack
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mkdir -p ~/.local/share/llama-stack
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mkdir -p ~/.local/state/llama-stack
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# Move configuration files
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mv ~/.llama/distributions ~/.config/llama-stack/
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mv ~/.llama/providers.d ~/.config/llama-stack/
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# Move data files
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mv ~/.llama/checkpoints ~/.local/share/llama-stack/
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# Move state files
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mv ~/.llama/runtime ~/.local/state/llama-stack/
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# Clean up empty legacy directory
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rmdir ~/.llama
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```
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### Environment Variables in Configurations
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Template configurations now use XDG-compliant environment variables:
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```yaml
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# Old format
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/registry.db
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# New format
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_STATE_HOME:-~/.local/state}/llama-stack/distributions/ollama}/registry.db
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```
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## Configuration Examples
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### Using Custom XDG Directories
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```bash
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# Set custom XDG directories
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export XDG_CONFIG_HOME="/opt/llama-stack/config"
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export XDG_DATA_HOME="/opt/llama-stack/data"
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export XDG_STATE_HOME="/opt/llama-stack/state"
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# Start Llama Stack
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llama stack run my-distribution.yaml
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```
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### Using Legacy Directory
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```bash
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# Continue using legacy directory
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export LLAMA_STACK_CONFIG_DIR="/home/user/.llama"
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# Start Llama Stack
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llama stack run my-distribution.yaml
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```
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### Custom Database and File Locations
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```bash
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# Override specific directories
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export SQLITE_STORE_DIR="/fast/ssd/llama-stack/databases"
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export FILES_STORAGE_DIR="/large/disk/llama-stack/files"
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# Start Llama Stack
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llama stack run my-distribution.yaml
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```
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## Benefits of XDG Compliance
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1. **Standards Compliance**: Follows established Linux/Unix conventions
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2. **Better Organization**: Separates configuration, data, cache, and state files
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3. **Flexibility**: Easy to customize storage locations
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4. **Backup-Friendly**: Easier to backup just data files or just configuration
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5. **Multi-User Support**: Better support for shared systems
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6. **Cross-Platform**: Works consistently across different environments
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## Template Updates
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All distribution templates have been updated to use XDG-compliant paths:
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- Database files use `XDG_STATE_HOME`
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- Model checkpoints use `XDG_DATA_HOME`
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- Configuration files use `XDG_CONFIG_HOME`
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- Cache files use `XDG_CACHE_HOME`
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## Troubleshooting
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### Migration Issues
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If you encounter issues during migration:
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1. **Check Permissions**: Ensure you have write permissions to target directories
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2. **Disk Space**: Verify sufficient disk space in target locations
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3. **Existing Files**: Handle conflicts with existing files in target locations
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### Environment Variable Conflicts
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If you have multiple environment variables set:
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1. `LLAMA_STACK_CONFIG_DIR` takes highest precedence
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2. Individual `XDG_*` variables override defaults
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3. Fallback to legacy `~/.llama` if it exists
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4. Default to XDG standard paths for new installations
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### Debugging Path Resolution
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To see which paths Llama Stack is using:
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```python
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from llama_stack.distribution.utils.xdg_utils import (
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get_llama_stack_config_dir,
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get_llama_stack_data_dir,
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get_llama_stack_state_dir,
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)
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print(f"Config: {get_llama_stack_config_dir()}")
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print(f"Data: {get_llama_stack_data_dir()}")
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print(f"State: {get_llama_stack_state_dir()}")
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```
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## Future Considerations
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- Container deployments will continue to use `/app` or similar paths
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- Cloud deployments may use provider-specific storage systems
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- The XDG specification primarily applies to local development and single-user systems
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@ -16,10 +16,10 @@ Meta's reference implementation of an agent system that can use tools, access ve
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```yaml
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persistence_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/agents_store.db
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/agents_store.db
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responses_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/responses_store.db
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/responses_store.db
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```
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|
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@ -15,7 +15,7 @@ Local filesystem-based dataset I/O provider for reading and writing datasets to
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```yaml
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/localfs_datasetio.db
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/localfs_datasetio.db
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```
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|
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@ -15,7 +15,7 @@ HuggingFace datasets provider for accessing and managing datasets from the Huggi
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```yaml
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/huggingface_datasetio.db
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/huggingface_datasetio.db
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```
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|
|
|
@ -15,7 +15,7 @@ Meta's reference implementation of evaluation tasks with support for multiple la
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```yaml
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/meta_reference_eval.db
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```
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|
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@ -15,10 +15,10 @@ Local filesystem-based file storage provider for managing files and documents lo
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## Sample Configuration
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```yaml
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storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/dummy/files}
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storage_dir: ${env.FILES_STORAGE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy/files}
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/files_metadata.db
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db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/files_metadata.db
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```
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|
|
|
@ -11,14 +11,14 @@ Meta's reference implementation of telemetry and observability using OpenTelemet
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| `otel_exporter_otlp_endpoint` | `str \| None` | No | | The OpenTelemetry collector endpoint URL (base URL for traces, metrics, and logs). If not set, the SDK will use OTEL_EXPORTER_OTLP_ENDPOINT environment variable. |
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| `service_name` | `<class 'str'>` | No | | The service name to use for telemetry |
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| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [<TelemetrySink.CONSOLE: 'console'>, <TelemetrySink.SQLITE: 'sqlite'>] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console) |
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| `sqlite_db_path` | `<class 'str'>` | No | ~/.llama/runtime/trace_store.db | The path to the SQLite database to use for storing traces |
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| `sqlite_db_path` | `<class 'str'>` | No | ${env.XDG_STATE_HOME:-~/.local/state}/llama-stack/runtime/trace_store.db | The path to the SQLite database to use for storing traces |
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## Sample Configuration
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```yaml
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service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
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sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
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sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/trace_store.db
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sqlite_db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/trace_store.db
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otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
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```
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|
|
|
@ -44,7 +44,7 @@ more details about Faiss in general.
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```yaml
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kvstore:
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type: sqlite
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||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/faiss_store.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -15,7 +15,7 @@ Meta's reference implementation of a vector database.
|
|||
```yaml
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/faiss_store.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -17,10 +17,10 @@ Please refer to the remote provider documentation.
|
|||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
db_path: ${env.MILVUS_DB_PATH:=~/.llama/dummy}/milvus.db
|
||||
db_path: ${env.MILVUS_DB_PATH:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/milvus.db
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_registry.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/milvus_registry.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -55,7 +55,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
|
|||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
|
||||
path: ${env.QDRANT_PATH:=~/.llama/${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/qdrant.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -211,10 +211,10 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
|
|||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/sqlite_vec.db
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/sqlite_vec_registry.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -16,10 +16,10 @@ Please refer to the sqlite-vec provider documentation.
|
|||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/sqlite_vec.db
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/sqlite_vec_registry.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -126,7 +126,7 @@ uri: ${env.MILVUS_ENDPOINT}
|
|||
token: ${env.MILVUS_TOKEN}
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/milvus_remote_registry.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -52,7 +52,7 @@ user: ${env.PGVECTOR_USER}
|
|||
password: ${env.PGVECTOR_PASSWORD}
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/pgvector_registry.db
|
||||
|
||||
```
|
||||
|
||||
|
|
|
@ -38,7 +38,7 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
|
|||
```yaml
|
||||
kvstore:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db
|
||||
db_path: ${env.SQLITE_STORE_DIR:=${env.XDG_DATA_HOME:-~/.local/share}/llama-stack/dummy}/weaviate_registry.db
|
||||
|
||||
```
|
||||
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue