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as title
test plan:
```yaml
# custom-ollama-run.yaml
version: 2
image_name: starter
external_providers_dir: /.llama/providers.d
apis:
- inference
- vector_io
- files
- safety
- tool_runtime
- agents
providers:
inference:
# Single Ollama provider for all models
- provider_id: ollama
provider_type: remote::ollama
config:
url: ${env.OLLAMA_URL:=http://localhost:11434}
vector_io:
- provider_id: faiss
provider_type: inline::faiss
config:
persistence:
namespace: vector_io::faiss
backend: kv_default
files:
- provider_id: meta-reference-files
provider_type: inline::localfs
config:
storage_dir: /.llama/files
metadata_store:
table_name: files_metadata
backend: sql_default
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config:
excluded_categories: []
tool_runtime:
- provider_id: rag-runtime
provider_type: inline::rag-runtime
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
storage:
backends:
kv_default:
type: kv_sqlite
db_path: /.llama/kvstore.db
sql_default:
type: sql_sqlite
db_path: /.llama/sql_store.db
stores:
metadata:
namespace: registry
backend: kv_default
inference:
table_name: inference_store
backend: sql_default
max_write_queue_size: 10000
num_writers: 4
conversations:
table_name: openai_conversations
backend: sql_default
registered_resources:
models:
# All models use the same 'ollama' provider
- model_id: llama3.2-vision:latest
provider_id: ollama
provider_model_id: llama3.2-vision:latest
model_type: llm
- model_id: llama3.2:3b
provider_id: ollama
provider_model_id: llama3.2:3b
model_type: llm
# Embedding models
- model_id: nomic-embed-text-v2-moe
provider_id: ollama
provider_model_id: toshk0/nomic-embed-text-v2-moe:Q6_K
model_type: embedding
metadata:
embedding_dimension: 768
shields: []
vector_dbs: []
datasets: []
scoring_fns: []
benchmarks: []
tool_groups: []
server:
port: 8321
telemetry:
enabled: true
vector_stores:
default_provider_id: faiss
default_embedding_model:
provider_id: ollama
model_id: toshk0/nomic-embed-text-v2-moe:Q6_K
```
```bash
docker run
-it
--pull always
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT
-v ~/.llama:/root/.llama
-v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml
-e RUN_CONFIG_PATH=/app/custom-run.yaml
-e OLLAMA_URL=http://host.docker.internal:11434/
llamastack/distribution-starter:0.3.0
--port $LLAMA_STACK_PORT
```
|
||
|---|---|---|
| .. | ||
| docs | ||
| notebooks | ||
| openapi_generator | ||
| scripts | ||
| src | ||
| static | ||
| supplementary | ||
| zero_to_hero_guide | ||
| docusaurus.config.ts | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| original_rfc.md | ||
| package-lock.json | ||
| package.json | ||
| quick_start.ipynb | ||
| README.md | ||
| sidebars.ts | ||
| tsconfig.json | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our Github page.
Render locally
From the llama-stack docs/ directory, run the following commands to render the docs locally:
npm install
npm run gen-api-docs all
npm run build
npm run serve
You can open up the docs in your browser at http://localhost:3000
File Import System
This documentation uses remark-code-import to import files directly from the repository, eliminating copy-paste maintenance. Files are automatically embedded during build time.
Importing Code Files
To import Python code (or any code files) with syntax highlighting, use this syntax in .mdx files:
```python file=./demo_script.py title="demo_script.py"
This automatically imports the file content and displays it as a formatted code block with Python syntax highlighting.
**Note:** Paths are relative to the current `.mdx` file location, not the repository root.
### Importing Markdown Files as Content
For importing and rendering markdown files (like CONTRIBUTING.md), use the raw-loader approach:
```jsx
import Contributing from '!!raw-loader!../../../CONTRIBUTING.md';
import ReactMarkdown from 'react-markdown';
<ReactMarkdown>{Contributing}</ReactMarkdown>
Requirements:
- Install dependencies:
npm install --save-dev raw-loader react-markdown
Path Resolution:
- For
remark-code-import: Paths are relative to the current.mdxfile location - For
raw-loader: Paths are relative to the current.mdxfile location - Use
../to navigate up directories as needed
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack