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docs: concepts and building_applications migration (#3534)

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# What does this PR do?

- Migrates the remaining documentation sections to the new documentation format

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## Test Plan

- Partial migration

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# Starter Distribution
```{toctree}
:maxdepth: 2
:hidden:
self
```
The `llamastack/distribution-starter` distribution is a comprehensive, multi-provider distribution that includes most of the available inference providers in Llama Stack. It's designed to be a one-stop solution for developers who want to experiment with different AI providers without having to configure each one individually.
## Provider Composition
The starter distribution consists of the following provider configurations:
| API | Provider(s) |
|-----|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| agents | `inline::meta-reference` |
| datasetio | `remote::huggingface`, `inline::localfs` |
| eval | `inline::meta-reference` |
| files | `inline::localfs` |
| inference | `remote::openai`, `remote::fireworks`, `remote::together`, `remote::ollama`, `remote::anthropic`, `remote::gemini`, `remote::groq`, `remote::sambanova`, `remote::vllm`, `remote::tgi`, `remote::cerebras`, `remote::llama-openai-compat`, `remote::nvidia`, `remote::hf::serverless`, `remote::hf::endpoint`, `inline::sentence-transformers` |
| safety | `inline::llama-guard` |
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
| telemetry | `inline::meta-reference` |
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
| vector_io | `inline::faiss`, `inline::sqlite-vec`, `inline::milvus`, `remote::chromadb`, `remote::pgvector` |
## Inference Providers
The starter distribution includes a comprehensive set of inference providers:
### Hosted Providers
- **[OpenAI](https://openai.com/api/)**: GPT-4, GPT-3.5, O1, O3, O4 models and text embeddings -
provider ID: `openai` - reference documentation: [openai](../../providers/inference/remote_openai.md)
- **[Fireworks](https://fireworks.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models and
embeddings - provider ID: `fireworks` - reference documentation: [fireworks](../../providers/inference/remote_fireworks.md)
- **[Together](https://together.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models and
embeddings - provider ID: `together` - reference documentation: [together](../../providers/inference/remote_together.md)
- **[Anthropic](https://www.anthropic.com/)**: Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 3.5 Haiku, and Voyage embeddings - provider ID: `anthropic` - reference documentation: [anthropic](../../providers/inference/remote_anthropic.md)
- **[Gemini](https://gemini.google.com/)**: Gemini 1.5, 2.0, 2.5 models and text embeddings - provider ID: `gemini` - reference documentation: [gemini](../../providers/inference/remote_gemini.md)
- **[Groq](https://groq.com/)**: Fast Llama models (3.1, 3.2, 3.3, 4 Scout, 4 Maverick) - provider ID: `groq` - reference documentation: [groq](../../providers/inference/remote_groq.md)
- **[SambaNova](https://www.sambanova.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models - provider ID: `sambanova` - reference documentation: [sambanova](../../providers/inference/remote_sambanova.md)
- **[Cerebras](https://www.cerebras.ai/)**: Cerebras AI models - provider ID: `cerebras` - reference documentation: [cerebras](../../providers/inference/remote_cerebras.md)
- **[NVIDIA](https://www.nvidia.com/)**: NVIDIA NIM - provider ID: `nvidia` - reference documentation: [nvidia](../../providers/inference/remote_nvidia.md)
- **[HuggingFace](https://huggingface.co/)**: Serverless and endpoint models - provider ID: `hf::serverless` and `hf::endpoint` - reference documentation: [huggingface-serverless](../../providers/inference/remote_hf_serverless.md) and [huggingface-endpoint](../../providers/inference/remote_hf_endpoint.md)
- **[Bedrock](https://aws.amazon.com/bedrock/)**: AWS Bedrock models - provider ID: `bedrock` - reference documentation: [bedrock](../../providers/inference/remote_bedrock.md)
### Local/Remote Providers
- **[Ollama](https://ollama.ai/)**: Local Ollama models - provider ID: `ollama` - reference documentation: [ollama](../../providers/inference/remote_ollama.md)
- **[vLLM](https://docs.vllm.ai/en/latest/)**: Local or remote vLLM server - provider ID: `vllm` - reference documentation: [vllm](../../providers/inference/remote_vllm.md)
- **[TGI](https://github.com/huggingface/text-generation-inference)**: Text Generation Inference server - Dell Enterprise Hub's custom TGI container too (use `DEH_URL`) - provider ID: `tgi` - reference documentation: [tgi](../../providers/inference/remote_tgi.md)
- **[Sentence Transformers](https://www.sbert.net/)**: Local embedding models - provider ID: `sentence-transformers` - reference documentation: [sentence-transformers](../../providers/inference/inline_sentence-transformers.md)
All providers are disabled by default. So you need to enable them by setting the environment variables.
## Vector IO
The starter distribution includes a comprehensive set of vector IO providers:
- **[FAISS](https://github.com/facebookresearch/faiss)**: Local FAISS vector store - enabled by
default - provider ID: `faiss`
- **[SQLite](https://www.sqlite.org/index.html)**: Local SQLite vector store - disabled by default - provider ID: `sqlite-vec`
- **[ChromaDB](https://www.trychroma.com/)**: Remote ChromaDB vector store - disabled by default - provider ID: `chromadb`
- **[PGVector](https://github.com/pgvector/pgvector)**: PostgreSQL vector store - disabled by default - provider ID: `pgvector`
- **[Milvus](https://milvus.io/)**: Milvus vector store - disabled by default - provider ID: `milvus`
## Environment Variables
The following environment variables can be configured:
### Server Configuration
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
### API Keys for Hosted Providers
- `OPENAI_API_KEY`: OpenAI API key
- `FIREWORKS_API_KEY`: Fireworks API key
- `TOGETHER_API_KEY`: Together API key
- `ANTHROPIC_API_KEY`: Anthropic API key
- `GEMINI_API_KEY`: Google Gemini API key
- `GROQ_API_KEY`: Groq API key
- `SAMBANOVA_API_KEY`: SambaNova API key
- `CEREBRAS_API_KEY`: Cerebras API key
- `LLAMA_API_KEY`: Llama API key
- `NVIDIA_API_KEY`: NVIDIA API key
- `HF_API_TOKEN`: HuggingFace API token
### Local Provider Configuration
- `OLLAMA_URL`: Ollama server URL (default: `http://localhost:11434`)
- `VLLM_URL`: vLLM server URL (default: `http://localhost:8000/v1`)
- `VLLM_MAX_TOKENS`: vLLM max tokens (default: `4096`)
- `VLLM_API_TOKEN`: vLLM API token (default: `fake`)
- `VLLM_TLS_VERIFY`: vLLM TLS verification (default: `true`)
- `TGI_URL`: TGI server URL
### Model Configuration
- `INFERENCE_MODEL`: HuggingFace model for serverless inference
- `INFERENCE_ENDPOINT_NAME`: HuggingFace endpoint name
### Vector Database Configuration
- `SQLITE_STORE_DIR`: SQLite store directory (default: `~/.llama/distributions/starter`)
- `ENABLE_SQLITE_VEC`: Enable SQLite vector provider
- `ENABLE_CHROMADB`: Enable ChromaDB provider
- `ENABLE_PGVECTOR`: Enable PGVector provider
- `CHROMADB_URL`: ChromaDB server URL
- `PGVECTOR_HOST`: PGVector host (default: `localhost`)
- `PGVECTOR_PORT`: PGVector port (default: `5432`)
- `PGVECTOR_DB`: PGVector database name
- `PGVECTOR_USER`: PGVector username
- `PGVECTOR_PASSWORD`: PGVector password
### Tool Configuration
- `BRAVE_SEARCH_API_KEY`: Brave Search API key
- `TAVILY_SEARCH_API_KEY`: Tavily Search API key
### Telemetry Configuration
- `OTEL_SERVICE_NAME`: OpenTelemetry service name
- `TELEMETRY_SINKS`: Telemetry sinks (default: `console,sqlite`)
## Enabling Providers
You can enable specific providers by setting appropriate environment variables. For example,
```bash
# self-hosted
export OLLAMA_URL=http://localhost:11434 # enables the Ollama inference provider
export VLLM_URL=http://localhost:8000/v1 # enables the vLLM inference provider
export TGI_URL=http://localhost:8000/v1 # enables the TGI inference provider
# cloud-hosted requiring API key configuration on the server
export CEREBRAS_API_KEY=your_cerebras_api_key # enables the Cerebras inference provider
export NVIDIA_API_KEY=your_nvidia_api_key # enables the NVIDIA inference provider
# vector providers
export MILVUS_URL=http://localhost:19530 # enables the Milvus vector provider
export CHROMADB_URL=http://localhost:8000/v1 # enables the ChromaDB vector provider
export PGVECTOR_DB=llama_stack_db # enables the PGVector vector provider
```
This distribution comes with a default "llama-guard" shield that can be enabled by setting the `SAFETY_MODEL` environment variable to point to an appropriate Llama Guard model id. Use `llama-stack-client models list` to see the list of available models.
## Running the Distribution
You can run the starter distribution via Docker or venv.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-e OPENAI_API_KEY=your_openai_key \
-e FIREWORKS_API_KEY=your_fireworks_key \
-e TOGETHER_API_KEY=your_together_key \
llamastack/distribution-starter \
--port $LLAMA_STACK_PORT
```
### Via venv
Ensure you have configured the starter distribution using the environment variables explained above.
```bash
uv run --with llama-stack llama stack build --distro starter --image-type venv --run
```
## Example Usage
Once the distribution is running, you can use any of the available models. Here are some examples:
### Using OpenAI Models
```bash
llama-stack-client --endpoint http://localhost:8321 \
inference chat-completion \
--model-id openai/gpt-4o \
--message "Hello, how are you?"
```
### Using Fireworks Models
```bash
llama-stack-client --endpoint http://localhost:8321 \
inference chat-completion \
--model-id fireworks/meta-llama/Llama-3.2-3B-Instruct \
--message "Write a short story about a robot."
```
### Using Local Ollama Models
```bash
# First, make sure Ollama is running and you have a model
ollama run llama3.2:3b
# Then use it through Llama Stack
export OLLAMA_INFERENCE_MODEL=llama3.2:3b
llama-stack-client --endpoint http://localhost:8321 \
inference chat-completion \
--model-id ollama/llama3.2:3b \
--message "Explain quantum computing in simple terms."
```
## Storage
The starter distribution uses SQLite for local storage of various components:
- **Metadata store**: `~/.llama/distributions/starter/registry.db`
- **Inference store**: `~/.llama/distributions/starter/inference_store.db`
- **FAISS store**: `~/.llama/distributions/starter/faiss_store.db`
- **SQLite vector store**: `~/.llama/distributions/starter/sqlite_vec.db`
- **Files metadata**: `~/.llama/distributions/starter/files_metadata.db`
- **Agents store**: `~/.llama/distributions/starter/agents_store.db`
- **Responses store**: `~/.llama/distributions/starter/responses_store.db`
- **Trace store**: `~/.llama/distributions/starter/trace_store.db`
- **Evaluation store**: `~/.llama/distributions/starter/meta_reference_eval.db`
- **Dataset I/O stores**: Various HuggingFace and local filesystem stores
## Benefits of the Starter Distribution
1. **Comprehensive Coverage**: Includes most popular AI providers in one distribution
2. **Flexible Configuration**: Easy to enable/disable providers based on your needs
3. **No Local GPU Required**: Most providers are cloud-based, making it accessible to developers without high-end hardware
4. **Easy Migration**: Start with hosted providers and gradually move to local ones as needed
5. **Production Ready**: Includes safety, evaluation, and telemetry components
6. **Tool Integration**: Comes with web search, RAG, and model context protocol tools
The starter distribution is ideal for developers who want to experiment with different AI providers, build prototypes quickly, or create applications that can work with multiple AI backends.