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Restructure docs (#494)
Rendered docs at: https://llama-stack.readthedocs.io/en/doc-simplify/
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Dinesh Yeduguru
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# Getting Started
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# Getting Started with Llama Stack
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```{toctree}
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:maxdepth: 2
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:hidden:
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distributions/self_hosted_distro/index
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distributions/remote_hosted_distro/index
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distributions/ondevice_distro/index
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In this guide, we'll walk through using ollama as the inference provider and build a simple python application that uses the Llama Stack Client SDK
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Llama stack consists of a distribution server and an accompanying client SDK. The distribution server can be configured for different providers for inference, memory, agents, evals etc. This configuration is defined in a yaml file called `run.yaml`.
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Running inference on the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options. Note that each option have different necessary prerequisites. We will use ollama as the inference provider as it is the easiest to get started with.
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### Step 1. Start the inference server
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```bash
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export LLAMA_STACK_PORT=5001
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export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
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# ollama names this model differently, and we must use the ollama name when loading the model
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export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
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ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
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```
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At the end of the guide, you will have learned how to:
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- get a Llama Stack server up and running
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- set up an agent (with tool-calling and vector stores) that works with the above server
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To see more example apps built using Llama Stack, see [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main).
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## Step 1. Starting Up Llama Stack Server
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### Decide Your Build Type
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There are two ways to start a Llama Stack:
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- **Docker**: we provide a number of pre-built Docker containers allowing you to get started instantly. If you are focused on application development, we recommend this option.
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- **Conda**: the `llama` CLI provides a simple set of commands to build, configure and run a Llama Stack server containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
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Both of these provide options to run model inference using our reference implementations, Ollama, TGI, vLLM or even remote providers like Fireworks, Together, Bedrock, etc.
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### Decide Your Inference Provider
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Running inference on the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options. Note that each option have different necessary prerequisites.
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- **Do you have access to a machine with powerful GPUs?**
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If so, we suggest:
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- [distribution-meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html)
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- [distribution-tgi](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/tgi.html)
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- **Are you running on a "regular" desktop machine?**
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If so, we suggest:
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- [distribution-ollama](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html)
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- **Do you have an API key for a remote inference provider like Fireworks, Together, etc.?** If so, we suggest:
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- [distribution-together](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/together.html)
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- [distribution-fireworks](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/fireworks.html)
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- **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
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- [iOS](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/ondevice_distro/ios_sdk.html)
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- [Android](https://github.com/meta-llama/llama-stack-client-kotlin) (coming soon)
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Please see our pages in detail for the types of distributions we offer:
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1. [Self-Hosted Distribution](./distributions/self_hosted_distro/index.md): If you want to run Llama Stack inference on your local machine.
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2. [Remote-Hosted Distribution](./distributions/remote_hosted_distro/index.md): If you want to connect to a remote hosted inference provider.
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3. [On-device Distribution](./distributions/ondevice_distro/index.md): If you want to run Llama Stack inference on your iOS / Android device.
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### Table of Contents
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Once you have decided on the inference provider and distribution to use, use the following guides to get started.
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##### 1.0 Prerequisite
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```
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$ git clone git@github.com:meta-llama/llama-stack.git
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```
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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##### System Requirements
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Access to Single-Node GPU to start a local server.
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##### Downloading Models
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Please make sure you have Llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models.
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```
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$ ls ~/.llama/checkpoints
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Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
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Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
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```
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:::
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:::{tab-item} vLLM
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##### System Requirements
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Access to Single-Node GPU to start a vLLM server.
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:::
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:::{tab-item} tgi
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##### System Requirements
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Access to Single-Node GPU to start a TGI server.
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:::
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:::{tab-item} ollama
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##### System Requirements
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Access to Single-Node CPU/GPU able to run ollama.
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:::
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:::{tab-item} together
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##### System Requirements
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Access to Single-Node CPU with Together hosted endpoint via API_KEY from [together.ai](https://api.together.xyz/signin).
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:::
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:::{tab-item} fireworks
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##### System Requirements
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Access to Single-Node CPU with Fireworks hosted endpoint via API_KEY from [fireworks.ai](https://fireworks.ai/).
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:::
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::::
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##### 1.1. Start the distribution
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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- [Start Meta Reference GPU Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html)
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:::
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:::{tab-item} vLLM
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- [Start vLLM Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/remote-vllm.html)
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:::
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:::{tab-item} tgi
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- [Start TGI Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html)
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:::
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:::{tab-item} ollama
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- [Start Ollama Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html)
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:::
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:::{tab-item} together
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- [Start Together Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/together.html)
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:::
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:::{tab-item} fireworks
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- [Start Fireworks Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/fireworks.html)
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:::
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::::
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##### Troubleshooting
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- If you encounter any issues, search through our [GitHub Issues](https://github.com/meta-llama/llama-stack/issues), or file an new issue.
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- Use `--port <PORT>` flag to use a different port number. For docker run, update the `-p <PORT>:<PORT>` flag.
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## Step 2. Run Llama Stack App
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### Chat Completion Test
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Once the server is set up, we can test it with a client to verify it's working correctly. The following command will send a chat completion request to the server's `/inference/chat_completion` API:
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### Step 2. Start the Llama Stack server
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```bash
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$ curl http://localhost:5000/alpha/inference/chat-completion \
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-H "Content-Type: application/json" \
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-d '{
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"model_id": "meta-llama/Llama-3.1-8B-Instruct",
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"messages": [
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export LLAMA_STACK_PORT=5001
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ~/.llama:/root/.llama \
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llamastack/distribution-ollama \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env OLLAMA_URL=http://host.docker.internal:11434
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```
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### Step 3. Use the Llama Stack client SDK
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```bash
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pip install llama-stack-client
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```
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We will use the `llama-stack-client` CLI to check the connectivity to the server. This should be installed in your environment if you installed the SDK.
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```bash
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llama-stack-client --endpoint http://localhost:5001 models list
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
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┃ identifier ┃ provider_id ┃ provider_resource_id ┃ metadata ┃
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┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
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│ meta-llama/Llama-3.2-3B-Instruct │ ollama │ llama3.2:3b-instruct-fp16 │ {} │
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└──────────────────────────────────┴─────────────┴───────────────────────────┴──────────┘
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```
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Chat completion using the CLI
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```bash
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llama-stack-client --endpoint http://localhost:5001 inference chat_completion --message "hello, what model are you?"
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```
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Simple python example using the client SDK
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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:5001")
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# List available models
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models = client.models.list()
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print(models)
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# Simple chat completion
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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=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Write me a 2 sentence poem about the moon"}
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],
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"sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
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}'
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Output:
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{'completion_message': {'role': 'assistant',
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'content': 'The moon glows softly in the midnight sky, \nA beacon of wonder, as it catches the eye.',
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'stop_reason': 'out_of_tokens',
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'tool_calls': []},
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'logprobs': null}
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{"role": "user", "content": "Write a haiku about coding"}
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]
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)
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print(response.completion_message.content)
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```
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### Run Agent App
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### Step 4. Your first RAG agent
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```python
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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To run an agent app, check out examples demo scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo. To run a simple agent app:
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import asyncio
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```bash
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$ git clone git@github.com:meta-llama/llama-stack-apps.git
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$ cd llama-stack-apps
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$ pip install -r requirements.txt
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import fire
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$ python -m examples.agents.client <host> <port>
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from llama_stack_client import LlamaStackClient
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from llama_stack_client.lib.agents.agent import Agent
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from llama_stack_client.lib.agents.event_logger import EventLogger
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from llama_stack_client.types import Attachment
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from llama_stack_client.types.agent_create_params import AgentConfig
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async def run_main(host: str, port: int, disable_safety: bool = False):
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urls = [
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"memory_optimizations.rst",
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"chat.rst",
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"llama3.rst",
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"datasets.rst",
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"qat_finetune.rst",
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"lora_finetune.rst",
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]
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attachments = [
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Attachment(
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
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mime_type="text/plain",
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)
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for i, url in enumerate(urls)
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]
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client = LlamaStackClient(
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base_url=f"http://{host}:{port}",
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)
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available_shields = [shield.identifier for shield in client.shields.list()]
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if not available_shields:
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print("No available shields. Disable safety.")
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else:
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print(f"Available shields found: {available_shields}")
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available_models = [model.identifier for model in client.models.list()]
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if not available_models:
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raise ValueError("No available models")
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else:
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selected_model = available_models[0]
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print(f"Using model: {selected_model}")
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agent_config = AgentConfig(
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model=selected_model,
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instructions="You are a helpful assistant",
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sampling_params={
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"strategy": "greedy",
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"temperature": 1.0,
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"top_p": 0.9,
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},
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tools=[
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{
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"type": "memory",
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"memory_bank_configs": [],
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"query_generator_config": {"type": "default", "sep": " "},
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"max_tokens_in_context": 4096,
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"max_chunks": 10,
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},
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],
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tool_choice="auto",
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tool_prompt_format="json",
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input_shields=available_shields if available_shields else [],
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output_shields=available_shields if available_shields else [],
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enable_session_persistence=False,
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)
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agent = Agent(client, agent_config)
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session_id = agent.create_session("test-session")
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print(f"Created session_id={session_id} for Agent({agent.agent_id})")
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user_prompts = [
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(
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"I am attaching some documentation for Torchtune. Help me answer questions I will ask next.",
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attachments,
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),
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(
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"What are the top 5 topics that were explained? Only list succinct bullet points.",
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None,
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),
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(
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"Was anything related to 'Llama3' discussed, if so what?",
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None,
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),
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(
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"Tell me how to use LoRA",
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None,
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),
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(
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"What about Quantization?",
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None,
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),
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]
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for prompt in user_prompts:
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": prompt[0],
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}
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],
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attachments=prompt[1],
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session_id=session_id,
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)
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async for log in EventLogger().log(response):
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log.print()
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def main(host: str, port: int):
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asyncio.run(run_main(host, port))
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if __name__ == "__main__":
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fire.Fire(main)
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```
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You will see outputs of the form --
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```
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User> I am planning a trip to Switzerland, what are the top 3 places to visit?
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inference> Switzerland is a beautiful country with a rich history, stunning landscapes, and vibrant culture. Here are three must-visit places to add to your itinerary:
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...
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## Next Steps
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User> What is so special about #1?
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inference> Jungfraujoch, also known as the "Top of Europe," is a unique and special place for several reasons:
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...
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- You can mix and match different providers for inference, memory, agents, evals etc. See [Building custom distributions](../distributions/index.md)
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- [Developer Cookbook](developer_cookbook.md)
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User> What other countries should I consider to club?
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inference> Considering your interest in Switzerland, here are some neighboring countries that you may want to consider visiting:
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```
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For example applications and more detailed tutorials, visit our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repository.
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