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Resolved merge conflicts in:
- Documentation files: updated vector IO provider docs to include both kvstore fields and embedding model configuration
- Config files: merged kvstore requirements from upstream with embedding model fields
- Dependencies: updated to latest client versions while preserving llama-models dependency
- Regenerated lockfiles to ensure consistency

All embedding model configuration features preserved while incorporating upstream changes.
This commit is contained in:
skamenan7 2025-07-16 19:57:02 -04:00
commit 6634b21a76
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72
docs/source/providers/index.md
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@ -1,4 +1,4 @@
# Providers Overview
# API Providers Overview
The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
- LLM inference providers (e.g., Meta Reference, Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, OpenAI, Anthropic, Gemini, WatsonX, etc.),
@ -13,13 +13,25 @@ Providers come in two flavors:
Importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally.
## External Providers
Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently.
```{toctree}
:maxdepth: 1
external
external.md
```
```{include} openai.md
:start-after: ## OpenAI API Compatibility
```
## Inference
Runs inference with an LLM.
```{toctree}
:maxdepth: 1
inference/index
```
## Agents
@ -40,33 +52,6 @@ Interfaces with datasets and data loaders.
datasetio/index
```
## Eval
Generates outputs (via Inference or Agents) and perform scoring.
```{toctree}
:maxdepth: 1
eval/index
```
## Inference
Runs inference with an LLM.
```{toctree}
:maxdepth: 1
inference/index
```
## Post Training
Fine-tunes a model.
```{toctree}
:maxdepth: 1
post_training/index
```
## Safety
Applies safety policies to the output at a Systems (not only model) level.
@ -76,15 +61,6 @@ Applies safety policies to the output at a Systems (not only model) level.
safety/index
```
## Scoring
Evaluates the outputs of the system.
```{toctree}
:maxdepth: 1
scoring/index
```
## Telemetry
Collects telemetry data from the system.
@ -94,15 +70,6 @@ Collects telemetry data from the system.
telemetry/index
```
## Tool Runtime
Is associated with the ToolGroup resouces.
```{toctree}
:maxdepth: 1
tool_runtime/index
```
## Vector IO
Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents.
@ -114,3 +81,12 @@ io and database are used to store and retrieve documents for retrieval.
vector_io/index
```
## Tool Runtime
Is associated with the ToolGroup resources.
```{toctree}
:maxdepth: 1
tool_runtime/index
```

193
docs/source/providers/openai.md Normal file
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@ -0,0 +1,193 @@
## OpenAI API Compatibility
### Server path
Llama Stack exposes an OpenAI-compatible API endpoint at `/v1/openai/v1`. So, for a Llama Stack server running locally on port `8321`, the full url to the OpenAI-compatible API endpoint is `http://localhost:8321/v1/openai/v1`.
### Clients
You should be able to use any client that speaks OpenAI APIs with Llama Stack. We regularly test with the official Llama Stack clients as well as OpenAI's official Python client.
#### Llama Stack Client
When using the Llama Stack client, set the `base_url` to the root of your Llama Stack server. It will automatically route OpenAI-compatible requests to the right server endpoint for you.
```python
from llama_stack_client import LlamaStackClient
client = LlamaStackClient(base_url="http://localhost:8321")
```
#### OpenAI Client
When using an OpenAI client, set the `base_url` to the `/v1/openai/v1` path on your Llama Stack server.
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none")
```
Regardless of the client you choose, the following code examples should all work the same.
### APIs implemented
#### Models
Many of the APIs require you to pass in a model parameter. To see the list of models available in your Llama Stack server:
```python
models = client.models.list()
```
#### Responses
:::{note}
The Responses API implementation is still in active development. While it is quite usable, there are still unimplemented parts of the API. We'd love feedback on any use-cases you try that do not work to help prioritize the pieces left to implement. Please open issues in the [meta-llama/llama-stack](https://github.com/meta-llama/llama-stack) GitHub repository with details of anything that does not work.
:::
##### Simple inference
Request:
```
response = client.responses.create(
model="meta-llama/Llama-3.2-3B-Instruct",
input="Write a haiku about coding."
)
print(response.output_text)
```
Example output:
```text
Pixels dancing slow
Syntax whispers secrets sweet
Code's gentle silence
```
##### Structured Output
Request:
```python
response = client.responses.create(
model="meta-llama/Llama-3.2-3B-Instruct",
input=[
{
"role": "system",
"content": "Extract the participants from the event information.",
},
{
"role": "user",
"content": "Alice and Bob are going to a science fair on Friday.",
},
],
text={
"format": {
"type": "json_schema",
"name": "participants",
"schema": {
"type": "object",
"properties": {
"participants": {"type": "array", "items": {"type": "string"}}
},
"required": ["participants"],
},
}
},
)
print(response.output_text)
```
Example output:
```text
{ "participants": ["Alice", "Bob"] }
```
#### Chat Completions
##### Simple inference
Request:
```python
chat_completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=[{"role": "user", "content": "Write a haiku about coding."}],
)
print(chat_completion.choices[0].message.content)
```
Example output:
```text
Lines of code unfold
Logic flows like a river
Code's gentle beauty
```
##### Structured Output
Request:
```python
chat_completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=[
{
"role": "system",
"content": "Extract the participants from the event information.",
},
{
"role": "user",
"content": "Alice and Bob are going to a science fair on Friday.",
},
],
response_format={
"type": "json_schema",
"json_schema": {
"name": "participants",
"schema": {
"type": "object",
"properties": {
"participants": {"type": "array", "items": {"type": "string"}}
},
"required": ["participants"],
},
},
},
)
print(chat_completion.choices[0].message.content)
```
Example output:
```text
{ "participants": ["Alice", "Bob"] }
```
#### Completions
##### Simple inference
Request:
```python
completion = client.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct", prompt="Write a haiku about coding."
)
print(completion.choices[0].text)
```
Example output:
```text
Lines of code unfurl
Logic whispers in the dark
Art in hidden form
```

122
docs/source/providers/post_training/huggingface.md
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@ -1,122 +0,0 @@
---
orphan: true
---
# HuggingFace SFTTrainer
[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets
## Features
- Simple access through the post_training API
- Fully integrated with Llama Stack
- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders)
## Usage
To use the HF SFTTrainer in your Llama Stack project, follow these steps:
1. Configure your Llama Stack project to use this provider.
2. Kick off a SFT job using the Llama Stack post_training API.
## Setup
You can access the HuggingFace trainer via the `ollama` distribution:
```bash
llama stack build --template starter --image-type venv
llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
```
## Run Training
You can access the provider and the `supervised_fine_tune` method via the post_training API:
```python
import time
import uuid
from llama_stack_client.types import (
post_training_supervised_fine_tune_params,
algorithm_config_param,
)
def create_http_client():
from llama_stack_client import LlamaStackClient
return LlamaStackClient(base_url="http://localhost:8321")
client = create_http_client()
# Example Dataset
client.datasets.register(
purpose="post-training/messages",
source={
"type": "uri",
"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
},
dataset_id="simpleqa",
)
training_config = post_training_supervised_fine_tune_params.TrainingConfig(
data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
batch_size=32,
data_format="instruct",
dataset_id="simpleqa",
shuffle=True,
),
gradient_accumulation_steps=1,
max_steps_per_epoch=0,
max_validation_steps=1,
n_epochs=4,
)
algorithm_config = algorithm_config_param.LoraFinetuningConfig( # this config is also currently mandatory but should not be
alpha=1,
apply_lora_to_mlp=True,
apply_lora_to_output=False,
lora_attn_modules=["q_proj"],
rank=1,
type="LoRA",
)
job_uuid = f"test-job{uuid.uuid4()}"
# Example Model
training_model = "ibm-granite/granite-3.3-8b-instruct"
start_time = time.time()
response = client.post_training.supervised_fine_tune(
job_uuid=job_uuid,
logger_config={},
model=training_model,
hyperparam_search_config={},
training_config=training_config,
algorithm_config=algorithm_config,
checkpoint_dir="output",
)
print("Job: ", job_uuid)
# Wait for the job to complete!
while True:
status = client.post_training.job.status(job_uuid=job_uuid)
if not status:
print("Job not found")
break
print(status)
if status.status == "completed":
break
print("Waiting for job to complete...")
time.sleep(5)
end_time = time.time()
print("Job completed in", end_time - start_time, "seconds!")
print("Artifacts:")
print(client.post_training.job.artifacts(job_uuid=job_uuid))
```

163
docs/source/providers/post_training/nvidia_nemo.md
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@ -1,163 +0,0 @@
---
orphan: true
---
# NVIDIA NEMO
[NVIDIA NEMO](https://developer.nvidia.com/nemo-framework) is a remote post training provider for Llama Stack. It provides enterprise-grade fine-tuning capabilities through NVIDIA's NeMo Customizer service.
## Features
- Enterprise-grade fine-tuning capabilities
- Support for LoRA and SFT fine-tuning
- Integration with NVIDIA's NeMo Customizer service
- Support for various NVIDIA-optimized models
- Efficient training with NVIDIA hardware acceleration
## Usage
To use NVIDIA NEMO in your Llama Stack project, follow these steps:
1. Configure your Llama Stack project to use this provider.
2. Set up your NVIDIA API credentials.
3. Kick off a fine-tuning job using the Llama Stack post_training API.
## Setup
You'll need to set the following environment variables:
```bash
export NVIDIA_API_KEY="your-api-key"
export NVIDIA_DATASET_NAMESPACE="default"
export NVIDIA_CUSTOMIZER_URL="your-customizer-url"
export NVIDIA_PROJECT_ID="your-project-id"
export NVIDIA_OUTPUT_MODEL_DIR="your-output-model-dir"
```
## Run Training
You can access the provider and the `supervised_fine_tune` method via the post_training API:
```python
import time
import uuid
from llama_stack_client.types import (
post_training_supervised_fine_tune_params,
algorithm_config_param,
)
def create_http_client():
from llama_stack_client import LlamaStackClient
return LlamaStackClient(base_url="http://localhost:8321")
client = create_http_client()
# Example Dataset
client.datasets.register(
purpose="post-training/messages",
source={
"type": "uri",
"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
},
dataset_id="simpleqa",
)
training_config = post_training_supervised_fine_tune_params.TrainingConfig(
data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
batch_size=8, # Default batch size for NEMO
data_format="instruct",
dataset_id="simpleqa",
shuffle=True,
),
n_epochs=50, # Default epochs for NEMO
optimizer_config=post_training_supervised_fine_tune_params.TrainingConfigOptimizerConfig(
lr=0.0001, # Default learning rate
weight_decay=0.01, # NEMO-specific parameter
),
# NEMO-specific parameters
log_every_n_steps=None,
val_check_interval=0.25,
sequence_packing_enabled=False,
hidden_dropout=None,
attention_dropout=None,
ffn_dropout=None,
)
algorithm_config = algorithm_config_param.LoraFinetuningConfig(
alpha=16, # Default alpha for NEMO
type="LoRA",
)
job_uuid = f"test-job{uuid.uuid4()}"
# Example Model - must be a supported NEMO model
training_model = "meta/llama-3.1-8b-instruct"
start_time = time.time()
response = client.post_training.supervised_fine_tune(
job_uuid=job_uuid,
logger_config={},
model=training_model,
hyperparam_search_config={},
training_config=training_config,
algorithm_config=algorithm_config,
checkpoint_dir="output",
)
print("Job: ", job_uuid)
# Wait for the job to complete!
while True:
status = client.post_training.job.status(job_uuid=job_uuid)
if not status:
print("Job not found")
break
print(status)
if status.status == "completed":
break
print("Waiting for job to complete...")
time.sleep(5)
end_time = time.time()
print("Job completed in", end_time - start_time, "seconds!")
print("Artifacts:")
print(client.post_training.job.artifacts(job_uuid=job_uuid))
```
## Supported Models
Currently supports the following models:
- meta/llama-3.1-8b-instruct
- meta/llama-3.2-1b-instruct
## Supported Parameters
### TrainingConfig
- n_epochs (default: 50)
- data_config
- optimizer_config
- log_every_n_steps
- val_check_interval (default: 0.25)
- sequence_packing_enabled (default: False)
- hidden_dropout (0.0-1.0)
- attention_dropout (0.0-1.0)
- ffn_dropout (0.0-1.0)
### DataConfig
- dataset_id
- batch_size (default: 8)
### OptimizerConfig
- lr (default: 0.0001)
- weight_decay (default: 0.01)
### LoRA Config
- alpha (default: 16)
- type (must be "LoRA")
Note: Some parameters from the standard Llama Stack API are not supported and will be ignored with a warning.

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docs/source/providers/post_training/torchtune.md
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@ -1,125 +0,0 @@
---
orphan: true
---
# TorchTune
[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch.
## Features
- Simple access through the post_training API
- Fully integrated with Llama Stack
- GPU support and single device capabilities.
- Support for LoRA
## Usage
To use TorchTune in your Llama Stack project, follow these steps:
1. Configure your Llama Stack project to use this provider.
2. Kick off a fine-tuning job using the Llama Stack post_training API.
## Setup
You can access the TorchTune trainer by writing your own yaml pointing to the provider:
```yaml
post_training:
- provider_id: torchtune
provider_type: inline::torchtune
config: {}
```
you can then build and run your own stack with this provider.
## Run Training
You can access the provider and the `supervised_fine_tune` method via the post_training API:
```python
import time
import uuid
from llama_stack_client.types import (
post_training_supervised_fine_tune_params,
algorithm_config_param,
)
def create_http_client():
from llama_stack_client import LlamaStackClient
return LlamaStackClient(base_url="http://localhost:8321")
client = create_http_client()
# Example Dataset
client.datasets.register(
purpose="post-training/messages",
source={
"type": "uri",
"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
},
dataset_id="simpleqa",
)
training_config = post_training_supervised_fine_tune_params.TrainingConfig(
data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
batch_size=32,
data_format="instruct",
dataset_id="simpleqa",
shuffle=True,
),
gradient_accumulation_steps=1,
max_steps_per_epoch=0,
max_validation_steps=1,
n_epochs=4,
)
algorithm_config = algorithm_config_param.LoraFinetuningConfig(
alpha=1,
apply_lora_to_mlp=True,
apply_lora_to_output=False,
lora_attn_modules=["q_proj"],
rank=1,
type="LoRA",
)
job_uuid = f"test-job{uuid.uuid4()}"
# Example Model
training_model = "meta-llama/Llama-2-7b-hf"
start_time = time.time()
response = client.post_training.supervised_fine_tune(
job_uuid=job_uuid,
logger_config={},
model=training_model,
hyperparam_search_config={},
training_config=training_config,
algorithm_config=algorithm_config,
checkpoint_dir="output",
)
print("Job: ", job_uuid)
# Wait for the job to complete!
while True:
status = client.post_training.job.status(job_uuid=job_uuid)
if not status:
print("Job not found")
break
print(status)
if status.status == "completed":
break
print("Waiting for job to complete...")
time.sleep(5)
end_time = time.time()
print("Job completed in", end_time - start_time, "seconds!")
print("Artifacts:")
print(client.post_training.job.artifacts(job_uuid=job_uuid))
```

5
docs/source/providers/vector_io/remote_milvus.md
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@ -114,7 +114,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
| `uri` | `<class 'str'>` | No | PydanticUndefined | The URI of the Milvus server |
| `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server |
| `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
| `embedding_model` | `str \| None` | No | | Optional default embedding model for this provider. If not specified, will use system default. |
| `embedding_dimension` | `int \| None` | No | | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
| `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |
@ -126,6 +126,9 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
```yaml
uri: ${env.MILVUS_ENDPOINT}
token: ${env.MILVUS_TOKEN}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db
```

4
docs/source/providers/vector_io/remote_pgvector.md
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@ -40,6 +40,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
| `db` | `str \| None` | No | postgres | |
| `user` | `str \| None` | No | postgres | |
| `password` | `str \| None` | No | mysecretpassword | |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) |
| `embedding_model` | `str \| None` | No | | Optional default embedding model for this provider. If not specified, will use system default. |
| `embedding_dimension` | `int \| None` | No | | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
@ -51,6 +52,9 @@ port: ${env.PGVECTOR_PORT:=5432}
db: ${env.PGVECTOR_DB}
user: ${env.PGVECTOR_USER}
password: ${env.PGVECTOR_PASSWORD}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db
```

4
docs/source/providers/vector_io/remote_weaviate.md
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@ -43,7 +43,9 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
## Sample Configuration
```yaml
{}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db
```