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Merge remote-tracking branch 'origin/main' into kill_client

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Ashwin Bharambe 2025-11-07 09:28:58 -08:00
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8
client-sdks/stainless/config-not-source-of-truth-yet.yml
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@ -115,9 +115,6 @@ resources:
sampling_params: SamplingParams sampling_params: SamplingParams
scoring_result: ScoringResult scoring_result: ScoringResult
system_message: SystemMessage system_message: SystemMessage
query_result: RAGQueryResult
document: RAGDocument
query_config: RAGQueryConfig
toolgroups: toolgroups:
models: models:
tool_group: ToolGroup tool_group: ToolGroup
@ -143,11 +140,6 @@ resources:
endpoint: get /v1/tool-runtime/list-tools endpoint: get /v1/tool-runtime/list-tools
paginated: false paginated: false
invoke_tool: post /v1/tool-runtime/invoke invoke_tool: post /v1/tool-runtime/invoke
subresources:
rag_tool:
methods:
insert: post /v1/tool-runtime/rag-tool/insert
query: post /v1/tool-runtime/rag-tool/query
responses: responses:
models: models:

3
docs/docs/building_applications/index.mdx
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@ -35,9 +35,6 @@ Here are the key topics that will help you build effective AI applications:
- **[Telemetry](./telemetry.mdx)** - Monitor and analyze your agents' performance and behavior - **[Telemetry](./telemetry.mdx)** - Monitor and analyze your agents' performance and behavior
- **[Safety](./safety.mdx)** - Implement guardrails and safety measures to ensure responsible AI behavior - **[Safety](./safety.mdx)** - Implement guardrails and safety measures to ensure responsible AI behavior
### 🎮 **Interactive Development**
- **[Playground](./playground.mdx)** - Interactive environment for testing and developing applications
## Application Patterns ## Application Patterns
### 🤖 **Conversational Agents** ### 🤖 **Conversational Agents**

298
docs/docs/building_applications/playground.mdx
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@ -1,298 +0,0 @@
---
title: Llama Stack Playground
description: Interactive interface to explore and experiment with Llama Stack capabilities
sidebar_label: Playground
sidebar_position: 10
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Llama Stack Playground
:::note[Experimental Feature]
The Llama Stack Playground is currently experimental and subject to change. We welcome feedback and contributions to help improve it.
:::
The Llama Stack Playground is a simple interface that aims to:
- **Showcase capabilities and concepts** of Llama Stack in an interactive environment
- **Demo end-to-end application code** to help users get started building their own applications
- **Provide a UI** to help users inspect and understand Llama Stack API providers and resources
## Key Features
### Interactive Playground Pages
The playground provides interactive pages for users to explore Llama Stack API capabilities:
#### Chatbot Interface
<video
controls
autoPlay
playsInline
muted
loop
style={{width: '100%'}}
>
<source src="https://github.com/user-attachments/assets/8d2ef802-5812-4a28-96e1-316038c84cbf" type="video/mp4" />
Your browser does not support the video tag.
</video>
<Tabs>
<TabItem value="chat" label="Chat">
**Simple Chat Interface**
- Chat directly with Llama models through an intuitive interface
- Uses the `/chat/completions` streaming API under the hood
- Real-time message streaming for responsive interactions
- Perfect for testing model capabilities and prompt engineering
</TabItem>
<TabItem value="rag" label="RAG Chat">
**Document-Aware Conversations**
- Upload documents to create memory banks
- Chat with a RAG-enabled agent that can query your documents
- Uses Llama Stack's `/agents` API to create and manage RAG sessions
- Ideal for exploring knowledge-enhanced AI applications
</TabItem>
</Tabs>
#### Evaluation Interface
<video
controls
autoPlay
playsInline
muted
loop
style={{width: '100%'}}
>
<source src="https://github.com/user-attachments/assets/6cc1659f-eba4-49ca-a0a5-7c243557b4f5" type="video/mp4" />
Your browser does not support the video tag.
</video>
<Tabs>
<TabItem value="scoring" label="Scoring Evaluations">
**Custom Dataset Evaluation**
- Upload your own evaluation datasets
- Run evaluations using available scoring functions
- Uses Llama Stack's `/scoring` API for flexible evaluation workflows
- Great for testing application performance on custom metrics
</TabItem>
<TabItem value="benchmarks" label="Benchmark Evaluations">
<video
controls
autoPlay
playsInline
muted
loop
style={{width: '100%', marginBottom: '1rem'}}
>
<source src="https://github.com/user-attachments/assets/345845c7-2a2b-4095-960a-9ae40f6a93cf" type="video/mp4" />
Your browser does not support the video tag.
</video>
**Pre-registered Evaluation Tasks**
- Evaluate models or agents on pre-defined tasks
- Uses Llama Stack's `/eval` API for comprehensive evaluation
- Combines datasets and scoring functions for standardized testing
**Setup Requirements:**
Register evaluation datasets and benchmarks first:
```bash
# Register evaluation dataset
llama-stack-client datasets register \
--dataset-id "mmlu" \
--provider-id "huggingface" \
--url "https://huggingface.co/datasets/llamastack/evals" \
--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string"}, "chat_completion_input": {"type": "string"}}'
# Register benchmark task
llama-stack-client benchmarks register \
--eval-task-id meta-reference-mmlu \
--provider-id meta-reference \
--dataset-id mmlu \
--scoring-functions basic::regex_parser_multiple_choice_answer
```
</TabItem>
</Tabs>
#### Inspection Interface
<video
controls
autoPlay
playsInline
muted
loop
style={{width: '100%'}}
>
<source src="https://github.com/user-attachments/assets/01d52b2d-92af-4e3a-b623-a9b8ba22ba99" type="video/mp4" />
Your browser does not support the video tag.
</video>
<Tabs>
<TabItem value="providers" label="API Providers">
**Provider Management**
- Inspect available Llama Stack API providers
- View provider configurations and capabilities
- Uses the `/providers` API for real-time provider information
- Essential for understanding your deployment's capabilities
</TabItem>
<TabItem value="resources" label="API Resources">
**Resource Exploration**
- Inspect Llama Stack API resources including:
- **Models**: Available language models
- **Datasets**: Registered evaluation datasets
- **Memory Banks**: Vector databases and knowledge stores
- **Benchmarks**: Evaluation tasks and scoring functions
- **Shields**: Safety and content moderation tools
- Uses `/<resources>/list` APIs for comprehensive resource visibility
- For detailed information about resources, see [Core Concepts](/docs/concepts)
</TabItem>
</Tabs>
## Getting Started
### Quick Start Guide
<Tabs>
<TabItem value="setup" label="Setup">
**1. Start the Llama Stack API Server**
```bash
llama stack list-deps together | xargs -L1 uv pip install
llama stack run together
```
**2. Start the Streamlit UI**
```bash
# Launch the playground interface
uv run --with ".[ui]" streamlit run llama_stack.core/ui/app.py
```
</TabItem>
<TabItem value="usage" label="Usage Tips">
**Making the Most of the Playground:**
- **Start with Chat**: Test basic model interactions and prompt engineering
- **Explore RAG**: Upload sample documents to see knowledge-enhanced responses
- **Try Evaluations**: Use the scoring interface to understand evaluation metrics
- **Inspect Resources**: Check what providers and resources are available
- **Experiment with Settings**: Adjust parameters to see how they affect results
</TabItem>
</Tabs>
### Available Distributions
The playground works with any Llama Stack distribution. Popular options include:
<Tabs>
<TabItem value="together" label="Together AI">
```bash
llama stack list-deps together | xargs -L1 uv pip install
llama stack run together
```
**Features:**
- Cloud-hosted models
- Fast inference
- Multiple model options
</TabItem>
<TabItem value="ollama" label="Ollama (Local)">
```bash
llama stack list-deps ollama | xargs -L1 uv pip install
llama stack run ollama
```
**Features:**
- Local model execution
- Privacy-focused
- No internet required
</TabItem>
<TabItem value="meta-reference" label="Meta Reference">
```bash
llama stack list-deps meta-reference | xargs -L1 uv pip install
llama stack run meta-reference
```
**Features:**
- Reference implementation
- All API features available
- Best for development
</TabItem>
</Tabs>
## Use Cases & Examples
### Educational Use Cases
- **Learning Llama Stack**: Hands-on exploration of API capabilities
- **Prompt Engineering**: Interactive testing of different prompting strategies
- **RAG Experimentation**: Understanding how document retrieval affects responses
- **Evaluation Understanding**: See how different metrics evaluate model performance
### Development Use Cases
- **Prototype Testing**: Quick validation of application concepts
- **API Exploration**: Understanding available endpoints and parameters
- **Integration Planning**: Seeing how different components work together
- **Demo Creation**: Showcasing Llama Stack capabilities to stakeholders
### Research Use Cases
- **Model Comparison**: Side-by-side testing of different models
- **Evaluation Design**: Understanding how scoring functions work
- **Safety Testing**: Exploring shield effectiveness with different inputs
- **Performance Analysis**: Measuring model behavior across different scenarios
## Best Practices
### 🚀 **Getting Started**
- Begin with simple chat interactions to understand basic functionality
- Gradually explore more advanced features like RAG and evaluations
- Use the inspection tools to understand your deployment's capabilities
### 🔧 **Development Workflow**
- Use the playground to prototype before writing application code
- Test different parameter settings interactively
- Validate evaluation approaches before implementing them programmatically
### 📊 **Evaluation & Testing**
- Start with simple scoring functions before trying complex evaluations
- Use the playground to understand evaluation results before automation
- Test safety features with various input types
### 🎯 **Production Preparation**
- Use playground insights to inform your production API usage
- Test edge cases and error conditions interactively
- Validate resource configurations before deployment
## Related Resources
- **[Getting Started Guide](../getting_started/quickstart)** - Complete setup and introduction
- **[Core Concepts](/docs/concepts)** - Understanding Llama Stack fundamentals
- **[Agents](./agent)** - Building intelligent agents
- **[RAG (Retrieval Augmented Generation)](./rag)** - Knowledge-enhanced applications
- **[Evaluations](./evals)** - Comprehensive evaluation framework
- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation

19
docs/docs/providers/inference/remote_bedrock.mdx
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@ -1,5 +1,5 @@
--- ---
description: "AWS Bedrock inference provider for accessing various AI models through AWS's managed service." description: "AWS Bedrock inference provider using OpenAI compatible endpoint."
sidebar_label: Remote - Bedrock sidebar_label: Remote - Bedrock
title: remote::bedrock title: remote::bedrock
--- ---
@ -8,7 +8,7 @@ title: remote::bedrock
## Description ## Description
AWS Bedrock inference provider for accessing various AI models through AWS's managed service. AWS Bedrock inference provider using OpenAI compatible endpoint.
## Configuration ## Configuration
@ -16,19 +16,12 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man
|-------|------|----------|---------|-------------| |-------|------|----------|---------|-------------|
| `allowed_models` | `list[str \| None` | No | | List of models that should be registered with the model registry. If None, all models are allowed. | | `allowed_models` | `list[str \| None` | No | | List of models that should be registered with the model registry. If None, all models are allowed. |
| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider | | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
| `aws_access_key_id` | `str \| None` | No | | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID | | `api_key` | `pydantic.types.SecretStr \| None` | No | | Authentication credential for the provider |
| `aws_secret_access_key` | `str \| None` | No | | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY | | `region_name` | `<class 'str'>` | No | us-east-2 | AWS Region for the Bedrock Runtime endpoint |
| `aws_session_token` | `str \| None` | No | | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
| `region_name` | `str \| None` | No | | The default AWS Region to use, for example, us-west-1 or us-west-2.Default use environment variable: AWS_DEFAULT_REGION |
| `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE |
| `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS |
| `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE |
| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. |
| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. |
| `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). |
## Sample Configuration ## Sample Configuration
```yaml ```yaml
{} api_key: ${env.AWS_BEDROCK_API_KEY:=}
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
``` ```

8
pyproject.toml
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@ -51,14 +51,6 @@ dependencies = [
"sqlalchemy[asyncio]>=2.0.41", # server - for conversations "sqlalchemy[asyncio]>=2.0.41", # server - for conversations
] ]
[project.optional-dependencies]
ui = [
"streamlit",
"pandas",
"llama-stack-client>=0.3.0",
"streamlit-option-menu",
]
[dependency-groups] [dependency-groups]
dev = [ dev = [
"pytest>=8.4", "pytest>=8.4",

4
src/llama_stack/core/routers/inference.py
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@ -190,7 +190,7 @@ class InferenceRouter(Inference):
response = await provider.openai_completion(params) response = await provider.openai_completion(params)
response.model = request_model_id response.model = request_model_id
if self.telemetry_enabled: if self.telemetry_enabled and response.usage is not None:
metrics = self._construct_metrics( metrics = self._construct_metrics(
prompt_tokens=response.usage.prompt_tokens, prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens, completion_tokens=response.usage.completion_tokens,
@ -253,7 +253,7 @@ class InferenceRouter(Inference):
if self.store: if self.store:
asyncio.create_task(self.store.store_chat_completion(response, params.messages)) asyncio.create_task(self.store.store_chat_completion(response, params.messages))
if self.telemetry_enabled: if self.telemetry_enabled and response.usage is not None:
metrics = self._construct_metrics( metrics = self._construct_metrics(
prompt_tokens=response.usage.prompt_tokens, prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens, completion_tokens=response.usage.completion_tokens,

11
src/llama_stack/core/ui/Containerfile
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@ -1,11 +0,0 @@
# More info on playground configuration can be found here:
# https://llama-stack.readthedocs.io/en/latest/playground
FROM python:3.12-slim
WORKDIR /app
COPY . /app/
RUN /usr/local/bin/python -m pip install --upgrade pip && \
/usr/local/bin/pip3 install -r requirements.txt
EXPOSE 8501
ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

50
src/llama_stack/core/ui/README.md
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@ -1,50 +0,0 @@
# (Experimental) LLama Stack UI
## Docker Setup
:warning: This is a work in progress.
## Developer Setup
1. Start up Llama Stack API server. More details [here](https://llamastack.github.io/latest/getting_started/index.htmll).
```
llama stack list-deps together | xargs -L1 uv pip install
llama stack run together
```
2. (Optional) Register datasets and eval tasks as resources. If you want to run pre-configured evaluation flows (e.g. Evaluations (Generation + Scoring) Page).
```bash
llama-stack-client datasets register \
--dataset-id "mmlu" \
--provider-id "huggingface" \
--url "https://huggingface.co/datasets/llamastack/evals" \
--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
```
```bash
llama-stack-client benchmarks register \
--eval-task-id meta-reference-mmlu \
--provider-id meta-reference \
--dataset-id mmlu \
--scoring-functions basic::regex_parser_multiple_choice_answer
```
3. Start Streamlit UI
```bash
uv run --with ".[ui]" streamlit run llama_stack.core/ui/app.py
```
## Environment Variables
| Environment Variable | Description | Default Value |
|----------------------------|------------------------------------|---------------------------|
| LLAMA_STACK_ENDPOINT | The endpoint for the Llama Stack | http://localhost:8321 |
| FIREWORKS_API_KEY | API key for Fireworks provider | (empty string) |
| TOGETHER_API_KEY | API key for Together provider | (empty string) |
| SAMBANOVA_API_KEY | API key for SambaNova provider | (empty string) |
| OPENAI_API_KEY | API key for OpenAI provider | (empty string) |

55
src/llama_stack/core/ui/app.py
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@ -1,55 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
def main():
# Evaluation pages
application_evaluation_page = st.Page(
"page/evaluations/app_eval.py",
title="Evaluations (Scoring)",
icon="📊",
default=False,
)
native_evaluation_page = st.Page(
"page/evaluations/native_eval.py",
title="Evaluations (Generation + Scoring)",
icon="📊",
default=False,
)
# Playground pages
chat_page = st.Page("page/playground/chat.py", title="Chat", icon="💬", default=True)
rag_page = st.Page("page/playground/rag.py", title="RAG", icon="💬", default=False)
tool_page = st.Page("page/playground/tools.py", title="Tools", icon="🛠", default=False)
# Distribution pages
resources_page = st.Page("page/distribution/resources.py", title="Resources", icon="🔍", default=False)
provider_page = st.Page(
"page/distribution/providers.py",
title="API Providers",
icon="🔍",
default=False,
)
pg = st.navigation(
{
"Playground": [
chat_page,
rag_page,
tool_page,
application_evaluation_page,
native_evaluation_page,
],
"Inspect": [provider_page, resources_page],
},
expanded=False,
)
pg.run()
if __name__ == "__main__":
main()

5
src/llama_stack/core/ui/modules/__init__.py
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@ -1,5 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

32
src/llama_stack/core/ui/modules/api.py
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@ -1,32 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
from llama_stack_client import LlamaStackClient
class LlamaStackApi:
def __init__(self):
self.client = LlamaStackClient(
base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:8321"),
provider_data={
"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""),
"together_api_key": os.environ.get("TOGETHER_API_KEY", ""),
"sambanova_api_key": os.environ.get("SAMBANOVA_API_KEY", ""),
"openai_api_key": os.environ.get("OPENAI_API_KEY", ""),
"tavily_search_api_key": os.environ.get("TAVILY_SEARCH_API_KEY", ""),
},
)
def run_scoring(self, row, scoring_function_ids: list[str], scoring_params: dict | None):
"""Run scoring on a single row"""
if not scoring_params:
scoring_params = dict.fromkeys(scoring_function_ids)
return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params)
llama_stack_api = LlamaStackApi()

42
src/llama_stack/core/ui/modules/utils.py
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@ -1,42 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import base64
import os
import pandas as pd
import streamlit as st
def process_dataset(file):
if file is None:
return "No file uploaded", None
try:
# Determine file type and read accordingly
file_ext = os.path.splitext(file.name)[1].lower()
if file_ext == ".csv":
df = pd.read_csv(file)
elif file_ext in [".xlsx", ".xls"]:
df = pd.read_excel(file)
else:
return "Unsupported file format. Please upload a CSV or Excel file.", None
return df
except Exception as e:
st.error(f"Error processing file: {str(e)}")
return None
def data_url_from_file(file) -> str:
file_content = file.getvalue()
base64_content = base64.b64encode(file_content).decode("utf-8")
mime_type = file.type
data_url = f"data:{mime_type};base64,{base64_content}"
return data_url

5
src/llama_stack/core/ui/page/__init__.py
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@ -1,5 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

5
src/llama_stack/core/ui/page/distribution/__init__.py
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@ -1,5 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

18
src/llama_stack/core/ui/page/distribution/datasets.py
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@ -1,18 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def datasets():
st.header("Datasets")
datasets_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()}
if len(datasets_info) > 0:
selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
st.json(datasets_info[selected_dataset], expanded=True)

20
src/llama_stack/core/ui/page/distribution/eval_tasks.py
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@ -1,20 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def benchmarks():
# Benchmarks Section
st.header("Benchmarks")
benchmarks_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.benchmarks.list()}
if len(benchmarks_info) > 0:
selected_benchmark = st.selectbox("Select an eval task", list(benchmarks_info.keys()), key="benchmark_inspect")
st.json(benchmarks_info[selected_benchmark], expanded=True)

18
src/llama_stack/core/ui/page/distribution/models.py
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@ -1,18 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def models():
# Models Section
st.header("Models")
models_info = {m.id: m.model_dump() for m in llama_stack_api.client.models.list()}
selected_model = st.selectbox("Select a model", list(models_info.keys()))
st.json(models_info[selected_model])

27
src/llama_stack/core/ui/page/distribution/providers.py
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@ -1,27 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def providers():
st.header("🔍 API Providers")
apis_providers_lst = llama_stack_api.client.providers.list()
api_to_providers = {}
for api_provider in apis_providers_lst:
if api_provider.api in api_to_providers:
api_to_providers[api_provider.api].append(api_provider)
else:
api_to_providers[api_provider.api] = [api_provider]
for api in api_to_providers.keys():
st.markdown(f"###### {api}")
st.dataframe([x.to_dict() for x in api_to_providers[api]], width=500)
providers()

48
src/llama_stack/core/ui/page/distribution/resources.py
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@ -1,48 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from streamlit_option_menu import option_menu
from llama_stack.core.ui.page.distribution.datasets import datasets
from llama_stack.core.ui.page.distribution.eval_tasks import benchmarks
from llama_stack.core.ui.page.distribution.models import models
from llama_stack.core.ui.page.distribution.scoring_functions import scoring_functions
from llama_stack.core.ui.page.distribution.shields import shields
def resources_page():
options = [
"Models",
"Shields",
"Scoring Functions",
"Datasets",
"Benchmarks",
]
icons = ["magic", "shield", "file-bar-graph", "database", "list-task"]
selected_resource = option_menu(
None,
options,
icons=icons,
orientation="horizontal",
styles={
"nav-link": {
"font-size": "12px",
},
},
)
if selected_resource == "Benchmarks":
benchmarks()
elif selected_resource == "Datasets":
datasets()
elif selected_resource == "Models":
models()
elif selected_resource == "Scoring Functions":
scoring_functions()
elif selected_resource == "Shields":
shields()
resources_page()

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src/llama_stack/core/ui/page/distribution/scoring_functions.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def scoring_functions():
st.header("Scoring Functions")
scoring_functions_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.scoring_functions.list()}
selected_scoring_function = st.selectbox("Select a scoring function", list(scoring_functions_info.keys()))
st.json(scoring_functions_info[selected_scoring_function], expanded=True)

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src/llama_stack/core/ui/page/distribution/shields.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def shields():
# Shields Section
st.header("Shields")
shields_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list()}
selected_shield = st.selectbox("Select a shield", list(shields_info.keys()))
st.json(shields_info[selected_shield])

5
src/llama_stack/core/ui/page/evaluations/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

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src/llama_stack/core/ui/page/evaluations/app_eval.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
import pandas as pd
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
from llama_stack.core.ui.modules.utils import process_dataset
def application_evaluation_page():
st.set_page_config(page_title="Evaluations (Scoring)", page_icon="🦙")
st.title("📊 Evaluations (Scoring)")
# File uploader
uploaded_file = st.file_uploader("Upload Dataset", type=["csv", "xlsx", "xls"])
if uploaded_file is None:
st.error("No file uploaded")
return
# Process uploaded file
df = process_dataset(uploaded_file)
if df is None:
st.error("Error processing file")
return
# Display dataset information
st.success("Dataset loaded successfully!")
# Display dataframe preview
st.subheader("Dataset Preview")
st.dataframe(df)
# Select Scoring Functions to Run Evaluation On
st.subheader("Select Scoring Functions")
scoring_functions = llama_stack_api.client.scoring_functions.list()
scoring_functions = {sf.identifier: sf for sf in scoring_functions}
scoring_functions_names = list(scoring_functions.keys())
selected_scoring_functions = st.multiselect(
"Choose one or more scoring functions",
options=scoring_functions_names,
help="Choose one or more scoring functions.",
)
available_models = llama_stack_api.client.models.list()
available_models = [m.identifier for m in available_models]
scoring_params = {}
if selected_scoring_functions:
st.write("Selected:")
for scoring_fn_id in selected_scoring_functions:
scoring_fn = scoring_functions[scoring_fn_id]
st.write(f"- **{scoring_fn_id}**: {scoring_fn.description}")
new_params = None
if scoring_fn.params:
new_params = {}
for param_name, param_value in scoring_fn.params.to_dict().items():
if param_name == "type":
new_params[param_name] = param_value
continue
if param_name == "judge_model":
value = st.selectbox(
f"Select **{param_name}** for {scoring_fn_id}",
options=available_models,
index=0,
key=f"{scoring_fn_id}_{param_name}",
)
new_params[param_name] = value
else:
value = st.text_area(
f"Enter value for **{param_name}** in {scoring_fn_id} in valid JSON format",
value=json.dumps(param_value, indent=2),
height=80,
)
try:
new_params[param_name] = json.loads(value)
except json.JSONDecodeError:
st.error(f"Invalid JSON for **{param_name}** in {scoring_fn_id}")
st.json(new_params)
scoring_params[scoring_fn_id] = new_params
# Add run evaluation button & slider
total_rows = len(df)
num_rows = st.slider("Number of rows to evaluate", 1, total_rows, total_rows)
if st.button("Run Evaluation"):
progress_text = "Running evaluation..."
progress_bar = st.progress(0, text=progress_text)
rows = df.to_dict(orient="records")
if num_rows < total_rows:
rows = rows[:num_rows]
# Create separate containers for progress text and results
progress_text_container = st.empty()
results_container = st.empty()
output_res = {}
for i, r in enumerate(rows):
# Update progress
progress = i / len(rows)
progress_bar.progress(progress, text=progress_text)
# Run evaluation for current row
score_res = llama_stack_api.run_scoring(
r,
scoring_function_ids=selected_scoring_functions,
scoring_params=scoring_params,
)
for k in r.keys():
if k not in output_res:
output_res[k] = []
output_res[k].append(r[k])
for fn_id in selected_scoring_functions:
if fn_id not in output_res:
output_res[fn_id] = []
output_res[fn_id].append(score_res.results[fn_id].score_rows[0])
# Display current row results using separate containers
progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
results_container.json(
score_res.to_json(),
expanded=2,
)
progress_bar.progress(1.0, text="Evaluation complete!")
# Display results in dataframe
if output_res:
output_df = pd.DataFrame(output_res)
st.subheader("Evaluation Results")
st.dataframe(output_df)
application_evaluation_page()

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src/llama_stack/core/ui/page/evaluations/native_eval.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
import pandas as pd
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
def select_benchmark_1():
# Select Benchmarks
st.subheader("1. Choose An Eval Task")
benchmarks = llama_stack_api.client.benchmarks.list()
benchmarks = {et.identifier: et for et in benchmarks}
benchmarks_names = list(benchmarks.keys())
selected_benchmark = st.selectbox(
"Choose an eval task.",
options=benchmarks_names,
help="Choose an eval task. Each eval task is parameterized by a dataset, and list of scoring functions.",
)
with st.expander("View Eval Task"):
st.json(benchmarks[selected_benchmark], expanded=True)
st.session_state["selected_benchmark"] = selected_benchmark
st.session_state["benchmarks"] = benchmarks
if st.button("Confirm", key="confirm_1"):
st.session_state["selected_benchmark_1_next"] = True
def define_eval_candidate_2():
if not st.session_state.get("selected_benchmark_1_next", None):
return
st.subheader("2. Define Eval Candidate")
st.info(
"""
Define the configurations for the evaluation candidate model or agent used for generation.
Select "model" if you want to run generation with inference API, or "agent" if you want to run generation with agent API through specifying AgentConfig.
"""
)
with st.expander("Define Eval Candidate", expanded=True):
# Define Eval Candidate
candidate_type = st.radio("Candidate Type", ["model", "agent"])
available_models = llama_stack_api.client.models.list()
available_models = [model.identifier for model in available_models]
selected_model = st.selectbox(
"Choose a model",
available_models,
index=0,
)
# Sampling Parameters
st.markdown("##### Sampling Parameters")
temperature = st.slider(
"Temperature",
min_value=0.0,
max_value=1.0,
value=0.0,
step=0.1,
help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
)
top_p = st.slider(
"Top P",
min_value=0.0,
max_value=1.0,
value=0.95,
step=0.1,
)
max_tokens = st.slider(
"Max Tokens",
min_value=0,
max_value=4096,
value=512,
step=1,
help="The maximum number of tokens to generate",
)
repetition_penalty = st.slider(
"Repetition Penalty",
min_value=1.0,
max_value=2.0,
value=1.0,
step=0.1,
help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.",
)
if candidate_type == "model":
if temperature > 0.0:
strategy = {
"type": "top_p",
"temperature": temperature,
"top_p": top_p,
}
else:
strategy = {"type": "greedy"}
eval_candidate = {
"type": "model",
"model": selected_model,
"sampling_params": {
"strategy": strategy,
"max_tokens": max_tokens,
"repetition_penalty": repetition_penalty,
},
}
elif candidate_type == "agent":
system_prompt = st.text_area(
"System Prompt",
value="You are a helpful AI assistant.",
help="Initial instructions given to the AI to set its behavior and context",
)
tools_json = st.text_area(
"Tools Configuration (JSON)",
value=json.dumps(
[
{
"type": "brave_search",
"engine": "brave",
"api_key": "ENTER_BRAVE_API_KEY_HERE",
}
]
),
help="Enter tool configurations in JSON format. Each tool should have a name, description, and parameters.",
height=200,
)
try:
tools = json.loads(tools_json)
except json.JSONDecodeError:
st.error("Invalid JSON format for tools configuration")
tools = []
eval_candidate = {
"type": "agent",
"config": {
"model": selected_model,
"instructions": system_prompt,
"tools": tools,
"tool_choice": "auto",
"tool_prompt_format": "json",
"input_shields": [],
"output_shields": [],
"enable_session_persistence": False,
},
}
st.session_state["eval_candidate"] = eval_candidate
if st.button("Confirm", key="confirm_2"):
st.session_state["selected_eval_candidate_2_next"] = True
def run_evaluation_3():
if not st.session_state.get("selected_eval_candidate_2_next", None):
return
st.subheader("3. Run Evaluation")
# Add info box to explain configurations being used
st.info(
"""
Review the configurations that will be used for this evaluation run, make any necessary changes, and then click the "Run Evaluation" button.
"""
)
selected_benchmark = st.session_state["selected_benchmark"]
benchmarks = st.session_state["benchmarks"]
eval_candidate = st.session_state["eval_candidate"]
dataset_id = benchmarks[selected_benchmark].dataset_id
rows = llama_stack_api.client.datasets.iterrows(
dataset_id=dataset_id,
)
total_rows = len(rows.data)
# Add number of examples control
num_rows = st.number_input(
"Number of Examples to Evaluate",
min_value=1,
max_value=total_rows,
value=5,
help="Number of examples from the dataset to evaluate. ",
)
benchmark_config = {
"type": "benchmark",
"eval_candidate": eval_candidate,
"scoring_params": {},
}
with st.expander("View Evaluation Task", expanded=True):
st.json(benchmarks[selected_benchmark], expanded=True)
with st.expander("View Evaluation Task Configuration", expanded=True):
st.json(benchmark_config, expanded=True)
# Add run button and handle evaluation
if st.button("Run Evaluation"):
progress_text = "Running evaluation..."
progress_bar = st.progress(0, text=progress_text)
rows = rows.data
if num_rows < total_rows:
rows = rows[:num_rows]
# Create separate containers for progress text and results
progress_text_container = st.empty()
results_container = st.empty()
output_res = {}
for i, r in enumerate(rows):
# Update progress
progress = i / len(rows)
progress_bar.progress(progress, text=progress_text)
# Run evaluation for current row
eval_res = llama_stack_api.client.eval.evaluate_rows(
benchmark_id=selected_benchmark,
input_rows=[r],
scoring_functions=benchmarks[selected_benchmark].scoring_functions,
benchmark_config=benchmark_config,
)
for k in r.keys():
if k not in output_res:
output_res[k] = []
output_res[k].append(r[k])
for k in eval_res.generations[0].keys():
if k not in output_res:
output_res[k] = []
output_res[k].append(eval_res.generations[0][k])
for scoring_fn in benchmarks[selected_benchmark].scoring_functions:
if scoring_fn not in output_res:
output_res[scoring_fn] = []
output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0])
progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
results_container.json(eval_res, expanded=2)
progress_bar.progress(1.0, text="Evaluation complete!")
# Display results in dataframe
if output_res:
output_df = pd.DataFrame(output_res)
st.subheader("Evaluation Results")
st.dataframe(output_df)
def native_evaluation_page():
st.set_page_config(page_title="Evaluations (Generation + Scoring)", page_icon="🦙")
st.title("📊 Evaluations (Generation + Scoring)")
select_benchmark_1()
define_eval_candidate_2()
run_evaluation_3()
native_evaluation_page()

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src/llama_stack/core/ui/page/playground/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

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src/llama_stack/core/ui/page/playground/chat.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import streamlit as st
from llama_stack.core.ui.modules.api import llama_stack_api
# Sidebar configurations
with st.sidebar:
st.header("Configuration")
available_models = llama_stack_api.client.models.list()
available_models = [
model.id
for model in available_models
if model.custom_metadata and model.custom_metadata.get("model_type") == "llm"
]
selected_model = st.selectbox(
"Choose a model",
available_models,
index=0,
)
temperature = st.slider(
"Temperature",
min_value=0.0,
max_value=1.0,
value=0.0,
step=0.1,
help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
)
top_p = st.slider(
"Top P",
min_value=0.0,
max_value=1.0,
value=0.95,
step=0.1,
)
max_tokens = st.slider(
"Max Tokens",
min_value=0,
max_value=4096,
value=512,
step=1,
help="The maximum number of tokens to generate",
)
repetition_penalty = st.slider(
"Repetition Penalty",
min_value=1.0,
max_value=2.0,
value=1.0,
step=0.1,
help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.",
)
stream = st.checkbox("Stream", value=True)
system_prompt = st.text_area(
"System Prompt",
value="You are a helpful AI assistant.",
help="Initial instructions given to the AI to set its behavior and context",
)
# Add clear chat button to sidebar
if st.button("Clear Chat", use_container_width=True):
st.session_state.messages = []
st.rerun()
# Main chat interface
st.title("🦙 Chat")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Chat input
if prompt := st.chat_input("Example: What is Llama Stack?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
if temperature > 0.0:
strategy = {
"type": "top_p",
"temperature": temperature,
"top_p": top_p,
}
else:
strategy = {"type": "greedy"}
response = llama_stack_api.client.inference.chat_completion(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
],
model_id=selected_model,
stream=stream,
sampling_params={
"strategy": strategy,
"max_tokens": max_tokens,
"repetition_penalty": repetition_penalty,
},
)
if stream:
for chunk in response:
if chunk.event.event_type == "progress":
full_response += chunk.event.delta.text
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
else:
full_response = response.completion_message.content
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})

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src/llama_stack/core/ui/page/playground/tools.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import enum
import json
import uuid
import streamlit as st
from llama_stack_client import Agent
from llama_stack_client.lib.agents.react.agent import ReActAgent
from llama_stack_client.lib.agents.react.tool_parser import ReActOutput
from llama_stack.core.ui.modules.api import llama_stack_api
class AgentType(enum.Enum):
REGULAR = "Regular"
REACT = "ReAct"
def tool_chat_page():
st.title("🛠 Tools")
client = llama_stack_api.client
models = client.models.list()
model_list = [model.identifier for model in models if model.api_model_type == "llm"]
tool_groups = client.toolgroups.list()
tool_groups_list = [tool_group.identifier for tool_group in tool_groups]
mcp_tools_list = [tool for tool in tool_groups_list if tool.startswith("mcp::")]
builtin_tools_list = [tool for tool in tool_groups_list if not tool.startswith("mcp::")]
selected_vector_stores = []
def reset_agent():
st.session_state.clear()
st.cache_resource.clear()
with st.sidebar:
st.title("Configuration")
st.subheader("Model")
model = st.selectbox(label="Model", options=model_list, on_change=reset_agent, label_visibility="collapsed")
st.subheader("Available ToolGroups")
toolgroup_selection = st.pills(
label="Built-in tools",
options=builtin_tools_list,
selection_mode="multi",
on_change=reset_agent,
format_func=lambda tool: "".join(tool.split("::")[1:]),
help="List of built-in tools from your llama stack server.",
)
if "builtin::rag" in toolgroup_selection:
vector_stores = llama_stack_api.client.vector_stores.list() or []
if not vector_stores:
st.info("No vector databases available for selection.")
vector_stores = [vector_store.identifier for vector_store in vector_stores]
selected_vector_stores = st.multiselect(
label="Select Document Collections to use in RAG queries",
options=vector_stores,
on_change=reset_agent,
)
mcp_selection = st.pills(
label="MCP Servers",
options=mcp_tools_list,
selection_mode="multi",
on_change=reset_agent,
format_func=lambda tool: "".join(tool.split("::")[1:]),
help="List of MCP servers registered to your llama stack server.",
)
toolgroup_selection.extend(mcp_selection)
grouped_tools = {}
total_tools = 0
for toolgroup_id in toolgroup_selection:
tools = client.tools.list(toolgroup_id=toolgroup_id)
grouped_tools[toolgroup_id] = [tool.name for tool in tools]
total_tools += len(tools)
st.markdown(f"Active Tools: 🛠 {total_tools}")
for group_id, tools in grouped_tools.items():
with st.expander(f"🔧 Tools from `{group_id}`"):
for idx, tool in enumerate(tools, start=1):
st.markdown(f"{idx}. `{tool.split(':')[-1]}`")
st.subheader("Agent Configurations")
st.subheader("Agent Type")
agent_type = st.radio(
label="Select Agent Type",
options=["Regular", "ReAct"],
on_change=reset_agent,
)
if agent_type == "ReAct":
agent_type = AgentType.REACT
else:
agent_type = AgentType.REGULAR
max_tokens = st.slider(
"Max Tokens",
min_value=0,
max_value=4096,
value=512,
step=64,
help="The maximum number of tokens to generate",
on_change=reset_agent,
)
for i, tool_name in enumerate(toolgroup_selection):
if tool_name == "builtin::rag":
tool_dict = dict(
name="builtin::rag",
args={
"vector_store_ids": list(selected_vector_stores),
},
)
toolgroup_selection[i] = tool_dict
@st.cache_resource
def create_agent():
if "agent_type" in st.session_state and st.session_state.agent_type == AgentType.REACT:
return ReActAgent(
client=client,
model=model,
tools=toolgroup_selection,
response_format={
"type": "json_schema",
"json_schema": ReActOutput.model_json_schema(),
},
sampling_params={"strategy": {"type": "greedy"}, "max_tokens": max_tokens},
)
else:
return Agent(
client,
model=model,
instructions="You are a helpful assistant. When you use a tool always respond with a summary of the result.",
tools=toolgroup_selection,
sampling_params={"strategy": {"type": "greedy"}, "max_tokens": max_tokens},
)
st.session_state.agent_type = agent_type
agent = create_agent()
if "agent_session_id" not in st.session_state:
st.session_state["agent_session_id"] = agent.create_session(session_name=f"tool_demo_{uuid.uuid4()}")
session_id = st.session_state["agent_session_id"]
if "messages" not in st.session_state:
st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
for msg in st.session_state.messages:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
if prompt := st.chat_input(placeholder=""):
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
turn_response = agent.create_turn(
session_id=session_id,
messages=[{"role": "user", "content": prompt}],
stream=True,
)
def response_generator(turn_response):
if st.session_state.get("agent_type") == AgentType.REACT:
return _handle_react_response(turn_response)
else:
return _handle_regular_response(turn_response)
def _handle_react_response(turn_response):
current_step_content = ""
final_answer = None
tool_results = []
for response in turn_response:
if not hasattr(response.event, "payload"):
yield (
"\n\n🚨 :red[_Llama Stack server Error:_]\n"
"The response received is missing an expected `payload` attribute.\n"
"This could indicate a malformed response or an internal issue within the server.\n\n"
f"Error details: {response}"
)
return
payload = response.event.payload
if payload.event_type == "step_progress" and hasattr(payload.delta, "text"):
current_step_content += payload.delta.text
continue
if payload.event_type == "step_complete":
step_details = payload.step_details
if step_details.step_type == "inference":
yield from _process_inference_step(current_step_content, tool_results, final_answer)
current_step_content = ""
elif step_details.step_type == "tool_execution":
tool_results = _process_tool_execution(step_details, tool_results)
current_step_content = ""
else:
current_step_content = ""
if not final_answer and tool_results:
yield from _format_tool_results_summary(tool_results)
def _process_inference_step(current_step_content, tool_results, final_answer):
try:
react_output_data = json.loads(current_step_content)
thought = react_output_data.get("thought")
action = react_output_data.get("action")
answer = react_output_data.get("answer")
if answer and answer != "null" and answer is not None:
final_answer = answer
if thought:
with st.expander("🤔 Thinking...", expanded=False):
st.markdown(f":grey[__{thought}__]")
if action and isinstance(action, dict):
tool_name = action.get("tool_name")
tool_params = action.get("tool_params")
with st.expander(f'🛠 Action: Using tool "{tool_name}"', expanded=False):
st.json(tool_params)
if answer and answer != "null" and answer is not None:
yield f"\n\n✅ **Final Answer:**\n{answer}"
except json.JSONDecodeError:
yield f"\n\nFailed to parse ReAct step content:\n```json\n{current_step_content}\n```"
except Exception as e:
yield f"\n\nFailed to process ReAct step: {e}\n```json\n{current_step_content}\n```"
return final_answer
def _process_tool_execution(step_details, tool_results):
try:
if hasattr(step_details, "tool_responses") and step_details.tool_responses:
for tool_response in step_details.tool_responses:
tool_name = tool_response.tool_name
content = tool_response.content
tool_results.append((tool_name, content))
with st.expander(f'⚙️ Observation (Result from "{tool_name}")', expanded=False):
try:
parsed_content = json.loads(content)
st.json(parsed_content)
except json.JSONDecodeError:
st.code(content, language=None)
else:
with st.expander("⚙️ Observation", expanded=False):
st.markdown(":grey[_Tool execution step completed, but no response data found._]")
except Exception as e:
with st.expander("⚙️ Error in Tool Execution", expanded=False):
st.markdown(f":red[_Error processing tool execution: {str(e)}_]")
return tool_results
def _format_tool_results_summary(tool_results):
yield "\n\n**Here's what I found:**\n"
for tool_name, content in tool_results:
try:
parsed_content = json.loads(content)
if tool_name == "web_search" and "top_k" in parsed_content:
yield from _format_web_search_results(parsed_content)
elif "results" in parsed_content and isinstance(parsed_content["results"], list):
yield from _format_results_list(parsed_content["results"])
elif isinstance(parsed_content, dict) and len(parsed_content) > 0:
yield from _format_dict_results(parsed_content)
elif isinstance(parsed_content, list) and len(parsed_content) > 0:
yield from _format_list_results(parsed_content)
except json.JSONDecodeError:
yield f"\n**{tool_name}** was used but returned complex data. Check the observation for details.\n"
except (TypeError, AttributeError, KeyError, IndexError) as e:
print(f"Error processing {tool_name} result: {type(e).__name__}: {e}")
def _format_web_search_results(parsed_content):
for i, result in enumerate(parsed_content["top_k"], 1):
if i <= 3:
title = result.get("title", "Untitled")
url = result.get("url", "")
content_text = result.get("content", "").strip()
yield f"\n- **{title}**\n {content_text}\n [Source]({url})\n"
def _format_results_list(results):
for i, result in enumerate(results, 1):
if i <= 3:
if isinstance(result, dict):
name = result.get("name", result.get("title", "Result " + str(i)))
description = result.get("description", result.get("content", result.get("summary", "")))
yield f"\n- **{name}**\n {description}\n"
else:
yield f"\n- {result}\n"
def _format_dict_results(parsed_content):
yield "\n```\n"
for key, value in list(parsed_content.items())[:5]:
if isinstance(value, str) and len(value) < 100:
yield f"{key}: {value}\n"
else:
yield f"{key}: [Complex data]\n"
yield "```\n"
def _format_list_results(parsed_content):
yield "\n"
for _, item in enumerate(parsed_content[:3], 1):
if isinstance(item, str):
yield f"- {item}\n"
elif isinstance(item, dict) and "text" in item:
yield f"- {item['text']}\n"
elif isinstance(item, dict) and len(item) > 0:
first_value = next(iter(item.values()))
if isinstance(first_value, str) and len(first_value) < 100:
yield f"- {first_value}\n"
def _handle_regular_response(turn_response):
for response in turn_response:
if hasattr(response.event, "payload"):
print(response.event.payload)
if response.event.payload.event_type == "step_progress":
if hasattr(response.event.payload.delta, "text"):
yield response.event.payload.delta.text
if response.event.payload.event_type == "step_complete":
if response.event.payload.step_details.step_type == "tool_execution":
if response.event.payload.step_details.tool_calls:
tool_name = str(response.event.payload.step_details.tool_calls[0].tool_name)
yield f'\n\n🛠 :grey[_Using "{tool_name}" tool:_]\n\n'
else:
yield "No tool_calls present in step_details"
else:
yield f"Error occurred in the Llama Stack Cluster: {response}"
with st.chat_message("assistant"):
response_content = st.write_stream(response_generator(turn_response))
st.session_state.messages.append({"role": "assistant", "content": response_content})
tool_chat_page()

5
src/llama_stack/core/ui/requirements.txt
View file

@ -1,5 +0,0 @@
llama-stack>=0.2.1
llama-stack-client>=0.2.1
pandas
streamlit
streamlit-option-menu

3
src/llama_stack/distributions/ci-tests/run.yaml
View file

@ -46,6 +46,9 @@ providers:
api_key: ${env.TOGETHER_API_KEY:=} api_key: ${env.TOGETHER_API_KEY:=}
- provider_id: bedrock - provider_id: bedrock
provider_type: remote::bedrock provider_type: remote::bedrock
config:
api_key: ${env.AWS_BEDROCK_API_KEY:=}
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
- provider_id: ${env.NVIDIA_API_KEY:+nvidia} - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
provider_type: remote::nvidia provider_type: remote::nvidia
config: config:

3
src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml
View file

@ -46,6 +46,9 @@ providers:
api_key: ${env.TOGETHER_API_KEY:=} api_key: ${env.TOGETHER_API_KEY:=}
- provider_id: bedrock - provider_id: bedrock
provider_type: remote::bedrock provider_type: remote::bedrock
config:
api_key: ${env.AWS_BEDROCK_API_KEY:=}
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
- provider_id: ${env.NVIDIA_API_KEY:+nvidia} - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
provider_type: remote::nvidia provider_type: remote::nvidia
config: config:

3
src/llama_stack/distributions/starter-gpu/run.yaml
View file

@ -46,6 +46,9 @@ providers:
api_key: ${env.TOGETHER_API_KEY:=} api_key: ${env.TOGETHER_API_KEY:=}
- provider_id: bedrock - provider_id: bedrock
provider_type: remote::bedrock provider_type: remote::bedrock
config:
api_key: ${env.AWS_BEDROCK_API_KEY:=}
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
- provider_id: ${env.NVIDIA_API_KEY:+nvidia} - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
provider_type: remote::nvidia provider_type: remote::nvidia
config: config:

3
src/llama_stack/distributions/starter/run-with-postgres-store.yaml
View file

@ -46,6 +46,9 @@ providers:
api_key: ${env.TOGETHER_API_KEY:=} api_key: ${env.TOGETHER_API_KEY:=}
- provider_id: bedrock - provider_id: bedrock
provider_type: remote::bedrock provider_type: remote::bedrock
config:
api_key: ${env.AWS_BEDROCK_API_KEY:=}
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
- provider_id: ${env.NVIDIA_API_KEY:+nvidia} - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
provider_type: remote::nvidia provider_type: remote::nvidia
config: config:

3
src/llama_stack/distributions/starter/run.yaml
View file

@ -46,6 +46,9 @@ providers:
api_key: ${env.TOGETHER_API_KEY:=} api_key: ${env.TOGETHER_API_KEY:=}
- provider_id: bedrock - provider_id: bedrock
provider_type: remote::bedrock provider_type: remote::bedrock
config:
api_key: ${env.AWS_BEDROCK_API_KEY:=}
region_name: ${env.AWS_DEFAULT_REGION:=us-east-2}
- provider_id: ${env.NVIDIA_API_KEY:+nvidia} - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
provider_type: remote::nvidia provider_type: remote::nvidia
config: config:

2
src/llama_stack/providers/inline/inference/meta_reference/inference.py
View file

@ -146,7 +146,7 @@ class MetaReferenceInferenceImpl(
def check_model(self, request) -> None: def check_model(self, request) -> None:
if self.model_id is None or self.llama_model is None: if self.model_id is None or self.llama_model is None:
raise RuntimeError( raise RuntimeError(
"No avaible model yet, please register your requested model or add your model in the resouces first" "No available model yet, please register your requested model or add your model in the resources first"
) )
elif request.model != self.model_id: elif request.model != self.model_id:
raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}") raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}")

2
src/llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py
View file

@ -91,7 +91,7 @@ class TorchtuneCheckpointer:
if checkpoint_format == "meta" or checkpoint_format is None: if checkpoint_format == "meta" or checkpoint_format is None:
self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only) self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only)
elif checkpoint_format == "huggingface": elif checkpoint_format == "huggingface":
# Note: for saving hugging face format checkpoints, we only suppport saving adapter weights now # Note: for saving hugging face format checkpoints, we only support saving adapter weights now
self._save_hf_format_checkpoint(model_file_path, state_dict) self._save_hf_format_checkpoint(model_file_path, state_dict)
else: else:
raise ValueError(f"Unsupported checkpoint format: {format}") raise ValueError(f"Unsupported checkpoint format: {format}")

2
src/llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py
View file

@ -25,7 +25,7 @@ def llama_stack_instruct_to_torchtune_instruct(
) )
input_messages = json.loads(sample[ColumnName.chat_completion_input.value]) input_messages = json.loads(sample[ColumnName.chat_completion_input.value])
assert len(input_messages) == 1, "llama stack intruct dataset format only supports 1 user message" assert len(input_messages) == 1, "llama stack instruct dataset format only supports 1 user message"
input_message = input_messages[0] input_message = input_messages[0]
assert "content" in input_message, "content not found in input message" assert "content" in input_message, "content not found in input message"

5
src/llama_stack/providers/registry/inference.py
View file

@ -138,10 +138,11 @@ def available_providers() -> list[ProviderSpec]:
api=Api.inference, api=Api.inference,
adapter_type="bedrock", adapter_type="bedrock",
provider_type="remote::bedrock", provider_type="remote::bedrock",
pip_packages=["boto3"], pip_packages=[],
module="llama_stack.providers.remote.inference.bedrock", module="llama_stack.providers.remote.inference.bedrock",
config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig", config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
description="AWS Bedrock inference provider for accessing various AI models through AWS's managed service.", provider_data_validator="llama_stack.providers.remote.inference.bedrock.config.BedrockProviderDataValidator",
description="AWS Bedrock inference provider using OpenAI compatible endpoint.",
), ),
RemoteProviderSpec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,

2
src/llama_stack/providers/remote/inference/bedrock/__init__.py
View file

@ -11,7 +11,7 @@ async def get_adapter_impl(config: BedrockConfig, _deps):
assert isinstance(config, BedrockConfig), f"Unexpected config type: {type(config)}" assert isinstance(config, BedrockConfig), f"Unexpected config type: {type(config)}"
impl = BedrockInferenceAdapter(config) impl = BedrockInferenceAdapter(config=config)
await impl.initialize() await impl.initialize()

191
src/llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -4,139 +4,124 @@
# This source code is licensed under the terms described in the LICENSE file in # This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree. # the root directory of this source tree.
import json from collections.abc import AsyncIterator, Iterable
from collections.abc import AsyncIterator
from botocore.client import BaseClient from openai import AuthenticationError
from llama_stack.apis.inference import ( from llama_stack.apis.inference import (
ChatCompletionRequest, OpenAIChatCompletion,
Inference, OpenAIChatCompletionChunk,
OpenAIChatCompletionRequestWithExtraBody, OpenAIChatCompletionRequestWithExtraBody,
OpenAICompletion,
OpenAICompletionRequestWithExtraBody, OpenAICompletionRequestWithExtraBody,
OpenAIEmbeddingsRequestWithExtraBody, OpenAIEmbeddingsRequestWithExtraBody,
OpenAIEmbeddingsResponse, OpenAIEmbeddingsResponse,
) )
from llama_stack.apis.inference.inference import ( from llama_stack.core.telemetry.tracing import get_current_span
OpenAIChatCompletion, from llama_stack.log import get_logger
OpenAIChatCompletionChunk, from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
OpenAICompletion,
)
from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
from llama_stack.providers.utils.bedrock.client import create_bedrock_client
from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_strategy_options,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
)
from .models import MODEL_ENTRIES from .config import BedrockConfig
REGION_PREFIX_MAP = { logger = get_logger(name=__name__, category="inference::bedrock")
"us": "us.",
"eu": "eu.",
"ap": "ap.",
}
def _get_region_prefix(region: str | None) -> str: class BedrockInferenceAdapter(OpenAIMixin):
# AWS requires region prefixes for inference profiles """
if region is None: Adapter for AWS Bedrock's OpenAI-compatible API endpoints.
return "us." # default to US when we don't know
# Handle case insensitive region matching Supports Llama models across regions and GPT-OSS models (us-west-2 only).
region_lower = region.lower()
for prefix in REGION_PREFIX_MAP:
if region_lower.startswith(f"{prefix}-"):
return REGION_PREFIX_MAP[prefix]
# Fallback to US for anything we don't recognize Note: Bedrock's OpenAI-compatible endpoint does not support /v1/models
return "us." for dynamic model discovery. Models must be pre-registered in the config.
"""
config: BedrockConfig
provider_data_api_key_field: str = "aws_bedrock_api_key"
def _to_inference_profile_id(model_id: str, region: str = None) -> str: def get_base_url(self) -> str:
# Return ARNs unchanged """Get base URL for OpenAI client."""
if model_id.startswith("arn:"): return f"https://bedrock-runtime.{self.config.region_name}.amazonaws.com/openai/v1"
return model_id
# Return inference profile IDs that already have regional prefixes async def list_provider_model_ids(self) -> Iterable[str]:
if any(model_id.startswith(p) for p in REGION_PREFIX_MAP.values()): """
return model_id Bedrock's OpenAI-compatible endpoint does not support the /v1/models endpoint.
Returns empty list since models must be pre-registered in the config.
"""
return []
# Default to US East when no region is provided async def check_model_availability(self, model: str) -> bool:
if region is None: """
region = "us-east-1" Bedrock doesn't support dynamic model listing via /v1/models.
Always return True to accept all models registered in the config.
return _get_region_prefix(region) + model_id """
return True
class BedrockInferenceAdapter(
ModelRegistryHelper,
Inference,
):
def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
self._config = config
self._client = None
@property
def client(self) -> BaseClient:
if self._client is None:
self._client = create_bedrock_client(self._config)
return self._client
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
if self._client is not None:
self._client.close()
async def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> dict:
bedrock_model = request.model
sampling_params = request.sampling_params
options = get_sampling_strategy_options(sampling_params)
if sampling_params.max_tokens:
options["max_gen_len"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0:
options["repetition_penalty"] = sampling_params.repetition_penalty
prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model))
# Convert foundation model ID to inference profile ID
region_name = self.client.meta.region_name
inference_profile_id = _to_inference_profile_id(bedrock_model, region_name)
return {
"modelId": inference_profile_id,
"body": json.dumps(
{
"prompt": prompt,
**options,
}
),
}
async def openai_embeddings( async def openai_embeddings(
self, self,
params: OpenAIEmbeddingsRequestWithExtraBody, params: OpenAIEmbeddingsRequestWithExtraBody,
) -> OpenAIEmbeddingsResponse: ) -> OpenAIEmbeddingsResponse:
raise NotImplementedError() """Bedrock's OpenAI-compatible API does not support the /v1/embeddings endpoint."""
raise NotImplementedError(
"Bedrock's OpenAI-compatible API does not support /v1/embeddings endpoint. "
"See https://docs.aws.amazon.com/bedrock/latest/userguide/inference-chat-completions.html"
)
async def openai_completion( async def openai_completion(
self, self,
params: OpenAICompletionRequestWithExtraBody, params: OpenAICompletionRequestWithExtraBody,
) -> OpenAICompletion: ) -> OpenAICompletion:
raise NotImplementedError("OpenAI completion not supported by the Bedrock provider") """Bedrock's OpenAI-compatible API does not support the /v1/completions endpoint."""
raise NotImplementedError(
"Bedrock's OpenAI-compatible API does not support /v1/completions endpoint. "
"Only /v1/chat/completions is supported. "
"See https://docs.aws.amazon.com/bedrock/latest/userguide/inference-chat-completions.html"
)
async def openai_chat_completion( async def openai_chat_completion(
self, self,
params: OpenAIChatCompletionRequestWithExtraBody, params: OpenAIChatCompletionRequestWithExtraBody,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
raise NotImplementedError("OpenAI chat completion not supported by the Bedrock provider") """Override to enable streaming usage metrics and handle authentication errors."""
# Enable streaming usage metrics when telemetry is active
if params.stream and get_current_span() is not None:
if params.stream_options is None:
params.stream_options = {"include_usage": True}
elif "include_usage" not in params.stream_options:
params.stream_options = {**params.stream_options, "include_usage": True}
try:
logger.debug(f"Calling Bedrock OpenAI API with model={params.model}, stream={params.stream}")
result = await super().openai_chat_completion(params=params)
logger.debug(f"Bedrock API returned: {type(result).__name__ if result is not None else 'None'}")
if result is None:
logger.error(f"Bedrock OpenAI client returned None for model={params.model}, stream={params.stream}")
raise RuntimeError(
f"Bedrock API returned no response for model '{params.model}'. "
"This may indicate the model is not supported or a network/API issue occurred."
)
return result
except AuthenticationError as e:
error_msg = str(e)
# Check if this is a token expiration error
if "expired" in error_msg.lower() or "Bearer Token has expired" in error_msg:
logger.error(f"AWS Bedrock authentication token expired: {error_msg}")
raise ValueError(
"AWS Bedrock authentication failed: Bearer token has expired. "
"The AWS_BEDROCK_API_KEY environment variable contains an expired pre-signed URL. "
"Please refresh your token by generating a new pre-signed URL with AWS credentials. "
"Refer to AWS Bedrock documentation for details on OpenAI-compatible endpoints."
) from e
else:
logger.error(f"AWS Bedrock authentication failed: {error_msg}")
raise ValueError(
f"AWS Bedrock authentication failed: {error_msg}. "
"Please verify your API key is correct in the provider config or x-llamastack-provider-data header. "
"The API key should be a valid AWS pre-signed URL for Bedrock's OpenAI-compatible endpoint."
) from e
except Exception as e:
logger.error(f"Unexpected error calling Bedrock API: {type(e).__name__}: {e}", exc_info=True)
raise

27
src/llama_stack/providers/remote/inference/bedrock/config.py
View file

@ -4,8 +4,29 @@
# This source code is licensed under the terms described in the LICENSE file in # This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree. # the root directory of this source tree.
from llama_stack.providers.utils.bedrock.config import BedrockBaseConfig import os
from pydantic import BaseModel, Field
from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
class BedrockConfig(BedrockBaseConfig): class BedrockProviderDataValidator(BaseModel):
pass aws_bedrock_api_key: str | None = Field(
default=None,
description="API key for Amazon Bedrock",
)
class BedrockConfig(RemoteInferenceProviderConfig):
region_name: str = Field(
default_factory=lambda: os.getenv("AWS_DEFAULT_REGION", "us-east-2"),
description="AWS Region for the Bedrock Runtime endpoint",
)
@classmethod
def sample_run_config(cls, **kwargs):
return {
"api_key": "${env.AWS_BEDROCK_API_KEY:=}",
"region_name": "${env.AWS_DEFAULT_REGION:=us-east-2}",
}

29
src/llama_stack/providers/remote/inference/bedrock/models.py
View file

@ -1,29 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.models.llama.sku_types import CoreModelId
from llama_stack.providers.utils.inference.model_registry import (
build_hf_repo_model_entry,
)
SAFETY_MODELS_ENTRIES = []
# https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
MODEL_ENTRIES = [
build_hf_repo_model_entry(
"meta.llama3-1-8b-instruct-v1:0",
CoreModelId.llama3_1_8b_instruct.value,
),
build_hf_repo_model_entry(
"meta.llama3-1-70b-instruct-v1:0",
CoreModelId.llama3_1_70b_instruct.value,
),
build_hf_repo_model_entry(
"meta.llama3-1-405b-instruct-v1:0",
CoreModelId.llama3_1_405b_instruct.value,
),
] + SAFETY_MODELS_ENTRIES

0
src/llama_stack/core/ui/__init__.py → tests/integration/agents/__init__.py
View file

59
tests/integration/agents/recordings/007a9180a7aa38e17c1135ebf1f75e0d5ce1ea58e2261deba8c41e51196078ec.json generated Normal file
View file

@ -0,0 +1,59 @@
{
"test_id": "tests/integration/agents/test_openai_responses.py::test_output_safety_guardrails_unsafe_content[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-False]",
"request": {
"method": "POST",
"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama-guard3:1b",
"messages": [
{
"role": "user",
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