diff --git a/docs/docs/building_applications/index.mdx b/docs/docs/building_applications/index.mdx index a4b71efd7..935a02f8a 100644 --- a/docs/docs/building_applications/index.mdx +++ b/docs/docs/building_applications/index.mdx @@ -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 - **[Safety](./safety.mdx)** - Implement guardrails and safety measures to ensure responsible AI behavior -### šŸŽ® **Interactive Development** -- **[Playground](./playground.mdx)** - Interactive environment for testing and developing applications - ## Application Patterns ### šŸ¤– **Conversational Agents** diff --git a/docs/docs/building_applications/playground.mdx b/docs/docs/building_applications/playground.mdx deleted file mode 100644 index f3290a356..000000000 --- a/docs/docs/building_applications/playground.mdx +++ /dev/null @@ -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 - - - - - - -**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 - - - - -**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 - - - - -#### Evaluation Interface - - - - - - -**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 - - - - - - -**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 -``` - - - - -#### Inspection Interface - - - - - - -**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 - - - - -**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 `//list` APIs for comprehensive resource visibility -- For detailed information about resources, see [Core Concepts](/docs/concepts) - - - - -## Getting Started - -### Quick Start Guide - - - - -**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 -``` - - - - -**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 - - - - -### Available Distributions - -The playground works with any Llama Stack distribution. Popular options include: - - - - -```bash -llama stack list-deps together | xargs -L1 uv pip install -llama stack run together -``` - -**Features:** -- Cloud-hosted models -- Fast inference -- Multiple model options - - - - -```bash -llama stack list-deps ollama | xargs -L1 uv pip install -llama stack run ollama -``` - -**Features:** -- Local model execution -- Privacy-focused -- No internet required - - - - -```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 - - - - -## 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 - -### šŸš€ **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 - -### šŸ”§ **Development Workflow** -- Use the playground to prototype before writing application code -- Test different parameter settings interactively -- Validate evaluation approaches before implementing them programmatically - -### šŸ“Š **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 - -### šŸŽÆ **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 diff --git a/pyproject.toml b/pyproject.toml index 8f07f9cbd..f8577ad2b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,14 +51,6 @@ dependencies = [ "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] dev = [ "pytest>=8.4", diff --git a/src/llama_stack/core/ui/Containerfile b/src/llama_stack/core/ui/Containerfile deleted file mode 100644 index 0126d1867..000000000 --- a/src/llama_stack/core/ui/Containerfile +++ /dev/null @@ -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"] diff --git a/src/llama_stack/core/ui/README.md b/src/llama_stack/core/ui/README.md deleted file mode 100644 index 37f1501c9..000000000 --- a/src/llama_stack/core/ui/README.md +++ /dev/null @@ -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) | diff --git a/src/llama_stack/core/ui/__init__.py b/src/llama_stack/core/ui/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/src/llama_stack/core/ui/__init__.py +++ /dev/null @@ -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. diff --git a/src/llama_stack/core/ui/app.py b/src/llama_stack/core/ui/app.py deleted file mode 100644 index 441f65d20..000000000 --- a/src/llama_stack/core/ui/app.py +++ /dev/null @@ -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="šŸ“Š", - default=False, - ) - native_evaluation_page = st.Page( - "page/evaluations/native_eval.py", - title="Evaluations (Generation + Scoring)", - icon="šŸ“Š", - default=False, - ) - - # Playground pages - chat_page = st.Page("page/playground/chat.py", title="Chat", icon="šŸ’¬", default=True) - rag_page = st.Page("page/playground/rag.py", title="RAG", icon="šŸ’¬", default=False) - tool_page = st.Page("page/playground/tools.py", title="Tools", icon="šŸ› ", default=False) - - # Distribution pages - resources_page = st.Page("page/distribution/resources.py", title="Resources", icon="šŸ”", default=False) - provider_page = st.Page( - "page/distribution/providers.py", - title="API Providers", - icon="šŸ”", - 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() diff --git a/src/llama_stack/core/ui/modules/__init__.py b/src/llama_stack/core/ui/modules/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/src/llama_stack/core/ui/modules/__init__.py +++ /dev/null @@ -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. diff --git a/src/llama_stack/core/ui/modules/api.py b/src/llama_stack/core/ui/modules/api.py deleted file mode 100644 index 9db87b280..000000000 --- a/src/llama_stack/core/ui/modules/api.py +++ /dev/null @@ -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() diff --git a/src/llama_stack/core/ui/modules/utils.py b/src/llama_stack/core/ui/modules/utils.py deleted file mode 100644 index 67cce98fa..000000000 --- a/src/llama_stack/core/ui/modules/utils.py +++ /dev/null @@ -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 diff --git a/src/llama_stack/core/ui/page/__init__.py b/src/llama_stack/core/ui/page/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/src/llama_stack/core/ui/page/__init__.py +++ /dev/null @@ -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. diff --git a/src/llama_stack/core/ui/page/distribution/__init__.py b/src/llama_stack/core/ui/page/distribution/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/src/llama_stack/core/ui/page/distribution/__init__.py +++ /dev/null @@ -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. diff --git a/src/llama_stack/core/ui/page/distribution/datasets.py b/src/llama_stack/core/ui/page/distribution/datasets.py deleted file mode 100644 index aab0901ac..000000000 --- a/src/llama_stack/core/ui/page/distribution/datasets.py +++ /dev/null @@ -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) diff --git a/src/llama_stack/core/ui/page/distribution/eval_tasks.py b/src/llama_stack/core/ui/page/distribution/eval_tasks.py deleted file mode 100644 index 1a0ce502b..000000000 --- a/src/llama_stack/core/ui/page/distribution/eval_tasks.py +++ /dev/null @@ -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) diff --git a/src/llama_stack/core/ui/page/distribution/models.py b/src/llama_stack/core/ui/page/distribution/models.py deleted file mode 100644 index e00b327ae..000000000 --- a/src/llama_stack/core/ui/page/distribution/models.py +++ /dev/null @@ -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]) diff --git a/src/llama_stack/core/ui/page/distribution/providers.py b/src/llama_stack/core/ui/page/distribution/providers.py deleted file mode 100644 index 3ec6026d1..000000000 --- a/src/llama_stack/core/ui/page/distribution/providers.py +++ /dev/null @@ -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("šŸ” 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() diff --git a/src/llama_stack/core/ui/page/distribution/resources.py b/src/llama_stack/core/ui/page/distribution/resources.py deleted file mode 100644 index 6e7122ceb..000000000 --- a/src/llama_stack/core/ui/page/distribution/resources.py +++ /dev/null @@ -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() diff --git a/src/llama_stack/core/ui/page/distribution/scoring_functions.py b/src/llama_stack/core/ui/page/distribution/scoring_functions.py deleted file mode 100644 index 2a5196fa9..000000000 --- a/src/llama_stack/core/ui/page/distribution/scoring_functions.py +++ /dev/null @@ -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 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) diff --git a/src/llama_stack/core/ui/page/distribution/shields.py b/src/llama_stack/core/ui/page/distribution/shields.py deleted file mode 100644 index ecce2f12b..000000000 --- a/src/llama_stack/core/ui/page/distribution/shields.py +++ /dev/null @@ -1,19 +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 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]) diff --git a/src/llama_stack/core/ui/page/evaluations/__init__.py b/src/llama_stack/core/ui/page/evaluations/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/src/llama_stack/core/ui/page/evaluations/__init__.py +++ /dev/null @@ -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. diff --git a/src/llama_stack/core/ui/page/evaluations/app_eval.py b/src/llama_stack/core/ui/page/evaluations/app_eval.py deleted file mode 100644 index 07e6349c9..000000000 --- a/src/llama_stack/core/ui/page/evaluations/app_eval.py +++ /dev/null @@ -1,143 +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 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="šŸ¦™") - st.title("šŸ“Š 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() diff --git a/src/llama_stack/core/ui/page/evaluations/native_eval.py b/src/llama_stack/core/ui/page/evaluations/native_eval.py deleted file mode 100644 index 2bef63b2f..000000000 --- a/src/llama_stack/core/ui/page/evaluations/native_eval.py +++ /dev/null @@ -1,253 +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 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="šŸ¦™") - st.title("šŸ“Š Evaluations (Generation + Scoring)") - - select_benchmark_1() - define_eval_candidate_2() - run_evaluation_3() - - -native_evaluation_page() diff --git a/src/llama_stack/core/ui/page/playground/__init__.py b/src/llama_stack/core/ui/page/playground/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/src/llama_stack/core/ui/page/playground/__init__.py +++ /dev/null @@ -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. diff --git a/src/llama_stack/core/ui/page/playground/chat.py b/src/llama_stack/core/ui/page/playground/chat.py deleted file mode 100644 index c813f05dc..000000000 --- a/src/llama_stack/core/ui/page/playground/chat.py +++ /dev/null @@ -1,134 +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 - -# 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("šŸ¦™ 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 + "ā–Œ") - 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}) diff --git a/src/llama_stack/core/ui/page/playground/tools.py b/src/llama_stack/core/ui/page/playground/tools.py deleted file mode 100644 index 16fd464ee..000000000 --- a/src/llama_stack/core/ui/page/playground/tools.py +++ /dev/null @@ -1,352 +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 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("šŸ›  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: šŸ›  {total_tools}") - - for group_id, tools in grouped_tools.items(): - with st.expander(f"šŸ”§ 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\nšŸšØ :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("šŸ¤” 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'šŸ›  Action: Using tool "{tool_name}"', expanded=False): - st.json(tool_params) - - if answer and answer != "null" and answer is not None: - yield f"\n\nāœ… **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'āš™ļø 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("āš™ļø Observation", expanded=False): - st.markdown(":grey[_Tool execution step completed, but no response data found._]") - except Exception as e: - with st.expander("āš™ļø 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\nšŸ›  :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() diff --git a/src/llama_stack/core/ui/requirements.txt b/src/llama_stack/core/ui/requirements.txt deleted file mode 100644 index 53a1e7bf3..000000000 --- a/src/llama_stack/core/ui/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -llama-stack>=0.2.1 -llama-stack-client>=0.2.1 -pandas -streamlit -streamlit-option-menu diff --git a/uv.lock b/uv.lock index de1c8879c..b2e562abc 100644 --- a/uv.lock +++ b/uv.lock @@ -1963,14 +1963,6 @@ dependencies = [ { name = "uvicorn" }, ] -[package.optional-dependencies] -ui = [ - { name = "llama-stack-client" }, - { name = "pandas" }, - { name = "streamlit" }, - { name = "streamlit-option-menu" }, -] - [package.dev-dependencies] benchmark = [ { name = "locust" }, @@ -2097,11 +2089,9 @@ requires-dist = [ { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, { name = "llama-stack-client", specifier = ">=0.3.0" }, - { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.3.0" }, { name = "openai", specifier = ">=2.5.0" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, - { name = "pandas", marker = "extra == 'ui'" }, { name = "pillow" }, { name = "prompt-toolkit" }, { name = "pydantic", specifier = ">=2.11.9" }, @@ -2111,13 +2101,10 @@ requires-dist = [ { name = "rich" }, { name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0.41" }, { name = "starlette" }, - { name = "streamlit", marker = "extra == 'ui'" }, - { name = "streamlit-option-menu", marker = "extra == 'ui'" }, { name = "termcolor" }, { name = "tiktoken" }, { name = "uvicorn", specifier = ">=0.34.0" }, ] -provides-extras = ["ui"] [package.metadata.requires-dev] benchmark = [{ name = "locust", specifier = ">=2.39.1" }]