forked from phoenix-oss/llama-stack-mirror
[llama stack ui] add native eval & inspect distro & playground pages (#541)
# What does this PR do? New Pages Added: - (1) Inspect Distro - (2) Evaluations: - (a) native evaluations (including generation) - (b) application evaluations (no generation, scoring only) - (3) Playground: - (a) chat - (b) RAG ## Test Plan ``` streamlit run app.py ``` #### Playground https://github.com/user-attachments/assets/6ca617e8-32ca-49b2-9774-185020ff5204 #### Inspect https://github.com/user-attachments/assets/01d52b2d-92af-4e3a-b623-a9b8ba22ba99 #### Evaluations (Generation + Scoring) https://github.com/user-attachments/assets/345845c7-2a2b-4095-960a-9ae40f6a93cf #### Evaluations (Scoring) https://github.com/user-attachments/assets/6cc1659f-eba4-49ca-a0a5-7c243557b4f5 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
This commit is contained in:
Xi Yan
GitHub
parent
caf1dac114
commit
16769256b7
22 changed files with 1000 additions and 166 deletions
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@ -2,6 +2,12 @@
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[!NOTE] This is a work in progress.
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## Prerequisite
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- Start up Llama Stack Server
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```
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llama stack run
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```
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## Running Streamlit App
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```
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@ -3,170 +3,54 @@
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import pandas as pd
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import streamlit as st
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from modules.api import LlamaStackEvaluation
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from modules.utils import process_dataset
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EVALUATION_API = LlamaStackEvaluation()
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def main():
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# Add collapsible sidebar
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with st.sidebar:
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# Add collapse button
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if "sidebar_state" not in st.session_state:
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st.session_state.sidebar_state = True
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if st.session_state.sidebar_state:
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st.title("Navigation")
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page = st.radio(
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"Select a Page",
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["Application Evaluation"],
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index=0,
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)
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else:
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page = "Application Evaluation" # Default page when sidebar is collapsed
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# Main content area
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st.title("🦙 Llama Stack Evaluations")
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if page == "Application Evaluation":
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application_evaluation_page()
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def application_evaluation_page():
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# File uploader
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uploaded_file = st.file_uploader("Upload Dataset", type=["csv", "xlsx", "xls"])
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if uploaded_file is None:
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st.error("No file uploaded")
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return
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# Process uploaded file
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df = process_dataset(uploaded_file)
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if df is None:
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st.error("Error processing file")
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return
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# Display dataset information
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st.success("Dataset loaded successfully!")
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# Display dataframe preview
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st.subheader("Dataset Preview")
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st.dataframe(df)
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# Select Scoring Functions to Run Evaluation On
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st.subheader("Select Scoring Functions")
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scoring_functions = EVALUATION_API.list_scoring_functions()
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scoring_functions = {sf.identifier: sf for sf in scoring_functions}
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scoring_functions_names = list(scoring_functions.keys())
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selected_scoring_functions = st.multiselect(
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"Choose one or more scoring functions",
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options=scoring_functions_names,
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help="Choose one or more scoring functions.",
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# Evaluation pages
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application_evaluation_page = st.Page(
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"page/evaluations/app_eval.py",
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title="Evaluations (Scoring)",
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icon="📊",
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default=False,
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)
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native_evaluation_page = st.Page(
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"page/evaluations/native_eval.py",
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title="Evaluations (Generation + Scoring)",
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icon="📊",
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default=False,
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)
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available_models = EVALUATION_API.list_models()
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available_models = [m.identifier for m in available_models]
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# Playground pages
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chat_page = st.Page(
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"page/playground/chat.py", title="Chat", icon="💬", default=True
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)
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rag_page = st.Page("page/playground/rag.py", title="RAG", icon="💬", default=False)
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scoring_params = {}
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if selected_scoring_functions:
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st.write("Selected:")
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for scoring_fn_id in selected_scoring_functions:
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scoring_fn = scoring_functions[scoring_fn_id]
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st.write(f"- **{scoring_fn_id}**: {scoring_fn.description}")
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new_params = None
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if scoring_fn.params:
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new_params = {}
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for param_name, param_value in scoring_fn.params.to_dict().items():
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if param_name == "type":
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new_params[param_name] = param_value
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continue
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# Distribution pages
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resources_page = st.Page(
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"page/distribution/resources.py", title="Resources", icon="🔍", default=False
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)
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provider_page = st.Page(
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"page/distribution/providers.py",
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title="API Providers",
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icon="🔍",
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default=False,
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)
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if param_name == "judge_model":
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value = st.selectbox(
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f"Select **{param_name}** for {scoring_fn_id}",
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options=available_models,
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index=0,
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key=f"{scoring_fn_id}_{param_name}",
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)
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new_params[param_name] = value
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else:
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value = st.text_area(
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f"Enter value for **{param_name}** in {scoring_fn_id} in valid JSON format",
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value=json.dumps(param_value, indent=2),
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height=80,
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)
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try:
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new_params[param_name] = json.loads(value)
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except json.JSONDecodeError:
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st.error(
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f"Invalid JSON for **{param_name}** in {scoring_fn_id}"
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)
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st.json(new_params)
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scoring_params[scoring_fn_id] = new_params
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# Add run evaluation button & slider
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total_rows = len(df)
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num_rows = st.slider("Number of rows to evaluate", 1, total_rows, total_rows)
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if st.button("Run Evaluation"):
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progress_text = "Running evaluation..."
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progress_bar = st.progress(0, text=progress_text)
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rows = df.to_dict(orient="records")
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if num_rows < total_rows:
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rows = rows[:num_rows]
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# Create separate containers for progress text and results
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progress_text_container = st.empty()
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results_container = st.empty()
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output_res = {}
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for i, r in enumerate(rows):
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# Update progress
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progress = i / len(rows)
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progress_bar.progress(progress, text=progress_text)
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# Run evaluation for current row
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score_res = EVALUATION_API.run_scoring(
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r,
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scoring_function_ids=selected_scoring_functions,
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scoring_params=scoring_params,
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)
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for k in r.keys():
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if k not in output_res:
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output_res[k] = []
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output_res[k].append(r[k])
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for fn_id in selected_scoring_functions:
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if fn_id not in output_res:
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output_res[fn_id] = []
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output_res[fn_id].append(score_res.results[fn_id].score_rows[0])
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# Display current row results using separate containers
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progress_text_container.write(
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f"Expand to see current processed result ({i+1}/{len(rows)})"
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)
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results_container.json(
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score_res.to_json(),
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expanded=2,
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)
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progress_bar.progress(1.0, text="Evaluation complete!")
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# Display results in dataframe
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if output_res:
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output_df = pd.DataFrame(output_res)
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st.subheader("Evaluation Results")
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st.dataframe(output_df)
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pg = st.navigation(
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{
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"Playground": [
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chat_page,
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rag_page,
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application_evaluation_page,
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native_evaluation_page,
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],
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"Inspect": [provider_page, resources_page],
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},
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expanded=False,
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)
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pg.run()
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if __name__ == "__main__":
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5
llama_stack/distribution/ui/modules/__init__.py
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5
llama_stack/distribution/ui/modules/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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@ -11,7 +11,7 @@ from typing import Optional
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from llama_stack_client import LlamaStackClient
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class LlamaStackEvaluation:
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class LlamaStackApi:
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def __init__(self):
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self.client = LlamaStackClient(
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base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:5000"),
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},
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)
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def list_scoring_functions(self):
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"""List all available scoring functions"""
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return self.client.scoring_functions.list()
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def list_models(self):
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"""List all available judge models"""
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return self.client.models.list()
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def run_scoring(
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self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]
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):
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return self.client.scoring.score(
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input_rows=[row], scoring_functions=scoring_params
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)
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llama_stack_api = LlamaStackApi()
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import base64
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import os
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import pandas as pd
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except Exception as e:
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st.error(f"Error processing file: {str(e)}")
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return None
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def data_url_from_file(file) -> str:
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file_content = file.getvalue()
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base64_content = base64.b64encode(file_content).decode("utf-8")
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mime_type = file.type
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data_url = f"data:{mime_type};base64,{base64_content}"
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return data_url
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5
llama_stack/distribution/ui/page/__init__.py
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5
llama_stack/distribution/ui/page/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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19
llama_stack/distribution/ui/page/distribution/datasets.py
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19
llama_stack/distribution/ui/page/distribution/datasets.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import streamlit as st
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from modules.api import llama_stack_api
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def datasets():
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st.header("Datasets")
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datasets_info = {
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d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()
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}
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selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
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st.json(datasets_info[selected_dataset], expanded=True)
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22
llama_stack/distribution/ui/page/distribution/eval_tasks.py
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22
llama_stack/distribution/ui/page/distribution/eval_tasks.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import streamlit as st
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from modules.api import llama_stack_api
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def eval_tasks():
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# Eval Tasks Section
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st.header("Eval Tasks")
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eval_tasks_info = {
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d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list()
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}
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selected_eval_task = st.selectbox(
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"Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect"
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)
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st.json(eval_tasks_info[selected_eval_task], expanded=True)
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import streamlit as st
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from modules.api import llama_stack_api
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def memory_banks():
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st.header("Memory Banks")
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memory_banks_info = {
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m.identifier: m.to_dict() for m in llama_stack_api.client.memory_banks.list()
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}
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if len(memory_banks_info) > 0:
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selected_memory_bank = st.selectbox(
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"Select a memory bank", list(memory_banks_info.keys())
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)
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st.json(memory_banks_info[selected_memory_bank])
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else:
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st.info("No memory banks found")
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19
llama_stack/distribution/ui/page/distribution/models.py
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19
llama_stack/distribution/ui/page/distribution/models.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import streamlit as st
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from modules.api import llama_stack_api
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def models():
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# Models Section
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st.header("Models")
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models_info = {
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m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()
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}
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selected_model = st.selectbox("Select a model", list(models_info.keys()))
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st.json(models_info[selected_model])
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20
llama_stack/distribution/ui/page/distribution/providers.py
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20
llama_stack/distribution/ui/page/distribution/providers.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import streamlit as st
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from modules.api import llama_stack_api
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def providers():
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st.header("🔍 API Providers")
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apis_providers_info = llama_stack_api.client.providers.list()
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# selected_api = st.selectbox("Select an API", list(apis_providers_info.keys()))
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for api in apis_providers_info.keys():
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st.markdown(f"###### {api}")
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st.dataframe([p.to_dict() for p in apis_providers_info[api]], width=500)
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providers()
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52
llama_stack/distribution/ui/page/distribution/resources.py
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52
llama_stack/distribution/ui/page/distribution/resources.py
Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from page.distribution.datasets import datasets
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from page.distribution.eval_tasks import eval_tasks
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from page.distribution.memory_banks import memory_banks
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from page.distribution.models import models
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from page.distribution.scoring_functions import scoring_functions
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from page.distribution.shields import shields
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from streamlit_option_menu import option_menu
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def resources_page():
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options = [
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"Models",
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"Memory Banks",
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"Shields",
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"Scoring Functions",
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"Datasets",
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"Eval Tasks",
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]
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icons = ["magic", "memory", "shield", "file-bar-graph", "database", "list-task"]
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selected_resource = option_menu(
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None,
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options,
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icons=icons,
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orientation="horizontal",
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styles={
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"nav-link": {
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"font-size": "12px",
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},
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},
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)
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if selected_resource == "Eval Tasks":
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eval_tasks()
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elif selected_resource == "Memory Banks":
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memory_banks()
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elif selected_resource == "Datasets":
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datasets()
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elif selected_resource == "Models":
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models()
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elif selected_resource == "Scoring Functions":
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scoring_functions()
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elif selected_resource == "Shields":
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shields()
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resources_page()
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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|
||||
import streamlit as st
|
||||
from 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)
|
||||
20
llama_stack/distribution/ui/page/distribution/shields.py
Normal file
20
llama_stack/distribution/ui/page/distribution/shields.py
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
# 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 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
llama_stack/distribution/ui/page/evaluations/__init__.py
Normal file
5
llama_stack/distribution/ui/page/evaluations/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
# 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.
|
||||
148
llama_stack/distribution/ui/page/evaluations/app_eval.py
Normal file
148
llama_stack/distribution/ui/page/evaluations/app_eval.py
Normal file
|
|
@ -0,0 +1,148 @@
|
|||
# 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 modules.api import llama_stack_api
|
||||
from 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()
|
||||
257
llama_stack/distribution/ui/page/evaluations/native_eval.py
Normal file
257
llama_stack/distribution/ui/page/evaluations/native_eval.py
Normal file
|
|
@ -0,0 +1,257 @@
|
|||
# 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 modules.api import llama_stack_api
|
||||
|
||||
|
||||
def select_eval_task_1():
|
||||
# Select Eval Tasks
|
||||
st.subheader("1. Choose An Eval Task")
|
||||
eval_tasks = llama_stack_api.client.eval_tasks.list()
|
||||
eval_tasks = {et.identifier: et for et in eval_tasks}
|
||||
eval_tasks_names = list(eval_tasks.keys())
|
||||
selected_eval_task = st.selectbox(
|
||||
"Choose an eval task.",
|
||||
options=eval_tasks_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(eval_tasks[selected_eval_task], expanded=True)
|
||||
|
||||
st.session_state["selected_eval_task"] = selected_eval_task
|
||||
st.session_state["eval_tasks"] = eval_tasks
|
||||
if st.button("Confirm", key="confirm_1"):
|
||||
st.session_state["selected_eval_task_1_next"] = True
|
||||
|
||||
|
||||
def define_eval_candidate_2():
|
||||
if not st.session_state.get("selected_eval_task_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")
|
||||
strategy = st.selectbox(
|
||||
"Strategy",
|
||||
["greedy", "top_p", "top_k"],
|
||||
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.",
|
||||
)
|
||||
if candidate_type == "model":
|
||||
eval_candidate = {
|
||||
"type": "model",
|
||||
"model": selected_model,
|
||||
"sampling_params": {
|
||||
"strategy": strategy,
|
||||
"temperature": temperature,
|
||||
"top_p": top_p,
|
||||
"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_eval_task = st.session_state["selected_eval_task"]
|
||||
eval_tasks = st.session_state["eval_tasks"]
|
||||
eval_candidate = st.session_state["eval_candidate"]
|
||||
|
||||
dataset_id = eval_tasks[selected_eval_task].dataset_id
|
||||
rows = llama_stack_api.client.datasetio.get_rows_paginated(
|
||||
dataset_id=dataset_id,
|
||||
rows_in_page=-1,
|
||||
)
|
||||
total_rows = len(rows.rows)
|
||||
# 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. ",
|
||||
)
|
||||
|
||||
eval_task_config = {
|
||||
"type": "benchmark",
|
||||
"eval_candidate": eval_candidate,
|
||||
"scoring_params": {},
|
||||
}
|
||||
|
||||
with st.expander("View Evaluation Task", expanded=True):
|
||||
st.json(eval_tasks[selected_eval_task], expanded=True)
|
||||
with st.expander("View Evaluation Task Configuration", expanded=True):
|
||||
st.json(eval_task_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.rows
|
||||
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(
|
||||
task_id=selected_eval_task,
|
||||
input_rows=[r],
|
||||
scoring_functions=eval_tasks[selected_eval_task].scoring_functions,
|
||||
task_config=eval_task_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 eval_tasks[selected_eval_task].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_eval_task_1()
|
||||
define_eval_candidate_2()
|
||||
run_evaluation_3()
|
||||
|
||||
|
||||
native_evaluation_page()
|
||||
5
llama_stack/distribution/ui/page/playground/__init__.py
Normal file
5
llama_stack/distribution/ui/page/playground/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
# 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.
|
||||
123
llama_stack/distribution/ui/page/playground/chat.py
Normal file
123
llama_stack/distribution/ui/page/playground/chat.py
Normal file
|
|
@ -0,0 +1,123 @@
|
|||
# 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 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.identifier for model in available_models]
|
||||
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 = ""
|
||||
|
||||
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={
|
||||
"temperature": temperature,
|
||||
"top_p": top_p,
|
||||
"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
|
||||
message_placeholder.markdown(full_response + "▌")
|
||||
message_placeholder.markdown(full_response)
|
||||
else:
|
||||
full_response = response
|
||||
message_placeholder.markdown(full_response.completion_message.content)
|
||||
|
||||
st.session_state.messages.append(
|
||||
{"role": "assistant", "content": full_response}
|
||||
)
|
||||
188
llama_stack/distribution/ui/page/playground/rag.py
Normal file
188
llama_stack/distribution/ui/page/playground/rag.py
Normal file
|
|
@ -0,0 +1,188 @@
|
|||
# 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_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.event_logger import EventLogger
|
||||
from llama_stack_client.types.agent_create_params import AgentConfig
|
||||
from llama_stack_client.types.memory_insert_params import Document
|
||||
|
||||
from modules.api import llama_stack_api
|
||||
from modules.utils import data_url_from_file
|
||||
|
||||
|
||||
def rag_chat_page():
|
||||
st.title("🦙 RAG")
|
||||
|
||||
with st.sidebar:
|
||||
# File/Directory Upload Section
|
||||
st.subheader("Upload Documents")
|
||||
uploaded_files = st.file_uploader(
|
||||
"Upload file(s) or directory",
|
||||
accept_multiple_files=True,
|
||||
type=["txt", "pdf", "doc", "docx"], # Add more file types as needed
|
||||
)
|
||||
# Process uploaded files
|
||||
if uploaded_files:
|
||||
st.success(f"Successfully uploaded {len(uploaded_files)} files")
|
||||
# Add memory bank name input field
|
||||
memory_bank_name = st.text_input(
|
||||
"Memory Bank Name",
|
||||
value="rag_bank",
|
||||
help="Enter a unique identifier for this memory bank",
|
||||
)
|
||||
if st.button("Create Memory Bank"):
|
||||
documents = [
|
||||
Document(
|
||||
document_id=uploaded_file.name,
|
||||
content=data_url_from_file(uploaded_file),
|
||||
)
|
||||
for i, uploaded_file in enumerate(uploaded_files)
|
||||
]
|
||||
|
||||
providers = llama_stack_api.client.providers.list()
|
||||
llama_stack_api.client.memory_banks.register(
|
||||
memory_bank_id=memory_bank_name, # Use the user-provided name
|
||||
params={
|
||||
"embedding_model": "all-MiniLM-L6-v2",
|
||||
"chunk_size_in_tokens": 512,
|
||||
"overlap_size_in_tokens": 64,
|
||||
},
|
||||
provider_id=providers["memory"][0].provider_id,
|
||||
)
|
||||
|
||||
# insert documents using the custom bank name
|
||||
llama_stack_api.client.memory.insert(
|
||||
bank_id=memory_bank_name, # Use the user-provided name
|
||||
documents=documents,
|
||||
)
|
||||
st.success("Memory bank created successfully!")
|
||||
|
||||
st.subheader("Configure Agent")
|
||||
# select memory banks
|
||||
memory_banks = llama_stack_api.client.memory_banks.list()
|
||||
memory_banks = [bank.identifier for bank in memory_banks]
|
||||
selected_memory_banks = st.multiselect(
|
||||
"Select Memory Banks",
|
||||
memory_banks,
|
||||
)
|
||||
memory_bank_configs = [
|
||||
{"bank_id": bank_id, "type": "vector"} for bank_id in selected_memory_banks
|
||||
]
|
||||
|
||||
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,
|
||||
)
|
||||
system_prompt = st.text_area(
|
||||
"System Prompt",
|
||||
value="You are a helpful assistant. ",
|
||||
help="Initial instructions given to the AI to set its behavior and context",
|
||||
)
|
||||
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,
|
||||
)
|
||||
|
||||
# Add clear chat button to sidebar
|
||||
if st.button("Clear Chat", use_container_width=True):
|
||||
st.session_state.messages = []
|
||||
st.rerun()
|
||||
|
||||
# Chat Interface
|
||||
if "messages" not in st.session_state:
|
||||
st.session_state.messages = []
|
||||
|
||||
# Display chat history
|
||||
for message in st.session_state.messages:
|
||||
with st.chat_message(message["role"]):
|
||||
st.markdown(message["content"])
|
||||
|
||||
selected_model = llama_stack_api.client.models.list()[0].identifier
|
||||
|
||||
agent_config = AgentConfig(
|
||||
model=selected_model,
|
||||
instructions=system_prompt,
|
||||
sampling_params={
|
||||
"strategy": "greedy",
|
||||
"temperature": temperature,
|
||||
"top_p": top_p,
|
||||
},
|
||||
tools=[
|
||||
{
|
||||
"type": "memory",
|
||||
"memory_bank_configs": memory_bank_configs,
|
||||
"query_generator_config": {"type": "default", "sep": " "},
|
||||
"max_tokens_in_context": 4096,
|
||||
"max_chunks": 10,
|
||||
}
|
||||
],
|
||||
tool_choice="auto",
|
||||
tool_prompt_format="json",
|
||||
input_shields=[],
|
||||
output_shields=[],
|
||||
enable_session_persistence=False,
|
||||
)
|
||||
|
||||
agent = Agent(llama_stack_api.client, agent_config)
|
||||
session_id = agent.create_session("rag-session")
|
||||
|
||||
# Chat input
|
||||
if prompt := st.chat_input("Ask a question about your documents"):
|
||||
# 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)
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": prompt,
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
)
|
||||
|
||||
# Display assistant response
|
||||
with st.chat_message("assistant"):
|
||||
retrieval_message_placeholder = st.empty()
|
||||
message_placeholder = st.empty()
|
||||
full_response = ""
|
||||
retrieval_response = ""
|
||||
for log in EventLogger().log(response):
|
||||
log.print()
|
||||
if log.role == "memory_retrieval":
|
||||
retrieval_response += log.content.replace("====", "").strip()
|
||||
retrieval_message_placeholder.info(retrieval_response)
|
||||
else:
|
||||
full_response += log.content
|
||||
message_placeholder.markdown(full_response + "▌")
|
||||
message_placeholder.markdown(full_response)
|
||||
|
||||
st.session_state.messages.append(
|
||||
{"role": "assistant", "content": full_response}
|
||||
)
|
||||
|
||||
|
||||
rag_chat_page()
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
streamlit
|
||||
pandas
|
||||
llama-stack-client>=0.0.55
|
||||
streamlit-option-menu
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
from llama_stack.apis.common.type_system import NumberType
|
||||
from llama_stack.apis.scoring_functions import ScoringFn
|
||||
from llama_stack.apis.scoring_functions import LLMAsJudgeScoringFnParams, ScoringFn
|
||||
|
||||
|
||||
llm_as_judge_base = ScoringFn(
|
||||
|
|
@ -14,4 +14,8 @@ llm_as_judge_base = ScoringFn(
|
|||
return_type=NumberType(),
|
||||
provider_id="llm-as-judge",
|
||||
provider_resource_id="llm-as-judge-base",
|
||||
params=LLMAsJudgeScoringFnParams(
|
||||
judge_model="meta-llama/Llama-3.1-405B-Instruct",
|
||||
prompt_template="Enter custom LLM as Judge Prompt Template",
|
||||
),
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue