mirror of
https://github.com/microsoft/graphrag.git
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0e1a6e3770
* unified search app added to graphrag repository * ignore print statements * update words for unified-search * fix lint errors * fix lint error * fix module name --------- Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>
261 lines
9.1 KiB
Python
261 lines
9.1 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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"""Home Page module."""
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import asyncio
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import streamlit as st
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from app_logic import dataset_name, initialize, run_all_searches, run_generate_questions
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from rag.typing import SearchType
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from st_tabs import TabBar
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from state.session_variables import SessionVariables
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from ui.full_graph import create_full_graph_ui
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from ui.questions_list import create_questions_list_ui
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from ui.report_details import create_report_details_ui
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from ui.report_list import create_report_list_ui
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from ui.search import display_citations, format_suggested_questions, init_search_ui
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from ui.sidebar import create_side_bar
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async def main():
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"""Return main streamlit component to render the app."""
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sv = initialize()
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create_side_bar(sv)
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st.markdown(
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"#### GraphRAG: A Novel Knowledge Graph-based Approach to Retrieval Augmented Generation (RAG)"
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)
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st.markdown("##### Dataset selected: " + dataset_name(sv.dataset.value, sv))
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st.markdown(sv.dataset_config.value.description)
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def on_click_reset(sv: SessionVariables):
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sv.generated_questions.value = []
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sv.selected_question.value = ""
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sv.show_text_input.value = True
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def on_change(sv: SessionVariables):
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sv.question.value = st.session_state[question_input]
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question_input = "question_input"
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generate_questions = st.button("Suggest some questions")
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question = ""
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if len(sv.question.value.strip()) > 0:
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question = sv.question.value
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if generate_questions:
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with st.spinner("Generating suggested questions..."):
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try:
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result = await run_generate_questions(
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query=f"Generate numbered list only with the top {sv.suggested_questions.value} most important questions of this dataset (numbered list only without titles or anything extra)",
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sv=sv,
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)
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for result_item in result:
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questions = format_suggested_questions(result_item.response)
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sv.generated_questions.value = questions
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sv.show_text_input.value = False
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except Exception as e: # noqa: BLE001
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print(f"Search exception: {e}") # noqa T201
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st.write(e)
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if sv.show_text_input.value is True:
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st.text_input(
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"Ask a question to compare the results",
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key=question_input,
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on_change=on_change,
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value=question,
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kwargs={"sv": sv},
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)
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if len(sv.generated_questions.value) != 0:
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create_questions_list_ui(sv)
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if sv.show_text_input.value is False:
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st.button(label="Reset", on_click=on_click_reset, kwargs={"sv": sv})
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tab_id = TabBar(
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tabs=["Search", "Graph Explorer"],
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color="#fc9e9e",
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activeColor="#ff4b4b",
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default=0,
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)
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if tab_id == 0:
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if len(sv.question.value.strip()) > 0:
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question = sv.question.value
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if sv.selected_question.value != "":
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question = sv.selected_question.value
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sv.question.value = question
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if question:
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st.write(f"##### Answering the question: *{question}*")
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ss_basic = None
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ss_local = None
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ss_global = None
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ss_drift = None
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ss_basic_citations = None
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ss_local_citations = None
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ss_global_citations = None
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ss_drift_citations = None
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count = sum([
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sv.include_basic_rag.value,
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sv.include_local_search.value,
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sv.include_global_search.value,
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sv.include_drift_search.value,
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])
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if count > 0:
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columns = st.columns(count)
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index = 0
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if sv.include_basic_rag.value:
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ss_basic = columns[index]
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index += 1
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if sv.include_local_search.value:
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ss_local = columns[index]
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index += 1
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if sv.include_global_search.value:
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ss_global = columns[index]
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index += 1
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if sv.include_drift_search.value:
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ss_drift = columns[index]
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else:
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st.write("Please select at least one search option from the sidebar.")
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with st.container():
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if ss_basic:
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with ss_basic:
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init_search_ui(
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container=ss_basic,
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search_type=SearchType.Basic,
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title="##### GraphRAG: Basic RAG",
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caption="###### Answer context: Fixed number of text chunks of raw documents",
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)
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if ss_local:
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with ss_local:
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init_search_ui(
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container=ss_local,
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search_type=SearchType.Local,
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title="##### GraphRAG: Local Search",
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caption="###### Answer context: Graph index query results with relevant document text chunks",
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)
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if ss_global:
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with ss_global:
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init_search_ui(
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container=ss_global,
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search_type=SearchType.Global,
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title="##### GraphRAG: Global Search",
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caption="###### Answer context: AI-generated network reports covering all input documents",
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)
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if ss_drift:
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with ss_drift:
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init_search_ui(
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container=ss_drift,
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search_type=SearchType.Drift,
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title="##### GraphRAG: Drift Search",
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caption="###### Answer context: Includes community information",
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)
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count = sum([
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sv.include_basic_rag.value,
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sv.include_local_search.value,
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sv.include_global_search.value,
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sv.include_drift_search.value,
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])
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if count > 0:
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columns = st.columns(count)
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index = 0
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if sv.include_basic_rag.value:
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ss_basic_citations = columns[index]
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index += 1
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if sv.include_local_search.value:
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ss_local_citations = columns[index]
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index += 1
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if sv.include_global_search.value:
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ss_global_citations = columns[index]
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index += 1
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if sv.include_drift_search.value:
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ss_drift_citations = columns[index]
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with st.container():
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if ss_basic_citations:
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with ss_basic_citations:
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st.empty()
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if ss_local_citations:
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with ss_local_citations:
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st.empty()
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if ss_global_citations:
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with ss_global_citations:
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st.empty()
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if ss_drift_citations:
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with ss_drift_citations:
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st.empty()
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if question != "" and question != sv.question_in_progress.value:
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sv.question_in_progress.value = question
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try:
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await run_all_searches(query=question, sv=sv)
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if "response_lengths" not in st.session_state:
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st.session_state.response_lengths = []
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for result in st.session_state.response_lengths:
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if result["search"] == SearchType.Basic.value.lower():
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display_citations(
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container=ss_basic_citations,
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result=result["result"],
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)
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if result["search"] == SearchType.Local.value.lower():
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display_citations(
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container=ss_local_citations,
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result=result["result"],
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)
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if result["search"] == SearchType.Global.value.lower():
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display_citations(
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container=ss_global_citations,
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result=result["result"],
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)
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elif result["search"] == SearchType.Drift.value.lower():
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display_citations(
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container=ss_drift_citations,
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result=result["result"],
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)
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except Exception as e: # noqa: BLE001
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print(f"Search exception: {e}") # noqa T201
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st.write(e)
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if tab_id == 1:
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report_list, graph, report_content = st.columns([0.20, 0.55, 0.25])
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with report_list:
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st.markdown("##### Community Reports")
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create_report_list_ui(sv)
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with graph:
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title, dropdown = st.columns([0.80, 0.20])
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title.markdown("##### Entity Graph (All entities)")
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dropdown.selectbox(
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"Community level", options=[0, 1], key=sv.graph_community_level.key
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)
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create_full_graph_ui(sv)
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with report_content:
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st.markdown("##### Selected Report")
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create_report_details_ui(sv)
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if __name__ == "__main__":
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asyncio.run(main())
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