disable save embeddings and bug fixes (#82)

fixes

app/components/app_mode.py

@@ -17,11 +17,12 @@ def __init__(self, sv=None):
             self.sv = SessionVariables("home")
 
     def config(self):
-        cache = st.sidebar.toggle(
-            "Save embeddings",
-            value=self.sv.save_cache.value,
-            help="Enable caching of embeddings to speed up the application.",
-        )
-        if cache != self.sv.save_cache.value:
-            self.sv.save_cache.value = cache
-            st.rerun()
+        print("save embeddings not available")
+        # cache = st.sidebar.toggle(
+        #     "Save embeddings",
+        #     value=self.sv.save_cache.value,
+        #     help="Enable caching of embeddings to speed up the application.",
+        # )
+        # if cache != self.sv.save_cache.value:
+        #     self.sv.save_cache.value = cache
+        #     st.rerun()

app/util/ui_components.py

@@ -54,7 +54,7 @@ def dataframe_with_selections(df, selections, selection_col, label, key, height=
     return selected_rows.drop(label, axis=1)
 
 
-def report_download_ui(report_var, name):
+def report_download_ui(report_var, name) -> None:
     if type(report_var) == str:
         if len(report_var) == 0:
             return
@@ -89,15 +89,18 @@ def report_download_ui(report_var, name):
             )
 
 
-def generative_ai_component(system_prompt_var, variables):
+def generative_ai_component(
+    system_prompt_var, variables
+) -> tuple[bool, list[dict[str, str]], bool]:
     st.markdown("##### Generative AI instructions")
+
     with st.expander(
         "Edit AI system prompt used to generate output report", expanded=True
     ):
+        reset_prompt = st.button("Discard prompt text changes")
         instructions_text = st.text_area(
             "Prompt text", value=system_prompt_var.value["user_prompt"], height=200
         )
-        reset_prompt = st.button("Reset to default")
 
     st.warning(
         "AI outputs may contain errors. Please verify details independently."
@@ -122,21 +125,21 @@ def generative_ai_component(system_prompt_var, variables):
         if ratio <= 100:
             st.info(message)
         else:
-            st.warning(message)
+            st.error(message)
     return generate, messages, reset_prompt
 
 
 def generative_batch_ai_component(
     system_prompt_var, variables, batch_name, batch_val, batch_size
-):
+) -> tuple[bool, list, bool]:
     st.markdown("##### Generative AI instructions")
     with st.expander("Edit AI System Prompt", expanded=True):
+        reset_prompt = st.button("Discard prompt text changes")
         instructions_text = st.text_area(
             "Contents of System Prompt used to generate AI outputs.",
             value=system_prompt_var.value["user_prompt"],
             height=200,
         )
-        reset_prompt = st.button("Reset to default")
 
     st.warning(
         "AI outputs may contain errors. Please verify details independently."
@@ -182,7 +185,7 @@ def single_csv_uploader(
     key,
     show_rows=10000,
     height=250,
-):
+) -> None:
     if f"{workflow}_uploader_index" not in st.session_state:
         st.session_state[f"{workflow}_uploader_index"] = str(random.randint(0, 100))
     file = st.file_uploader(
@@ -799,12 +802,27 @@ def build_validation_ui(
                     mime="text/json",
                 )
 
-def check_ai_configuration():
+def check_ai_configuration(enforce_structured_output=False):
     ai_configuration = UIOpenAIConfiguration().get_configuration()
     if ai_configuration.api_key == "":
         st.warning("Please set your OpenAI API key in the Settings page.")
     if ai_configuration.model == "":
         st.warning("Please set your OpenAI model in the Settings page.")
 
+    list_enforce_structured_output = [
+        "gpt-4o",
+        "gpt-4o-2024-08-06",
+        "gpt-4o-mini",
+        "gpt-4o-mini-2024-07-18",
+    ]
+    if (
+        enforce_structured_output
+        and ai_configuration.model not in list_enforce_structured_output
+    ):
+        st.warning(
+            "Your current OpenAI model does not support this workflow. Please use the Settings page to use `gpt-4o` or `gpt-4o-mini` as OpenAI Deployment Name."
+        )
+
+
 def format_report_group_options(group_dict, existing_groups) -> str:
     return " & ".join([f"{key}: {group_dict[key]}" for key in existing_groups])

app/workflows/compare_case_groups/README.md

@@ -1,6 +1,8 @@
 # Compare Case Groups
 
-The **Compare Case Groups** workflow generates intelligence reports by defining and comparing groups of case records.
+The [`Compare Case Groups`]((https://github.com/microsoft/intelligence-toolkit/blob/main/app/workflows/compare_case_groups/README.md)) workflow generates intelligence reports by defining and comparing groups of case records.
+
+Select the `View example outputs` tab (in app) or navigate to [example_outputs/compare_case_groups](https://github.com/microsoft/intelligence-toolkit/tree/main/example_outputs/compare_case_groups) (on GitHub) for examples.
 
 ## How it works
 

app/workflows/detect_entity_networks/workflow.py

@@ -316,7 +316,7 @@ def on_inferring_batch_change(
                 st.markdown(f"*Number of links inferred*: {inferred_links_count}")
                 inferred_df = den.inferred_nodes_df()
                 st.dataframe(
-                    inferred_df.to_pandas(), hide_index=True, use_container_width=True
+                    inferred_df.to_pandas(), hide_index=True, use_container_width=False
                 )
             else:
                 st.markdown(f"*No inferred links*")

app/workflows/extract_record_data/workflow.py

@@ -1,5 +1,5 @@
 import os
-from json import dumps, loads
+from json import dumps
 
 import pandas as pd
 import streamlit as st
@@ -8,7 +8,6 @@
 import app.util.schema_ui as schema_ui
 import app.util.ui_components as ui_components
 import app.workflows.extract_record_data.variables as variables
-import intelligence_toolkit.extract_record_data.data_extractor as data_extractor
 from app.util.download_pdf import add_download_pdf
 from app.util.openai_wrapper import UIOpenAIConfiguration
 
@@ -21,7 +20,7 @@ def get_intro():
 
 
 async def create(sv: variables.SessionVariables, workflow: None):
-    ui_components.check_ai_configuration()
+    ui_components.check_ai_configuration(enforce_structured_output=True)
     erd = sv.workflow_object.value
     erd.set_ai_configuration(ai_configuration)
     intro_tab, schema_tab, generator_tab, mock_tab = st.tabs(['Extract Record Data workflow:', 'Prepare data schema', 'Extract structured records', 'View example outputs'])

app/workflows/generate_mock_data/workflow.py

@@ -21,7 +21,7 @@ def get_intro():
 
 
 async def create(sv: bds_variables.SessionVariables, workflow: None):
-    ui_components.check_ai_configuration()
+    ui_components.check_ai_configuration(enforce_structured_output=True)
     gmd: GenerateMockData = sv.workflow_object.value
     gmd.set_ai_configuration(ai_configuration)
     intro_tab, schema_tab, record_generator_tab, text_generator_tab, mock_tab = st.tabs(['Generate Mock Data workflow:', 'Prepare data schema', 'Generate mock records', 'Generate mock texts', 'View example outputs'])

app/workflows/match_entity_records/workflow.py

@@ -82,7 +82,7 @@ async def create(sv: rm_variables.SessionVariable, workflow=None) -> None:
                 )
                 entity_id_col = st.selectbox(
                     "Entity ID column (optional)",
-                    cols,
+                    ["", *selected_df.columns],
                     help="The column containing the unique identifier of the entity to be matched. If left blank, a unique ID will be generated for each entity based on the row number.",
                 )
                 filtered_cols = [
@@ -319,95 +319,101 @@ def on_embedding_batch_change(current, total):
                     )
 
     with evaluate_tab:
-        b1, b2 = st.columns([2, 3])
-        with b1:
-            batch_size = 100
-            data = sv.matching_matches_df.value.drop(
-                [
-                    "Entity ID",
-                    "Dataset",
-                    "Name similarity",
-                ]
-            ).to_pandas()
-            generate, batch_messages, reset = (
-                ui_components.generative_batch_ai_component(
-                    sv.matching_system_prompt, {}, "data", data, batch_size
+        if (
+            sv.matching_matches_df.value is None
+            or len(sv.matching_matches_df.value) == 0
+        ):
+            st.warning("Detect record groups to continue.")
+        else:
+            b1, b2 = st.columns([2, 3])
+            with b1:
+                batch_size = 100
+                data = sv.matching_matches_df.value.drop(
+                    [
+                        "Entity ID",
+                        "Dataset",
+                        "Name similarity",
+                    ]
+                ).to_pandas()
+                generate, batch_messages, reset = (
+                    ui_components.generative_batch_ai_component(
+                        sv.matching_system_prompt, {}, "data", data, batch_size
+                    )
                 )
-            )
-            if reset:
-                sv.matching_system_prompt.value["user_prompt"] = prompts.user_prompt
-                st.rerun()
-        with b2:
-            st.markdown("##### AI evaluation of record groups")
-            prefix = "```\nGroup ID,Relatedness,Explanation\n"
-            placeholder = st.empty()
-            gen_placeholder = st.empty()
+                if reset:
+                    sv.matching_system_prompt.value["user_prompt"] = prompts.user_prompt
+                    st.rerun()
+            with b2:
+                st.markdown("##### AI evaluation of record groups")
+                prefix = "```\nGroup ID,Relatedness,Explanation\n"
+                placeholder = st.empty()
+                gen_placeholder = st.empty()
 
-            if generate:
-                for messages in batch_messages:
-                    callback = ui_components.create_markdown_callback(
-                        placeholder, prefix
-                    )
-                    response = ui_components.generate_text(messages, [callback])
+                if generate:
+                    for messages in batch_messages:
+                        callback = ui_components.create_markdown_callback(
+                            placeholder, prefix
+                        )
+                        response = ui_components.generate_text(messages, [callback])
 
-                    if len(response.strip()) > 0:
-                        prefix = prefix + response + "\n"
-                result = prefix.replace("```\n", "").strip()
-                sv.matching_evaluations.value = result
-                lines = result.split("\n")
+                        if len(response.strip()) > 0:
+                            prefix = prefix + response + "\n"
+                    result = prefix.replace("```\n", "").strip()
+                    sv.matching_evaluations.value = result
+                    lines = result.split("\n")
 
-                if len(lines) > 30:
-                    lines = lines[:30]
-                    result = "\n".join(lines)
+                    if len(lines) > 30:
+                        lines = lines[:30]
+                        result = "\n".join(lines)
 
-                # validation, messages_to_llm = ui_components.validate_ai_report(
-                #     batch_messages[0], result
-                # )
-                # sv.matching_report_validation.value = validation
-                # sv.matching_report_validation_messages.value = messages_to_llm
-                st.rerun()
-            else:
-                if len(sv.matching_evaluations.value) == 0:
-                    gen_placeholder.warning(
-                        "Press the Generate button to create an AI report for the current record matches."
-                    )
-            placeholder.empty()
+                    # validation, messages_to_llm = ui_components.validate_ai_report(
+                    #     batch_messages[0], result
+                    # )
+                    # sv.matching_report_validation.value = validation
+                    # sv.matching_report_validation_messages.value = messages_to_llm
+                    st.rerun()
+                else:
+                    if len(sv.matching_evaluations.value) == 0:
+                        gen_placeholder.warning(
+                            "Press the Generate button to create an AI report for the current record matches."
+                        )
+                placeholder.empty()
 
-            if len(sv.matching_evaluations.value) > 0:
-                try:
-                    mer.evaluations_df = pl.read_csv(
-                        io.StringIO(sv.matching_evaluations.value)
-                    )
-                    value = mer.evaluations_df.drop_nulls()
+                if len(sv.matching_evaluations.value) > 0:
+                    try:
+                        mer.evaluations_df = pl.read_csv(
+                            io.StringIO(sv.matching_evaluations.value)
+                        )
+                        value = mer.evaluations_df.drop_nulls()
 
-                    st.dataframe(
-                        value.to_pandas(),
-                        height=700,
-                        use_container_width=True,
-                        hide_index=True,
-                    )
-                    c1, c2 = st.columns([1, 1])
-                    with c1:
-                        st.download_button(
-                            "Download AI match reports",
-                            data=value.write_csv(),
-                            file_name="record_group_match_reports.csv",
-                            mime="text/csv",
+                        st.dataframe(
+                            value.to_pandas(),
+                            height=700,
+                            use_container_width=True,
+                            hide_index=True,
                         )
-                    with c2:
-                        st.download_button(
-                            "Download integrated results",
-                            data=mer.integrated_results.write_csv(),
-                            file_name="integrated_record_match_results.csv",
-                            mime="text/csv",
+                        c1, c2 = st.columns([1, 1])
+                        with c1:
+                            st.download_button(
+                                "Download AI match reports",
+                                data=value.write_csv(),
+                                file_name="record_group_match_reports.csv",
+                                mime="text/csv",
+                            )
+                        with c2:
+                            st.download_button(
+                                "Download integrated results",
+                                data=mer.integrated_results.write_csv(),
+                                file_name="integrated_record_match_results.csv",
+                                mime="text/csv",
+                            )
+                    except:
+                        st.markdown(sv.matching_evaluations.value)
+                        add_download_pdf(
+                            "record_groups_evaluated.pdf",
+                            sv.matching_evaluations.value,
+                            "Download AI match report",
                         )
-                except:
-                    st.markdown(sv.matching_evaluations.value)
-                    add_download_pdf(
-                        "record_groups_evaluated.pdf",
-                        sv.matching_evaluations.value,
-                        "Download AI match report",
-                    )
 
     with examples_tab:
         example_outputs_ui.create_example_outputs_ui(examples_tab, workflow)