From 2e9b4e6d63ba21f63335e58162aeb3d99df3607a Mon Sep 17 00:00:00 2001 From: Nick Karpov Date: Thu, 19 Mar 2026 16:20:53 -0700 Subject: [PATCH 1/9] feat(support): add support use case target with generator, agent, and scoring stream Add new `support` target to databricks.yml with full DAG: - Support request generator (deterministic synthetic data from delivered events) - Support triage agent (DSPy ReAct with UC function tools, policy-based guardrails) - Scoring stream with fake-it-till-up pattern and inference capping Based on work from PR #63. Lakebase + App stages to follow. Co-authored-by: Andre Landgraf Co-authored-by: Isaac --- databricks.yml | 66 ++ jobs/support_request_agent_stream.ipynb | 320 ++++++++ jobs/support_request_generator.ipynb | 191 +++++ stages/support_request_agent.ipynb | 688 ++++++++++++++++++ stages/support_request_agent_stream.ipynb | 89 +++ stages/support_request_generator_stream.ipynb | 90 +++ 6 files changed, 1444 insertions(+) create mode 100644 jobs/support_request_agent_stream.ipynb create mode 100644 jobs/support_request_generator.ipynb create mode 100644 stages/support_request_agent.ipynb create mode 100644 stages/support_request_agent_stream.ipynb create mode 100644 stages/support_request_generator_stream.ipynb diff --git a/databricks.yml b/databricks.yml index a382049..caf7775 100644 --- a/databricks.yml +++ b/databricks.yml @@ -339,6 +339,72 @@ targets: notebook_task: notebook_path: ${workspace.root_path}/stages/menu_supervisor + support: + resources: + jobs: + caspers: + name: "Casper's Initializer" + queue: + enabled: true + performance_target: PERFORMANCE_OPTIMIZED + + parameters: + - name: CATALOG + default: ${var.catalog} + - name: EVENTS_VOLUME + default: events + - name: LLM_MODEL + default: databricks-meta-llama-3-3-70b-instruct + - name: SUPPORT_AGENT_ENDPOINT_NAME + default: ${var.catalog}_support_agent + - name: SUPPORT_RATE + default: "0.18" + - name: SIMULATOR_SCHEMA + default: simulator + - name: START_DAY + default: "70" + - name: SPEED_MULTIPLIER + default: "60.0" + - name: SCHEDULE_MINUTES + default: "3" + - name: PIPELINE_SCHEDULE_MINUTES + default: "0" + + tasks: + - task_key: Canonical_Data + notebook_task: + notebook_path: ${workspace.root_path}/stages/canonical_data + + - task_key: Spark_Declarative_Pipeline + depends_on: + - task_key: Canonical_Data + notebook_task: + notebook_path: ${workspace.root_path}/stages/lakeflow + + - task_key: Support_Request_Agent + depends_on: + - task_key: Spark_Declarative_Pipeline + notebook_task: + notebook_path: ${workspace.root_path}/stages/support_request_agent + + - task_key: Support_Request_Generator_Stream + depends_on: + - task_key: Spark_Declarative_Pipeline + notebook_task: + notebook_path: ${workspace.root_path}/stages/support_request_generator_stream + + - task_key: Support_Request_Agent_Stream + depends_on: + - task_key: Support_Request_Generator_Stream + notebook_task: + notebook_path: ${workspace.root_path}/stages/support_request_agent_stream + + # - task_key: Support_Lakebase_And_App + # depends_on: + # - task_key: Support_Request_Agent_Stream + # notebook_task: + # notebook_path: ${workspace.root_path}/stages/support_lakebase + all: resources: jobs: diff --git a/jobs/support_request_agent_stream.ipynb b/jobs/support_request_agent_stream.ipynb new file mode 100644 index 0000000..8458a62 --- /dev/null +++ b/jobs/support_request_agent_stream.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Support Request Agent Stream\n", + "\n", + "Stream support requests through the support agent endpoint and persist reports.\n", + "\n", + "Uses fake-it-till-up pattern: deterministic responses during backfill and when\n", + "the agent endpoint is not yet ready, real inference with capping once available." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DATABRICKS_TOKEN = dbutils.notebook.entry_point.getDbutils().notebook().getContext().apiToken().getOrElse(None)\n", + "DATABRICKS_HOST = dbutils.notebook.entry_point.getDbutils().notebook().getContext().apiUrl().getOrElse(None)\n", + "\n", + "CATALOG = dbutils.widgets.get(\"CATALOG\")\n", + "SUPPORT_AGENT_ENDPOINT_NAME = dbutils.widgets.get(\"SUPPORT_AGENT_ENDPOINT_NAME\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install openai" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import random\n", + "import os\n", + "\n", + "from pyspark.sql import functions as F\n", + "from pyspark.sql.types import StringType\n", + "from pyspark.sql.functions import udf\n", + "from pyspark.sql.window import Window\n", + "from openai import OpenAI\n", + "\n", + "CHECKPOINT_PATH = f\"/Volumes/{CATALOG}/support/checkpoints/support_request_agent_stream\"\n", + "MAX_INFERENCES_PER_BATCH = 50\n", + "\n", + "\n", + "def is_first_run():\n", + " \"\"\"Check if checkpoint exists (indicates this is NOT the first run)\"\"\"\n", + " return not os.path.exists(CHECKPOINT_PATH) or len(os.listdir(CHECKPOINT_PATH)) == 0\n", + "\n", + "\n", + "# ── Deterministic fallback responses ──────────────────────────────────────────\n", + "# These mirror the policy resolution logic from the agent, providing realistic\n", + "# structured responses when the endpoint is unavailable or during backfill.\n", + "\n", + "fake_responses = [\n", + " {\n", + " \"support_request_id\": \"\", \"user_id\": \"\", \"order_id\": \"\",\n", + " \"credit_recommendation\": {\"amount_usd\": 3.0, \"reason\": \"Goodwill credit for minor service issue.\"},\n", + " \"refund_recommendation\": {\"amount_usd\": 0.0, \"reason\": \"No verified severe failure.\"},\n", + " \"draft_response\": \"Thanks for contacting us, and I am really sorry about this experience. We are offering a $3.00 credit on this case.\",\n", + " \"past_interactions_summary\": \"No prior interactions found.\",\n", + " \"order_details_summary\": \"Standard delivery order.\",\n", + " \"decision_confidence\": \"medium\",\n", + " \"escalation_flag\": False,\n", + " },\n", + " {\n", + " \"support_request_id\": \"\", \"user_id\": \"\", \"order_id\": \"\",\n", + " \"credit_recommendation\": {\"amount_usd\": 5.0, \"reason\": \"Moderate service issue with customer impact.\"},\n", + " \"refund_recommendation\": {\"amount_usd\": 10.0, \"reason\": \"Moderate service issue with customer impact.\"},\n", + " \"draft_response\": \"Thanks for contacting us, and I am really sorry about this experience. We are offering a $10.00 refund and a $5.00 credit on this case.\",\n", + " \"past_interactions_summary\": \"No prior interactions found.\",\n", + " \"order_details_summary\": \"Delivery with reported delay.\",\n", + " \"decision_confidence\": \"high\",\n", + " \"escalation_flag\": False,\n", + " },\n", + " {\n", + " \"support_request_id\": \"\", \"user_id\": \"\", \"order_id\": \"\",\n", + " \"credit_recommendation\": {\"amount_usd\": 2.0, \"reason\": \"Minor issue or low-confidence compensable case.\"},\n", + " \"refund_recommendation\": {\"amount_usd\": 0.0, \"reason\": \"No severe failure identified.\"},\n", + " \"draft_response\": \"Thanks for contacting us, and I am really sorry about this experience. We are offering a $2.00 credit on this case.\",\n", + " \"past_interactions_summary\": \"No prior interactions found.\",\n", + " \"order_details_summary\": \"Standard delivery order.\",\n", + " \"decision_confidence\": \"medium\",\n", + " \"escalation_flag\": False,\n", + " },\n", + " {\n", + " \"support_request_id\": \"\", \"user_id\": \"\", \"order_id\": \"\",\n", + " \"credit_recommendation\": {\"amount_usd\": 5.0, \"reason\": \"Repeated or high-risk service failure.\"},\n", + " \"refund_recommendation\": {\"amount_usd\": 18.0, \"reason\": \"Repeated or high-risk service failure.\"},\n", + " \"draft_response\": \"I see this is at least the 2nd time you've had to contact us for similar issues, and that is not acceptable. We are offering a $18.00 refund and a $5.00 credit on this case.\",\n", + " \"past_interactions_summary\": \"Multiple prior contacts for similar issues.\",\n", + " \"order_details_summary\": \"Repeat issue with delivery.\",\n", + " \"decision_confidence\": \"high\",\n", + " \"escalation_flag\": False,\n", + " },\n", + " {\n", + " \"support_request_id\": \"\", \"user_id\": \"\", \"order_id\": \"\",\n", + " \"credit_recommendation\": {\"amount_usd\": 3.0, \"reason\": \"Abusive/spammy pattern detected; restricted compensation with escalation.\"},\n", + " \"refund_recommendation\": {\"amount_usd\": 0.0, \"reason\": \"Abusive/spammy pattern detected.\"},\n", + " \"draft_response\": \"Thanks for contacting us. We are escalating this case for manual review and will follow up quickly.\",\n", + " \"past_interactions_summary\": \"Pattern of escalation detected.\",\n", + " \"order_details_summary\": \"Order under review.\",\n", + " \"decision_confidence\": \"medium\",\n", + " \"escalation_flag\": True,\n", + " },\n", + "]\n", + "\n", + "\n", + "def get_fake_response(support_request_id: str, user_id: str, order_id: str) -> str:\n", + " \"\"\"Return a deterministic fake response with IDs filled in.\"\"\"\n", + " resp = random.choice(fake_responses).copy()\n", + " resp[\"support_request_id\"] = support_request_id\n", + " resp[\"user_id\"] = user_id\n", + " resp[\"order_id\"] = order_id\n", + " return json.dumps(resp)\n", + "\n", + "\n", + "def call_support_agent(payload_json: str) -> str:\n", + " \"\"\"Call the support agent endpoint with the case payload.\"\"\"\n", + " payload = json.loads(payload_json)\n", + " support_request_id = payload.get(\"support_request_id\", \"\")\n", + " user_id = payload.get(\"user_id\", \"\")\n", + " order_id = payload.get(\"order_id\", \"\")\n", + "\n", + " client = OpenAI(\n", + " api_key=DATABRICKS_TOKEN,\n", + " base_url=f\"{DATABRICKS_HOST}/serving-endpoints\",\n", + " )\n", + "\n", + " for _ in range(3):\n", + " try:\n", + " response_obj = client.responses.create(\n", + " model=SUPPORT_AGENT_ENDPOINT_NAME,\n", + " input=[{\"role\": \"user\", \"content\": payload_json}],\n", + " )\n", + " response = response_obj.output[-1].content[0].text\n", + " json.loads(response) # validate JSON\n", + " return response\n", + " except Exception:\n", + " continue\n", + "\n", + " # All retries failed — return fake\n", + " return get_fake_response(support_request_id, user_id, order_id)\n", + "\n", + "\n", + "call_support_agent_udf = udf(call_support_agent, StringType())\n", + "get_fake_response_udf = udf(get_fake_response, StringType())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spark.sql(f\"CREATE SCHEMA IF NOT EXISTS {CATALOG}.support\")\n", + "spark.sql(f\"CREATE VOLUME IF NOT EXISTS {CATALOG}.support.checkpoints\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spark.sql(f\"\"\"\n", + "CREATE TABLE IF NOT EXISTS {CATALOG}.support.support_agent_reports (\n", + " support_request_id STRING,\n", + " user_id STRING,\n", + " order_id STRING,\n", + " request_text STRING,\n", + " ts TIMESTAMP,\n", + " agent_response STRING\n", + ")\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Enable CDC for Lakebase sync\n", + "table_name = f\"{CATALOG}.support.support_agent_reports\"\n", + "\n", + "try:\n", + " props = spark.sql(f\"SHOW TBLPROPERTIES {table_name}\").collect()\n", + " cdc_enabled = any(row.key == \"delta.enableChangeDataFeed\" and row.value == \"true\" for row in props)\n", + "except Exception:\n", + " cdc_enabled = False\n", + "\n", + "if not cdc_enabled:\n", + " print(f\"Enabling CDC on {table_name}\")\n", + " spark.sql(f\"ALTER TABLE {table_name} SET TBLPROPERTIES (delta.enableChangeDataFeed = true)\")\n", + "else:\n", + " print(f\"CDC already enabled on {table_name}, skipping\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def process_batch(batch_df, batch_id):\n", + " \"\"\"Process each micro-batch with inference capping.\n", + "\n", + " - First run (no checkpoint): ALL rows get fake responses (fast backfill)\n", + " - Subsequent runs: First N rows get real inference, rest get fake\n", + " \"\"\"\n", + " if batch_df.isEmpty():\n", + " return\n", + "\n", + " first_run = is_first_run()\n", + " row_count = batch_df.count()\n", + "\n", + " print(f\"Processing batch {batch_id}: {row_count} rows, first_run={first_run}\")\n", + "\n", + " # Build payload JSON from row columns for the agent\n", + " with_payload = batch_df.withColumn(\n", + " \"payload_json\",\n", + " F.to_json(\n", + " F.struct(\n", + " F.col(\"support_request_id\"),\n", + " F.col(\"user_id\"),\n", + " F.col(\"order_id\"),\n", + " F.col(\"request_type\"),\n", + " F.col(\"edge_case_type\"),\n", + " F.col(\"request_text\"),\n", + " )\n", + " ),\n", + " )\n", + "\n", + " if first_run:\n", + " # First run: ALL rows get fake responses (fast backfill)\n", + " print(f\" -> First run detected, using fake responses for all {row_count} rows\")\n", + " result_df = with_payload.select(\n", + " F.col(\"support_request_id\"),\n", + " F.col(\"user_id\"),\n", + " F.col(\"order_id\"),\n", + " F.col(\"request_text\"),\n", + " F.current_timestamp().alias(\"ts\"),\n", + " get_fake_response_udf(\n", + " F.col(\"support_request_id\"), F.col(\"user_id\"), F.col(\"order_id\")\n", + " ).alias(\"agent_response\"),\n", + " )\n", + " else:\n", + " # Subsequent runs: first N rows get real inference, rest get fake\n", + " windowed = with_payload.withColumn(\n", + " \"row_num\",\n", + " F.row_number().over(Window.orderBy(F.col(\"ts\"), F.col(\"support_request_id\"))),\n", + " )\n", + "\n", + " real_count = min(row_count, MAX_INFERENCES_PER_BATCH)\n", + " fake_count = max(0, row_count - MAX_INFERENCES_PER_BATCH)\n", + " print(f\" -> Real inference: {real_count} rows, fake: {fake_count} rows\")\n", + "\n", + " real_inference_df = windowed.filter(f\"row_num <= {MAX_INFERENCES_PER_BATCH}\").select(\n", + " F.col(\"support_request_id\"),\n", + " F.col(\"user_id\"),\n", + " F.col(\"order_id\"),\n", + " F.col(\"request_text\"),\n", + " F.current_timestamp().alias(\"ts\"),\n", + " call_support_agent_udf(F.col(\"payload_json\")).alias(\"agent_response\"),\n", + " )\n", + "\n", + " fake_response_df = windowed.filter(f\"row_num > {MAX_INFERENCES_PER_BATCH}\").select(\n", + " F.col(\"support_request_id\"),\n", + " F.col(\"user_id\"),\n", + " F.col(\"order_id\"),\n", + " F.col(\"request_text\"),\n", + " F.current_timestamp().alias(\"ts\"),\n", + " get_fake_response_udf(\n", + " F.col(\"support_request_id\"), F.col(\"user_id\"), F.col(\"order_id\")\n", + " ).alias(\"agent_response\"),\n", + " )\n", + "\n", + " result_df = real_inference_df.union(fake_response_df)\n", + "\n", + " result_df.write.mode(\"append\").saveAsTable(f\"{CATALOG}.support.support_agent_reports\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read stream from raw support requests\n", + "support_requests = spark.readStream.table(f\"{CATALOG}.support.raw_support_requests\")\n", + "\n", + "support_requests.writeStream \\\n", + " .foreachBatch(process_batch) \\\n", + " .option(\"checkpointLocation\", CHECKPOINT_PATH) \\\n", + " .trigger(availableNow=True) \\\n", + " .start() \\\n", + " .awaitTermination()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/jobs/support_request_generator.ipynb b/jobs/support_request_generator.ipynb new file mode 100644 index 0000000..1402179 --- /dev/null +++ b/jobs/support_request_generator.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Support Request Generator\n", + "\n", + "Generate synthetic support requests from delivered orders with deterministic user IDs and edge-case distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CATALOG = dbutils.widgets.get(\"CATALOG\")\n", + "SUPPORT_RATE = float(dbutils.widgets.get(\"SUPPORT_RATE\"))\n", + "LLM_MODEL = dbutils.widgets.get(\"LLM_MODEL\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spark.sql(f\"CREATE SCHEMA IF NOT EXISTS {CATALOG}.support\")\n", + "spark.sql(f\"CREATE VOLUME IF NOT EXISTS {CATALOG}.support.checkpoints\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spark.sql(\n", + " f\"\"\"\n", + " CREATE TABLE IF NOT EXISTS {CATALOG}.support.raw_support_requests (\n", + " support_request_id STRING,\n", + " user_id STRING,\n", + " order_id STRING,\n", + " ts TIMESTAMP,\n", + " request_type STRING,\n", + " request_text STRING,\n", + " edge_case_type STRING,\n", + " request_attempt INT,\n", + " generated_by STRING\n", + " )\n", + " \"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql import functions as F\n", + "\n", + "# Deterministic repeatable user IDs and controlled edge-case distribution.\n", + "# - ~10% create an extra follow-up request for same order/user\n", + "# - ~3% create two extra follow-ups (spam/repeat bursts)\n", + "# - ~5% spammy/escalation phrasing\n", + "# - ~2% abusive phrasing\n", + "source = (\n", + " spark.readStream\n", + " .table(f\"{CATALOG}.lakeflow.all_events\")\n", + " .filter(F.col(\"event_type\") == \"delivered\")\n", + " .filter(F.rand() < SUPPORT_RATE)\n", + " .withColumn(\"seed\", F.abs(F.hash(F.col(\"order_id\"))))\n", + " .withColumn(\"hash_bucket\", F.col(\"seed\") % F.lit(25))\n", + " .withColumn(\"user_id\", F.concat(F.lit(\"user-\"), F.format_string(\"%03d\", F.col(\"hash_bucket\"))))\n", + " .withColumn(\n", + " \"request_type\",\n", + " F.expr(\n", + " \"\"\"\n", + " CASE\n", + " WHEN hash_bucket % 5 = 0 THEN 'delivery_delay'\n", + " WHEN hash_bucket % 5 = 1 THEN 'missing_items'\n", + " WHEN hash_bucket % 5 = 2 THEN 'food_quality'\n", + " WHEN hash_bucket % 5 = 3 THEN 'billing'\n", + " ELSE 'general_support'\n", + " END\n", + " \"\"\"\n", + " ),\n", + " )\n", + " .withColumn(\n", + " \"edge_case_type\",\n", + " F.expr(\n", + " \"\"\"\n", + " CASE\n", + " WHEN seed % 100 < 2 THEN 'abusive_language'\n", + " WHEN seed % 100 < 7 THEN 'spammy_escalation'\n", + " WHEN seed % 100 < 15 THEN 'repeat_follow_up'\n", + " WHEN seed % 100 < 21 THEN 'non_compensable_confusion'\n", + " ELSE 'standard'\n", + " END\n", + " \"\"\"\n", + " ),\n", + " )\n", + " .withColumn(\n", + " \"repeat_multiplier\",\n", + " F.expr(\n", + " \"\"\"\n", + " CASE\n", + " WHEN seed % 100 < 3 THEN 3\n", + " WHEN seed % 100 < 13 THEN 2\n", + " ELSE 1\n", + " END\n", + " \"\"\"\n", + " ),\n", + " )\n", + ")\n", + "\n", + "expanded = (\n", + " source\n", + " .withColumn(\"repeat_idx\", F.explode(F.sequence(F.lit(1), F.col(\"repeat_multiplier\"))))\n", + " .withColumn(\"support_request_id\", F.expr(\"uuid()\"))\n", + " .withColumn(\"request_attempt\", F.col(\"repeat_idx\"))\n", + " .withColumn(\n", + " \"request_text\",\n", + " F.expr(\n", + " \"\"\"\n", + " CASE\n", + " WHEN edge_case_type = 'abusive_language' THEN\n", + " 'This is unacceptable and ridiculous. Your team keeps messing this up. Fix this now.'\n", + " WHEN edge_case_type = 'spammy_escalation' AND request_attempt > 1 THEN\n", + " 'Third message: still unresolved. Refund me immediately and escalate this to a supervisor now.'\n", + " WHEN edge_case_type = 'spammy_escalation' THEN\n", + " 'Second request today. Please resolve this immediately. I need clear compensation details now.'\n", + " WHEN edge_case_type = 'repeat_follow_up' AND request_attempt > 1 THEN\n", + " 'Following up again because this is not the first time this happened. Please review previous cases and offer a fair resolution.'\n", + " WHEN request_type = 'delivery_delay' AND request_attempt > 1 THEN\n", + " 'This is another delay issue for me. I need a concrete resolution with refund and credit details.'\n", + " WHEN request_type = 'delivery_delay' THEN\n", + " 'My delivery arrived much later than expected. Can you help with a credit or refund?'\n", + " WHEN request_type = 'missing_items' AND request_attempt > 1 THEN\n", + " 'This is the second time items were missing from my order. Please make this right with a concrete offer.'\n", + " WHEN request_type = 'missing_items' THEN\n", + " 'My order was missing one or more items. Please review and advise next steps.'\n", + " WHEN request_type = 'food_quality' THEN\n", + " 'I had a quality issue with my food. I would like support and possible compensation.'\n", + " WHEN request_type = 'billing' THEN\n", + " 'I think there is a billing issue on my recent order. Can you verify the charge?'\n", + " WHEN edge_case_type = 'non_compensable_confusion' THEN\n", + " 'I changed my mind after ordering and wanted to check if I can still get a courtesy credit.'\n", + " ELSE\n", + " 'I need help with a recent order. Please review my request and let me know what you can do.'\n", + " END\n", + " \"\"\"\n", + " ),\n", + " )\n", + " .select(\n", + " \"support_request_id\",\n", + " \"user_id\",\n", + " \"order_id\",\n", + " F.current_timestamp().alias(\"ts\"),\n", + " \"request_type\",\n", + " \"request_text\",\n", + " \"edge_case_type\",\n", + " \"request_attempt\",\n", + " F.lit(\"generator-v2\").alias(\"generated_by\"),\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "expanded.writeStream \\\n", + " .option(\"checkpointLocation\", f\"/Volumes/{CATALOG}/support/checkpoints/support_request_generator\") \\\n", + " .trigger(availableNow=True) \\\n", + " .table(f\"{CATALOG}.support.raw_support_requests\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/stages/support_request_agent.ipynb b/stages/support_request_agent.ipynb new file mode 100644 index 0000000..eab6d9b --- /dev/null +++ b/stages/support_request_agent.ipynb @@ -0,0 +1,688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Support Request Agent\n", + "\n", + "Build and deploy a support-request triage agent with order context and user interaction history.\n", + "\n", + "Outputs include:\n", + "- credit recommendation\n", + "- refund recommendation\n", + "- draft customer response\n", + "- past interactions summary\n", + "- order details summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tool & View Registration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CATALOG = dbutils.widgets.get(\"CATALOG\")\n", + "LLM_MODEL = dbutils.widgets.get(\"LLM_MODEL\")\n", + "SUPPORT_AGENT_ENDPOINT_NAME = dbutils.widgets.get(\"SUPPORT_AGENT_ENDPOINT_NAME\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%sql\n", + "CREATE SCHEMA IF NOT EXISTS ${CATALOG}.ai;\n", + "CREATE SCHEMA IF NOT EXISTS ${CATALOG}.support;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%sql\n", + "CREATE OR REPLACE FUNCTION ${CATALOG}.ai.get_order_overview(oid STRING)\n", + "RETURNS TABLE (\n", + " order_id STRING,\n", + " location STRING,\n", + " items_json STRING,\n", + " customer_address STRING,\n", + " order_created_ts TIMESTAMP\n", + ")\n", + "RETURN\n", + " SELECT\n", + " order_id,\n", + " location,\n", + " get_json_object(body, '$.items') AS items_json,\n", + " get_json_object(body, '$.customer_addr') AS customer_address,\n", + " try_to_timestamp(ts) AS order_created_ts\n", + " FROM ${CATALOG}.lakeflow.all_events\n", + " WHERE order_id = oid AND event_type = 'order_created'\n", + " LIMIT 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%sql\n", + "CREATE OR REPLACE FUNCTION ${CATALOG}.ai.get_order_timing(oid STRING)\n", + "RETURNS TABLE (\n", + " order_id STRING,\n", + " order_created_ts TIMESTAMP,\n", + " delivered_ts TIMESTAMP,\n", + " delivery_duration_minutes FLOAT\n", + ")\n", + "RETURN\n", + " WITH order_events AS (\n", + " SELECT order_id, event_type, try_to_timestamp(ts) AS event_ts\n", + " FROM ${CATALOG}.lakeflow.all_events\n", + " WHERE order_id = oid\n", + " )\n", + " SELECT\n", + " order_id,\n", + " MIN(CASE WHEN event_type='order_created' THEN event_ts END) AS order_created_ts,\n", + " MAX(CASE WHEN event_type='delivered' THEN event_ts END) AS delivered_ts,\n", + " CAST((UNIX_TIMESTAMP(MAX(CASE WHEN event_type='delivered' THEN event_ts END)) - UNIX_TIMESTAMP(MIN(CASE WHEN event_type='order_created' THEN event_ts END))) / 60 AS FLOAT) AS delivery_duration_minutes\n", + " FROM order_events\n", + " GROUP BY order_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%sql\n", + "CREATE OR REPLACE FUNCTION ${CATALOG}.ai.get_user_support_history(uid STRING)\n", + "RETURNS TABLE (\n", + " support_request_id STRING,\n", + " order_id STRING,\n", + " ts TIMESTAMP,\n", + " request_text STRING,\n", + " agent_response STRING\n", + ")\n", + "RETURN\n", + " SELECT support_request_id, order_id, ts, request_text, agent_response\n", + " FROM ${CATALOG}.support.support_agent_reports\n", + " WHERE user_id = uid\n", + " ORDER BY ts DESC\n", + " LIMIT 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%sql\n", + "CREATE OR REPLACE FUNCTION ${CATALOG}.ai.get_user_request_history(uid STRING)\n", + "RETURNS TABLE (\n", + " support_request_id STRING,\n", + " order_id STRING,\n", + " ts TIMESTAMP,\n", + " request_type STRING,\n", + " edge_case_type STRING,\n", + " request_text STRING\n", + ")\n", + "RETURN\n", + " SELECT support_request_id, order_id, ts, request_type, edge_case_type, request_text\n", + " FROM ${CATALOG}.support.raw_support_requests\n", + " WHERE user_id = uid\n", + " ORDER BY ts DESC\n", + " LIMIT 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -U -qqqq typing_extensions dspy-ai mlflow unitycatalog-openai[databricks] openai databricks-sdk databricks-agents pydantic\n", + "%restart_python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CATALOG = dbutils.widgets.get(\"CATALOG\")\n", + "LLM_MODEL = dbutils.widgets.get(\"LLM_MODEL\")\n", + "SUPPORT_AGENT_ENDPOINT_NAME = dbutils.widgets.get(\"SUPPORT_AGENT_ENDPOINT_NAME\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow\n", + "import sys\n", + "\n", + "sys.path.append('../utils')\n", + "from uc_state import add\n", + "\n", + "dev_experiment = mlflow.set_experiment(f\"/Shared/{CATALOG}_support_agent_dev\")\n", + "add(CATALOG, \"experiments\", {\"experiment_id\": dev_experiment.experiment_id, \"name\": dev_experiment.name})\n", + "print(f\"Using dev experiment: {dev_experiment.name} (ID: {dev_experiment.experiment_id})\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "from IPython.core.magic import register_cell_magic\n", + "\n", + "@register_cell_magic\n", + "def writefilev(line, cell):\n", + " \"\"\"\n", + " %%writefilev file.py\n", + " Allows {{var}} substitutions while leaving normal {} intact.\n", + " \"\"\"\n", + " filename = line.strip()\n", + "\n", + " def replacer(match):\n", + " expr = match.group(1)\n", + " return str(eval(expr, globals(), locals()))\n", + "\n", + " content = re.sub(r\"\\{\\{(.*?)\\}\\}\", replacer, cell)\n", + "\n", + " with open(filename, \"w\") as f:\n", + " f.write(content)\n", + " print(f\"Wrote file with substitutions: {filename}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefilev agent.py\n", + "import json\n", + "import re\n", + "import warnings\n", + "from typing import Any, Dict, Literal, Optional\n", + "from uuid import uuid4\n", + "\n", + "import dspy\n", + "import mlflow\n", + "from mlflow.pyfunc import ResponsesAgent\n", + "from mlflow.types.responses import ResponsesAgentRequest, ResponsesAgentResponse\n", + "from pydantic import BaseModel, Field\n", + "from unitycatalog.ai.core.base import get_uc_function_client\n", + "\n", + "warnings.filterwarnings(\"ignore\", message=\".*Ignoring the default notebook Spark session.*\")\n", + "\n", + "mlflow.dspy.autolog(log_traces=True)\n", + "\n", + "LLM_MODEL = \"{{LLM_MODEL}}\"\n", + "CATALOG = \"{{CATALOG}}\"\n", + "\n", + "lm = dspy.LM(f\"databricks/{LLM_MODEL}\", max_tokens=2000)\n", + "dspy.configure(lm=lm)\n", + "uc_client = get_uc_function_client()\n", + "\n", + "\n", + "class SupportReport(BaseModel):\n", + " support_request_id: str\n", + " user_id: str\n", + " order_id: str\n", + " credit_recommendation: Optional[dict] = None\n", + " refund_recommendation: Optional[dict] = None\n", + " draft_response: str\n", + " past_interactions_summary: str\n", + " order_details_summary: str\n", + " decision_confidence: Literal[\"high\", \"medium\", \"low\"] = Field(default=\"medium\")\n", + " escalation_flag: bool = Field(default=False)\n", + "\n", + "\n", + "class SupportTriage(dspy.Signature):\n", + " \"\"\"Produce a final support resolution report using full context.\n", + "\n", + " Required inputs to reason over:\n", + " - support request text and edge-case metadata\n", + " - order details and delivery timing\n", + " - prior support history for this user\n", + "\n", + " Decision policy:\n", + " - Severe repeat/high-risk issue: provide concrete refund + credit and apology.\n", + " - Moderate issue: provide bounded partial refund and/or credit with rationale.\n", + " - Minor/non-compensable issue: typically no refund, small goodwill credit or clear denial.\n", + " - Spam/abusive patterns: avoid excessive compensation, set escalation_flag=true.\n", + "\n", + " Never return placeholder text like \"we will look into this\".\n", + " Always return a final customer-ready message with concrete next steps.\n", + " \"\"\"\n", + "\n", + " support_request: str = dspy.InputField(desc=\"Structured case payload JSON string\")\n", + " support_request_id: str = dspy.OutputField()\n", + " user_id: str = dspy.OutputField()\n", + " order_id: str = dspy.OutputField()\n", + " credit_recommendation_json: str = dspy.OutputField(desc=\"JSON object with amount_usd/reason\")\n", + " refund_recommendation_json: str = dspy.OutputField(desc=\"JSON object with amount_usd/reason\")\n", + " draft_response: str = dspy.OutputField()\n", + " past_interactions_summary: str = dspy.OutputField()\n", + " order_details_summary: str = dspy.OutputField()\n", + " decision_confidence: str = dspy.OutputField(desc=\"high, medium, low\")\n", + " escalation_flag: str = dspy.OutputField(desc=\"true or false\")\n", + "\n", + "\n", + "def get_order_overview(order_id: str) -> str:\n", + " return str(uc_client.execute_function(f\"{CATALOG}.ai.get_order_overview\", {\"oid\": order_id}).value)\n", + "\n", + "\n", + "def get_order_timing(order_id: str) -> str:\n", + " return str(uc_client.execute_function(f\"{CATALOG}.ai.get_order_timing\", {\"oid\": order_id}).value)\n", + "\n", + "\n", + "def get_user_support_history(user_id: str) -> str:\n", + " return str(uc_client.execute_function(f\"{CATALOG}.ai.get_user_support_history\", {\"uid\": user_id}).value)\n", + "\n", + "\n", + "def get_user_request_history(user_id: str) -> str:\n", + " return str(uc_client.execute_function(f\"{CATALOG}.ai.get_user_request_history\", {\"uid\": user_id}).value)\n", + "\n", + "\n", + "def _parse_case_payload(raw: str) -> Dict[str, Any]:\n", + " try:\n", + " payload = json.loads(raw)\n", + " return payload if isinstance(payload, dict) else {}\n", + " except Exception:\n", + " parsed: Dict[str, Any] = {}\n", + " for key in [\"support_request_id\", \"user_id\", \"order_id\", \"text\"]:\n", + " match = re.search(rf\"{key}=([^\\s]+)\", raw)\n", + " if match:\n", + " parsed[key] = match.group(1)\n", + " if \"text\" in parsed:\n", + " parsed[\"request_text\"] = parsed[\"text\"]\n", + " return parsed\n", + "\n", + "\n", + "def _safe_float(value: Any, default: float = 0.0) -> float:\n", + " try:\n", + " return float(value)\n", + " except Exception:\n", + " return default\n", + "\n", + "\n", + "def _safe_int(value: Any, default: int = 0) -> int:\n", + " try:\n", + " return int(value)\n", + " except Exception:\n", + " return default\n", + "\n", + "\n", + "def _compute_policy_resolution(payload: Dict[str, Any]) -> Dict[str, Any]:\n", + " request_text = str(payload.get(\"request_text\", \"\"))\n", + " edge_case_type = str(payload.get(\"edge_case_type\", \"standard\"))\n", + "\n", + " repeat_count = _safe_int(payload.get(\"repeat_complaints_30d\"), 0)\n", + " risk_score = _safe_float(payload.get(\"risk_score\"), 0.25)\n", + " policy_limit = max(0.0, _safe_float(payload.get(\"policy_limit_usd\"), 20.0))\n", + "\n", + " text_lower = request_text.lower()\n", + " mentions_missing = \"missing\" in text_lower\n", + " mentions_delay = \"late\" in text_lower or \"delay\" in text_lower\n", + " abusive = edge_case_type == \"abusive_language\"\n", + " spammy = edge_case_type == \"spammy_escalation\"\n", + "\n", + " if abusive or (spammy and repeat_count >= 4 and risk_score < 0.4):\n", + " refund_amount = 0.0\n", + " credit_amount = min(3.0, policy_limit)\n", + " escalation = True\n", + " confidence = \"medium\"\n", + " reason = \"Abusive/spammy pattern detected; restricted compensation with escalation.\"\n", + " elif repeat_count >= 2 or risk_score >= 0.75 or (mentions_missing and mentions_delay):\n", + " refund_amount = round(min(policy_limit, max(policy_limit * 0.9, 15.0)), 2)\n", + " credit_amount = round(min(10.0, max(5.0, policy_limit * 0.25)), 2)\n", + " escalation = False\n", + " confidence = \"high\"\n", + " reason = \"Repeated or high-risk service failure.\"\n", + " elif repeat_count == 1 or risk_score >= 0.45 or mentions_missing or mentions_delay:\n", + " refund_amount = round(min(policy_limit, max(policy_limit * 0.5, 6.0)), 2)\n", + " credit_amount = round(min(7.0, max(3.0, policy_limit * 0.15)), 2)\n", + " escalation = False\n", + " confidence = \"high\"\n", + " reason = \"Moderate service issue with customer impact.\"\n", + " else:\n", + " refund_amount = 0.0\n", + " credit_amount = round(min(5.0, max(2.0, policy_limit * 0.1)), 2)\n", + " escalation = False\n", + " confidence = \"medium\"\n", + " reason = \"Minor issue or low-confidence compensable case.\"\n", + "\n", + " return {\n", + " \"refund_amount\": refund_amount,\n", + " \"credit_amount\": credit_amount,\n", + " \"escalation\": escalation,\n", + " \"confidence\": confidence,\n", + " \"reason\": reason,\n", + " \"repeat_count\": repeat_count,\n", + " }\n", + "\n", + "\n", + "def _ordinal(value: int) -> str:\n", + " if 10 <= value % 100 <= 20:\n", + " suffix = \"th\"\n", + " else:\n", + " suffix = {1: \"st\", 2: \"nd\", 3: \"rd\"}.get(value % 10, \"th\")\n", + " return f\"{value}{suffix}\"\n", + "\n", + "\n", + "def _compose_customer_message(payload: Dict[str, Any], policy: Dict[str, Any]) -> str:\n", + " request_text = str(payload.get(\"request_text\", \"\")).strip()\n", + " repeat_count = policy[\"repeat_count\"]\n", + " refund_amount = policy[\"refund_amount\"]\n", + " credit_amount = policy[\"credit_amount\"]\n", + "\n", + " lead = \"Thanks for contacting us, and I am really sorry about this experience.\"\n", + " if repeat_count >= 2:\n", + " lead = (\n", + " f\"I see this is at least the {_ordinal(repeat_count)} time you've had to contact us for similar issues, \"\n", + " \"and that is not acceptable.\"\n", + " )\n", + "\n", + " resolution_parts = []\n", + " if refund_amount > 0:\n", + " resolution_parts.append(f\"a ${refund_amount:.2f} refund\")\n", + " if credit_amount > 0:\n", + " resolution_parts.append(f\"a ${credit_amount:.2f} credit\")\n", + "\n", + " if resolution_parts:\n", + " resolution = \"We are offering \" + \" and \".join(resolution_parts) + \" on this case.\"\n", + " else:\n", + " resolution = \"We are escalating this case for manual review and will follow up quickly.\"\n", + "\n", + " context_line = f\"Issue noted: {request_text}\" if request_text else \"Issue noted from your support ticket.\"\n", + "\n", + " return f\"{lead} {resolution} {context_line}\"\n", + "\n", + "\n", + "class SupportModule(dspy.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.react = dspy.ReAct(\n", + " signature=SupportTriage,\n", + " tools=[\n", + " get_order_overview,\n", + " get_order_timing,\n", + " get_user_support_history,\n", + " get_user_request_history,\n", + " ],\n", + " max_iters=10,\n", + " )\n", + "\n", + " def forward(self, support_request: str) -> SupportReport:\n", + " payload = _parse_case_payload(support_request)\n", + " result = self.react(support_request=support_request)\n", + "\n", + " policy = _compute_policy_resolution(payload)\n", + " final_message = _compose_customer_message(payload, policy)\n", + "\n", + " credit_recommendation = {\n", + " \"amount_usd\": policy[\"credit_amount\"],\n", + " \"reason\": policy[\"reason\"],\n", + " }\n", + " refund_recommendation = {\n", + " \"amount_usd\": policy[\"refund_amount\"],\n", + " \"reason\": policy[\"reason\"],\n", + " }\n", + "\n", + " return SupportReport(\n", + " support_request_id=str(payload.get(\"support_request_id\") or result.support_request_id),\n", + " user_id=str(payload.get(\"user_id\") or result.user_id),\n", + " order_id=str(payload.get(\"order_id\") or result.order_id),\n", + " credit_recommendation=credit_recommendation,\n", + " refund_recommendation=refund_recommendation,\n", + " draft_response=final_message,\n", + " past_interactions_summary=str(result.past_interactions_summary),\n", + " order_details_summary=str(result.order_details_summary),\n", + " decision_confidence=policy[\"confidence\"],\n", + " escalation_flag=bool(policy[\"escalation\"]),\n", + " )\n", + "\n", + "\n", + "class DSPySupportAgent(ResponsesAgent):\n", + " def __init__(self):\n", + " self.module = SupportModule()\n", + "\n", + " def predict(self, request: ResponsesAgentRequest) -> ResponsesAgentResponse:\n", + " user_message = None\n", + " for msg in request.input:\n", + " msg_dict = msg.model_dump() if hasattr(msg, \"model_dump\") else msg\n", + " if msg_dict.get(\"role\") == \"user\":\n", + " user_message = msg_dict.get(\"content\", \"\")\n", + " break\n", + " if not user_message:\n", + " raise ValueError(\"No user message found\")\n", + "\n", + " result = self.module(support_request=user_message)\n", + " return ResponsesAgentResponse(\n", + " output=[self.create_text_output_item(text=result.model_dump_json(), id=str(uuid4()))],\n", + " custom_outputs=request.custom_inputs,\n", + " )\n", + "\n", + "\n", + "AGENT = DSPySupportAgent()\n", + "mlflow.models.set_model(AGENT)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "sample_order_id = None\n", + "for attempt in range(12):\n", + " rows = spark.sql(f\"\"\"\n", + " SELECT order_id \n", + " FROM {CATALOG}.lakeflow.all_events \n", + " WHERE event_type='delivered'\n", + " LIMIT 1\n", + " \"\"\").collect()\n", + " if rows:\n", + " sample_order_id = rows[0]['order_id']\n", + " break\n", + " print(f\"No delivered events yet (attempt {attempt+1}/12). Waiting 30s for pipeline data...\")\n", + " time.sleep(30)\n", + "\n", + "if not sample_order_id:\n", + " raise RuntimeError(\n", + " f\"No delivered events found in {CATALOG}.lakeflow.all_events after 6 minutes. \"\n", + " \"Ensure the Canonical_Data and Lakeflow pipeline stages completed and processed data.\"\n", + " )\n", + "\n", + "print(f\"Sample order_id: {sample_order_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import mlflow\n", + "from mlflow.models.resources import DatabricksFunction, DatabricksServingEndpoint\n", + "from pkg_resources import get_distribution\n", + "\n", + "payload = {\n", + " \"support_request_id\": \"sample-1\",\n", + " \"user_id\": \"user-001\",\n", + " \"order_id\": sample_order_id,\n", + " \"request_type\": \"missing_items\",\n", + " \"edge_case_type\": \"standard\",\n", + " \"request_text\": \"My order was missing one or more items. Please review and advise next steps.\",\n", + "}\n", + "\n", + "resources = [DatabricksServingEndpoint(endpoint_name=LLM_MODEL)]\n", + "for fn in [\n", + " f\"{CATALOG}.ai.get_order_overview\",\n", + " f\"{CATALOG}.ai.get_order_timing\",\n", + " f\"{CATALOG}.ai.get_user_support_history\",\n", + " f\"{CATALOG}.ai.get_user_request_history\",\n", + "]:\n", + " resources.append(DatabricksFunction(function_name=fn))\n", + "\n", + "input_example = {\"input\": [{\"role\": \"user\", \"content\": json.dumps(payload)}]}\n", + "\n", + "conda_env = {\n", + " \"channels\": [\"conda-forge\"],\n", + " \"dependencies\": [\n", + " \"python=3.11\",\n", + " \"pip\",\n", + " {\n", + " \"pip\": [\n", + " \"mlflow==3.6\",\n", + " f\"typing_extensions=={get_distribution('typing_extensions').version}\",\n", + " f\"dspy-ai=={get_distribution('dspy-ai').version}\",\n", + " f\"unitycatalog-openai[databricks]=={get_distribution('unitycatalog-openai').version}\",\n", + " f\"pydantic=={get_distribution('pydantic').version}\",\n", + " ]\n", + " }\n", + " ],\n", + " \"name\": \"mlflow-env\"\n", + "}\n", + "\n", + "with mlflow.start_run():\n", + " logged_agent_info = mlflow.pyfunc.log_model(\n", + " name=\"support_agent\",\n", + " python_model=\"agent.py\",\n", + " input_example=input_example,\n", + " resources=resources,\n", + " conda_env=conda_env,\n", + " )\n", + "\n", + "mlflow.set_active_model(model_id=logged_agent_info.model_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Log to Unity Catalog" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlflow.set_registry_uri(\"databricks-uc\")\n", + "\n", + "UC_MODEL_NAME = f\"{CATALOG}.ai.support_agent\"\n", + "\n", + "uc_registered_model_info = mlflow.register_model(\n", + " model_uri=logged_agent_info.model_uri, name=UC_MODEL_NAME\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deploy to Model Serving" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow\n", + "from datetime import timedelta\n", + "\n", + "from databricks import agents\n", + "from databricks.sdk import WorkspaceClient\n", + "from databricks.sdk.service.serving import EndpointStateReady\n", + "\n", + "import sys\n", + "sys.path.append('../utils')\n", + "from uc_state import add\n", + "\n", + "prod_experiment = mlflow.set_experiment(f\"/Shared/{CATALOG}_support_agent_prod\")\n", + "add(CATALOG, \"experiments\", {\"experiment_id\": prod_experiment.experiment_id, \"name\": prod_experiment.name})\n", + "\n", + "deployment_info = agents.deploy(\n", + " model_name=UC_MODEL_NAME,\n", + " model_version=uc_registered_model_info.version,\n", + " scale_to_zero=False,\n", + " endpoint_name=SUPPORT_AGENT_ENDPOINT_NAME,\n", + " environment_vars={\"MLFLOW_EXPERIMENT_ID\": str(prod_experiment.experiment_id)},\n", + ")\n", + "\n", + "workspace = WorkspaceClient()\n", + "ready_endpoint = workspace.serving_endpoints.wait_get_serving_endpoint_not_updating(\n", + " name=SUPPORT_AGENT_ENDPOINT_NAME,\n", + " timeout=timedelta(minutes=30),\n", + ")\n", + "\n", + "if ready_endpoint.state.ready != EndpointStateReady.READY:\n", + " raise RuntimeError(\n", + " f\"Endpoint {SUPPORT_AGENT_ENDPOINT_NAME} is {ready_endpoint.state.ready} after deployment; retry or investigate.\"\n", + " )\n", + "\n", + "print(f\"Endpoint {SUPPORT_AGENT_ENDPOINT_NAME} is READY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Record in State" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "add(CATALOG, \"endpoints\", deployment_info)\n", + "print(f\"Registered {SUPPORT_AGENT_ENDPOINT_NAME} in UC state\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/stages/support_request_agent_stream.ipynb b/stages/support_request_agent_stream.ipynb new file mode 100644 index 0000000..2109659 --- /dev/null +++ b/stages/support_request_agent_stream.ipynb @@ -0,0 +1,89 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Support Request Agent Stream\n", + "\n", + "This notebook creates a scheduled job to process support requests through the support agent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade databricks-sdk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from databricks.sdk import WorkspaceClient\n", + "import databricks.sdk.service.jobs as j\n", + "import os\n", + "\n", + "w = WorkspaceClient()\n", + "\n", + "CATALOG = dbutils.widgets.get(\"CATALOG\")\n", + "\n", + "notebook_abs_path = os.path.abspath(\"../jobs/support_request_agent_stream\")\n", + "notebook_dbx_path = notebook_abs_path.replace(\n", + " os.environ.get(\"DATABRICKS_WORKSPACE_ROOT\", \"/Workspace\"),\n", + " \"/Workspace\"\n", + ")\n", + "\n", + "job_name = f\"Support Request Agent Stream ({CATALOG})\"\n", + "\n", + "task_def = [\n", + " j.Task(\n", + " task_key=\"support_request_agent_stream\",\n", + " notebook_task=j.NotebookTask(\n", + " notebook_path=notebook_dbx_path,\n", + " base_parameters={\n", + " \"CATALOG\": CATALOG,\n", + " \"SUPPORT_AGENT_ENDPOINT_NAME\": dbutils.widgets.get(\"SUPPORT_AGENT_ENDPOINT_NAME\"),\n", + " },\n", + " )\n", + " )\n", + "]\n", + "schedule_def = j.CronSchedule(\n", + " quartz_cron_expression=\"0 0/10 * * * ?\",\n", + " timezone_id=\"UTC\",\n", + " pause_status=j.PauseStatus.UNPAUSED,\n", + ")\n", + "\n", + "existing = [jb for jb in w.jobs.list(name=job_name) if jb.settings.name == job_name]\n", + "if existing:\n", + " job_id = existing[0].job_id\n", + " w.jobs.reset(job_id=job_id, new_settings=j.JobSettings(\n", + " name=job_name, tasks=task_def, schedule=schedule_def,\n", + " ))\n", + " print(f\"\\u267b\\ufe0f Updated existing job_id={job_id}\")\n", + "else:\n", + " job = w.jobs.create(name=job_name, tasks=task_def, schedule=schedule_def)\n", + " job_id = job.job_id\n", + " import sys\n", + " sys.path.append('../utils')\n", + " from uc_state import add\n", + " add(CATALOG, \"jobs\", job)\n", + " print(f\"\\u2705 Created job_id={job_id}\")\n", + "\n", + "w.jobs.run_now(job_id=job_id)\n", + "print(f\"\\U0001f680 Started run of {job_name}\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/stages/support_request_generator_stream.ipynb b/stages/support_request_generator_stream.ipynb new file mode 100644 index 0000000..2c48eb1 --- /dev/null +++ b/stages/support_request_generator_stream.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Support Request Generator Stream\n", + "\n", + "This notebook creates a scheduled job to generate synthetic support requests from delivered orders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade databricks-sdk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from databricks.sdk import WorkspaceClient\n", + "import databricks.sdk.service.jobs as j\n", + "import os\n", + "\n", + "w = WorkspaceClient()\n", + "\n", + "CATALOG = dbutils.widgets.get(\"CATALOG\")\n", + "\n", + "notebook_abs_path = os.path.abspath(\"../jobs/support_request_generator\")\n", + "notebook_dbx_path = notebook_abs_path.replace(\n", + " os.environ.get(\"DATABRICKS_WORKSPACE_ROOT\", \"/Workspace\"),\n", + " \"/Workspace\"\n", + ")\n", + "\n", + "job_name = f\"Support Request Generator ({CATALOG})\"\n", + "\n", + "task_def = [\n", + " j.Task(\n", + " task_key=\"support_request_generator\",\n", + " notebook_task=j.NotebookTask(\n", + " notebook_path=notebook_dbx_path,\n", + " base_parameters={\n", + " \"CATALOG\": CATALOG,\n", + " \"SUPPORT_RATE\": dbutils.widgets.get(\"SUPPORT_RATE\"),\n", + " \"LLM_MODEL\": dbutils.widgets.get(\"LLM_MODEL\"),\n", + " },\n", + " )\n", + " )\n", + "]\n", + "schedule_def = j.CronSchedule(\n", + " quartz_cron_expression=\"0 0/10 * * * ?\",\n", + " timezone_id=\"UTC\",\n", + " pause_status=j.PauseStatus.UNPAUSED,\n", + ")\n", + "\n", + "existing = [jb for jb in w.jobs.list(name=job_name) if jb.settings.name == job_name]\n", + "if existing:\n", + " job_id = existing[0].job_id\n", + " w.jobs.reset(job_id=job_id, new_settings=j.JobSettings(\n", + " name=job_name, tasks=task_def, schedule=schedule_def,\n", + " ))\n", + " print(f\"\\u267b\\ufe0f Updated existing job_id={job_id}\")\n", + "else:\n", + " job = w.jobs.create(name=job_name, tasks=task_def, schedule=schedule_def)\n", + " job_id = job.job_id\n", + " import sys\n", + " sys.path.append('../utils')\n", + " from uc_state import add\n", + " add(CATALOG, \"jobs\", job)\n", + " print(f\"\\u2705 Created job_id={job_id}\")\n", + "\n", + "w.jobs.run_now(job_id=job_id)\n", + "print(f\"\\U0001f680 Started run of {job_name}\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From b05f1a9900e0b62f41151034be7dfab6f560f8b8 Mon Sep 17 00:00:00 2001 From: Nick Karpov Date: Thu, 19 Mar 2026 16:42:31 -0700 Subject: [PATCH 2/9] fix(support): fix DAG dependencies and location column in agent SQL function - Agent stream now depends on SDP directly (not generator), matching the refund pattern where all stages fan out from SDP - Fix get_order_overview to JOIN simulator.locations for location name (all_events has location_id, not location) Co-authored-by: Andre Landgraf Co-authored-by: Isaac --- databricks.yml | 2 +- stages/support_request_agent.ipynb | 22 +--------------------- 2 files changed, 2 insertions(+), 22 deletions(-) diff --git a/databricks.yml b/databricks.yml index caf7775..ae231bb 100644 --- a/databricks.yml +++ b/databricks.yml @@ -395,7 +395,7 @@ targets: - task_key: Support_Request_Agent_Stream depends_on: - - task_key: Support_Request_Generator_Stream + - task_key: Spark_Declarative_Pipeline notebook_task: notebook_path: ${workspace.root_path}/stages/support_request_agent_stream diff --git a/stages/support_request_agent.ipynb b/stages/support_request_agent.ipynb index eab6d9b..72fa54c 100644 --- a/stages/support_request_agent.ipynb +++ b/stages/support_request_agent.ipynb @@ -50,27 +50,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "%sql\n", - "CREATE OR REPLACE FUNCTION ${CATALOG}.ai.get_order_overview(oid STRING)\n", - "RETURNS TABLE (\n", - " order_id STRING,\n", - " location STRING,\n", - " items_json STRING,\n", - " customer_address STRING,\n", - " order_created_ts TIMESTAMP\n", - ")\n", - "RETURN\n", - " SELECT\n", - " order_id,\n", - " location,\n", - " get_json_object(body, '$.items') AS items_json,\n", - " get_json_object(body, '$.customer_addr') AS customer_address,\n", - " try_to_timestamp(ts) AS order_created_ts\n", - " FROM ${CATALOG}.lakeflow.all_events\n", - " WHERE order_id = oid AND event_type = 'order_created'\n", - " LIMIT 1" - ] + "source": "%sql\nCREATE OR REPLACE FUNCTION ${CATALOG}.ai.get_order_overview(oid STRING)\nRETURNS TABLE (\n order_id STRING,\n location STRING,\n items_json STRING,\n customer_address STRING,\n order_created_ts TIMESTAMP\n)\nRETURN\n SELECT\n ae.order_id,\n loc.name AS location,\n get_json_object(ae.body, '$.items') AS items_json,\n get_json_object(ae.body, '$.customer_addr') AS customer_address,\n try_to_timestamp(ae.ts) AS order_created_ts\n FROM ${CATALOG}.lakeflow.all_events ae\n LEFT JOIN ${CATALOG}.simulator.locations loc ON ae.location_id = loc.location_id\n WHERE ae.order_id = oid AND ae.event_type = 'order_created'\n LIMIT 1" }, { "cell_type": "code", From 2c9b03acbf99d6aad32b20d0947fadf88860eea3 Mon Sep 17 00:00:00 2001 From: Nick Karpov Date: Thu, 19 Mar 2026 16:44:07 -0700 Subject: [PATCH 3/9] fix(support): agent stages depend on generator, not SDP Generator is the split point - both Support_Request_Agent (leaf) and Support_Request_Agent_Stream fan out from Support_Request_Generator_Stream. Co-authored-by: Andre Landgraf Co-authored-by: Isaac --- databricks.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/databricks.yml b/databricks.yml index ae231bb..9f643a7 100644 --- a/databricks.yml +++ b/databricks.yml @@ -381,21 +381,21 @@ targets: notebook_task: notebook_path: ${workspace.root_path}/stages/lakeflow - - task_key: Support_Request_Agent + - task_key: Support_Request_Generator_Stream depends_on: - task_key: Spark_Declarative_Pipeline notebook_task: - notebook_path: ${workspace.root_path}/stages/support_request_agent + notebook_path: ${workspace.root_path}/stages/support_request_generator_stream - - task_key: Support_Request_Generator_Stream + - task_key: Support_Request_Agent depends_on: - - task_key: Spark_Declarative_Pipeline + - task_key: Support_Request_Generator_Stream notebook_task: - notebook_path: ${workspace.root_path}/stages/support_request_generator_stream + notebook_path: ${workspace.root_path}/stages/support_request_agent - task_key: Support_Request_Agent_Stream depends_on: - - task_key: Spark_Declarative_Pipeline + - task_key: Support_Request_Generator_Stream notebook_task: notebook_path: ${workspace.root_path}/stages/support_request_agent_stream From 79dd993005e5a9e26b7e8f905b02144aa736df3f Mon Sep 17 00:00:00 2001 From: Nick Karpov Date: Thu, 19 Mar 2026 17:51:04 -0700 Subject: [PATCH 4/9] feat(support): add Lakebase + App stage and support console app - Add support_lakebase stage: Lakebase instance, synced table for support_agent_reports, OLTP tables (operator_actions, support_replies, request_status, response_ratings), warehouse, app deployment - Port supportconsolek React/TS app from PR #63 - Dynamic app.yaml generation with computed warehouse/endpoint values - Add sync exclusions for supportconsolek build artifacts - Wire Support_Lakebase_And_App into DAG (depends on agent stream) Co-authored-by: Andre Landgraf Co-authored-by: Isaac --- .../supportconsolek/.env.example | 4 + .../supportconsolek/.gitignore | 10 + .../supportconsolek/.prettierignore | 36 + .../supportconsolek/.prettierrc.json | 12 + apps/supportconsolek/supportconsolek/app.yaml | 14 + .../supportconsolek/appkit.plugins.json | 41 + .../supportconsolek/client/components.json | 21 + .../supportconsolek/client/index.html | 18 + .../supportconsolek/client/postcss.config.js | 6 + .../client/public/apple-touch-icon.png | Bin 0 -> 2547 bytes .../client/public/favicon-16x16.png | Bin 0 -> 302 bytes .../client/public/favicon-192x192.png | Bin 0 -> 2762 bytes .../client/public/favicon-32x32.png | Bin 0 -> 492 bytes .../client/public/favicon-48x48.png | Bin 0 -> 686 bytes .../client/public/favicon-512x512.png | Bin 0 -> 10325 bytes .../supportconsolek/client/public/favicon.svg | 6 + .../client/public/site.webmanifest | 19 + .../supportconsolek/client/src/App.tsx | 860 + .../client/src/ErrorBoundary.tsx | 75 + .../supportconsolek/client/src/index.css | 63 + .../supportconsolek/client/src/lib/utils.ts | 6 + .../supportconsolek/client/src/main.tsx | 19 + .../supportconsolek/client/src/vite-env.d.ts | 1 + .../supportconsolek/client/tailwind.config.ts | 10 + .../supportconsolek/client/vite.config.ts | 25 + .../supportconsolek/databricks.yml | 20 + .../supportconsolek/eslint.config.js | 91 + .../supportconsolek/package-lock.json | 14167 ++++++++++++++++ .../supportconsolek/package.json | 85 + .../supportconsolek/playwright.config.ts | 26 + .../supportconsolek/server/lib/lakebase.ts | 63 + .../supportconsolek/server/server.ts | 11 + .../supportconsolek/server/support-plugin.ts | 1085 ++ .../supportconsolek/tests/smoke.spec.ts | 108 + .../supportconsolek/vitest.config.ts | 16 + databricks.yml | 13 +- stages/support_lakebase.ipynb | 504 + 37 files changed, 17430 insertions(+), 5 deletions(-) create mode 100644 apps/supportconsolek/supportconsolek/.env.example create mode 100644 apps/supportconsolek/supportconsolek/.gitignore create mode 100644 apps/supportconsolek/supportconsolek/.prettierignore create mode 100644 apps/supportconsolek/supportconsolek/.prettierrc.json create mode 100644 apps/supportconsolek/supportconsolek/app.yaml create mode 100644 apps/supportconsolek/supportconsolek/appkit.plugins.json create mode 100644 apps/supportconsolek/supportconsolek/client/components.json create mode 100644 apps/supportconsolek/supportconsolek/client/index.html create mode 100644 apps/supportconsolek/supportconsolek/client/postcss.config.js create mode 100644 apps/supportconsolek/supportconsolek/client/public/apple-touch-icon.png create mode 100644 apps/supportconsolek/supportconsolek/client/public/favicon-16x16.png create mode 100644 apps/supportconsolek/supportconsolek/client/public/favicon-192x192.png create mode 100644 apps/supportconsolek/supportconsolek/client/public/favicon-32x32.png create mode 100644 apps/supportconsolek/supportconsolek/client/public/favicon-48x48.png create mode 100644 apps/supportconsolek/supportconsolek/client/public/favicon-512x512.png create mode 100644 apps/supportconsolek/supportconsolek/client/public/favicon.svg create mode 100644 apps/supportconsolek/supportconsolek/client/public/site.webmanifest create mode 100644 apps/supportconsolek/supportconsolek/client/src/App.tsx create mode 100644 apps/supportconsolek/supportconsolek/client/src/ErrorBoundary.tsx create mode 100644 apps/supportconsolek/supportconsolek/client/src/index.css create mode 100644 apps/supportconsolek/supportconsolek/client/src/lib/utils.ts create mode 100644 apps/supportconsolek/supportconsolek/client/src/main.tsx create mode 100644 apps/supportconsolek/supportconsolek/client/src/vite-env.d.ts create mode 100644 apps/supportconsolek/supportconsolek/client/tailwind.config.ts create mode 100644 apps/supportconsolek/supportconsolek/client/vite.config.ts create mode 100644 apps/supportconsolek/supportconsolek/databricks.yml create mode 100644 apps/supportconsolek/supportconsolek/eslint.config.js create mode 100644 apps/supportconsolek/supportconsolek/package-lock.json create mode 100644 apps/supportconsolek/supportconsolek/package.json create mode 100644 apps/supportconsolek/supportconsolek/playwright.config.ts create mode 100644 apps/supportconsolek/supportconsolek/server/lib/lakebase.ts create mode 100644 apps/supportconsolek/supportconsolek/server/server.ts create mode 100644 apps/supportconsolek/supportconsolek/server/support-plugin.ts create mode 100644 apps/supportconsolek/supportconsolek/tests/smoke.spec.ts create mode 100644 apps/supportconsolek/supportconsolek/vitest.config.ts create mode 100644 stages/support_lakebase.ipynb diff --git a/apps/supportconsolek/supportconsolek/.env.example b/apps/supportconsolek/supportconsolek/.env.example new file mode 100644 index 0000000..bc164a3 --- /dev/null +++ b/apps/supportconsolek/supportconsolek/.env.example @@ -0,0 +1,4 @@ +DATABRICKS_HOST=https://... +DATABRICKS_APP_PORT=8000 +DATABRICKS_APP_NAME=caspers-supportconsole +FLASK_RUN_HOST=0.0.0.0 diff --git a/apps/supportconsolek/supportconsolek/.gitignore b/apps/supportconsolek/supportconsolek/.gitignore new file mode 100644 index 0000000..f2abc32 --- /dev/null +++ b/apps/supportconsolek/supportconsolek/.gitignore @@ -0,0 +1,10 @@ +.DS_Store +node_modules/ +client/dist/ +dist/ +build/ +.env +.databricks/ +.smoke-test/ +test-results/ +playwright-report/ diff --git a/apps/supportconsolek/supportconsolek/.prettierignore b/apps/supportconsolek/supportconsolek/.prettierignore new file mode 100644 index 0000000..7d3d77c --- /dev/null +++ b/apps/supportconsolek/supportconsolek/.prettierignore @@ -0,0 +1,36 @@ +# Dependencies +node_modules + +# Build outputs +dist +build +client/dist +.next +.databricks/ + +# Environment files +.env +.env.local +.env.*.local + +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Coverage +coverage + +# Cache +.cache +.turbo + +# Lock files +package-lock.json +yarn.lock +pnpm-lock.yaml + +# Vendor +vendor diff --git a/apps/supportconsolek/supportconsolek/.prettierrc.json b/apps/supportconsolek/supportconsolek/.prettierrc.json new file mode 100644 index 0000000..d95a63f --- /dev/null +++ b/apps/supportconsolek/supportconsolek/.prettierrc.json @@ -0,0 +1,12 @@ +{ + "semi": true, + "trailingComma": "es5", + "singleQuote": true, + "printWidth": 120, + "tabWidth": 2, + "useTabs": false, + "arrowParens": "always", + "endOfLine": "lf", + "bracketSpacing": true, + "jsxSingleQuote": false +} diff --git a/apps/supportconsolek/supportconsolek/app.yaml b/apps/supportconsolek/supportconsolek/app.yaml new file mode 100644 index 0000000..95576a0 --- /dev/null +++ b/apps/supportconsolek/supportconsolek/app.yaml @@ -0,0 +1,14 @@ +command: ['npm', 'run', 'start'] +env: + - name: PGPORT + value: "5432" + - name: PGDATABASE + value: "databricks_postgres" + - name: PGSSLMODE + value: "require" + - name: LAKEBASE_ENDPOINT + value: "projects/casperskitchens-support-db/branches/production/endpoints/primary" + - name: DATABRICKS_WAREHOUSE_ID + value: "e5ed18828056f3cf" + - name: SUPPORT_AGENT_ENDPOINT_NAME + value: "caspers_support_agent" \ No newline at end of file diff --git a/apps/supportconsolek/supportconsolek/appkit.plugins.json b/apps/supportconsolek/supportconsolek/appkit.plugins.json new file mode 100644 index 0000000..67f3874 --- /dev/null +++ b/apps/supportconsolek/supportconsolek/appkit.plugins.json @@ -0,0 +1,41 @@ +{ + "$schema": "https://databricks.github.io/appkit/schemas/template-plugins.schema.json", + "version": "1.0", + "plugins": { + "analytics": { + "name": "analytics", + "displayName": "Analytics Plugin", + "description": "SQL query execution against Databricks SQL Warehouses", + "package": "@databricks/appkit", + "resources": { + "required": [ + { + "type": "sql_warehouse", + "alias": "SQL Warehouse", + "resourceKey": "sql-warehouse", + "description": "SQL Warehouse for executing analytics queries", + "permission": "CAN_USE", + "fields": { + "id": { + "env": "DATABRICKS_WAREHOUSE_ID", + "description": "SQL Warehouse ID" + } + } + } + ], + "optional": [] + } + }, + "server": { + "name": "server", + "displayName": "Server Plugin", + "description": "HTTP server with Express, static file serving, and Vite dev mode support", + "package": "@databricks/appkit", + "requiredByTemplate": true, + "resources": { + "required": [], + "optional": [] + } + } + } +} diff --git a/apps/supportconsolek/supportconsolek/client/components.json b/apps/supportconsolek/supportconsolek/client/components.json new file mode 100644 index 0000000..13e1db0 --- /dev/null +++ b/apps/supportconsolek/supportconsolek/client/components.json @@ -0,0 +1,21 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "", + "css": "src/index.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "iconLibrary": "lucide" +} diff --git a/apps/supportconsolek/supportconsolek/client/index.html b/apps/supportconsolek/supportconsolek/client/index.html new file mode 100644 index 0000000..26f3416 --- /dev/null +++ b/apps/supportconsolek/supportconsolek/client/index.html @@ -0,0 +1,18 @@ + + + + + + + + + + + + caspers-supportconsole + + +
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Create SQL files in config/queries/ + * 2. Run `npm run typegen` to generate query types + * 3. Check appKitTypes.d.ts for available types + * + * Common Mistakes: + * - DataTable does NOT accept `data` or `columns` props + * - Charts use `xKey` and `yKey`, NOT `seriesKey`/`nameKey`/`valueKey` + * - useAnalyticsQuery has no `enabled` option - use conditional rendering + */ +import { useEffect, useRef, useState } from "react"; +import { + Badge, + Button, + Card, + CardContent, + CardHeader, + CardTitle, + Input, + Sheet, + SheetClose, + SheetContent, + SheetDescription, + SheetFooter, + SheetHeader, + SheetTitle, + Textarea, +} from "@databricks/appkit-ui/react"; +import { Toaster, toast } from "sonner"; + +type Summary = { requests: number; actions: number; replies: number }; + +type Recommendation = { + amount_usd?: number | null; + reason?: string; +}; + +type Report = { + draft_response?: string; + past_interactions_summary?: string; + order_details_summary?: string; + decision_confidence?: string; + escalation_flag?: boolean; + refund_recommendation?: Recommendation | null; + credit_recommendation?: Recommendation | null; +}; + +type CaseState = { + case_status: "pending" | "in_progress" | "done" | "blocked"; + next_action: string; + has_reply: boolean; + has_refund: boolean; + has_credit: boolean; + action_count: number; + reply_count: number; + regen_count: number; + last_action_type?: string | null; + last_event_at?: string | null; + latest_report_source?: string | null; +}; + +type TimelineEvent = { + event_type: string; + event_at: string; + actor?: string | null; + details?: Record; +}; + +type RegenerationItem = { + regenerated_report_id: number; + operator_context?: string | null; + actor?: string | null; + created_at: string; + report: Report; +}; + +type ResponseRating = { + rating_id: number; + rating: "thumbs_up" | "thumbs_down"; + reason_code?: string | null; + feedback_notes?: string | null; + actor?: string | null; + created_at: string; +}; + +type RequestItem = { + support_request_id: string; + user_id: string; + user_display_name?: string | null; + order_id: string; + ts: string; + request_text?: string | null; + report: Report; + case_state?: CaseState; +}; + +type RequestDetails = RequestItem & { + actions: Array>; + replies: Array>; + ratings?: ResponseRating[]; + latest_rating?: ResponseRating | null; + regenerations?: RegenerationItem[]; + timeline?: TimelineEvent[]; +}; + +type NoticeState = { + kind: "success" | "error"; + message: string; + supportRequestId?: string; +} | null; + +const RATING_REASON_OPTIONS: Array<{ value: string; label: string }> = [ + { value: "incorrect_facts", label: "Incorrect facts in response" }, + { value: "wrong_refund_amount", label: "Wrong refund amount" }, + { value: "wrong_credit_amount", label: "Wrong credit amount" }, + { value: "should_escalate", label: "Should have escalated" }, + { value: "should_not_escalate", label: "Should not have escalated" }, + { value: "poor_tone", label: "Poor tone or wording" }, + { value: "unclear_response", label: "Unclear or incomplete response" }, + { value: "other", label: "Other" }, +]; + +function App() { + const PAGE_SIZE = 50; + const [summary, setSummary] = useState(null); + const [requests, setRequests] = useState([]); + const [totalRequests, setTotalRequests] = useState(0); + const [currentPage, setCurrentPage] = useState(1); + const [selected, setSelected] = useState(null); + const [replyText, setReplyText] = useState(""); + const [operatorContext, setOperatorContext] = useState(""); + const [actor, setActor] = useState(""); + const [refundAmount, setRefundAmount] = useState(""); + const [creditAmount, setCreditAmount] = useState(""); + const [ratingChoice, setRatingChoice] = useState<"thumbs_up" | "thumbs_down">("thumbs_up"); + const [ratingReason, setRatingReason] = useState(""); + const [ratingNotes, setRatingNotes] = useState(""); + const [loading, setLoading] = useState(false); + const [detailsLoading, setDetailsLoading] = useState(false); + const [isDrawerOpen, setIsDrawerOpen] = useState(false); + const [pendingAction, setPendingAction] = useState<"apply_refund" | "apply_credit" | "send_reply" | "regenerate" | "rate_response" | null>(null); + const [notice, setNotice] = useState(null); + const [error, setError] = useState(null); + const drawerScrollRef = useRef(null); + + useEffect(() => { + void refresh(); + }, [currentPage]); + + const formatCurrency = (value?: number | null) => + typeof value === "number" ? `$${value.toFixed(2)}` : "No recommendation"; + + const formatTs = (value: string) => { + const date = new Date(value); + if (Number.isNaN(date.getTime())) { + return "Recent"; + } + return date.toLocaleString(); + }; + + const statusLabel = (status: CaseState["case_status"] | undefined) => { + if (status === "done") return "Done"; + if (status === "in_progress") return "In Progress"; + if (status === "blocked") return "Blocked"; + return "Pending"; + }; + + const statusVariant = (status: CaseState["case_status"] | undefined): "default" | "secondary" | "outline" => { + if (status === "done") return "default"; + if (status === "in_progress") return "secondary"; + return "outline"; + }; + + const nextActionLabel = (nextAction: string | undefined) => { + const mapping: Record = { + review_report: "Review report", + apply_resolution_or_regenerate: "Apply resolution or re-gen", + send_customer_reply: "Send customer reply", + monitor: "Monitor case", + investigate_blocker: "Investigate blocker", + continue_investigation: "Continue investigation", + }; + return mapping[nextAction ?? ""] ?? "Review report"; + }; + + const suggestedRefund = selected?.report?.refund_recommendation?.amount_usd ?? null; + const suggestedCredit = selected?.report?.credit_recommendation?.amount_usd ?? null; + const selectedCaseState = selected?.case_state; + const latestRating = selected?.latest_rating; + const latestRatingLabel = latestRating?.rating === "thumbs_up" + ? "Agent Rating: Thumbs Up" + : latestRating?.rating === "thumbs_down" + ? "Agent Rating: Thumbs Down" + : "Agent Rating: Not rated"; + + const appliedRefund = selected?.actions.find((a) => a.action_type === "apply_refund"); + const appliedCredit = selected?.actions.find((a) => a.action_type === "apply_credit"); + + const parseErrorMessage = async (res: Response): Promise => { + try { + const body = (await res.json()) as { message?: string; error?: string }; + return body.message || body.error || `Request failed (${res.status})`; + } catch { + return `Request failed (${res.status})`; + } + }; + + const showNotice = (nextNotice: Exclude) => { + setNotice(nextNotice); + if (nextNotice.kind === "success") { + toast.success(nextNotice.message); + } else { + toast.error(nextNotice.message); + } + }; + + const refresh = async () => { + setLoading(true); + setError(null); + try { + const offset = (currentPage - 1) * PAGE_SIZE; + const [summaryRes, reqRes] = await Promise.all([ + fetch("/api/support/summary"), + fetch(`/api/support/requests?limit=${PAGE_SIZE}&offset=${offset}`), + ]); + if (!summaryRes.ok || !reqRes.ok) { + throw new Error("Failed to load support data"); + } + const summaryJson = (await summaryRes.json()) as Summary; + const reqJson = (await reqRes.json()) as { items?: RequestItem[]; total?: number }; + setSummary(summaryJson); + setRequests(reqJson.items || []); + setTotalRequests(reqJson.total ?? 0); + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + } finally { + setLoading(false); + } + }; + + const openDetails = async ( + supportRequestId: string, + options?: { showLoading?: boolean; preserveScroll?: boolean }, + ) => { + const showLoading = options?.showLoading ?? true; + const preserveScroll = options?.preserveScroll ?? false; + const previousScrollTop = preserveScroll ? drawerScrollRef.current?.scrollTop ?? 0 : 0; + setIsDrawerOpen(true); + if (showLoading) { + setDetailsLoading(true); + } + setNotice((prev) => (prev?.supportRequestId === supportRequestId ? prev : null)); + try { + const res = await fetch(`/api/support/requests/${supportRequestId}`); + if (!res.ok) { + throw new Error("Failed to load request details"); + } + const json = (await res.json()) as RequestDetails; + setSelected(json); + setReplyText(json?.report?.draft_response ?? ""); + setRefundAmount( + typeof json?.report?.refund_recommendation?.amount_usd === "number" + ? String(json.report.refund_recommendation.amount_usd) + : "", + ); + setCreditAmount( + typeof json?.report?.credit_recommendation?.amount_usd === "number" + ? String(json.report.credit_recommendation.amount_usd) + : "", + ); + setRatingChoice(json?.latest_rating?.rating === "thumbs_down" ? "thumbs_down" : "thumbs_up"); + setRatingReason(json?.latest_rating?.reason_code ?? ""); + setRatingNotes(json?.latest_rating?.feedback_notes ?? ""); + setError(null); + if (preserveScroll) { + requestAnimationFrame(() => { + if (drawerScrollRef.current) { + drawerScrollRef.current.scrollTop = previousScrollTop; + } + }); + } + return true; + } catch (e) { + setError(e instanceof Error ? e.message : String(e)); + return false; + } finally { + if (showLoading) { + setDetailsLoading(false); + } + } + }; + + const totalPages = Math.max(1, Math.ceil(totalRequests / PAGE_SIZE)); + + const applyAction = async (actionType: "apply_refund" | "apply_credit", amount: string) => { + if (!selected) return; + if (!amount || Number.isNaN(Number(amount))) return; + setPendingAction(actionType); + setNotice(null); + try { + const res = await fetch("/api/support/actions", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + support_request_id: selected.support_request_id, + order_id: selected.order_id, + user_id: selected.user_id, + action_type: actionType, + amount_usd: Number(amount), + actor: actor || null, + payload: { source: "appkit-ui" }, + }), + }); + if (!res.ok) { + throw new Error(await parseErrorMessage(res)); + } + showNotice({ + kind: "success", + message: actionType === "apply_credit" ? "Credits applied successfully." : "Refund applied successfully.", + supportRequestId: selected.support_request_id, + }); + await openDetails(selected.support_request_id, { showLoading: false, preserveScroll: true }); + await refresh(); + } catch (e) { + showNotice({ + kind: "error", + message: e instanceof Error ? e.message : String(e), + }); + } finally { + setPendingAction(null); + } + }; + + const sendReply = async () => { + if (!selected) return; + setPendingAction("send_reply"); + setNotice(null); + try { + const res = await fetch("/api/support/replies", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + support_request_id: selected.support_request_id, + order_id: selected.order_id, + user_id: selected.user_id, + message_text: replyText, + sent_by: actor || null, + }), + }); + if (!res.ok) { + throw new Error(await parseErrorMessage(res)); + } + showNotice({ + kind: "success", + message: "Reply sent successfully.", + supportRequestId: selected.support_request_id, + }); + await openDetails(selected.support_request_id, { showLoading: false, preserveScroll: true }); + await refresh(); + } catch (e) { + showNotice({ + kind: "error", + message: e instanceof Error ? e.message : String(e), + }); + } finally { + setPendingAction(null); + } + }; + + const regenerateReport = async () => { + if (!selected) return; + setPendingAction("regenerate"); + setNotice(null); + try { + const res = await fetch("/api/support/regenerate", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + support_request_id: selected.support_request_id, + order_id: selected.order_id, + user_id: selected.user_id, + actor: actor || null, + operator_context: operatorContext || null, + current_report: selected.report, + }), + }); + if (!res.ok) { + throw new Error(await parseErrorMessage(res)); + } + const body = (await res.json()) as { warning?: string }; + showNotice({ + kind: "success", + message: body.warning + ? `Report regenerated with fallback. ${body.warning}` + : "Report regenerated with operator context.", + supportRequestId: selected.support_request_id, + }); + await openDetails(selected.support_request_id, { showLoading: false, preserveScroll: true }); + await refresh(); + } catch (e) { + showNotice({ + kind: "error", + message: e instanceof Error ? e.message : String(e), + }); + } finally { + setPendingAction(null); + } + }; + + const submitRating = async () => { + if (!selected) return; + setPendingAction("rate_response"); + setNotice(null); + try { + const res = await fetch("/api/support/ratings", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + support_request_id: selected.support_request_id, + order_id: selected.order_id, + user_id: selected.user_id, + rating: ratingChoice, + reason_code: ratingReason || null, + feedback_notes: ratingNotes || null, + actor: actor || null, + }), + }); + if (!res.ok) { + throw new Error(await parseErrorMessage(res)); + } + showNotice({ + kind: "success", + message: "Agent response rating saved.", + supportRequestId: selected.support_request_id, + }); + await openDetails(selected.support_request_id, { showLoading: false, preserveScroll: true }); + await refresh(); + } catch (e) { + showNotice({ + kind: "error", + message: e instanceof Error ? e.message : String(e), + }); + } finally { + setPendingAction(null); + } + }; + + return ( +
+ +
+
+

Support Console

+

+ Triage support requests, review agent analysis, and take operator actions. +

+
+ +
+ + {error && ( + + Error: {error} + + )} + +
+ + + Requests + + +
{summary?.requests ?? "-"}
+ + + + + Actions + + +
{summary?.actions ?? "-"}
+ + + + + Replies + + +
{summary?.replies ?? "-"}
+ + +
+ +
+ + + Support Requests + + + {loading && requests.length === 0 && ( + <> + {Array.from({ length: 4 }).map((_, idx) => ( +
+
+
+
+
+ ))} + + )} + {requests.length === 0 && ( +
+ No support requests yet. +
+ )} + {requests.map((r) => { + const previewSource = + r.report?.draft_response || + r.report?.order_details_summary || + "Support request ready for review."; + const preview = previewSource.replace(/\s+/g, " ").trim(); + return ( +
+
+
+
+ {r.user_display_name ?? "Customer"} +
+ + {statusLabel(r.case_state?.case_status)} + + + Next: {nextActionLabel(r.case_state?.next_action)} + + {r.case_state?.has_reply && Replied} + {r.case_state?.has_refund && Refund Applied} + {r.case_state?.has_credit && Credits Applied} + + Refund: {formatCurrency(r.report?.refund_recommendation?.amount_usd)} + + + Credit: {formatCurrency(r.report?.credit_recommendation?.amount_usd)} + +
+
+ Updated {formatTs(r.case_state?.last_event_at ?? r.ts)} + + Replies {r.case_state?.reply_count ?? 0} + + + Actions {r.case_state?.action_count ?? 0} + + + Re-gens {r.case_state?.regen_count ?? 0} + +
+
{preview}
+
+ +
+ ); + })} +
+
+ Page {currentPage} of {totalPages} +
+
+ + +
+
+ + +
+ + +
+ + Request Details + + Review agent analysis and take operator actions for the selected support case. + + +
+ {notice && ( + + + {notice.message} + + + )} + {detailsLoading &&
Loading request details...
} + {!detailsLoading && !selected && ( +
Select a request from the list.
+ )} + {!detailsLoading && selected && ( + <> +
+
+ {selected.user_display_name ?? "Customer"} + + Updated: {formatTs(selectedCaseState?.last_event_at ?? selected.ts)} + + + Confidence: {selected.report?.decision_confidence ?? "unknown"} + + + {statusLabel(selectedCaseState?.case_status)} + +
+
+
+
History
+
+ + Current Status: {statusLabel(selectedCaseState?.case_status)} + + Next: {nextActionLabel(selectedCaseState?.next_action)} + Last Action: {selectedCaseState?.last_action_type ?? "none"} + {latestRatingLabel} +
+
+ {(selected.timeline ?? []).slice(0, 8).map((event, idx) => ( +
+
{event.event_type.replaceAll("_", " ")}
+
+ {formatTs(event.event_at)}{event.actor ? ` · ${event.actor}` : ""} +
+
+ ))} + {(selected.timeline ?? []).length === 0 && ( +
No activity yet.
+ )} +
+
+ +
+
Support Request & Actions
+
+
Raw Support Message
+
+ {selected.request_text || "Raw support message not available for this request yet."} +
+
+
+
Suggested Agent Response
+