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Fix link to Loading Conversations notebook (#96)
* Fix the link to a notebook * Fix the badge link * Output undetected outcome counts in results summary
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‎README.md‎

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[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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[![Twitter Follow](https://img.shields.io/twitter/follow/agentune_sb?style=social)](https://x.com/agentune_sb)
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[![Discord](https://img.shields.io/discord/1375004885845807114?color=7289da&label=discord&logo=discord&logoColor=white)](https://discord.gg/Hx5YYAaebz)
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[![Discord](https://img.shields.io/badge/discord-join-blue?logo=discord&logoColor=white)](https://discord.gg/Hx5YYAaebz)
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‎agentune_simulate/README.md‎

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[![PyPI version](https://badge.fury.io/py/agentune-simulate.svg)](https://pypi.org/project/agentune-simulate/)
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[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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[![Twitter Follow](https://img.shields.io/twitter/follow/agentune_sb?style=social)](https://x.com/agentune_sb)
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[![Discord](https://img.shields.io/discord/1375004885845807114?color=7289da&label=discord&logo=discord&logoColor=white)](https://discord.gg/Hx5YYAaebz)
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[![Discord](https://img.shields.io/badge/discord-join-blue?logo=discord&logoColor=white)](https://discord.gg/Hx5YYAaebz)
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@@ -62,7 +62,7 @@ Running a simulation with Agentune Simulate generates realistic conversations be
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1. **Quick Start** - [`getting_started.ipynb`](https://github.com/SparkBeyond/agentune/blob/main/agentune_simulate/examples/getting_started.ipynb) for a quick getting started example
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2. **Production Setup** - [`persistent_storage_example.ipynb`](https://github.com/SparkBeyond/agentune/blob/main/agentune_simulate/examples/persistent_storage_example.ipynb) for a closer to real life, scalable, persistent example
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3. **Validate _Your_ Data** - Adapt the 2nd example to load _your_ conversations data and validate the simulation.
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Here is an example of how to load conversations from tabular data: [`load_conversations_from_csv.ipynb`](https://github.com/SparkBeyond/agentune/blob/main/agentune_simulate/examples/load_conversations_from_csv.ipynb)
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Here is an example of how to load conversations from tabular data: [`loading_conversations.ipynb`](https://github.com/SparkBeyond/agentune/blob/main/agentune_simulate/examples/loading_conversations.ipynb)
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4. **Connect Real Agent** - [`real_agent_integration.ipynb`](https://github.com/SparkBeyond/agentune/blob/main/agentune_simulate/examples/real_agent_integration.ipynb) for integrating your existing agent systems
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📧 **Need help? Have feedback?** Contact us at [agentune-dev@sparkbeyond.com](mailto:agentune-dev@sparkbeyond.com)

‎agentune_simulate/agentune/simulate/models/results.py‎

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lines.append(f"{outcome:<20} {orig_count:>3} ({orig_pct:>4.1f}%) {sim_count:>3} ({sim_pct:>4.1f}%)")
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# Add row for conversations without outcome if any exist
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if orig_dist.conversations_without_outcome > 0 or sim_dist.conversations_without_outcome > 0:
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orig_no_outcome = orig_dist.conversations_without_outcome
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sim_no_outcome = sim_dist.conversations_without_outcome
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orig_no_outcome_pct = orig_dist.no_outcome_percentage
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sim_no_outcome_pct = sim_dist.no_outcome_percentage
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lines.append(f"{'No outcome':<20} {orig_no_outcome:>3} ({orig_no_outcome_pct:>4.1f}%) {sim_no_outcome:>3} ({sim_no_outcome_pct:>4.1f}%)")
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# Show a sample conversation
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if self.simulated_conversations:
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sample_conv = self.simulated_conversations[0].conversation

‎agentune_simulate/examples/persistent_storage_example.ipynb‎

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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:19:52.750523Z",
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"start_time": "2025-07-23T07:19:48.896707Z"
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"end_time": "2025-07-23T14:03:27.619459Z",
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"start_time": "2025-07-23T14:03:23.931925Z"
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}
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},
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"source": [
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:19:54.091900Z",
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"start_time": "2025-07-23T07:19:52.853485Z"
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"end_time": "2025-07-23T14:03:29.772500Z",
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"start_time": "2025-07-23T14:03:27.775396Z"
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}
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},
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"source": [
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:19:56.049115Z",
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"start_time": "2025-07-23T07:19:54.276165Z"
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"end_time": "2025-07-23T14:03:38.533472Z",
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"start_time": "2025-07-23T14:03:29.782917Z"
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}
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},
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"cell_type": "code",
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:19:56.060520Z",
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"start_time": "2025-07-23T07:19:56.058656Z"
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"end_time": "2025-07-23T14:03:38.581039Z",
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"start_time": "2025-07-23T14:03:38.577880Z"
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}
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"cell_type": "code",
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"metadata": {
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"end_time": "2025-07-23T07:19:57.374782Z",
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"start_time": "2025-07-23T07:19:57.297830Z"
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"end_time": "2025-07-23T14:03:38.905803Z",
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"start_time": "2025-07-23T14:03:38.653397Z"
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}
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"source": [
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:19:57.579752Z",
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"start_time": "2025-07-23T07:19:57.566833Z"
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"end_time": "2025-07-23T14:03:38.950116Z",
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"start_time": "2025-07-23T14:03:38.925006Z"
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}
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:19:58.769910Z",
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"start_time": "2025-07-23T07:19:58.767878Z"
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"end_time": "2025-07-23T14:03:38.999734Z",
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"start_time": "2025-07-23T14:03:38.995622Z"
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}
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:20:00.941262Z",
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"start_time": "2025-07-23T07:20:00.828130Z"
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"end_time": "2025-07-23T14:03:39.310715Z",
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"start_time": "2025-07-23T14:03:39.044874Z"
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}
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"cell_type": "code",
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:20:04.180391Z",
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"start_time": "2025-07-23T07:20:01.398536Z"
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"end_time": "2025-07-23T14:03:43.677868Z",
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"start_time": "2025-07-23T14:03:39.327150Z"
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}
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"cell_type": "code",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2025-07-23 10:20:01,411 - Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n"
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"2025-07-23 17:03:39,346 - Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n"
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]
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},
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:20:53.891715Z",
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"start_time": "2025-07-23T07:20:04.188558Z"
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"end_time": "2025-07-23T14:04:31.597482Z",
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"start_time": "2025-07-23T14:03:43.699526Z"
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}
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"source": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2025-07-23 10:20:04,189 - Starting intent extraction on 5 conversations\n",
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"2025-07-23 10:20:07,182 - Finished extracting original intents; generated 5 scenarios\n",
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"2025-07-23 10:20:07,182 - Starting conversation simulations (self.max_concurrent_conversations=20)\n",
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"2025-07-23 10:20:12,184 - Progress: 0/5 scenarios completed\n",
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"2025-07-23 10:20:22,187 - Progress: 1/5 scenarios completed\n",
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"2025-07-23 10:20:27,188 - Progress: 2/5 scenarios completed\n",
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"2025-07-23 10:20:37,191 - Progress: 3/5 scenarios completed\n",
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"2025-07-23 10:20:47,193 - Progress: 5/5 scenarios completed\n",
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"2025-07-23 10:20:47,194 - Finished simulating conversations; simulated 5 conversations, with 0 failures\n",
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"2025-07-23 10:20:47,194 - Starting analysis of simulation results\n",
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"2025-07-23 10:20:53,890 - Finished analyzing results\n"
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"2025-07-23 17:03:43,702 - Starting intent extraction on 5 conversations\n",
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"2025-07-23 17:03:52,403 - Finished extracting original intents; generated 5 scenarios\n",
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"2025-07-23 17:03:52,404 - Starting conversation simulations (self.max_concurrent_conversations=20)\n",
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"2025-07-23 17:03:57,406 - Progress: 0/5 scenarios completed\n",
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"2025-07-23 17:04:07,408 - Progress: 2/5 scenarios completed\n",
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"2025-07-23 17:04:12,410 - Progress: 3/5 scenarios completed\n",
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"2025-07-23 17:04:22,413 - Progress: 5/5 scenarios completed\n",
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"2025-07-23 17:04:22,414 - Finished simulating conversations; simulated 5 conversations, with 0 failures\n",
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"2025-07-23 17:04:22,415 - Starting analysis of simulation results\n",
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"2025-07-23 17:04:31,594 - Finished analyzing results\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-23T07:20:53.909318Z",
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"start_time": "2025-07-23T07:20:53.901864Z"
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"end_time": "2025-07-23T14:04:31.627201Z",
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"start_time": "2025-07-23T14:04:31.614360Z"
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}
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},
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"source": [
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"# Save results to file using built-in method\n",
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"output_file = \"chroma_simulation_results.json\"\n",
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"chroma_result.save_to_file(output_file)\n",
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"print(f\"\\n✓ Results saved to {Path(output_file).absolute()}\")"
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"print(f\"\\n✓ Results saved to {Path(output_file).resolve().relative_to(Path('../../').resolve())}\")"
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"outputs": [
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{
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"\n",
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"Average messages per conversation:\n",
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" Original: 4.4\n",
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" Simulated: 4.2\n",
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" Simulated: 2.8\n",
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"\n",
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"Outcome distribution comparison:\n",
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"Outcome Original Simulated \n",
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"--------------------------------------------------\n",
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"resolved 4 (80.0%) 4 (80.0%)\n",
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"unresolved 1 (20.0%) 0 ( 0.0%)\n",
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"resolved 4 (80.0%) 3 (60.0%)\n",
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"unresolved 1 (20.0%) 2 (40.0%)\n",
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"\n",
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"Sample conversation (6 messages):\n",
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"Sample conversation (4 messages):\n",
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" 1. customer: Last night, I waited in line for 2 hours in the business office, but because I only had a copy of my...\n",
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" 2. agent: I'm sorry to hear about the inconvenience you've experienced. It sounds frustrating to have to make ...\n",
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" 3. customer: I have the set-top box with me now. Can you confirm if this is all I need to proceed with the cancel...\n",
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" 4. agent: Thank you for confirming that you have the set-top box with you. To proceed with the cancellation, p...\n",
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" ... and 2 more messages\n",
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" 2. agent: I apologize for the inconvenience and frustration you've experienced with the cancellation process. ...\n",
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" 3. customer: I understand the need for the original ID and the set-top box now, but my issue is with the lack of ...\n",
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" 4. agent: I completely understand your frustration, and I apologize for the inconvenience this has caused. I w...\n",
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"========================================\n",
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"\n",
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"✓ Results saved to /Users/erik/Dev/agentune/agentune_simulate/examples/chroma_simulation_results.json\n"
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"✓ Results saved to agentune_simulate/examples/chroma_simulation_results.json\n"
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]
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}
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],

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