Build a Better ChatBot LiveLab (#540)

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Signed-off-by: Mark Nelson <[email protected]>

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Signed-off-by: Mark Nelson <[email protected]>
Co-authored-by: Mark Nelson <[email protected]>
Co-authored-by: Lorenzo De Marchis <[email protected]>

Author: 3 people

ai-optimizer/desktop/evaluating/evaluating.md

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+# Evaluating Performance
+
+## Introduction
+
+We are confident that adjusting certain parameters can improve the quality and accuracy of the chatbot’s responses. However, can we be sure that a specific configuration remains reliable when scaled to hundreds or even thousands of different questions?
+
+In this lab, you will explore the *Testbed* feature. The Testbed allows you to evaluate your chatbot at scale by generating a Q&A test dataset and automatically running it against your current configuration.
+
+**Note**: The example shown in this lab relies on gpt-4o-mini. Feel free to use your local LLMs (e.g. llama3.1) if you choose to or can't use OpenAI LLMs.
+
+Estimated Time: 15 minutes
+
+### Objectives
+
+In this lab, you will:
+
+* Explore the *Testbed* tab
+* Generate a Q&A Test dataset
+* Perform an evaluation on the Q&A Testset
+
+### Prerequisites
+
+* All previous labs successfully completed
+
+## Task 1: Navigate to the Testbed tab
+
+Access the *Testbed* from the left-hand menu:
+
+![testbed](./images/testbed.png)
+
+As a first step, you can either upload an existing Q&A test set—either from a local file or from a saved collection in the database—or generate a new one from a local PDF file.
+
+## Task 2: Generate a Q&A Test dataset
+
+The AI Optimizer allows you to generate as many questions and answers as you need, based on a single document from your knowledge base. To enable test dataset generation, simply select the corresponding radio button:
+
+![generate](./images/generatenew.png)
+
+1. Upload a document
+
+    Upload the same document that was used to create the vector store. You can easily download it from [this link](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/ai-vector-search-users-guide.pdf).
+
+2. Increase the number of questions to be generated to 10 or more
+
+    Keep in mind that the process can take a significant amount of time, especially if you are using a local LLM without sufficient hardware resources. If you choose to use a remote OpenAI model instead, the generation time will be less affected by the number of Q&A pairs to create.
+
+3. Leave the default option for:
+    * Q&A Language Model: **gpt-4o-mini**
+    * Q&A Embedding Model: **text-embedding-3-small**
+
+4. Click on **Generate Q&A** button and wait until the process is over:
+
+    ![patience](./images/patience.png)
+
+5. Browse the questions and answers generated:
+
+    ![qa-browse](./images/qa-browse.png)
+
+    Note that the **Question** and **Answer** fields are editable, allowing you to modify the proposed Q&A pairs based on the **Context** (which is randomly extracted and not editable) and the **Metadata** generated by the Testbed engine.
+
+    In the *Metadata* field you'll find a **topic** tag that classifies each Q&A pair. The topic list is generated automatically by analyzing the document content and is assigned to each Q&A pair. It will be used in the final report to break down the **Overall Correctness Score** and highlight areas where the chatbot lacks precision.
+
+    You can also export the generated Q&A dataset using the **Download** button. This allows you to edit and review it—e.g., in Visual Studio Code.
+
+    ![qa-json](./images/qa-json.png)
+
+6. Update the **Test Set Name**
+
+    Replace the automatically generated default name to make it easier to identify the test dataset later, especially when running repeated tests with different chatbot configurations. For example, change it from:
+
+    ![default-test-set](./images/default-test-set.png)
+
+    to something more descriptive, like:
+
+      ![test-rename](./images/test-rename.png)
+
+## Task 3: Evaluate the Q&A Testset
+
+Now you are ready to perform an evaluation on the Q&As you generated in the previous step.
+
+1. In the left-hand menu:
+
+    * Under **Language Model Parameters**, select **gpt-4o-mini** from the **Chat model** dropdown list.
+
+    * Ensure **Enable RAG?** is selected (if it wasn't already)
+
+    * In the **Select Alias** dropdown list, choose the **TEST2** value.
+
+    * Leave all other parameters unchanged
+
+2. With **gpt-4o-mini** selected as the evaluation model, click the **Start Evaluation** button and wait a few seconds. All questions from your dataset will be submitted to the chatbot using the configuration defined in the left pane:
+
+    ![start-eval](./images/start-eval.png)
+
+3. Let's examine the result report, starting with the first section:
+
+    ![result](./images/result-topic.png)
+
+    This section displays:
+
+      * The chatbot's **Evaluation Settings**, as configured in the left-hand pane before launching the massive test.
+
+      * The **RAG Settings** including the database and vector store used, the name of the embedding **model** used, and all associated parameters (e.g., **chunk size**, **top-k**).
+
+      * The **Overall Correctness Score**, representing the percentage of questions for which the LLM judged the chatbot's response as correct compared to the reference answer
+
+      * The **Correctness By Topic**, which breaks down the results based on the automatically generated topics assigned to each Q&A pair in the dataset.
+
+    The second section of the report contains details on each question submitted, with a focus on the **Failures** collection and the **Full Report** list. To view all fields, scroll horizontally. In the image below, the second frame has been scrolled to the right:
+
+      ![result](./images/result-question.png)
+
+    The main fields displayed are:
+
+      * **question**: the submitted question
+      * **reference_answer**: the expected answer used as a benchmark
+      * **reference_context**: the source document section used to generate the Q&A pair
+      * **agent_answer**: the response provided by the chatbot based on the current configuration and vector store
+      * **correctness_reason**: an explanation (if any) of why the response was considered incorrect. If correct, this field will display **None**.
+
+      You can download the results in different formats:
+
+      * Click the **Download Report** button to generate an HTML summary of the *Overall Correctness Score* and *Correctness by Topic*
+
+      * To export the **Full Report** and the **Failures** list, download them as .csv files using the download icons shown in the interface:
+
+        ![csv](./images/download-csv.png)
+
+## Task 4 (optional): Try a different Q&A Testset
+
+Now let's perform a test using an external saved test dataset, which you can download [here](https://raw.githubusercontent.com/markxnelson/developer/refs/heads/main/ai-optimizer/getting_started-30_testset.json). This file contains 30 pre-generated questions.
+
+If you wish to remove any Q&A pairs that you consider irrelevant or unhelpful, you can edit the file, save it, and then reload it as a local file—following the steps shown in the screenshot below:
+
+  ![load-tests](./images/load-tests.png)
+
+Next, let’s update the Chat Model parameters by setting the **Temperature** to **0** in the left-hand pane under the **Language Model Parameters** section.
+Why? Q&A datasets are typically generated with a low level of creativity to minimize randomness and focus on expressing core concepts clearly—avoiding unnecessary "frills" in the answers.
+Now, repeat the test to see whether there are improvements in the **Overall Correctness Score**.
+
+* To compare with previous results, open the dropdown under **Previous Evaluations for...** and click on the **View** button to display the associated report.
+
+  ![previous](./images/previous.png)
+
+* You can repeat the tests as many times as needed, changing the **Vector Store**, **Search Type**, and **Top K** parameters to apply the same tuning strategies you've used previously with individual questions—now extended to a full test using curated and reproducible data.
+
+## Acknowledgements
+
+* **Author** - Lorenzo De Marchis, Developer Evangelist, May 2025
+* **Contributors** - Mark Nelson, John Lathouwers, Corrado De Bari, Jorge Ortiz Fuentes, Andy Tael
+* **Last Updated By** - Lorenzo De Marchis, May 2025