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https://github.com/deepbiolab/custom-copilot-with-azure
Tutorial about building, testing, and deployment of a copilot capable of retrieving relevant information from an indexed dataset and delivering accurate, real-time responses to customer inquiries
https://github.com/deepbiolab/custom-copilot-with-azure
azure copilot prompt-flow rag
Last synced: about 2 months ago
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Tutorial about building, testing, and deployment of a copilot capable of retrieving relevant information from an indexed dataset and delivering accurate, real-time responses to customer inquiries
- Host: GitHub
- URL: https://github.com/deepbiolab/custom-copilot-with-azure
- Owner: deepbiolab
- License: mit
- Created: 2024-11-21T02:55:45.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-21T08:51:47.000Z (about 2 months ago)
- Last Synced: 2024-11-22T05:07:25.264Z (about 2 months ago)
- Topics: azure, copilot, prompt-flow, rag
- Homepage:
- Size: 14.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Custom Copilot with Azure
### Project Overview
These project instructions will guide you through creating and deploying a custom AI copilot using Azure AI Studio. Follow the steps below carefully, and be sure to complete each deliverable for submission.
In this project, you will:
1. Create an AI Studio project and hub.
2. Deploy an AI model and upload product data for indexing.
3. Build and test a custom copilot app using Prompt Flow.
4. Evaluate the app with both automated and manual prompt evaluation.
5. Deploy and test the copilot application.
### Step-by-Step Guide
#### 1. Create an AI Studio Hub and Project
- Log in to [Azure AI Studio](https://ai.azure.com/).
- Click **Create Project**, name your project (`outlander-ai-project`), and create a new hub (`outlander-ai-hub`).
- When we first time create this project and not create hub yet, after click **Customize**, we will customize below settings. For key settings in below you can custom by the instruction.
- Leave default configurations unless necessary adjustments are needed(below is my settings), and click **Next** in below.
- Ensure you have an **Azure AI Search** service for indexing data. And finally review the information and click **Create**
#### 2. Deploy an AI Model
- With your project selected, go to **Models + endpoints**.
- Click **+Deploy Model**, choose a base model like `gpt-4o`, and confirm the deployment.
#### 3. Upload Product Data to Azure AI Studio
- Download and unzip the `product-info.zip` from [this GitHub link](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/tree/main/tutorial/data).
- In AI Studio, select the **Data + indexes** blade and click **+New Data**.
- Upload the unzipped product data files or connect to your storage account if preferred.
- After upload files, you can see the files structure in **Data + indexes** panel.
#### 4. Create an AI Search Index
- Create a Embedding model, we can transform the document (uploaded above) to dense embedding which we can use it in RAG system later. Like procedure created for `gpt-4o`, we choose `Models + endpoints` >>> `Deploy model` >>> `Deploy base model` in sequentially, and choose `text-embedding-ada-002` model to create.
- Still you can custom below settings, but i leave here by default and click **Deploy**
- After deployed, we can see the model in the service we created before.
- Go to the **Data + indexes** blade and click **+New Index** after **Indexes**
- Select **Data in Azure AI Studio** as the data source, choose the uploaded data, and proceed.
- Choose your deployed model for text embeddings and create the index.
#### 5. Build the Copilot App
- Navigate to the **Chat** blade under Project Playground.
- Select your deployed model and add your data by choosing the created index.
- Click **Prompt Flow**, name your flow (e.g., `Outlander AI Copilot`), and open it.
- Review and understand the default components, such as data retrieval and response generation.
#### 6. Test the Copilot
- Start a compute session by clicking **Start compute session**.
- Test the copilot by entering sample questions such as:
> Below questions basically stick with the documents we uploaded, so ideally it will reply the relevant response based the document that is purpose of RAG.
- `How much do the TrailWalker Hiking Shoes cost?`
- `Which tent is the most waterproof?`
- `Can the warranty for TrailBlaze pants be transferred?`
- After test, click **Save** for this copilot.
#### 7. Perform Automated Prompt Evaluation
- Create a JSONL or CSV file with evaluation questions and answers (see below for format).
> [Sample Evaluation Data (JSONL format)](./data/qa_evaluation_data.jsonl)
>
> ```
> {"chat_input": "Which tent is the most waterproof?",
> "truth": "The Alpine Explorer Tent has the highest rainfly waterproof rating at 3000m",
> "chat_history": []}
> {"chat_input": "How much do the TrailWalker Hiking Shoes cost?",
> "truth": "The TrailWalker Hiking Shoes are priced at $110",
> "chat_history": []}
> ```
>
> [Sample Evaluation Data (CSV format)](./data/qa_evaluation_data.csv)
>
> | chat_input | truth | chat_history |
> | ----------------------------------------------- | ------------------------------------------------------------ | ------------ |
> | Which tent is the most waterproof? | The Alpine Explorer Tent has the highest rainfly waterproof rating at 3000m | [] |
> | How much do the TrailWalker Hiking Shoes cost? | The TrailWalker Hiking Shoes are priced at $110 | [] |
- In **Prompt Flow**, click **Evaluate**, choose **Automated evaluation**, and then and use your dataset.
- Map inputs and outputs and **Submit** on the next page and then run the evaluation.
#### 8. Manual Prompt Evaluation
- Go to **Evaluation** in AI Studio and Create a **Manual evaluations**
- Add questions and expected responses and run evaluations, right here I only add one.
- Input: `How much do the TrailWalker Hiking Shoes cost?`
- Expected response: `The TrailWalker Hiking Shoes are priced at $110.`
- After run, you can provide feedback using thumbs up or down for the response.
#### 9. Deploy the Copilot
- In **Prompt Flow**, click **Deploy** and name the deployment.
- Verify the deployment details and create, finally you will see the created endpoint in below image.
- Finally, click the created endpoint `outlander-ai-project-znsmp-1` and you will see ready-to-use deployment info
- And we can test some question.
🎉Good luck, and enjoy building your custom Azure AI copilot✨!