Adding CQA Retriever
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CQA_Retriever/_studio_dependencies/README.md
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CQA_Retriever/_studio_dependencies/README.md
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# CQA Retriever
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This package provides an example RAG process using Azure AI Search for Retrieval and the Verint DaVinci Contextual Question Answer (CQA) Service.
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## What's New: Updated [24/05/2025]
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- Initial Release
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## Setup Instructions
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### Prerequisites
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- This package requires the CQA Widget to be installed and configured.
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### Installation
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1. Copy the CQA_Retriever and CQA_RetieverSettings files into **Global Variables**
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2. _Optional:_ Import the Example Conversation Flow and Intent
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### Configuration
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#### CQA_RetieverSettings
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Fill out the settings below.
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```json
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{
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"azure_aisearch": {
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"endpoint": "https://iva-demo-vector-service.search.windows.net",
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"key": "<azure_aisearch_key>",
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"index_name": "iva-vector-demo"
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},
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"azure_openai_api": {
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"key": "<azure_openai_key>",
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"instance_name": "iva-open-ai",
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"deployment_name": "text-embedding-3-small",
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"embeddings_deployment_name": null,
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"version": "2024-08-01-preview"
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},
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"filterExpression": "search.in(company, 'Verint')",
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"debug": true
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}
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```
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## Package Content Details
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### CQA_RetieverSettings
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A global variable JSON object with environment-specific settings. See above for details.
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### CQA_Retriever
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A global variable function which handles the logic and API calls used to retrieve documents for Context.
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### Example code block that should be using in your Conversation Flows
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This is included in the Example Conversation Flow if you have imported that.
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```javascript
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(async () => {
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console.log(`CQA Retrieval: ${conversationData.cqa_question}`);
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conversationData.cqa_source = await CQA_Retriever().retrieve(
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conversationData.cqa_question
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);
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})()
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.catch((error) => {
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console.log(error.message);
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recognizedObject.answers.push(error.message);
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recognizedObject.errorInfo = {
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...recognizedObject.errorInfo,
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label: {
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data: error.toJSON ? error.toJSON() : {},
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message: error.message,
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},
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};
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})
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.finally(() => {
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next(0);
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});
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```
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