This repository contains the backend and frontend code for the Contoso Financials Assistant. Contoso Financial Assistant caters to below types of queries:

• Contoso financial financial performance in year 2023
• Contoso financial product lines
• Late EMI related queries for paurchases made using Conto Premier Credit Card

• Code Interpreter
• Custom Function to Categorize User Query
• Data file for interest charged on late EMIs
• Data file for info on Contoso Finacial Product portfolio and financial performance

1. Clone the repository.

2. Install the necessary dependencies for the backend using requirements.txt

3. Create a .env file in backend folder and specify values of following variables OPEN_AI_EMBEDDING_ENDPOINT=<your_open_ai_embedding_endpoint> OPEN_AI_EMBEDDING_KEY=<your_open_ai_embedding_key> OPEN_AI_EMBEDDING_DEPLOYMENT_NAME=<your_open_ai_embedding_deployment_name>

   OPEN_AI_ENDPOINT=<your_open_ai_endpoint> OPEN_AI_KEY=<your_open_ai_key> OPEN_AI_DEPLOYMENT_NAME=<your_open_ai_deployment_name>

   SEARCH_ENDPOINT=<your_search_endpoint> SEARCH_KEY=<your_search_key> SEARCH_INDEX_NAME=<your_search_index_name>

   BING_KEY=<your_bing_key> BING_ENDPOINT=<your_bing_endpoint>

1. Create AI Search index for user query categorization by executing get_intent_init.py
2. Start the backend server using command flask run --host 0.0.0.0 --port 5007
3. Launch the frontend application by opening assistant.html file in your browser.
4. Sample conversation: Tell me about performance of contoso financials in 2023. Or I purchased a Washing Machine on EMI Card. My EMI is 1000 Rs for 12 months.
   I missed my last EMI. How much do i pay now?