PAC

PAC is a tool for Prioritization and Categorization of support tickets. It enables quick and effortless categorization of support tickets for any kind of product. The main goal of a project is to remove manual human labor and automate the process. It reaches the goal using Vector Semantic Search and LLM function calling.

Quick explanation of the logic behind it is as follows: support ticket data is received by PAC using Kafka topic. PAC first vectorizes ticket data and searches similar vectors in Vector DB using COSINE similarity. In the resulting search result list it takes the most similar and checks the distance between what's given and most similar vector from Vector DB, if the distance is greater than some specific threshold, then the received ticket will be assigned the same category and priority. If the search and threshold check failed, then request to LLM is made, which should return category and priority for the ticket. After the ticket is assigned with priority and category it is inserted into Vector DB. The same process is applied for all the other incoming tickets. This approach saves up costs for LLM requests by first checking the Vector DB and if there is no similar enough ticket, only then it makes the request.

PAC also generates a response event with original ticket data and priority and category and sends it to output topic. So that this event can be further sent to data lake or other storage for later BI or other type of analysis.

In case if priority or category of a certain ticket was assigned incorrectly, there is an API so that correct priority or category can be assigned manually. If such case happens, app sends separate correction event to a separate topic, so that it will be taken to account during analysis.

Process Flow

flowchart TB
    A[Support Ticket] -->|Received via Kafka Topic| B[Text Normalization]
    B --> C[Request to LLM for Vectorization]
    C -->|Vector Embedding| D{Vector DB Search}

    D -->|If match| E[Check Distance]
    E -->|Below Threshold| F[Assign Category & Priority]
    E -->|Above Threshold| G[LLM Function Call for Priority and Category]
    G --> F
    D -->|No match| G
    
    F -->|Insert into Vector DB| H[Vector DB]
    F --> I[Generate Response Event]
    I -->|Send to Output Topic| J[Data Lake / Storage]
    
    K[Manual API Correction] -.->|If needed| F
    K -->|Correction Event| L[Corrected Tickets Topic]

    style A fill:#4f77f6,stroke:#333,stroke-width:2px
    style B fill:#ffcf33,stroke:#333,stroke-width:4px
    style C fill:#7fd3a4,stroke:#333,stroke-width:4px
    style D fill:#4095c6,stroke:#333,stroke-width:2px
    style E fill:#f98b88,stroke:#333,stroke-width:2px
    style F fill:#8bc34a,stroke:#333,stroke-width:2px
    style G fill:#f06292,stroke:#333,stroke-width:2px
    style H fill:#795548,stroke:#333,stroke-width:2px
    style I fill:#64b5f6,stroke:#333,stroke-width:2px
    style J fill:#ba68c8,stroke:#333,stroke-width:2px
    style K fill:#ffeb3b,stroke:#333,stroke-width:2px
    style L fill:#e91e63,stroke:#333,stroke-width:2px

Tech Stack

• Python 3.10
• Milvus
• Kafka and Zookeeper
• Docker
• OpenAI

Components

1. Text Normalizer

   • Removes Noise: Strips out irrelevant characters.
   • Standardizes: Converts all characters to lowercase to ensure consistency.
   • Anonymizes: Replaces names, email adresses, phone numbers, and any other user-specific data with generic placeholders.
   • Normalizes URLs and Paths: Converts URLs, file paths, or specific codes to generic placeholders or remove them if they are not relevant to the understanding of the ticket.

2. Vectorizer: creates a vector embedding from given text.

3. Vector DB Repository

   • Searches Similar Tickets
   • Inserts into Vector DB
   • Updates Record in Vector DB
   • Removes Record from Vector DB
   • Gets a Record by ID from Vector DB

4. PAC: given a ticket prioritizes and categorizes it to be one of available categories.

5. Updater: corrects already PACed ticket with given priority and category.

Getting Started

This section provides instructions on how to set up and run the project using Poetry as the package manager.

Prerequisites

Ensure you have Docker and Poetry installed on your system. These tools are required to run the services and the application.

Setup and Running Services

Start Milvus

To start the Milvus database, run the following command:

make start-milvus

Start Kafka

To start Kafka for message queuing, execute:

make start-kafka

Install Dependencies

Install the project dependencies using Poetry:

poetry install

Create Vector Database Collection

Before running the application, ensure to create the vector database collection with:

make create-collection

Run the Application

Start the FastAPI application with the following command:

make run

Testing Utilities

Create Input Topic

You can create a Kafka topic for input tickets by running:

make create-input-topic CONTAINER_ID=<your_kafka_container_id>

Write to Input Topic

To send a test ticket to the input topic, use:

make write-to-input-topic CONTAINER_ID=<your_kafka_container_id>

Then, input your test ticket JSON data, for example:

{"id": 123, "email": "test@test.com", "text": "peripherals you sent me are not working. i wanna return them today"}

Monitor Processed Tickets

To monitor processed tickets:

make monitor-processed-tickets CONTAINER_ID=<your_kafka_container_id>

Monitor Corrected Tickets

For monitoring corrected tickets:

make monitor-corrected-tickets CONTAINER_ID=<your_kafka_container_id>

Stopping Services

To stop the services, use the following commands:

Stop Kafka:

make stop-kafka

Stop Milvus:

make stop-milvus

API Documentation

For detailed API documentation, visit the FastAPI generated API documentation once the application is running on http://127.0.0.1:8000/docs#/