call-center-ai

Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!

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Call Center AI is an AI-powered call center solution that leverages Azure and OpenAI GPT. It is a proof of concept demonstrating the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI to build an automated call center solution. The project showcases features like accessing claims on a public website, customer conversation history, language change during conversation, bot interaction via phone number, multiple voice tones, lexicon understanding, todo list creation, customizable prompts, content filtering, GPT-4 Turbo for customer requests, specific data schema for claims, documentation database access, SMS report sending, conversation resumption, and more. The system architecture includes components like RAG AI Search, SMS gateway, call gateway, moderation, Cosmos DB, event broker, GPT-4 Turbo, Redis cache, translation service, and more. The tool can be deployed remotely using GitHub Actions and locally with prerequisites like Azure environment setup, configuration file creation, and resource hosting. Advanced usage includes custom training data with AI Search, prompt customization, language customization, moderation level customization, claim data schema customization, OpenAI compatible model usage for the LLM, and Twilio integration for SMS.

README:

Call Center AI

AI-powered call center solution with Azure and OpenAI GPT.

Overview

Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!

Insurance, IT support, customer service, and more. The bot can be customized in few seconds (really) to fit your needs.

# Ask the bot to call a phone number
data='{
  "bot_company": "Contoso",
  "bot_name": "Amélie",
  "phone_number": "+11234567890",
  "task": "Help the customer with their digital workplace. Assistant is working for the IT support department. The objective is to help the customer with their issue and gather information in the claim.",
  "agent_phone_number": "+33612345678",
  "claim": [
    {
      "name": "hardware_info",
      "type": "text"
    },
    {
      "name": "first_seen",
      "type": "datetime"
    },
    {
      "name": "building_location",
      "type": "text"
    }
  ]
}'

curl \
  --header 'Content-Type: application/json' \
  --request POST \
  --url https://xxx/call \
  --data $data

Features

[!NOTE] This project is a proof of concept. It is not intended to be used in production. This demonstrates how can be combined Azure Communication Services, Azure Cognitive Services and Azure OpenAI to build an automated call center solution.

[x] Access the claim on a public website
[x] Access to customer conversation history
[x] Allow user to change the language of the conversation
[x] Assistant can send SMS to the user for futher information
[x] Bot can be called from a phone number
[x] Bot use multiple voice tones (e.g. happy, sad, neutral) to keep the conversation engaging
[x] Company products (= lexicon) can be understood by the bot (e.g. a name of a specific insurance product)
[x] Create by itself a todo list of tasks to complete the claim
[x] Customizable prompts
[x] Disengaging from a human agent when needed
[x] Filter out inappropriate content from the LLM, like profanity or concurrence company names
[x] Fine understanding of the customer request with GPT-4 Turbo
[x] Follow a specific data schema for the claim
[x] Has access to a documentation database (few-shot training / RAG)
[x] Help the user to find the information needed to complete the claim
[x] Jailbreak detection
[x] Lower AI Search cost by usign a Redis cache
[x] Monitoring and tracing with Application Insights
[x] Receive SMS during a conversation for explicit wordings
[x] Responses are streamed from the LLM to the user, to avoid long pauses
[x] Send a SMS report after the call
[x] Take back a conversation after a disengagement
[ ] Call back the user when needed
[ ] Simulate a IVR workflow

Demo

A French demo is avaialble on YouTube. Do not hesitate to watch the demo in x1.5 speed to get a quick overview of the project.

Main interactions shown in the demo:

User calls the call center
The bot answers and the conversation starts
The bot stores conversation, claim and todo list in the database

Extract of the data stored during the call:

{
  "claim": {
    "incident_date_time": "2024-01-11T19:33:41",
    "incident_description": "The vehicle began to travel with a burning smell and the driver pulled over to the side of the freeway.",
    "policy_number": "B01371946",
    "policyholder_phone": "[number masked for the demo]",
    "policyholder_name": "Clémence Lesne",
    "vehicle_info": "Ford Fiesta 2003"
  },
  "reminders": [
    {
      "description": "Check that all the information in Clémence Lesne's file is correct and complete.",
      "due_date_time": "2024-01-18T16:00:00",
      "title": "Check Clémence file"
    }
  ]
}

User report after the call

A report is available at https://[your_domain]/report/[phone_number] (like http://localhost:8080/report/%2B133658471534). It shows the conversation history, claim data and reminders.

High level architecture

---
title: System diagram (C4 model)
---
graph
  user(["User"])
  agent(["Agent"])

  app["Call Center AI"]

  app -- Transfer to --> agent
  app -. Send voice .-> user
  user -- Call --> app

Component level architecture

---
title: Claim AI component diagram (C4 model)
---
graph LR
  agent(["Agent"])
  user(["User"])

  subgraph "Claim AI"
    ada["Embedding\n(ADA)"]
    app["App\n(Functions App)"]
    communication_services["Call & SMS gateway\n(Communication Services)"]
    db[("Conversations and claims\n(Cosmos DB / SQLite)")]
    eventgrid["Broker\n(Event Grid)"]
    gpt["LLM\n(GPT-4o)"]
    queues[("Queues\n(Azure Storage)")]
    redis[("Cache\n(Redis)")]
    search[("RAG\n(AI Search)")]
    sounds[("Sounds\n(Azure Storage)")]
    sst["Speech-to-Text\n(Cognitive Services)"]
    translation["Translation\n(Cognitive Services)"]
    tts["Text-to-Speech\n(Cognitive Services)"]
  end

  app -- Respond with text --> communication_services
  app -- Ask for translation --> translation
  app -- Ask to transfer --> communication_services
  app -- Few-shot training --> search
  app -- Generate completion --> gpt
  app -- Get cached data --> redis
  app -- Save conversation --> db
  app -- Send SMS report --> communication_services
  app -. Watch .-> queues

  communication_services -- Generate voice --> tts
  communication_services -- Load sound --> sounds
  communication_services -- Notifies --> eventgrid
  communication_services -- Send SMS --> user
  communication_services -- Transfer to --> agent
  communication_services -- Transform voice --> sst
  communication_services -. Send voice .-> user

  eventgrid -- Push to --> queues

  search -- Generate embeddings --> ada

  user -- Call --> communication_services

Sequence diagram

sequenceDiagram
    autonumber

    actor Customer
    participant PSTN
    participant Text to Speech
    participant Speech to Text
    actor Human agent
    participant Event Grid
    participant Communication Services
    participant App
    participant Cosmos DB
    participant OpenAI GPT
    participant AI Search

    App->>Event Grid: Subscribe to events
    Customer->>PSTN: Initiate a call
    PSTN->>Communication Services: Forward call
    Communication Services->>Event Grid: New call event
    Event Grid->>App: Send event to event URL (HTTP webhook)
    activate App
    App->>Communication Services: Accept the call and give inbound URL
    deactivate App
    Communication Services->>Speech to Text: Transform speech to text

    Communication Services->>App: Send text to the inbound URL
    activate App
    alt First call
        App->>Communication Services: Send static SSML text
    else Callback
        App->>AI Search: Gather training data
        App->>OpenAI GPT: Ask for a completion
        OpenAI GPT-->>App: Respond (HTTP/2 SSE)
        loop Over buffer
            loop Over multiple tools
                alt Is this a claim data update?
                    App->>Cosmos DB: Update claim data
                else Does the user want the human agent?
                    App->>Communication Services: Send static SSML text
                    App->>Communication Services: Transfer to a human
                    Communication Services->>Human agent: Call the phone number
                else Should we end the call?
                    App->>Communication Services: Send static SSML text
                    App->>Communication Services: End the call
                end
            end
        end
        App->>Cosmos DB: Persist conversation
    end
    deactivate App
    Communication Services->>PSTN: Send voice
    PSTN->>Customer: Forward voice

Remote deployment

Application is hosted by Azure Functions. Code will be pushed automatically make deploy, with after the deployment.

Steps to deploy:

Create a new resource group
- Prefer to use lowercase and no special characters other than dashes (e.g. ccai-customer-a)
Create a Communication Services resource
- Same name as the resource group
- Enable system managed identity
Buy a phone number
- From the Communication Services resource
- Allow inbound and outbound communication
- Enable voice (required) and SMS (optional) capabilities

Create a local config.yaml file

# config.yaml
conversation:
  initiate:
    # Phone number the bot will transfer the call to if customer asks for a human agent
    agent_phone_number: "+33612345678"
    bot_company: Contoso
    bot_name: Amélie
    lang: {}

communication_services:
  # Phone number purshased from Communication Services
  phone_number: "+33612345678"

sms: {}

prompts:
  llm: {}
  tts: {}

Connect to your Azure environment (e.g. az login)
Run deployment automation with make deploy name=my-rg-name
- Wait for the deployment to finish
Create a AI Search resource
- An index named trainings
- A semantic search configuration on the index named default

Get the logs with make logs name=my-rg-name.

Local installation

Prerequisites

Place a file called config.yaml in the root of the project with the following content:

# config.yaml
resources:
  public_url: "https://xxx.blob.core.windows.net/public"

conversation:
  initiate:
    agent_phone_number: "+33612345678"
    bot_company: Contoso
    bot_name: Robert

communication_services:
  access_key: xxx
  call_queue_name: call-33612345678
  endpoint: https://xxx.france.communication.azure.com
  phone_number: "+33612345678"
  post_queue_name: post-33612345678
  sms_queue_name: sms-33612345678

cognitive_service:
  # Must be of type "AI services multi-service account"
  endpoint: https://xxx.cognitiveservices.azure.com

llm:
  fast:
    mode: azure_openai
    azure_openai:
      api_key: xxx
      context: 16385
      deployment: gpt-35-turbo-0125
      endpoint: https://xxx.openai.azure.com
      model: gpt-35-turbo
      streaming: true
  slow:
    mode: azure_openai
    azure_openai:
      api_key: xxx
      context: 128000
      deployment: gpt-4o-2024-05-13
      endpoint: https://xxx.openai.azure.com
      model: gpt-4o
      streaming: true

ai_search:
  access_key: xxx
  endpoint: https://xxx.search.windows.net
  index: trainings

ai_translation:
  access_key: xxx
  endpoint: https://xxx.cognitiveservices.azure.com

To use a Service Principal to authenticate to Azure, you can also add the following in a .env file:

AZURE_CLIENT_ID=xxx
AZURE_CLIENT_SECRET=xxx
AZURE_TENANT_ID=xxx

To override a specific configuration value, you can also use environment variables. For example, to override the llm.fast.endpoint value, you can use the LLM__FAST__ENDPOINT variable:

LLM__FAST__ENDPOINT=https://xxx.openai.azure.com

Then run:

# Install dependencies
make install

Also, a public file server is needed to host the audio files. Upload the files with make copy-resources name=my-rg-name (my-rg-name is the storage account name), or manually.

For your knowledge, this resources folder contains:

Audio files (xxx.wav) to be played during the call
Lexicon file (lexicon.xml) to be used by the bot to understand the company products (note: any change makes up to 15 minutes to be taken into account)

Run

Finally, run:

# Start the local API server
make dev

Debug

Breakpoints can be added in the code to debug the application with your favorite IDE.

Also, local.py script is available to test the application without the need of a phone call (= without Communication Services). Run the script with:

python3 -m tests.local

Advanced usage

Add my custom training data with AI Search

Training data is stored on AI Search to be retrieved by the bot, on demand.

Required index schema:

Field Name	`Type`	Retrievable	Searchable	Dimensions	Vectorizer
answer	`Edm.String`	Yes	Yes
context	`Edm.String`	Yes	Yes
created_at	`Edm.String`	Yes	No
document_synthesis	`Edm.String`	Yes	Yes
file_path	`Edm.String`	Yes	No
id	`Edm.String`	Yes	No
question	`Edm.String`	Yes	Yes
vectors	`Collection(Edm.Single)`	No	Yes	1536	OpenAI ADA

Software to fill the index is included on Synthetic RAG Index repository.

Customize the languages

The bot can be used in multiple languages. It can understand the language the user chose.

See the list of supported languages for the Text-to-Speech service.

# config.yaml
[...]

conversation:
  initiate:
    lang:
      default_short_code: "fr-FR"
      availables:
        - pronunciations_en: ["French", "FR", "France"]
          short_code: "fr-FR"
          voice: "fr-FR-DeniseNeural"
        - pronunciations_en: ["Chinese", "ZH", "China"]
          short_code: "zh-CN"
          voice: "zh-CN-XiaoqiuNeural"

Customize the moderation levels

Levels are defined for each category of Content Safety. The higher the score, the more strict the moderation is, from 0 to 7. Moderation is applied on all bot data, including the web page and the conversation. Configure them in Azure OpenAI Content Filters.

Customize the claim data schema

Customization of the data schema is fully supported. You can add or remove fields as needed, depending on the requirements.

By default, the schema of composed of:

caller_email (email)
caller_name (text)
caller_phone (phone_number)

Values are validated to ensure the data format commit to your schema. They can be either:

datetime
email
phone_number (E164 format)
text

Finally, an optional description can be provided. The description must be short and meaningful, it will be passed to the LLM.

Default schema, for inbound calls, is defined in the configuration:

# config.yaml
[...]

conversation:
  default_initiate:
    claim:
      - name: additional_notes
        type: text
        # description: xxx
      - name: device_info
        type: text
        # description: xxx
      - name: incident_datetime
        type: datetime
        # description: xxx

Claim schema can be customized for each call, by adding the claim field in the POST /call API call.

Customize the call objective

The objective is a description of what the bot will do during the call. It is used to give a context to the LLM. It should be short, meaningful, and written in English.

This solution is priviledged instead of overriding the LLM prompt.

Default task, for inbound calls, is defined in the configuration:

# config.yaml
[...]

conversation:
  initiate:
    task: "Help the customer with their insurance claim. Assistant requires data from the customer to fill the claim. The latest claim data will be given. Assistant role is not over until all the relevant data is gathered."

Task can be customized for each call, by adding the task field in the POST /call API call.

Customize the conversation

Conversation options are documented in conversation.py. The options can all be overridden in config.yaml file:

# config.yaml
[...]

conversation:
  answer_hard_timeout_sec: 180
  answer_soft_timeout_sec: 30
  callback_timeout_hour: 72
  phone_silence_timeout_sec: 1
  slow_llm_for_chat: true
  voice_recognition_retry_max: 2

Use an OpenAI compatible model for the LLM

To use a model compatible with the OpenAI completion API, you need to create an account and get the following information:

API key
Context window size
Endpoint URL
Model name
Streaming capability

Then, add the following in the config.yaml file:

# config.yaml
[...]

llm:
  fast:
    mode: openai
    openai:
      api_key: xxx
      context: 16385
      endpoint: https://api.openai.com
      model: gpt-35-turbo
      streaming: true
  slow:
    mode: openai
    openai:
      api_key: xxx
      context: 128000
      endpoint: https://api.openai.com
      model: gpt-4
      streaming: true

Use Twilio for SMS

To use Twilio for SMS, you need to create an account and get the following information:

Account SID
Auth Token
Phone number

Then, add the following in the config.yaml file:

# config.yaml
[...]

sms:
  mode: twilio
  twilio:
    account_sid: xxx
    auth_token: xxx
    phone_number: "+33612345678"

Customize the prompts

Note that prompt examples contains {xxx} placeholders. These placeholders are replaced by the bot with the corresponding data. For example, {bot_name} is internally replaced by the bot name.

Be sure to write all the TTS prompts in English. This language is used as a pivot language for the conversation translation.

# config.yaml
[...]

prompts:
  tts:
    hello_tpl: |
      Hello, I'm {bot_name}, from {bot_company}! I'm an IT support specialist.

      Here's how I work: when I'm working, you'll hear a little music; then, at the beep, it's your turn to speak. You can speak to me naturally, I'll understand.

      Examples:
      - "I've got a problem with my computer, it won't turn on".
      - "The external screen is flashing, I don't know why".

      What's your problem?
  llm:
    default_system_tpl: |
      Assistant is called {bot_name} and is in a call center for the company {bot_company} as an expert with 20 years of experience in IT service.

      # Context
      Today is {date}. Customer is calling from {phone_number}. Call center number is {bot_phone_number}.
    chat_system_tpl: |
      # Objective
      Provide internal IT support to employees. Assistant requires data from the employee to provide IT support. The assistant's role is not over until the issue is resolved or the request is fulfilled.

      # Rules
      - Answers in {default_lang}, even if the customer speaks another language
      - Cannot talk about any topic other than IT support
      - Is polite, helpful, and professional
      - Rephrase the employee's questions as statements and answer them
      - Use additional context to enhance the conversation with useful details
      - When the employee says a word and then spells out letters, this means that the word is written in the way the employee spelled it (e.g. "I work in Paris PARIS", "My name is John JOHN", "My email is Clemence CLEMENCE at gmail GMAIL dot com COM")
      - You work for {bot_company}, not someone else

      # Required employee data to be gathered by the assistant
      - Department
      - Description of the IT issue or request
      - Employee name
      - Location

      # General process to follow
      1. Gather information to know the employee's identity (e.g. name, department)
      2. Gather details about the IT issue or request to understand the situation (e.g. description, location)
      3. Provide initial troubleshooting steps or solutions
      4. Gather additional information if needed (e.g. error messages, screenshots)
      5. Be proactive and create reminders for follow-up or further assistance

      # Support status
      {claim}

      # Reminders
      {reminders}

Q&A

Why no LLM framework is used?

At the time of development, no LLM framework was available to handle all of these features: streaming capability with multi-tools, backup models on availability issue, callbacks mechanisms in the triggered tools. So, OpenAI SDK is used directly and some algorithms are implemented to handle reliability.

For Tasks:

Click tags to check more tools for each tasks

handle customer calls manage conversation history create todo lists customize conversation prompts filter inappropriate content

For Jobs:

customer service representative ai developer azure developer call center agent it support specialist

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DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

github

: 3.2k

MegaDetector

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

github

: 106

leapfrogai

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

github

: 255

llava-docker

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

github

: 59

carrot

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

github

: 16.9k

TrustLLM

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

github

: 412

AI-YinMei

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.

github

: 529