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@clemlesne
clemlesne committed Jul 29, 2024
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33 changes: 18 additions & 15 deletions Makefile
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@@ -1,7 +1,7 @@
# Versioning
version_full ?= $(shell $(MAKE) --silent version-full)
version_small ?= $(shell $(MAKE) --silent version)
# DevTunnel configuration
# Dev tunnels configuration
tunnel_name := call-center-ai-$(shell hostname | sed 's/[^a-zA-Z0-9]//g' | tr '[:upper:]' '[:lower:]')
tunnel_url ?= $(shell res=$$(devtunnel show $(tunnel_name) | grep -o 'http[s]*://[^"]*' | xargs) && echo $${res%/})
# App location
Expand Down Expand Up @@ -121,6 +121,22 @@ dev:
VERSION=$(version_full) PUBLIC_DOMAIN=$(tunnel_url) func start

deploy:
$(MAKE) deploy-bicep

@echo "💤 Wait 10 secs for output to be available..."
sleep 10

@echo "🛠️ Deploying Function App..."
func azure functionapp publish $(function_app_name) \
--build local \
--build-native-deps \
--python

@echo "🚀 Call Center AI is running on $(app_url)"

@$(MAKE) deploy-post

deploy-bicep:
@echo "👀 Current subscription:"
@az account show --query "{subscriptionId:id, subscriptionName:name, tenantId:tenantId}" --output table

Expand All @@ -137,20 +153,7 @@ deploy:
--template-file bicep/main.bicep \
--name $(name_sanitized)

@echo "💤 Wait 10 secs for output to be available..."
sleep 10

@echo "🛠️ Deploying Function App..."
func azure functionapp publish $(function_app_name) \
--build local \
--build-native-deps \
--python

@echo "🚀 Call Center AI is running on $(app_url)"

@$(MAKE) post-deploy name=$(name_sanitized)

post-deploy:
deploy-post:
@$(MAKE) copy-resources \
name=$(blob_storage_public_name)

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169 changes: 99 additions & 70 deletions README.md
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Expand Up @@ -243,7 +243,7 @@ sequenceDiagram

## Deployment

Some prerequisites are needed to deploy the solution.
### Prerequisites

[Prefer using GitHub Codespaces for a quick start.](https://codespaces.new/microsoft/call-center-ai?quickstart=1) The environment will setup automatically with all the required tools.

Expand All @@ -258,65 +258,97 @@ For other systems, make sure you have the following installed:
- [Azure Functions Core Tools](https://github.com/Azure/azure-functions-core-tools?tab=readme-ov-file#installing)
- [Twilio CLI](https://www.twilio.com/docs/twilio-cli/getting-started/install) (optional)

### Remote (on Azure)
Then, Azure resources are needed:

#### 1. [Create a new resource group](https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/manage-resource-groups-portal)

- Prefer to use lowercase and no special characters other than dashes (e.g. `ccai-customer-a`)

Steps to deploy:
#### 2. [Create a Communication Services resource](https://learn.microsoft.com/en-us/azure/communication-services/quickstarts/create-communication-resource?tabs=linux&pivots=platform-azp)

1. [Create a new resource group](https://learn.microsoft.com/en-us/azure/azure-resource-manager/management/manage-resource-groups-portal)
- Same name as the resource group
- Enable system managed identity

- Prefer to use lowercase and no special characters other than dashes (e.g. `ccai-customer-a`)
#### 3. [Buy a phone number](https://learn.microsoft.com/en-us/azure/communication-services/quickstarts/telephony/get-phone-number?tabs=linux&pivots=platform-azp-new)

2. [Create a Communication Services resource](https://learn.microsoft.com/en-us/azure/communication-services/quickstarts/create-communication-resource?tabs=linux&pivots=platform-azp)
- From the Communication Services resource
- Allow inbound and outbound communication
- Enable voice (required) and SMS (optional) capabilities

- Same name as the resource group
- Enable system managed identity
Now that the prerequisites are configured (local + Azure), the deployment can be done.

### Remote (on Azure)

3. [Buy a phone number](https://learn.microsoft.com/en-us/azure/communication-services/quickstarts/telephony/get-phone-number?tabs=linux&pivots=platform-azp-new)
#### 1. Create the light config file

- From the Communication Services resource
- Allow inbound and outbound communication
- Enable voice (required) and SMS (optional) capabilities
File is named `config.yaml`:

4. Create a local `config.yaml` file
```yaml
# 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: {}
```
```yaml
# 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: {}
#### 2. Connect to your Azure environment
communication_services:
# Phone number purshased from Communication Services
phone_number: "+33612345678"
```zsh
az login
```

sms: {}
#### 3. Run deployment automation

prompts:
llm: {}
tts: {}
```
```zsh
make deploy name=my-rg-name
```

5. Connect to your Azure environment (e.g. `az login`)
6. Run deployment automation with `make deploy name=my-rg-name`
- Wait for the deployment to finish

- Wait for the deployment to finish
#### 4. [Create a AI Search resource](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal)

7. [Create a AI Search resource](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal)
- An index named `trainings`
- A semantic search configuration on the index named `default`

- An index named `trainings`
- A semantic search configuration on the index named `default`
#### 5. Get the logs

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

### Local (on your machine)

#### Prerequisites for local development
#### 1. Create the full config file

> [!TIP]
> To use a Service Principal to authenticate to Azure, you can also add the following in a `.env` file:
>
> ```dotenv
> AZURE_CLIENT_ID=xxx
> AZURE_CLIENT_SECRET=xxx
> AZURE_TENANT_ID=xxx
> ```
Place a file called `config.yaml` in the root of the project with the following content:
> [!TIP]
> If you already deployed the application to Azure and if it is working, you can:
>
> - Copy the configuration from the Azure Function App to your local machine by using the content of the `CONFIG_JSON` application setting
> - Then convert it to YAML format
File is named `config.yaml`:
```yaml
# config.yaml
Expand Down Expand Up @@ -372,52 +404,49 @@ ai_translation:
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:
#### 2. Run the deployment automation

```dotenv
AZURE_CLIENT_ID=xxx
AZURE_CLIENT_SECRET=xxx
AZURE_TENANT_ID=xxx
```zsh
make deploy-bicep deploy-post name=my-rg-name
```

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:
- This will deploy the Azure resources without the API server, allowing you to test the bot locally
- Wait for the deployment to finish

```dotenv
LLM__FAST__ENDPOINT=https://xxx.openai.azure.com
```
#### 3. Initialize local function config

Then run:
Copy `local.example.settings.json` to `local.settings.json`, then fill the required fields:

```bash
# Install dependencies
make install
```
- `APPLICATIONINSIGHTS_CONNECTION_STRING`, as the connection string of the Application Insights resource
- `AzureWebJobsStorage`, as the connection string of the Azure Storage account

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.
#### 4. Connect to Azure Dev tunnels with `devtunnel login`, then run it with `make tunnel`

For your knowledge, this `resources` folder contains:
> [!IMPORTANT]
> Tunnel requires to be run in a separate terminal, because it needs to be running all the time
- Audio files (`xxx.wav`) to be played during the call
- [Lexicon file (`lexicon.xml`)](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-synthesis-markup-pronunciation#custom-lexicon) to be used by the bot to understand the company products (note: any change [makes up to 15 minutes](https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-synthesis-markup-pronunciation#custom-lexicon-file) to be taken into account)
#### 5. Iterate quickly with the code

#### Run
> [!NOTE]
> To override a specific configuration value, you can use environment variables. For example, to override the `llm.fast.endpoint` value, you can use the `LLM__FAST__ENDPOINT` variable:
>
> ```dotenv
> LLM__FAST__ENDPOINT=https://xxx.openai.azure.com
> ```
Finally, run:
> [!NOTE]
> Also, `local.py` script is available to test the application without the need of a phone call (= without Communication Services). Run the script with:
>
> ```bash
> python3 -m tests.local
> ```
```bash
# Start the local API server
```zsh
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:

```bash
python3 -m tests.local
```
- Code is automatically reloaded on file changes, no need to restart the server
- The API server is available at `http://localhost:8080`

## Advanced usage

Expand Down

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