# Environment Variables

Sakhi API service supports different environment variables to configure your instance. You can specify the following variables in the `.env` file inside the `root` folder. Refer to the [<mark style="color:purple;">**.env.example**</mark>](https://github.com/Sunbird-AIAssistant/sakhi-api-service) file.<br>

| Variable                 | Description                                            | Type                                  | Default      |
| ------------------------ | ------------------------------------------------------ | ------------------------------------- | ------------ |
| SERVICE\_ENVIRONMENT     | Name of the application instance                       | String                                | `dev`        |
| LOG\_LEVEL               | It's used to define  the level of logs to be captured. | Enum String: `error`, `info`, `debug` | `info`       |
| CONFIG\_INI\_PATH        | Path of config.ini file                                | String                                | `config.ini` |
| REDIS\_HOST              | Redis host URL                                         | String                                | `localhost`  |
| REDIS\_PORT              | Redis port number                                      | Number                                | `6379`       |
| REDIS\_DB                | Redis index                                            | Number                                | `0`          |
| TELEMETRY\_ENDPOINT\_URL | Telemetry service host URL                             | String                                | -            |
| TELEMETRY\_LOG\_ENABLED  | Used to enable/disable telemetry logging.              | Enum String: `true`, `false`          | `true`       |

### LLM

A chat model is a language model that uses chat messages as inputs and returns chat messages as outputs.\
Sakhi API service currently supports 3 LLM types:

1. [OpenAI](#openai)
2. [Azure OpenAI](#azure-openai)
3. [Ollama](#ollama)

These LLMs can be configured with the following env variables:

#### OpenAI

```markdown
LLM_TYPE=openai
OPENAI_API_KEY=<openai_key>
GPT_MODEL=<model_name>
```

#### **Azure OpenAI**

```markdown
LLM_TYPE=azure
AZURE_OPENAI_ENDPOINT=<azure_openai_endpoint_url>
AZURE_OPENAI_API_KEY=<azure_openai_key>
OPENAI_API_VERSION=<azure_openai_api_version>
AZURE_MODEL=<azure_deployment_model>
```

#### **Ollama**

```markdown
LLM_TYPE=ollama
OLLAMA_API_ENDPOINT=<ollama_endpoint_url>
LLM_MODEL=<model_name>
```

{% hint style="info" %}
If no env variables are specified, the service will throw the runtime exception.
{% endhint %}

For more information, Please refer to [LLM](https://ai-assistant.sunbird.org/components/sakhi-api-service/pluggability-of-llm-chat-model) page.

### Storage

Storage is used for storing audio files when the user wants output as audio. Users should specify a configuration option, like BUCKET\_TYPE, to choose between different cloud provider services such as AWS S3, OCI, or GCP.

Here's the list of storage available to use in the Sakhi API Service:

1. [OCI](#oci-oracle)
2. [AWS](#aws-amazon)
3. [GCP](#gcp-google)

#### OCI (Oracle)

```markdown
BUCKET_TYPE=oci
BUCKET_ENDPOINT_URL=<oci_bucket_endpoint_url>
BUCKET_REGION_NAME=<oci_bucket_region_name>
BUCKET_NAME=<oci_bucket_name>
BUCKET_SECRET_ACCESS_KEY=<oci_bucket_access_key_id>
BUCKET_ACCESS_KEY_ID=<oci_bucket_access_key_id>
```

#### **AWS (Amazon)**

```markdown
BUCKET_TYPE=aws
BUCKET_REGION_NAME=<aws_bucket_region_name>
BUCKET_NAME=<aws_bucket_name>
BUCKET_SECRET_ACCESS_KEY=<aws_bucket_secret_access_key>
BUCKET_ACCESS_KEY_ID=<aws_bucket_access_key_id>
```

#### **GCP (Google)**

```markdown
BUCKET_TYPE=gcp
BUCKET_NAME=<gcp_bucket_name>
GCP_CONFIG_PATH=<gcp_application_credential_path>
```

{% hint style="info" %}
If no env variables are specified, the service will throw the runtime exception.
{% endhint %}

### Translation

Sakhi API service currently supports 3 translation services and can be configured with the following env variables:

1. [Bhashini Dhruva](#bhashini-dhruva)
2. [Ekstep Dhruva](#ekstep-dhruva)
3. [Google ](#google)

#### **Bhashini Dhruva**

```markdown
TRANSLATION_TYPE=bhashini
BHASHINI_ENDPOINT_URL=<bhashini_api_endpoint_url>
BHASHINI_API_KEY=<bhashini_api_key>
```

#### **Ekstep Dhruva**

```markdown
TRANSLATION_TYPE=dhruva
BHASHINI_ENDPOINT_URL=<bhashini_api_endpoint_url>
BHASHINI_API_KEY=<bhashini_api_key>
```

#### **Google**

```markdown
TRANSLATION_TYPE=google
GCP_CONFIG_PATH=<gcp_application_credential_path>
```

{% hint style="info" %}
If no env variables are specified, the service will throw the runtime exception.
{% endhint %}

### Vector Store

A vector store or vector database refers to a type of database system that specializes in storing and retrieving high-dimensional numerical vectors. These stores are designed for efficient management and indexing of vectors, enabling fast similarity searches.

Here is the list of vector stores/databases available to use in the Sakhi API Service:

1. [Marqo](#marqo)

#### **Marqo**

```shellscript
VECTOR_STORE_TYPE=marqo
VECTOR_STORE_ENDPOINT=http://localhost:8882
EMBEDDING_MODEL=flax-sentence-embeddings/all_datasets_v4_mpnet-base
VECTOR_COLLECTION_NAME=<vector_collection_name>
```

{% hint style="info" %}
If no env variables are specified, the service will throw the runtime exception.
{% endhint %}

For more information, Please refer to [Vector Store](https://ai-assistant.sunbird.org/components/sakhi-api-service/pluggability-of-vector-store) page.