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 .env.example file.

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

2. Azure OpenAI

3. Ollama

These LLMs can be configured with the following env variables:

OpenAI

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

Azure OpenAI

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

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

If no env variables are specified, the service will throw the runtime exception.

For more information, Please refer to LLM 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

2. AWS

3. GCP

OCI (Oracle)

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)

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)

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

If no env variables are specified, the service will throw the runtime exception.

Translation

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

1. Bhashini Dhruva

2. Ekstep Dhruva

3. Google

Bhashini Dhruva

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

Ekstep Dhruva

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

Google

TRANSLATION_TYPE=google
GCP_CONFIG_PATH=<gcp_application_credential_path>

If no env variables are specified, the service will throw the runtime exception.

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

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>

If no env variables are specified, the service will throw the runtime exception.

For more information, Please refer to Vector Store page.