# tEmbeddingAI Available in... * Cloud Data Fabric * Data Fabric * Qlik Talend Cloud Enterprise Edition * Real-Time Big Data Platform Leverages embedding models to efficiently process data with AI. For a quick introduction on using this component in your Job, see the following video: ## tEmbeddingAI Standard properties These properties are used to configure tEmbeddingAI running in the Standard Job framework. The Standard tEmbeddingAI component belongs to the AI family. ## Basic settings | Properties | Description | | --- | --- | | Schema and Edit schema | A schema is a row description, and
it defines the fields to be processed and passed on to the next component.

Built-In: You create and store the schema locally for this component only.
Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Click Edit schema to make changes to
the schema. If you make changes, the schema automatically becomes built-in.

View schema: choose this option to view the schema only.
Change to built-in property: choose this option to change the schema to Built-in for local changes.
Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion.

If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

If you are using Amazon Bedrock, only the Amazon Titan Text Embedding v1 and v2 models are supported, and the default credentials provider chain authentication method is used, which means the access to the AWS Service can be configured in a chain.
If you are using Google Vertex AI, the Application Default Credentials authentication method is used, which means no additional parameters need to be defined as credentials are automatically found based on the application environment.
If you are using Hugging Face, the server exception "Model thenlper/gte-large is currently loading" might occasionally happen and is due to the fact that the model used is a cold model. The error will disappear at the next retry.
If you are using ONNX, you may get out-of-memory errors as ONNX models are local and therefore memory-consuming. It is recommended to set the JVM parameters to 2GB minimum in the Job run profile.

| | Ali Bailian (DashScope) parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Token/API Key: Click the [...] button next to the field to enter the access token to your Dashscope platform.

You can fill the API server URL in the Base URL
parameter of the Advanced settings. | | Amazon Bedrock parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Region: Click the [...] button next to the field to select one of the Amazon multi-regional locations you want to use.

| | Azure OpenAI parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Token/API Key: Click the [...] button next to the field to enter the access token to your Azure OpenAI platform.
Azure endpoint: Enter the endpoint to access your Azure OpenAI service API.

| | Cohere parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Token/API Key: Click the [...] button next to the field to enter the access token to your Cohere platform.

You can fill the API server URL in the Base URL
parameter of the Advanced settings. | | Google Vertex AI parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Location: Enter one of the Google Cloud multi-regional locations you want to use. See Vertex AI locations for more information.
Google endpoint: Enter the appropriate regional endpoint corresponding to your location. See Vertex AI locations for more information.
Google project ID: Enter your Google Cloud project ID. See Find your Google Cloud project ID for more information.
Example: my-sample-project-370819

| | Hugging Face parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Token/API Key: Click the [...] button next to the field to enter the access token to your Hugging Face platform.

You can fill the API server URL in the Base URL
parameter of the Advanced settings. | | Mistral AI parameters |

Model name: Click the [...] button next to the field to select the embedding model of your choice.
Token/API Key: Click the [...] button next to the field to enter the access token to your Mistral AI platform.

You can fill the API server URL in the Base URL
parameter of the Advanced settings. | | ONNX parameters | Select the Use local ONNX file checkbox to use a local
tokenizer file. Specify the model and tokenizer paths, as well as the pooling
mode to apply. If unselected, the default embedded tokenizer file will be used.

See [Introduction to ONNX](https://onnx.ai/onnx/intro/index.html) for more information. | | Column for embedding | Select or enter the schema column you want to apply the embedding model on. | ## Advanced settings | Properties | Description | | --- | --- | | tStatCatcher Statistics | Select this checkbox to gather the Job processing metadata at the Job level
as well as at each component level. | | Base URL | Type in the base URL address of the API server you want to access.

This
option is available for the Ali Bailian, Cohere, Hugging Face, and Mistral AI
platforms. | ## Global Variables | Variables | Description | | --- | --- | | ERROR\_MESSAGE | The error message generated by the component when an error
occurs. This is an After variable and it returns a string. | | NB\_LINE | The number of rows processed. This is an After variable and
it returns an integer. | ## Usage | Usage guidance | Description | | --- | --- | | Usage rule | This component can be used as a standalone component or as a start component of
a Job or subJob. | **Related tasks** * [Indexing phone reviews with embeddings and vector database](https://help.qlik.com/talend/en-US/components/8.0/ai/tembeddingai-tpineconeclient-tmap-indexing-phone-reviews-with-embeddings-and-vector-db "This Job reads phone review text files from a folder, splits the content into smaller chunks for better analysis, generates vector embeddings using Azure OpenAI, and stores them in a Pinecone vector database to enable semantic search.")