Embedding

The Embedding controller convert the text into high-dimensional vector representation. The vector capture the meaning of words, or sentences, enabling various NLP tasks like:

• Semantic search: Find relevant results based on meaning, not just keywords.
• Text clustering: Group similar texts automatically.
• Similarity comparison: Measure how similar two pieces of text are.

Supported Providers

Choose from different AI providers for embedding generation: openai, mistral, gemini.

Parameters

You need to specify several parameters to tailor the embedding generation to your requirements:

• provider_name: The name of the AI service provider (openai, mistral, or gemini).
• api_key: Your API key or authentication token for the chosen provider.
• texts: A list of texts for which you want to generate embeddings. Each item in the list can be a word, sentence, or paragraph.
• model (optional): The specific model you wish to use for embedding generation.

Example

Initialize the Embedding controller with your chosen provider and API key:

from intelli.controller.remote_embed_model import RemoteEmbedModel
embed_model = RemoteEmbedModel(provider_name="openai", api_key="your_openai_api_key_here")

Generate embeddings for your texts by creating an EmbedInput instance and passing it to the controller's get_embeddings method:

from intelli.model.input.embed_input import EmbedInput

# define the texts to generate embeddings
texts = ["This is a test sentence for embeddings.", "Exploring the capabilities of AI models."]

# create an EmbedInput instance
embed_input = EmbedInput(texts)

# generate embeddings
embeddings = embed_model.get_embeddings(embed_input)