Add support for Embedding Tasks

### Issue 1
## Summary
> Wrap LiteLLM to support both Text and Image Embedding Models.

## Pain Points
> What challenges are users encountering without this feature?

1. Users may be limited to handling only one type of embedding model, either text or image, which restricts the versatility of their applications.
2. The lack of unified support for both model types may lead to inefficient workflows and integration issues.

## Current Workarounds
> Have you come across any alternatives or tried any workarounds?

1. Users might have to implement separate systems or tools for text and image embeddings, which can be complex and cumbersome.
2. Custom code might be used to bridge the gap between text and image embeddings, but this could result in inconsistent performance.

## Solution
> What solution would you like to see?

1. Wrap LiteLLM to provide seamless support for both text and image embedding models, simplifying integration and enhancing versatility.

## References
> Have you seen similar solutions or examples elsewhere?

1. Look into existing frameworks or libraries that offer support for both text and image embeddings for best practices and implementation guidance.

## Additional Notes
> Is there anything else you’d like us to know?

1. Integrating support for both embedding types will improve the functionality and usability of LiteLLM.

## Contribution
- [ ] I’d love help with this if needed!

A issue menciona encapsular o LiteLLM para suportar modelos de embedding de texto e imagem. Primeiro, examine a integração existente do LiteLLM na base de código, provavelmente em um módulo como `cyyrus/llm/` ou `cyyrus/integrations/`. Procure como os embeddings de texto são atualmente tratados. Em seguida, pesquise a API de embedding do LiteLLM e seus recursos de embedding de imagem. 'Concluído' significa que o wrapper pode aceitar entradas de texto e imagem e retornar embeddings, com testes adicionados.

Escrita pelo modelo de indexação a partir do texto da issue.