Cohere Embeddings

This component in Nappai uses Cohere’s AI to understand the meaning of text. It converts your text into a special numerical code (an embedding) that computers can easily process. This is crucial for tasks like searching, comparing, and organizing information.

Relationship with Cohere

This component directly interacts with the Cohere API, a service that provides advanced language AI models. It uses these models to create the numerical representations of your text. You’ll need a Cohere account and API key to use this component. Nappai will guide you through the process of connecting your Cohere account.

Inputs

Model: Choose the Cohere model to use. Different models offer different levels of accuracy and speed. The default (“embed-english-v2.0”) is a good starting point for English text.
Credential: This is your Cohere API key. It’s how Nappai connects to your Cohere account to access the AI models. Nappai will securely store this information.
Truncate: (Advanced) This option allows you to shorten very long pieces of text before processing. Leave this blank unless you have very long text that’s causing problems.
Max Retries: (Advanced) This sets the number of times Nappai will try to connect to Cohere if there’s a problem. The default is 3.
User Agent: (Advanced) This identifies Nappai to Cohere. Leave this as the default (“langchain”).
Request Timeout: (Advanced) This sets how long Nappai will wait for a response from Cohere before giving up. Leave this as the default unless you experience significant delays.

Outputs

The component produces Embeddings, which are numerical representations of your input text. These embeddings are then used by other Nappai components, such as the various vector databases, to perform tasks like:

Semantic Search: Finding similar pieces of text based on meaning, not just keywords.
Clustering: Grouping similar pieces of text together.
Recommendation: Suggesting related content.

These embeddings are invisible to you directly; they are used behind the scenes by other components in your Nappai workflow.

Usage Example

Imagine you have a collection of customer reviews. You can use the “Cohere Embeddings” component to convert each review into an embedding. Then, you can use a vector database component (like Pinecone or Weaviate) to store these embeddings. Finally, you can use a search component to quickly find reviews similar to a new customer review.

Templates

This component is used in many Nappai templates, including those involving semantic search and text analysis. The specific templates will be visible within the Nappai interface.

Semantic Text Splitter: This component breaks down large texts into smaller, more manageable chunks before embedding. This is useful for very long documents.
[List of Vector Databases]: These components store and search the embeddings generated by this component. Each offers different features and scalability options.
Text Embedder: This is another embedding component, but it may use different models than Cohere.

Tips and Best Practices

Start with the default “embed-english-v2.0” model unless you have a specific reason to choose a different one.
For very long texts, consider using the “Semantic Text Splitter” component to improve performance and accuracy.
If you encounter errors, check your Cohere API key and ensure you have sufficient quota.

Security Considerations

Always protect your Cohere API key. Never share it publicly or commit it to version control. Nappai securely stores your credentials.