Cohere Embeddings

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Node Details

• Name: CohereEmbedding_Embeddings
• Type: CohereEmbeddings
• Version: 3.0
• Category: Embeddings

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Base Classes

• CohereEmbeddings
• (Additional base classes from LangChain’s CohereEmbeddings)

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Parameters

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Credential (Required)

• Type: cohereApi
• Required Fields: cohereApiKey

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Inputs

1. Model Name
   • Type: asyncOptions
   • Default: “embed-english-v2.0”
   • Description: The name of the Cohere embedding model to use.
   • Note: Available models are loaded dynamically.
2. Input Type
   • Type: options
   • Default: “search_query”
   • Options:
     • search_document: For encoding documents to store in a vector database for search use-cases.
     • search_query: For querying a vector database to find relevant documents.
     • classification: For using embeddings as input to a text classifier.
     • clustering: For clustering embeddings.
   • Description: Specifies the type of input passed to the model. Required for embedding models v3 and higher.

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Functionality

1. The node first retrieves the necessary credentials (Cohere API key) and input parameters.
2. It then initializes a CohereEmbeddings instance with the provided configuration.
3. The resulting model can be used to generate embeddings for given text inputs.

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Use Cases

1. Semantic Search: Generate embeddings for documents and queries to perform semantic search operations.
2. Text Classification: Create embeddings as input features for text classification tasks.
3. Document Clustering: Generate embeddings to group similar documents together.
4. Information Retrieval: Enhance document retrieval systems by using semantic embeddings.

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Notes

• The node dynamically loads available embedding models specific to Cohere.
• It supports different input types, allowing for optimization based on the specific use case (search, classification, clustering).
• The Cohere API key is required and should be securely stored and accessed.