# Jina Embeddings Blocks for generating text embeddings using Jina AI. ## Jina Embedding ### What it is Generates embeddings using Jina AI ### How it works This block generates vector embeddings for text using Jina AI's embedding models. Embeddings are numerical representations that capture semantic meaning, enabling similarity search and clustering. Optionally specify which Jina model to use for embedding generation. ### Inputs | Input | Description | Type | Required | | ----- | --------------------------- | ---------- | -------- | | texts | List of texts to embed | List\[Any] | Yes | | model | Jina embedding model to use | str | No | ### Outputs | Output | Description | Type | | ---------- | ------------------------------------- | ---------- | | error | Error message if the operation failed | str | | embeddings | List of embeddings | List\[Any] | ### Possible use case **Semantic Search**: Generate embeddings to enable semantic similarity search over documents. **Vector Database**: Create embeddings for storage in vector databases like Pinecone or Weaviate. **Document Clustering**: Embed documents to cluster similar content or find related items. *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://agpt.co/docs/integrations/block-integrations/embeddings.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.