> For the complete documentation index, see [llms.txt](https://docs.rierino.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rierino.com/devops/api-event-and-process-flows/configuring-saga-steps/event-step/ml-and-ai-actions/perform-sparse-text-embedding.md). # Perform Sparse Text Embedding These actions convert text into sparse (lexical) embeddings for keyword-aware and hybrid search. ## Perform Sparse Text Embedding Actions ### **SparseEmbedText** Transforms text into a sparse embedding. Event metadata fields applicable for this action are as follows: {% tabs %} {% tab title="Fields (table)" %} | Field | Definition | Example | Default | | -------------- | -------------------------------------------------- | ------- | ------- | | Input Element | Json path for the input in request event payload | input | - | | Output Element | Json path for the output in response event payload | output | - | | {% endtab %} | | | | {% tab title="Fields (JSON Schema)" %} ```json { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "eventMeta": { "type": "object", "properties": { "inputElement": { "type": "string", "description": "Json path for the input in request event payload", "examples": ["input"], "default": null }, "outputElement": { "type": "string", "description": "Json path for the output in response event payload", "examples": ["output"], "default": null } } } } } ``` {% endtab %} {% endtabs %} With event metadata parameters as: {% tabs %} {% tab title="Parameters (table)" %} | Parameter | Definition | Example | Default | | ------------- | --------------------------------------------------------------------------------------------------- | ------------------------ | ------- | | Input Pattern | JMESpath pattern to apply on input element for getting text and metadata fields | {text: "", metadata: ""} | - | | Output Format | Format of the sparse vector, `indices` for index/value arrays or `tokens` for a token to weight map | tokens | indices | | Extra Action | Optional extra action after generating embedding, `add` to local index or `search` in local index | search | - | | Max Results | Maximum results to return if `search` extra action is used | 5 | - | | Min Score | Minimum dot-product score required if `search` extra action is used | 2.5 | - | | {% endtab %} | | | | {% tab title="Parameters (JSON Schema)" %} ```json { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "eventMeta": { "type": "object", "properties": { "parameters": { "type": "object", "properties": { "inputPattern": { "type": "string", "description": "JMESpath pattern to apply on input element for getting text and metadata fields", "examples": ["{text: \"\", metadata: \"\"}"], "default": null }, "outputFormat": { "type": "string", "description": "Format of the sparse vector in the output", "enum": ["indices", "tokens"], "examples": ["tokens"], "default": "indices" }, "extraAction": { "type": "string", "description": "Optional extra action to perform after generating embedding", "enum": ["add", "search"], "examples": ["search"], "default": null }, "maxResults": { "type": "integer", "description": "Maximum results to return if search extra action is used", "examples": [5], "default": null }, "minScore": { "type": "number", "description": "Minimum dot-product score required if search extra action is used", "examples": [2.5], "default": null } } } } } } } ``` {% endtab %} {% endtabs %} The input element (after the input pattern is applied) must be an object with a `text` field and an optional `metadata` object. The metadata is stored with the embedding when `add` is used, and returned with search results. The sparse vector is written to `embedding` in the output element: * `indices` format: `{"indices": [1012, 2054, ...], "values": [0.83, 0.41, ...]}` * `tokens` format: `{"refund": 1.42, "payment": 0.87, ...}`, sorted with the highest weight first. This format fits fields such as Elastic `sparse_vector`. With `add`, the ID of the stored embedding is written to `embeddingId`. With `search`, matches are written to `results` as `{score, embeddingId, text, metadata}` entries. {% hint style="info" %} Sparse scores are dot products, so they are non-negative and have no upper bound, unlike the cosine similarity scores of dense embeddings. Set `Min Score` for your model and data rather than on a 0–1 scale. {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.rierino.com/devops/api-event-and-process-flows/configuring-saga-steps/event-step/ml-and-ai-actions/perform-sparse-text-embedding.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.