[Embedding] generate from text

The Embedding generate from text operation create numeric vectors from a text (without ingestion into the vector database).

Input Fields

XML Configuration

Below is the XML configuration for this operation:

<
  vectors:embedding-generate-from-text 
  doc:name="Embedding generate from text" 
  doc:id="eacf5862-2b9a-4602-b7d7-0358885986c3" 
  config-ref="<YOUR_CONFIG>" 
  textToAdd="#[payload.text]"
/>

Output Field

This operation responds with a json payload.

Example Output

This output has been converted to JSON.

{
    "Embedding": "Embedding { vector = [0.0064053377, ..., -0.01793744] }",
    "Dimension": 1536,
    "Segment": "TextSegment { text = \"The capital of Switzerland is Bern\" metadata = {} }"
}

• Embedding: The vector embeddings of the input Text.
• Dimension: The dimension of the selected embedding model.
• Segment: The text segment of the input Text.

[Embedding] Add Document to Store

The Embedding add document to store operation adds a document into an embedding store and exports it to a file. The document is ingested into external vector databases.

Input Fields

XML Configuration

Below is the XML configuration for this operation:

<
  vectors:embedding-add-document-to-store 
  doc:name="Embedding add document to store" 
  doc:id="8c0ab3e2-7085-4cb9-bf55-9eb1bee6d7f4" 
  config-ref="<YOUR_CONFIG>" storeName="mschainaicollection" 
  contextPath="#[payload.path]" 
  maxSegmentSizeInChars="1000"
  maxOverlapSizeInChars="200" 
  fileType="#[payload.fileType]"
/>
 

Output Field

This operation responds with a json payload.

Example Output

This output has been converted to JSON.

{
    "filePath": "https://mac-project.ai/",
    "storeName": "gettingstarted",
    "fileType": "url",
    "status": "updated"
}

• filePath: The file path or url of the document to be extracted.
• storeName: The name of the vector store.
• fileType: The file type selected on the operation.
• status: The status of the operation.

[Embedding] Add Folder to Store

The Embedding add folder to store operation adds a complete folder into an embedding store. The documents are ingested into external vector databases.

Input Fields

XML Configuration

Below is the XML configuration for this operation:

<
  vectors:embedding-add-folder-to-store 
  doc:name="Embedding add folder to store" 
  doc:id="1b39985e-9804-4ddf-b730-5a0b3450c198" 
  config-ref="<YOUR_CONFIG>" 
  storeName="mschainaicollection"
  maxSegmentSizeInChars="1000" 
  maxOverlapSizeInChars="200" 
  folderPath="#[payload.path]"
/>
 
 

Output Field

This operation responds with a json payload.

Example Output

This output has been converted to JSON.

{
    "folderPath": "/Users/amir.khan/Documents/Downloads/patch 8",
    "filesCount": 6,
    "storeName": "gettingstarted",
    "status": "updated"
}

• folderPath: The folder path to the files to be ingested.
• storeName: The name of the vector store.
• filesCount: The number of files ingested.
• status: The status of the operation.

[Embedding] Add Text to Store

The Embedding add text to store operation adds a text into an embedding store.

Input Fields

XML Configuration

Below is the XML configuration for this operation:

<
  vectors:embedding-add-text-to-store 
  doc:name="Embedding add text to store" 
  doc:id="9ab96d9c-51fd-4c3e-9669-3de4624fa2c0" 
  config-ref="<YOUR_CONFIG>" 
  storeName="mschainaicollection" 
  textToAdd="#[payload.text]"
/>
 

Output Field

This operation responds with a json payload.

Example Output

This output has been converted to JSON.

{
    "textSegment": "TextSegment { text = \"The capital of Switzerland is Bern\" metadata = {} }",
    "textEmbedding": "Embedding { vector = [0.0064053377, .[..]., -0.01793744] }",
    "storeName": "gettingstarted",
    "status": "added"
}

• textSegment: The text segment of the request.
• textEmbedding: The vectorised embeddings for the text segment.
• storeName: The name of the vector store.
• status: The status of the operation.

[Embedding] Query from Store

The Embedding query from store operation retrieve information based on a plain text prompt from an embedding store.

Input Fields

XML Configuration

Below is the XML configuration for this operation:

<vectors:embedding-get-info-from-store 
  doc:name="Embedding get info from store" 
  doc:id="addc46ed-0f89-4ae4-85e3-4be6445450eb" 
  config-ref="MAC_AI_Llm_configuration" 
  storeName='#[mule.home ++ "/apps/" ++ app.name ++ "/knowledge-center.store"]' 
  data="#[payload.prompt]"
/>

Output Field

This operation responds with a json payload.

Example Output

This output has been converted to JSON.

{
    "question": "Tell me more about Cloudhub High Availability Feature",
    "sources": [
        {
            "absoluteDirectoryPath": "/Users/<user>/Documents/Downloads/patch 8",
            "textSegment": "= CloudHub High Availability Features\nifndef::env-site,env-github[]\ninclude::_attributes.adoc[]\nendif::[]\n:page-aliases: runtime-manager::cloudhub-fabric.adoc,\....\n\n== Worker Scale-out",
            "individualScore": 0.9282029356714594,
            "file_name": "docs-runtime-manager__cloudhub_modules_ROOT_pages_cloudhub-fabric.adoc",
            "full_path": "/Users/<user>/Documents/Downloads/patch 8docs-runtime-manager__cloudhub_modules_ROOT_pages_cloudhub-fabric.adoc"
        },
        {
          ...
        },
        {
          ...
        }
    ],
    "response": "= CloudHub High Availability Features\.. (...) \..distributes HTTP requests among your assigned workers.\n. Persistent message queues (see below)",
    "maxResults": 3,
    "storeName": "gettingstarted",
    "minimumScore": 0.7
}

• question: The question of the request.
• sources: The sources identified by the similarity search.
  • absoluteDirectoryPath: The full path to the file which contains relevant text segment.
  • textSegment: The relevant text segment.
  • individualScore: The score of the similarity search based on the question.
  • file_name: The name of the file, where the text segment was found.
  • full_path: The full path to the file.
• response: The collected response of all relevant text segment. This is the response will is sent to the LLM.
• maxResults: The maximum number of text segments considered.
• storeName: The name of the vector store.
• minimumScore: The minimum score for the result.

Example Use Cases

This operation can be particularly useful in scenarios such as:

• Knowledge Management Systems: Adding new documents to an organizational knowledge base.
• Customer Support: Storing customer interaction documents for quick retrieval and analysis.
• Content Management: Ingesting various types of documents (text, PDF, URL) into a centralized repository for easy access and searchability.