--- schema_version: "1" id: 6ff705813c8662c74a3f2a414a16654a896c20780930af2f836a955429345285 title: Configuring Store Operations collection: mulesoft version: current locale: en-us portal_url: https://docs.salesforce.com/mulesoft/current/en-us/mulesoft-vectors-connector/1.0/configuring-store-operations.md canonical_url: https://docs.mulesoft.com/mulesoft-vectors-connector/1.0/configuring-store-operations source_site: docs.mulesoft.com content_type: documentation content_hash: 6a175017f5724431243a6d4900a6d6a34663d40e594c2d49cfbeedb88f04e2d4 description: MuleSoft Documentation Site release: "1.0" latest: false --- --- product: MuleSoft Vectors Connector version: 1.0 --- # Configuring Store Operations > For the full documentation index, see: https://docs.mulesoft.com/llms.txt Configure the **\[Store\] Add**, **\[Store\] Query**, **\[Store\] Query All**, and **\[Store\] Remove** operations. ## Configure the Store Add Operation The **\[Store\] Add** operation adds a document or text into an embedding store. The **\[Store\] Add** operation must be preceded by the **\[Embedding\] Generate from text** operation to ingest the text into a vector store. To configure the **\[Store\] Add** operation: 1. Select the operation on the Anypoint Code Builder or Studio canvas. 2. In the **General** properties tab for the operation, enter these values: - **Store Name** Enter the name of the collection in the external vector database. - **Text Segments and Embeddings** Enter the text segments and embeddings to ingest into the vector database. It is typically the output of the **\[Embedding\] Generate from text** operation. - **Metadata entries** Enter a list-item (metadata entry), such as key (custom metadata key) and value (custom metadata value). This is the XML for this operation: ```xml ``` ### Output Configuration This operation responds with a JSON payload. This is an example response: ```json { "sourceId": "af44c7ef-4562-4712-af09-4498fc7f29a2", "embeddingIds": [ "81f257c6-6406-4936-8c22-0ae523cce5fd", "2127ef9b-08f4-4bfc-b769-1f488cdbf835", "639e9994-f406-4481-a08a-0058ed3d781e" ], "status": "updated" } ``` - `sourceId`: Unique identifier for the source document. - `embeddingIds`: List of unique identifiers for the embeddings added to the store. - `status`: Status of the operation. The operation also returns other attributes: - `storeName`: Name of the vector store collection. ## Configure the Store Query Operation The **\[Store\] Query** operation retrieves information from the embedding store based on an embedding (previously generated from a text prompt) and optionally a filter on metadata. It can be used for: - **Knowledge Management Systems** Retrieving documents from 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. The **\[Store\] Query** operation can be preceded by the **\[Embedding\] Generate from text** operation. The plain text to use when querying the store is first processed by the **\[Embedding\] Generate from text** operation that generates an embedding that can be used to perform the actual query and represents the input for the **\[Store\] Query** operation. > [!NOTE] When generating an embedding from text for query purposes, don’t provide any segmentation > fields. Leave the **Max Segment Size (Characters)** and **Max Overlap Size (Characters)** fields > blank. To configure the **\[Store\] Query** operation: 1. Select the operation on the Anypoint Code Builder or Studio canvas. 2. In the **General** properties tab for the operation, enter these values: - **Store Name** Enter the name of the collection in the external vector database. - **Text Segments and Embeddings** Enter the text segments and embeddings to query into the vector database. It is typically the output of the **\[Embedding\] Generate from text** operation. Text segments and embeddings must have only one element. - **Max Results** Enter the maximum number of results to query back. - **Min Score** Enter the minimum score for the similarity search (0-1). - **Metadata Condition** Enter the condition used for filtering results based on metadata. It supports SQL-like syntax. - Comparison operators are `=`, `!=`, `<`, `⇐`, `>`, and `>=`. - Special operators: - `CONTAINS(field_name, 'value')` - Check if the field contains the value. - Logical operators are `AND` and `OR`. Here is an example: `index=1 AND (CONTAINS(file_name,'example.pdf') OR file_type='any')` > [!NOTE] Both `CONTAINS(field_name, 'value')` and `field_name LIKE '%value%'` work most of > the time, but the behavior might differ for each store provider. For example, for Azure AI > Search, it maps to `search.ismatch('value', field_name)`. This is the XML for this operation: ```xml ``` ### Output Configuration This operation responds with a JSON payload. This is an example response: ```json { "question": "Tell me more about Cloudhub High Availability Feature", "sources": [ { "embeddingId": "", "text": "= 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", "score": 0.9282029356714594, "metadata": { "source_Id": "c426a871-1a6e-4a47-a8ab-027eec9303e1", "index": "0" "absolute_directory_path": "/Users//Documents/Downloads/patch 8", "file_name": "docs-runtime-manager__cloudhub_modules_ROOT_pages_cloudhub-fabric.adoc", "full_path": "/Users//Documents/Downloads/patch 8docs-runtime-manager__cloudhub_modules_ROOT_pages_cloudhub-fabric.adoc", "file_type": "any", "ingestion_datetime": "2024-11-20T20:34:41.691Z", "ingestion_timestamp": "1732134881691" } }, { ... }, { ... } ] "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. - `embeddingId`: The embedding UUID. - `text`: The relevant text segment. - `score`: The score of the similarity search based on the question. - `metadata`: The metadata key-value pairs. - `source_id`: The UUID for the uploaded data source. - `index`: The segment or chunk number for the uploaded data source. - `absolute_directory_path`: The full path to the file that contains relevant text segment. - `file_name`: The name of the file, in which the text segment is found. - `full_path`: The full path to the file. - `file_Type`: The file type. - `ingestion_datetime`: The ingestion date and time in ISO 8601 format (UTC). - `ingestion_timestamp`: The ingestion time in milliseconds. - `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. The operation also returns other attributes: - `storeName`: Name of the vector store collection. - `metadataCondition` (Optional): Filter condition used to query embeddings. ## Configure the Store Query All Operation The **\[Store\] Query All** operation lists all sources into the embedding store. To configure the **\[Store\] Query All** operation: 1. Select the operation on the Anypoint Code Builder or Studio canvas. 2. In the **General** properties tab for the operation, enter these values: - **Store Name** Enter the name of the collection in the external vector database. - **Retrieve Embeddings** If true, retrieve embeddings from the store. > [!NOTE] When querying the store along with embeddings using Azure AI Search, the connector > might return the > `Invalid expression: 'content_vector' is not a retrievable field. Only fields marked as retrievable in the index can be used in $select.\r\nParameter name: $select` > error. To resolve the issue, set \`content\_vector as a retrievable field. - **Page Size** Enter the page size to use when querying the store. This is the XML for this operation: ```xml ``` ### Output Configuration This operation responds with a JSON payload. This is an example response: ```json [ { "embeddingId": "81f257c6-6406-4936-8c22-0ae523cce5fd", "text": "E-commerce giants like Amazon and Alibaba have redefined ..", "metadata": { "index": "0", "source": "s3://ms-vectors/invoicesample.pdf", "file_type": "any", "file_name": "invoicesample.pdf" ... }, "embeddings": [-0.00683132, -0.0033572172, 0.02698761, -0.01291587, ...] } ] ``` - `embeddingId`: The embedding UUID. - `text`: The relevant text segment. - `metadata`: The metadata key-value pairs. - `index`: The segment or chunk number for the uploaded data source. - `source`: The source of the text segment. - `file_type`: The file type. - `file_name`: The name of the file, in which the text segment is found. - `embeddings`: The embeddings for the text segment. The operation also returns other attributes: - `storeName`: Name of the vector store collection. ## Configure the Store Remove Operation The **\[Store\] Remove** operation removes all embeddings from the store based on a metadata filter. To configure the **\[Store\] Remove** operation: 1. Select the operation on the Anypoint Code Builder or Studio canvas. 2. In the **General** properties tab for the operation, enter these values: - **Store Name** Enter the name of the collection in the external vector database. - **Ids** Enter the list of IDs to delete. - **Metadata Condition** Enter the condition used for filtering results based on metadata. It supports SQL-like syntax. - Comparison operators are `=`, `!=`, `<`, `⇐`, `>`, and `>=`. - Special operators: - `CONTAINS(field_name, 'value')` - Check if the field contains the value. - Logical operators are `AND` and `OR`. Here is an example: `index=1 AND (CONTAINS(file_name,'example.pdf') OR file_type='any')` > [!NOTE] Both `CONTAINS(field_name, 'value')` and `field_name LIKE '%value%'` work most of > the time, but the behavior might differ for each store provider. For example, for Azure AI > Search, it maps to `search.ismatch('value', field_name)`. This is the XML for this operation: ```xml ``` ### Output Configuration This operation responds with a JSON payload. This is an example response: ```json { "status": "deleted" } ``` - `status`: Status of the operation. The operation also returns other attributes: - `storeName`: Name of the vector store collection. - `ids` (Optional): IDs of the embeddings to remove. - `metadataCondition` (Optional): Filter condition used to remove embeddings. ## See Also - [MuleSoft Vectors Connector Overview](https://docs.mulesoft.com/mulesoft-vectors-connector/1.0/) - [MuleSoft Vectors Connector Reference](https://docs.mulesoft.com/mulesoft-vectors-connector/1.0/mulesoft-vectors-connector-reference) - [Salesforce Help](https://help.salesforce.com)