Azure AI Search

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Overview

AI-powered information retrieval platform by Microsoft Azure. Through Langdock’s integration, you can access and manage Azure AI Search directly from your conversations.

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Available Actions

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Search Documents

azureaisearch.searchDocuments

Searches the database for the most relevant information based on the query provided Requires Confirmation: No Parameters:

• query (VECTOR, Required): Vector query for semantic search

Output: Returns search results with the following structure:

• value: Array of search result objects containing:
  • @search.score: Relevance score
  • @search.highlights: Highlighted text snippets
  • Field values from the indexed documents
• @odata.count: Total number of results
• @odata.nextLink: Link to next page of results (if available)

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List Datasets

azureaisearch.listDatasets

Lists all datasets in a BigQuery project Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID containing the datasets

Output: Returns an array of datasets with their IDs, names, and metadata

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List Tables

azureaisearch.listTables

Lists all tables in a BigQuery dataset Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The dataset ID containing the tables

Output: Returns an array of tables with their IDs, names, and metadata

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Get Table Schema

azureaisearch.getTableSchema

Gets the schema information for a specific BigQuery table Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The dataset ID containing the table
  • tableId (TEXT, Required): The table ID to get schema information for

Output: Returns the table schema including field names, types, and constraints

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Execute Query

azureaisearch.executeQuery

Executes a SQL query in BigQuery and returns the results Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID to execute the query in
  • query (MULTI_LINE_TEXT, Required): The SQL query to execute in BigQuery
  • useLegacySql (BOOLEAN, Optional): Whether to use legacy SQL syntax (default: false for Standard SQL)

Output: Returns query results with the following structure:

• jobReference: Job reference information
• totalRows: Total number of rows in the result
• rows: Array of result rows containing field values
• schema: Schema of the result fields
• jobComplete: Whether the job completed successfully

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Get Table Data

azureaisearch.getTableData

Retrieves actual data rows from a BigQuery table Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The dataset ID containing the table
  • tableId (TEXT, Required): The table ID to retrieve data from
  • maxResults (NUMBER, Optional): Maximum number of rows to return (optional)

Output: Returns table data with rows and schema information

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Create Dataset

azureaisearch.createDataset

Creates a new dataset in BigQuery Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The ID for the new dataset
  • description (TEXT, Optional): Optional description for the dataset
  • location (TEXT, Optional): Geographic location for the dataset (e.g., US, EU)

Output: Returns the created dataset with its ID and metadata

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Create Table

azureaisearch.createTable

Creates a new table in a BigQuery dataset Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The dataset ID to create the table in
  • tableId (TEXT, Required): The ID for the new table
  • description (TEXT, Optional): Optional description for the table
  • schema (MULTI_LINE_TEXT, Optional): Table schema as JSON array of field objects (optional)

Output: Returns the created table with its ID and schema information

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Insert Table Data

azureaisearch.insertTableData

Inserts data rows into a BigQuery table Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The dataset ID containing the table
  • tableId (TEXT, Required): The table ID to insert data into
  • rows (MULTI_LINE_TEXT, Required): JSON array of row objects to insert
  • ignoreUnknownValues (BOOLEAN, Optional): Whether to ignore unknown values in the data
  • skipInvalidRows (BOOLEAN, Optional): Whether to skip rows that contain invalid data

Output: Returns insertion results with success/failure information

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Get Dataset Info

azureaisearch.getDatasetInfo

Gets detailed information about a BigQuery dataset Requires Confirmation: No Parameters:

• projectId (TEXT, Required): The Google Cloud project ID
  • datasetId (TEXT, Required): The dataset ID to get information for

Output: Returns dataset information including creation time, location, and access controls

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Common Use Cases

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Best Practices

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Troubleshooting

Issue	Solution
Authentication failed	Verify your API Key credentials
Rate limit exceeded	Reduce request frequency
Data not found	Check permissions and data availability
Connection timeout	Verify network connectivity

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Support

For additional help with the Azure AI Search integration, contact support@langdock.com