Artisan IMG > Jira Cloud (jira-cloud) (d63bf7a8-f971-4860-885c-04b648140115)
Artisan IMG > AWS Bedrock (aws-bedrock) (5a1f2dc4-52b2-418d-a8ae-99fbaebbc4fa)
Artisan IMG > Vector Tables (vector-tables) (248afc8f-aa04-4481-9db0-151db15c004d)

Knowledge Indexing - Jira

Project
Artificial Intelligence
Intermediate

This is a 'Project' template which means that it contains a group of workflows that work together to achieve a particular aim

Overview
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Extract data from their Jira tickets and turn them into Knowledge articles for a Support use-case by using AI.

If you need help reach out in the community in the #ai-help channel!

Set-up
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  1. Import the Project into your Tray instance.

  2. Create a Data Table in this Project with the following columns:

    1. `Ticket Key`

    2. `Ticket Link`

    3. `Decision`

    4. `Reason`

  3. Create a Vector Table in this Project with these configurations:

    1. `Name`: Something that suits your use-case

    2. `Dimensions`: 1024

    3. `Metric`: Cosine

    4. **Note**: The Embedding Model this Project uses is the Amazon Titan Embedding Text v2 via AWS Bedrock.

  4. Open the `Jira extract` workflow and configure the Config variables:

    1. `jql_to_query_tickets`: Define your query to select tickets that will be audited by AI and potentially converted to knowledge articles

  5. Open the `Jira Audit + KB Creation + Vector Table Push` workflow and configure these steps:

    1. `data-tables-1`: Select the Data Table created in Step 2

    2. Ensure columns are in this order:

      1. `Ticket Key`

      2. `Ticket Link`

      3. `Decision`

      4. `Reason`

    3. `vector-tables-1`: Select the Vector Table created in Step 3

Next Steps
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Pair this with our Slackbot and other AI Agent templates to compose your first enterprise Agent.

If you need help reach out in the community in the #ai-help channel!