update format

inventory/catalog.md

@@ -1,58 +1,61 @@
 # Bedrock Use Case Catalog
 
----
+| ID | Name |
+|-|-|
+| 20260127-1 | Field Skills Interview Training |
+| 20260420-1 | Valhalla |
+| 20260420-2 | Decennial Census Data Quality Evaluation |
+| 20260504-1 | QUANTM |
+| 20260505-1 | CEDSCI AI Assisted Code Development |
+| 20260505-2 | CEDSCI AI Agentic Semantic Search |
+
 
 ## [20260127-1](20260127-1.yml)
 
-**Name:** Field Skills Interview Training
+**Name:** Field Skills Interview Training  
 **Summary:** Responses to short real-world practice scenarios to provide personalized user feedback as part of online training.
+
 
 
----
-
 ## [20260420-1](20260420-1.yml)
 
-**Name:** Valhalla
-**Summary:** We are using LLMs to map unstructured data into a known schema (known variables, etc..)
+**Name:** Valhalla  
+**Summary:** We are using LLMs to map unstructured data into a known schema (known variables, etc..)  
 
----
 
 ## [20260420-2](20260420-2.yml)
 
-**Name:** Decennial Census Data Quality Evaluation
+**Name:** Decennial Census Data Quality Evaluation  
 **Summary:** This project will evaluate data quality throughout the 2020 Census production cycle using LLMs to inform Census Bureau and
 Department of Commerce leadership decisions regarding processes, methodologies, and business rules to be used for the 2030 Census.
+
 
 
----
-
 ## [20260504-1](20260504-1.yml)
 
-**Name:** QUANTM
+**Name:** QUANTM  
 **Summary:** As part of the quality operations of the US Census Bureau, computer matching and clerical operations are employed.  These efforts help in the assessment
 of the quality of data collection. The existing operations have constraints in their ability to accurately match and compare complex datasets, resulting in high number
 of cases being flagged or removed. This process is time-intensive and expensive, creating a significant burden on human resources. 	With the advanced AI capabilities
 available in the industry, QUANTM is assessing their feasibility to support the quality assessment efforts and reduce the clerical footprint. The bureau aims to explore
 whether large language model (LLM) technology can replicate and improve upon the reasoning processes used by human clerks.
+
 
 
----
-
 ## [20260505-1](20260505-1.yml)
 
-**Name:** CEDSCI AI Assisted Code Development
+**Name:** CEDSCI AI Assisted Code Development  
 **Summary:** The goal of this project is to support the controlled use of AWS Bedrock–hosted large language models (LLMs) to
 enable software development, refactoring, and application modernization activities within the CEDSCI DEV environment.
+
 
 
----
-
 ## [20260505-2](20260505-2.yml)
 
-**Name:** CEDSCI AI Agentic Semantic Search
+**Name:** CEDSCI AI Agentic Semantic Search  
 **Summary:** CEDSCI AI Agentic Semantic Search is a cloud-native, generative AI–driven capability that enables a
 natural-language interface for interacting with publicly released Census Bureau statistical data and metadata.
 The project introduces an agentic semantic search layer that combines large language models (LLMs) with structured
 metadata and data assets to provide a conversational AI interface, exposed through data.census.gov and api.census.gov.
-
+  
 

inventory/templates/bedrock_catalog.md.j2

@@ -1,11 +1,16 @@
 # Bedrock Use Case Catalog
 
+| ID | Name |
+|-|-|
+{% for record in records %}
+| {{ record.federal_standards_information.id }} | {{ record.federal_standards_information.project_name }} |
+{% endfor %}
+
 {% for record in records %}
----
 
 ## [{{ record.federal_standards_information.id }}]({{ record.federal_standards_information.id }}.yml)
 
-**Name:** {{ record.federal_standards_information.project_name }}
-**Summary:** {{ record.federal_standards_information.project_summary }}
+**Name:** {{ record.federal_standards_information.project_name }}  
+**Summary:** {{ record.federal_standards_information.project_summary }}  
 
 {% endfor %}