Update text2sql

Author: BenConstable9

deploy_ai_search/README.md

@@ -1,8 +1,8 @@
 # AI Search Indexing Pre-built Index Setup
 
-The associated scripts in this portion of the repository contains pre-built scripts to deploy the skillset with Azure Document Intelligence.
+The associated scripts in this portion of the repository contains pre-built scripts to deploy the skillsets needed for both Text2SQL and Image Processing.
 
-## Steps for Rag Documents Index Deployment (For Unstructured RAG)
+## Steps for Rag Documents Index Deployment (For Image Processing)
 
 1. Update `.env` file with the associated values. Not all values are required dependent on whether you are using System / User Assigned Identities or a Key based authentication.
 2. Adjust `rag_documents.py` with any changes to the index / indexer. The `get_skills()` method implements the skills pipeline. Make any adjustments here in the skills needed to enrich the data source.
@@ -13,7 +13,7 @@ The associated scripts in this portion of the repository contains pre-built scri
     - `rebuild`. Whether to delete and rebuild the index.
     - `suffix`. Optional parameter that will apply a suffix onto the deployed index and indexer. This is useful if you want deploy a test version, before overwriting the main version.
 
-## Steps for Text2SQL Index Deployment (For Structured RAG)
+## Steps for Text2SQL Index Deployment (For Text2SQL)
 
 ### Schema Store Index
 

text_2_sql/GETTING_STARTED.md

@@ -1,21 +1,26 @@
 # Getting Started with Agentic Text2SQL Component
 
-**Execute the following commands in the `text_2_sql` directory.**
-
 To get started, perform the following steps:
 
+**Execute the following commands in the `deploy_ai_search` directory:**
+
 1. Setup Azure OpenAI in your subscription with **gpt-4o-mini** & an embedding model, alongside a SQL Server sample database, AI Search and a storage account.
-2. Clone this repository and deploy the AI Search text2sql indexes from `deploy_ai_search`.
-3. Run `uv sync` within the text_2_sql directory to install dependencies.
+2. Create your `.env` file based on the provided sample `deploy_ai_search/.env.example`. Place this file in the same place in `deploy_ai_search/.env`.
+3. Clone this repository and deploy the AI Search text2sql indexes from `deploy_ai_search`. See the instructions in the **Steps for Text2SQL Index Deployment (For Structured RAG)** section of the `deploy_ai_search/README.md`.
+
+**Execute the following commands in the `text_2_sql_core` directory:**
+
+4. Create your `.env` file based on the provided sample `text_2_sql/.env.example`. Place this file in the same place in `text_2_sql/.env`.
+5. Run `uv sync` within the text_2_sql directory to install dependencies.
     - Install the optional dependencies if you need a database connector other than TSQL. `uv sync --extra <DATABASE ENGINE>`
     - See the supported connectors in `text_2_sql_core/src/text_2_sql_core/connectors`.
-4. Create your `.env` file based on the provided sample `.env.example`. Place this file in the same place as the `.env.example`.
-5. Generate a data dictionary for your target server using the instructions in the **Running** section of the `data_dictionary/README.md`.
-6. Upload these generated data dictionaries files to the relevant containers in your storage account. Wait for them to be automatically indexed with the included skillsets.
+6. Create your `.env` file based on the provided sample `text_2_sql/.env.example`. Place this file in the same place in `text_2_sql/.env`.
+7. Generate a data dictionary for your target server using the instructions in the **Running** section of the `data_dictionary/README.md`.
+8. Upload these generated data dictionaries files to the relevant containers in your storage account. Wait for them to be automatically indexed with the included skillsets.
 
-**Execute the following commands in the `autogen` directory.**
+**Execute the following commands in the `autogen` directory:**
 
-7. Run `uv sync` within the text_2_sql directory to install dependencies.
+9. Run `uv sync` within the text_2_sql directory to install dependencies.
     - Install the optional dependencies if you need a database connector other than TSQL. `uv sync --extra <DATABASE ENGINE>`
     - See the supported connectors in `text_2_sql_core/src/text_2_sql_core/connectors`.
-8. Navigate to `autogen` directory to view the AutoGen implementation. Follow the steps in `Iteration 5 - Agentic Vector Based Text2SQL.ipynb` to get started.
+10. Navigate to `autogen` directory to view the AutoGen implementation. Follow the steps in `Iteration 5 - Agentic Vector Based Text2SQL.ipynb` to get started.

text_2_sql/data_dictionary/README.md

@@ -224,11 +224,11 @@ If there is no pre-built script for your database engine, take one of the above
 
 ## Running
 
-**Execute the following commands in the `text_2_sql` directory.**
-
 To generate a data dictionary, perform the following steps:
 
 1. Create your `.env` file based on the provided sample `text_2_sql/.env.example`. Place this file in the same place in `text_2_sql/.env`.
+
+**Execute the following commands in the `text_2_sql_core` directory:**
 2. Package and install the `text_2_sql_core` library. See [build](https://docs.astral.sh/uv/concepts/projects/build/) if you want to build as a wheel and install on an agent. Or you can run from within a `uv` environment and skip packaging.
     - Install the optional dependencies if you need a database connector other than TSQL. `uv sync --extra <DATABASE ENGINE>`
 3. Run `data_dictionary <DATABASE ENGINE>`