Update instructions

Author: BenConstable9

text_2_sql/GETTING_STARTED.md

@@ -5,7 +5,7 @@ To get started, perform the following steps:
 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.
-    - Install the optional dependencies if you need a database connector other than TSQL.
+    - 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`.

text_2_sql/autogen/Iteration 5 - Agentic Vector Based Text2SQL.ipynb

@@ -35,11 +35,13 @@
         "\n",
         "### Dependencies\n",
         "\n",
-        "To install dependencies for this demo:\n",
+        "To install dependencies for this demo. Navigate to the autogen directory:\n",
         "\n",
-        "`uv sync --package autogen_text_2_sql`\n",
+        "`uv sync`\n",
         "\n",
-        "`uv add --editable text_2_sql_core`"
+        "If you need a differnet connector to TSQL.\n",
+        "\n",
+        "`uv sync --extra <DATABASE ENGINE>`"
       ]
     },
     {
@@ -87,6 +89,13 @@
         "agentic_text_2_sql = AutoGenText2Sql(use_case=\"Analysing sales data\")"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -100,7 +109,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "async for message in agentic_text_2_sql.process_user_message(UserMessagePayload(user_message=\"What is the total number of sales?\")):\n",
+        "async for message in agentic_text_2_sql.process_user_message(UserMessagePayload(user_message=\"what are the total sales\")):\n",
         "    logging.info(\"Received %s Message from Text2SQL System\", message)"
       ]
     },
@@ -128,7 +137,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.7"
+      "version": "3.12.8"
     }
   },
   "nbformat": 4,

text_2_sql/data_dictionary/README.md

@@ -225,7 +225,8 @@ If there is no pre-built script for your database engine, take one of the above
 ## Running
 
 1. Create your `.env` file based on the provided sample `.env.example`. Place this file in the same place as the `.env.example`.
-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.
+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>`
     - You can pass the following command line arguements:
         - `-- output_directory` or `-o`: Optional directory that the script will write the output files to.