Build Chatbot with Large Language Model (LLM) and Azure SQL Database

By:   |   Updated: 2024-06-14   |   Comments   |   Related: More > Artificial Intelligence


You have tons of text data stored in your company's SQL database. You already have access to Azure OpenAI. How do you enable a large language model (LLM) to answer questions based on your data stored in Azure SQL Database? How do you package this in a custom application, such as a chatbot, for user-friendly interaction?


The retrieval augmented generation (RAG) pattern is a powerful technique designed to ground LLMs in your knowledge resources. This grounding enables the model to answer questions about your data. This tip will focus on developing an example solution using Azure OpenAI, existing vectorized data from SQL Server, and Azure Bot Framework under Python. While getting into the intricacies of RAG itself is beyond the scope of this tip, check out this tutorial as a primer.


In your Azure tenant, you need to have access to:

  • Azure OpenAI.
  • An Azure AI Search index populated with vectorized data from an SQL data source. Please refer to the previous article on, where I explained in detail how to set up a vectorized SQL data index.
  • Access to a Python environment.

Solution Namespace

Let's begin by creating a project folder and opening it in VS Code. Then, navigate to the official Microsoft repo containing bot framework samples. As an example, we will go with the template for 03.Welcome bot. Either clone the whole repo or copy the files needed from the samples/python/03.welcome-user directory. Below is what the project namespace should look like after you copy or clone the files:

solution namespace overview

Create a Virtual Environment

Open the already created requirements.txt file and add the following two lines:


So, now it looks like this:

requirements file

Next, create the virtual environment. Hit Ctrl+Shift+P, select Python: Create environment, select venv, then your global Python interpreter. Check the requirements file for installing the required packages:

Check the requirements file for installing the required packages

Wait until your environment is created and selected.


Next, create a subfolder named cfg. Inside it, create a file called .env. Populate the file with the following lines:


The values for these variables are available in your Azure Open AI and AI Search services. Here is what my env file looks like:

.env file

The values for MicrosoftAppId and MicrosoftAppPassword can be left blank for now unless you have an existing app registration and want to deploy this bot.

Next, move the existing into the same folder. Modify the file like this:

01: import os
02: from dotenv import load_dotenv
04: load_dotenv()
06: class DefaultConfig:
07:     """ Bot Configuration """
09:     PORT = 3978
10:     APP_ID = os.environ.get("MicrosoftAppId", "")
11:     APP_PASSWORD = os.environ.get("MicrosoftAppPassword", "")
13: class OpenAIServiceConfig:
14:     """ Open AI Service Configuration """
16:     API_KEY = os.environ.get('OPENAI_API_KEY')
17:     SEARCH_KEY = os.environ.get('SEARCH_KEY')
18:     SEARCH_ENDPOINT = os.environ.get('SEARCH_ENDPOINT')
19:     SEARCH_INDEX_NAME = os.environ.get('SEARCH_INDEX_NAME')
21:     DEPLOYMENT_ID = os.environ.get('DEPLOYMENT_ID') 
22:     API_V = os.environ.get('API_V')

This is the result:

config classes overview

App File Update

Next, we need to modify the main application file,, because we moved the config file. Open and change line 21:

modify config import

Previous version

modify config import

Updated version

This modification ensures the import of the config class reflects its new location in the namespace.

Bot Framework Emulator

Finally, make sure to have the Bot Framework Emulator installed so you can test the bot. You can find the most recent release here.

Azure Bot Framework OpenAI Integration

Now, we need to 1) add the AI functionality to the bot and 2) adapt the core app file.

Azure Open AI (AOAI) Integration

In the solution namespace, create a folder named azr and add to it the file This file will contain the class for interacting with AOAI:

01: import json
02: from openai import AsyncAzureOpenAI
03: from cfg.config import OpenAIServiceConfig
05: CONFIG = OpenAIServiceConfig()
07: class OpenAIServiceResponder():
09:     @staticmethod
10:     async def get_completion(usr_msg: list[dict[str:str]]) -> tuple[str, list[tuple[int,str,str]]]:
12:         rag_client = AsyncAzureOpenAI(
13:             azure_endpoint=CONFIG.AZURE_OPENAI_ENDPOINT,
14:             api_key=CONFIG.API_KEY,
15:             api_version=CONFIG.API_V
16:         )
18:         completion = await
19:             model=CONFIG.DEPLOYMENT_ID,
20:             messages=usr_msg,
21:             extra_body={
22:                 'data_sources': [
23:                     {
24:                         'type': 'AzureCognitiveSearch',
25:                         'parameters': {
26:                             'endpoint': CONFIG.SEARCH_ENDPOINT,
27:                             'authentication': {
28:                                 'type': 'api_key',
29:                                 'key':CONFIG.SEARCH_KEY
30:                             },
31:                             'embedding_dependency':{
32:                                 'type':'deployment_name',
33:                                 'deployment_name': 'embeddings'
34:                             },
35:                             'index_name': CONFIG.SEARCH_INDEX_NAME,
36:                             'semantic_configuration': 'my-semantic-config', # name of the semantic config
37:                             'query_type': 'vector_simple_hybrid', # "simple" in case of non-vectorized extraction
38:                             'fields_mapping': {}, # If you are using your own index, you will be prompted in the Azure OpenAI Studio to define which fields you want to map for answering questions 
39:                             'role_information': 'You are a knowledgeable QnA bot.',
40:                             'filter': None, #
41:                             'strictness': 2, # Determines the system's aggressiveness in filtering search documents based on their similarity scores. 
42:                             'top_n_documents': 2, # how many documents to show
43:                             'in_scope': True, # limit responses from the model to the grounding data content
44:                         }
45:                     }
46:                 ]
47:             },
48:             temperature= 0.3, 
49:             top_p= 1,
50:             max_tokens= 400,
51:             stop= None
52:         )
53:         answer = json.loads(completion.model_dump_json(indent=2))['choices'][0]['message']['content']
54:         document_refs = json.loads(completion.model_dump_json(indent=2))['choices'][0]['message']['context']['citations']
55:         doc_data = [(nr+1, citation['title'], citation['content']) 
56:                           for nr, citation
57:                           in enumerate(document_refs)]
58:         return (answer, doc_data)

Let's break it down:

  • 05: Make CONFIG variable (instance of the config class) to reference the config values we configured earlier.
  • 12 – 16: Instantiate a RAG client using the AsyncAzureOpenAI class using parameters provided by the config.
  • 18 – 54: Get a completion, which is the model response. Several important configurations enable this function:
    • 20: The input user message, which will be a list of dictionaries (we will see it later).
    • 21: In the extra body, we specify how the completion will work. In this case, it is a RAG, so we need to configure the data sources.
    • 24 – 30: Configure the search service authentication.
    • 31 – 34: Configure the embeddings dependency.
    • 35 – 45: The specific configurations for the vectorized source data. Most notably:
      • 35: Index name.
      • 36: The semantic configuration if it exists (also refer to the previous article on SQL data vectorization).
      • 37: The query type. Ideally, you want to use the hybrid combination of vector and semantic. Here, we use the simple hybrid type because of the lower service tier of the search service.
      • 38: Empty, as we use the default mapping.
      • 39: Role information for the service.
      • 40: The filter configuration can be used to filter out documents based on RBAC or other rules. More information is available here.
      • 41: Determines the system's aggressiveness in filtering search documents based on their similarity scores.
      • 42: How many documents to show as references when answering.
      • 43: Limit responses from the model to the grounding data content.
    • 48: A low temperature for more deterministic responses.
    • 45: Max four hundred tokens per response.
    • 53: Assign the answer to a variable. Internally, the answer is a generator object that is populated word-by-word as the model generates the answer. The AsynAzureOpenAI class provided a handy method model_dump_json to easily access the answer as a json object.
    • 54: Using the internal json data structure, we can access the references to the grounding data that the model will generate.
    • 55 – 57: For the references, I want to output a list of tuples, where I have the index, the title, and the source URL.

This is the result:

open ai service responder classopen ai service responder class

Update the Bot App

Make a Private Method. Next, open the file At line 20, import the module we created for interacting with AOAI:

from azr.azropenaisvc import OpenAIServiceResponder
import the azure ai service responder class

At line 115, insert a new line and a new private method. This is the definition:

115:     async def __get_gpt_response(self, q: list[dict[str:str]]) -> HeroCard:
117:         gpt = OpenAIServiceResponder()
118:         response = await gpt.get_completion(q)
120:         card = HeroCard(
121:             title='Answer: ',
122:             text=response[0],
123:             buttons=None
124:         )
126:         bullet_points = "\n".join([f"{index}. {title}: {content}" for index, title, content in response[1]])
128:         card.text += "\n\n" + bullet_points
129:         return card

Here, we instantiate an object of type OpenAIServiceResponder – our custom class that takes care of connecting to AOAI.

  • 118: We get the response using the user query as parameter.
  • 120 – 124: Using the built-in HeroCard card type, we assign the first element of the response tuple to the text of the card.
  • 126: Remember those references to grounding data we extracted from the json response? Here, we make them in a nice bullet-point list.
  • 128: Add the bullet points to the answer.

Update the User Text Input Flow. Next, delete lines 108 through 113:

delete unnecessary intro code

Substitute with:

108:             query = [{"role": "user",
109:                     "content": text}]
110:             card = await self.__get_gpt_response(q=query)
111:             await turn_context.send_activity(MessageFactory.attachment(
112:                                                     CardFactory.hero_card(card))
113:                                                     )     

This code formats the user prompt and passes it to the custom private method. Using the send_activity method, we serve the result in the bot app.

Update the Rest of the Bot App. The template we used contains code with some example messages. We do not really need them. Let's adapt that code a bit. In the __init__ method, adapt line 33 with a welcome message and delete everything from line 35 up to 49:

adapt intro message

Note: The line number will no longer correspond to the original count.

Next, in the on_members_added_activity method, delete lines 57 up to 64:

update on_members_added_activity method

Next, in the on_message_activity method, adapt line 70 with an introductory message you want to see and delete lines 73 up to 77:

update on_message_activity method

Finally, save the changes to the file.

Running the Bot App

Open a terminal in VS code. Navigate to the .venv/Scripts folder and activate your environment:

activate venv

Type cd ../.. to navigate back to the root folder of the project.

navigate to project root

Then, type python to start the bot app:

run the bot app

Open the Bot Framework Emulator application, click Open Bot, and input the Bot URL http://localhost:3978/api/messages

open the bot app

At this point, you should be able to interact with your data using the GPT model configured earlier in the config file. In my case, I am using GPT 4.0:

open ai rag response part one

If you recall, the Alien Truth is one of Paul Graham's essays that we vectorized and indexed in AI Search here. The answer develops further, including two references to index data chunks:

open ai rag response part two


Using the Azure Bot Framework SDK for Python, Azure Open AI, and AI Search, we have built a chatbot solution to answer questions about your SQL Server data. This is a basic solution demonstrating the core principle. Further improvements may include automatic recurring indexing of new data, deploying the chatbot to different channels (web, MS Teams, etc.), adding authentication to the bot, and monitoring the solution telemetry.

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About the author
MSSQLTips author Hristo Hristov Hristo Hristov is a Data Scientist and Power Platform engineer with more than 12 years of experience. Between 2009 and 2016 he was a web engineering consultant working on projects for local and international clients. Since 2017, he has been working for Atlas Copco Airpower in Flanders, Belgium where he has tackled successfully multiple end-to-end digital transformation challenges. His focus is delivering advanced solutions in the analytics domain with predominantly Azure cloud technologies and Python. Hristo's real passion is predictive analytics and statistical analysis. He holds a masters degree in Data Science and multiple Microsoft certifications covering SQL Server, Power BI, Azure Data Factory and related technologies.

This author pledges the content of this article is based on professional experience and not AI generated.

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Article Last Updated: 2024-06-14

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