https://github.com/denniszielke/sql-ai-agents

Demo implementation of a reactive multi agent bot that can answer questions based on relational database information to demonstrate different architecture pattern.
https://github.com/denniszielke/sql-ai-agents

agent-based azure azure-openai azure-sql container-apps generative-ai langchain-python langraph

Last synced: 3 months ago
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Demo implementation of a reactive multi agent bot that can answer questions based on relational database information to demonstrate different architecture pattern.

• Host: GitHub
• URL: https://github.com/denniszielke/sql-ai-agents
• Owner: denniszielke
• License: mit
• Created: 2024-07-29T14:18:14.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-11-04T19:24:19.000Z (3 months ago)
• Last Synced: 2024-11-04T20:30:13.714Z (3 months ago)
• Topics: agent-based, azure, azure-openai, azure-sql, container-apps, generative-ai, langchain-python, langraph
• Language: TSQL
• Homepage:
• Size: 1.67 MB
• Stars: 6
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Talking to a database with a large language model

This project demonstrates different approaches on how to implement a bot that can talk to a relational database using different architecture patterns.

This allows you to enter abstract questions that the bot will try to answer by reasoning over the available data structures using a vector database, create a plan to query the data and iterate over possible queries and finally execute the query. If anything goes wrong or does not look correct the bot will identify issues and fix them automatically.

The question can be something like that:

![image info](./img/question.png)

The bot will use tools to solve the problem:

![image info](./img/tools_used.png)

After different iterations the bot will be able generate a SQL query:

![image info](./img/query.png)

And finally it will come up with a way to output the response in a suitable format:

![image info](./img/output.png)

It will also plot the path taken and the amount of tokens used

![image info](./img/plan.png)

## Implementation patterns

To compare efficiency, reliability, creativity and scalability we have implemented the following different architecture patterns for this:

1.) Simple prompt

![image info](./img/simple.png)

2.) ReAct based Prompt

![image info](./img/tools.png)

3.) Multi agent

![image info](./img/agents.png)

4.) AI Search optimized agents

![image info](./img/embeded-agents.png)

## Deploy Infrastructure

```

echo "log into azure dev cli - only once"

azd auth login

echo "provisioning all the resources with the azure dev cli"

azd up

echo "get and set the value for AZURE_ENV_NAME"

source <(azd env get-values | grep AZURE_ENV_NAME)

echo "building and deploying the streamlit user interface"

bash ./azd-hooks/deploy.sh sql-react $AZURE_ENV_NAME

```

## Starting up

```

conda create -n sqlagents python=3.12

conda activate sqlagents

python -m pip install -r requirements.txt   

python -m streamlit run app.py --server.port=8000

```

## Startin up with open telemetry

```

docker run --rm --name jaeger \

  -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \

  -p 6831:6831/udp \

  -p 6832:6832/udp \

  -p 5778:5778 \

  -p 16686:16686 \

  -p 4317:4317 \

  -p 4318:4318 \

  -p 14250:14250 \

  -p 14268:14268 \

  -p 14269:14269 \

  -p 9411:9411 \

  jaegertracing/all-in-one:1.60

OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

opentelemetry-instrument --service_name sql-ai-agent python -m streamlit run app.py --server.port=8000

open http://localhost:16686/search

```