Report Generation Engine

Report Generation Engine#

Overview#

Report Generation Engine UI

This Solution Blueprint illustrates how automated technical report generation can be implemented using AIMs. It employs a multi-stage LLM workflow with integrated web research, where the system plans, researches, writes, and compiles content to produce comprehensive, evidence-based technical documents on any topic.

The blueprint follows a four-stage pipeline:

  • Planning: Generates search queries and creates a section outline

  • Research: Conducts parallel web searches via the Tavily API

  • Writing: Generates content for each section using the gathered research

  • Compilation: Assembles the final markdown report with introduction and conclusion

AMD Solution Blueprints are packaged as Helm charts for deployment on a Kubernetes cluster. For development or further exploration, the source code is public and available in the Solution Blueprints GitHub repository.

Architecture#

Report generation is performed by a four-stage pipeline with LLM and web research integration. Report generation is performed by a four-stage pipeline with LLM and web research integration.

The blueprint integrates a Streamlit web UI, a FastAPI backend, an AIM LLM service, and Tavily web search. LangChain orchestrates the multi-stage workflow across planning, research, writing, and compilation.

Component

Role

Streamlit

Web-based user interface

FastAPI

REST API backend

AIM (LLM)

Technical writing and synthesis (default: Llama 3.3 70B Instruct)

LangChain

Orchestration of LLM calls

Tavily API

Web search for research integration

Key Features#

  • Generates comprehensive technical reports on any user-provided topic

  • Users can customize the report structure or use intelligent defaults

  • Real-time progress tracking shows each stage of the generation process

  • Web research results are automatically integrated and cited in the final report

Getting Started#

This is a quick start guide on how to deploy the blueprint. For advanced options, such as reusing an existing AIM, configuring search parameters, or overriding storage classes, see Deploying Solution Blueprints with Helm or explore the advanced deployment guide.

This blueprint supports AMD Instinct (default), AMD Radeon, and AMD EPYC platforms. The section below covers the default Instinct deployment. For Radeon, EPYC, and other advanced options, see:

Prerequisites#

System Requirements#

The blueprint requires the following cluster resources by default:

Resource

Default Configuration

GPUs

1 (for the LLM)

CPUs

2 CPU cores

RAM

68 Gi

To deploy to the Kubernetes cluster, ensure the following prerequisites are met:

  • kubectl: Installed and configured to communicate with the cluster

  • Helm 3.17 or higher: Installed on your local machine

  • Tavily API key for web search integration (free tier: 1,000 requests/month)

Deployment#

Solution Blueprints are packaged as OCI-compliant Helm charts in the Docker Hub registry and can be deployed to a Kubernetes cluster with a single command. Define the name (deployment name) and namespace (Kubernetes namespace), set config.tavily.apiKey to your Tavily API key, then pipe the output of helm template to kubectl apply -f -:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-report-generation-engine \
  --set config.tavily.apiKey=tvly-your-key-here \
  | kubectl apply -f - -n $namespace

Note: You can create a namespace using kubectl create namespace $namespace.

To check the status of the deployment, run:

kubectl get pods -n $namespace

Wait until all pods report Running and Ready.

Connect to UI#

To connect to the UI, port-forward to 8501. The UI will then be available at http://localhost:8501 in your browser.

kubectl port-forward services/aimsb-report-generation-engine-${name} 8501:8501 -n $namespace

Enter a topic, optionally customize the report structure, and start generation. Progress is shown for each pipeline stage.

Clean Up#

When you are finished, remove the deployed resources:

helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-report-generation-engine \
  | kubectl delete -f - -n $namespace

Third-party Components#

This Solution Blueprint utilizes multiple components. For third-party license information, refer to each component’s documentation. Key third-party components are listed below.

Component

License

FastAPI

MIT

LangChain

MIT

Streamlit

Apache 2.0

Tavily API: Web search API for research integration

  • Website: https://tavily.com

  • Terms of Use: https://tavily.com/terms-of-service

  • License: Commercial API service; requires API key

Terms of Use#

AMD Solution Blueprints are released under the MIT License, which governs the parts of the software and materials created by AMD. Third-party software and materials used within the Solution Blueprints are governed by their respective licenses.

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