AI-Powered Article Generation with Bedrock Deep Research

Bedrock Deep Research is a Streamlit-based application using Amazon Bedrock, LangGraph, and LangChain AWS libraries that automates article/report generation through AI-powered research, content writing, and image generation. It combines web research, structured content generation, and human feedback to produce comprehensive, well-researched articles with accompanying header images (generated by Amazon Bedrock Nova Canvas). This repo is inspired by LangChain's Deep Researcher.

Features

• Automated Research: Performs targeted web searches to gather relevant information
• Structured Content Generation: Creates cohesive article outlines and detailed section content
• Interactive Feedback Loop: Incorporates human feedback to refine article outlines
• AI-Generated Imagery: Produces relevant header images for visual appeal

Repository Structure

bedrock_deep_research/
├── bedrock_deep_research.py          # Main Streamlit application entry point
├── bedrock_deep_research/
│   ├── config.py             # Configuration settings and parameters
│   ├── graph.py              # Core workflow orchestration using LangGraph
│   ├── model.py              # Data models for articles and sections
│   ├── nodes/                # Individual workflow components
│   │   ├── article_head_image_generator.py    # Header image generation
│   │   ├── article_outline_generator.py       # Article outline creation
│   │   ├── section_writer.py                  # Section content generation
│   │   └── [other node files]                 # Additional workflow components
│   ├── utils.py              # Utility functions
│   └── web_search.py         # Web research integration using Tavily API
├── poetry.lock               # Poetry dependency lock file
└── pyproject.toml           # Project configuration and dependencies

Work Flow

The application follows a sequential workflow from topic input to final article generation, with feedback loops for refinement.

Key Components of the graph:

1. Initial Researcher: It performs initial web searches to gather context
2. Article Outline Generator: creates structured outline using research data
   • Human Feedback Provider: This incorporates human feedback for the outline
3. Section Writer: A subgraph that generates content after web research.
4. Compilation: combines all elements into a cohesive article
5. Final Section Generation: Generate the overview and the last paragraph based on the other sections.
6. Header Image Generator: creates relevant header image

Setup

The setup is meant to be used locally with AWS authentication, as well as within Amazon Sagemaker: either in JupyterLab or Code Editor instance.

Note: Current setup is assuming us-east-1 region (as defined in env.tmp file).

Prerequisites

• Python 3.12 (to install, visit this link: https://www.python.org/downloads/). Check your python version using python --version. If your global python isn't set as 3.12, follow the steps here: https://python-poetry.org/docs/managing-environments/)
• Poetry for dependency management
• Make sure you have enabled model access via AWS Bedrock access in us-east-1 region. You can find the supported models provided in SUPPORTED_MODELS variable in ./bedrock_deep_research/config.py.
• Tavily API key for web research capabilities.

1. Installation

# Clone the repository
git clone https://github.com/aws-samples/sample-bedrock-deep-researcher.git
cd sample-bedrock-deep-researcher

# Activate the virtual environment
poetry shell

# Install dependencies using Poetry
poetry install

2. Create and set your Tavily API key

Go to https://app.tavily.com/home and create a free API KEY. Copy the API Key and paste it into the env.tmp file.

3. Setup the environment variables

Copy the environment variables into the local environment.

cp env.tmp .env

4. Run the application

• Start the Streamlit application:

streamlit run bedrock_deep_research.py

Using the Application

Step	Description	Visual Reference
1. Enter Article Details	• Enter your article topic in the main input field • Add specific writing guidelines in the text area provided • Adjust search parameters using the configuration panel • Click the "Generate Outline" button to start the process
2. Review and Refine the Outline	• Review the AI-generated article outline • Provide specific feedback in the feedback field to improve the structure • Use the editing tools to make direct modifications if needed • Click "Accept Outline" when you're satisfied with the structure
3. Generate the Complete Article	• Review the fully researched and written article with its custom header image • Use the formatting tools to make any final adjustments • Click "Copy to Clipboard" to export your article • Or select "New Article" to start the process again

Configuration details

Writing Guideline samples: You could include instructions like:

- Strict 150-200 word limit
- Start with your most important insight in **bold**
- Include code examples where relevant
- Focus on practical implementation

Web Research Configuration:

number_of_queries = 2  # Number of search queries per section
max_search_depth = 2   # Maximum research iterations per section

Debug Mode:

# Enable debug logging
export LOG_LEVEL=DEBUG
streamlit run bedrock_deep_research.py

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or bug fixes. Read CONTRIBUTING.md for more details.

License

This library is licensed under the MIT-0 License. See the LICENSE file.