Context

As part of defining the data models for the Quality Share project, a key decision was required on how to represent different types of content that the AI pipeline will process. The initial requirement is to handle both blog posts and research papers, with the potential for other content types (e.g., videos, books) in the future.

Options Considered

1. A Single, Unified ContentItem Model

This approach uses a single model to represent all types of content. A content_type field (e.g., “BLOG_POST”, “RESEARCH_PAPER”) is used to differentiate between them. Fields that are specific to one type of content (e.g., doi for a research paper) are made optional/nullable.

2. Separate, Specific Models

This approach involves creating a distinct data model for each content type (e.g., a BlogPost model and a ResearchPaper model). Each model would have only the fields relevant to that specific type.

Decision

We have decided to adopt Option 1: A Single, Unified ContentItem Model.

Rationale

The primary driver for this decision is flexibility and long-term scalability. The “trusted librarian” vision for this project implies that the ability to incorporate new and varied types of content over time is a core strategic goal.

• Adaptability: A single model allows us to introduce new content types (e.g., “VIDEO”, “BOOK_SUMMARY”) simply by adding a new value to the content_type enum, without requiring changes to the database schema or creating new API endpoints.
• Simplicity of Queries: It is far simpler to query and manage a single collection of content items, for example, to retrieve “all content published in the last week,” regardless of type.
• Reduced Code Duplication: Core logic for fetching, storing, summarizing, and ranking content can be written once and applied to all content types.
• Alignment with Architectural Principles: This decision directly supports our goal of a “Living Architecture” that is designed to evolve and adapt over time.

Implications

• The ContentItem data model will contain some fields that are optional/nullable to accommodate the specific attributes of different content types.
• Application logic will need to handle these optional fields gracefully.
• The content_type field becomes a critical piece of data for any logic that needs to differentiate between content types.

Context

The AI-assisted content curation pipeline requires a method to persistently store metadata such as source URLs, last fetch timestamps, and a record of processed article URLs. This is crucial to prevent re-processing content and to manage the pipeline’s state effectively.

Alternatives Considered

• JSON File: A simple file-based approach for storing data in JSON format.
• Full-fledged Database (e.g., PostgreSQL, MySQL): A client-server database system.
• Cloud-based Key-Value Store (e.g., AWS DynamoDB, Google Cloud Firestore): A managed, serverless database service.

Decision

SQLite was chosen for persistent storage of the pipeline’s metadata.

Rationale

The selection of SQLite was driven by:

• Balance of Robustness and Simplicity: It offers the benefits of a relational database (data integrity, SQL querying) without the operational overhead of a separate server.
• Lightweight and Embedded: The database is a single file within the pipeline/ directory, making it easy to manage within the monorepo.
• Performance: Efficient for the expected volume of metadata.
• Querying Capabilities: Supports SQL queries, allowing for more flexible and powerful data retrieval and management.

Implications & Future Considerations

The pipeline/ component will utilize Python’s sqlite3 module to interact with the database. The SQLite database file will be part of the monorepo. Future considerations include defining the database schema and managing schema migrations as the pipeline’s data requirements evolve.

Context

To fulfill the project’s mission of providing a curated platform of high-quality technical content, a scalable and efficient content discovery and summarization mechanism was required. The goal was to leverage AI to assist the human curator, not replace them.

Alternatives Considered

• Purely Manual Curation: Relying solely on manual discovery and summarization, which would be time-consuming and limit scalability.
• Simple LLM Request/Response Scripts: Using basic scripts to interact with LLMs, which would lack the orchestration, modularity, and advanced agentic capabilities offered by a framework.

Decision

An AI-assisted Content Curation Pipeline will be implemented, with LangChain chosen as the orchestration framework.

Rationale

The decision was driven by:

• Automation and Scale: To efficiently process a large volume of potential content.
• Quality Control: To pre-filter content, ensuring human curators focus on high-potential articles.
• Intelligent Workflow: LangChain enables the creation of modular, intelligent components (agents) for fetching, ranking, and summarizing, allowing for dynamic decision-making within the pipeline.
• Flexibility: LangChain’s abstraction facilitates swapping LLM providers and integrating new tools.
• Skill Showcase: Demonstrating advanced AI/ML pipeline design and implementation.

Implications & Future Considerations

The pipeline will be housed in a dedicated pipeline/ directory within the monorepo and orchestrated by GitHub Actions. It will be implemented in Python. Critical aspects include implementing robust API rate limit handling (retries, backoff) and secure API key management using GitHub Secrets. The initial AI accuracy and bias risks will be managed through iterative refinement.

Context

Following the selection of Hugo as the Static Site Generator, a robust and automated deployment strategy was required to publish the website to a live environment. The primary goals were to ensure cost-effectiveness, automation, and high performance for the end-users.

Alternatives Considered

• Manual Deployment: Building the Hugo site locally and manually uploading the public/ directory to a web server.
• Other Hosting Providers: Services like Netlify or Vercel offer similar static site hosting and CI/CD capabilities.

Decision

GitHub Pages was selected for hosting, and GitHub Actions was chosen for automated CI/CD.

Rationale

The choice was based on:

• Cost-Effectiveness: Leveraging the free tiers of GitHub Pages and GitHub Actions.
• Automation & Efficiency (Best Practice): Implementing CI/CD significantly reduces manual work, automates the build and deployment process, and ensures continuous updates.
• Seamless Integration: Utilizing the native integration within the GitHub ecosystem.
• Performance: Benefiting from GitHub Pages’ global CDN for fast content delivery.

Implications & Future Considerations

This deployment strategy necessitates the use of a gh-pages branch for the built static assets, keeping the main branch clean. Initial setup involved careful configuration of the GitHub Actions workflow, particularly regarding the baseURL and publish_dir to ensure correct asset loading. The free tier limits for GitHub Actions and GitHub Pages were assessed and deemed sufficient for the project’s current and foreseeable scale.

Context

The initial phase of the ‘Quality Share’ project required the selection of a Static Site Generator (SSG) to build the website. The primary goals were to create a highly performant, maintainable, and scalable platform for curated technical content.

Alternatives Considered

• Jekyll: A Ruby-based SSG with native support on GitHub Pages, offering simplicity and ease of initial deployment.
• Next.js (Static Export): A React-based framework capable of generating static sites, offering a powerful JavaScript ecosystem and component-based development.

Decision

Hugo was selected as the Static Site Generator.

Rationale

The decision to use Hugo was driven by a combination of technical advantages and alignment with the project creator’s objectives:

• Performance: Hugo’s build times are exceptionally fast, leading to a highly performant end-user experience.
• Leveraging Existing Skills: The project creator’s background in Go provided a strong foundation for understanding and potentially extending Hugo, which is written in Go.
• Modern Tooling: Hugo represents a modern approach to static site generation.
• Scalability for Content: Its efficiency ensures that even with a large volume of curated articles, the site build process remains quick and manageable.

Implications & Future Considerations

The selection of Hugo requires a GitHub Actions workflow for automated deployment to GitHub Pages, as opposed to Jekyll’s native support. This adds a layer of CI/CD complexity. The theme customization will require Go templating knowledge, a risk mitigated by leveraging AI for code generation.