How We Built an AI Directory Pipeline with LangGraph and Contentful
For Quicknode’s Builder’s Guide, we built a pipeline that populates over 60 structured fields per tool. That makes the content model a useful starting point for understanding the work: each entry has to support discovery and comparison, so generating a readable summary addresses only part of the publishing problem. Our Quicknode case study documents a 5-node LangGraph workflow with retrieval,…
Quicknode's Builder's Guide details the creation of an AI directory pipeline utilizing LangGraph and Contentful. This pipeline populates over 60 structured fields per tool, emphasizing the importance of a content model in supporting discovery and comparison. The workflow comprises five nodes: retrieval, generation, category validation, quality scoring, and publication.
The guide underscores the necessity of assigning distinct roles to each concern, such as evidence collection, writing, classification, and publication, each with its own acceptance criteria.
The content model defines the fields and relationships within each entry, enabling a more granular evaluation of generated content than a simple assessment of its persuasiveness. For instance, a descriptive summary requires contextual information to explain the tool's purpose, while a pricing field necessitates specific information about the offer. Assigning categories demands clear definitions to verify the choice against a predefined set of criteria.
Missing information must be explicitly addressed; an empty field, an explicit unknown, or a generated guess can significantly impact the reader's experience. The pipeline retrieves data using the Tavily API and generates descriptions, pricing tiers, and use cases with GPT-4o. The relevance of the retrieved material to the intended fields must be determined, especially concerning pricing information.
Verification of the source's current offer, limited promotion, or older announcement is crucial before accepting generated values.
Classification against the directory's taxonomy should be performed using AI reasoning, ensuring the assigned category aligns with the directory's structure. Overlapping categories require careful consideration of how they should be handled in the content model. The workflow should separately validate classification from prose quality, as an accurate explanation of a tool does not necessarily guarantee correct categorization.
Publication criteria are defined using the Contentful Management API, which handles entry creation and asset linking. Scoring content quality before publishing allows for concrete decision-making when evaluating entries. Possible criteria for scoring include factual support, completion of required fields, and category fit, among others. These criteria should be tested against representative entries before implementation to ensure effective decision-making during the publishing process.
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