The tutorial covers the installation of necessary software, the creation of a database, the modification of the schema to include a GraphQL query and a handlebars template, and the indexing of embeddings. The author demonstrates how to use OpenAI to get vectors for embedding documents and how to check the status of indexing. The tutorial concludes with an example of cross-language entity resolution, showing how similar records in different languages can be identified. The author notes that future work will need to address automatic record merging and the representation of merged records.
Key takeaways:
- Entity resolution, also known as record linkage, data matching and data linkage, is the process of determining whether two records should be considered the same. This process can be enhanced using Large Language Models (LLMs) and AI.
- AI solutions for entity resolution have several advantages, including increased performance of matches, reduced programmer tuning for record structure, and the ability to perform incremental updates to the database.
- VectorLink and OpenAI can be used to create and index embeddings for entity resolution. This process involves modifying the schema to include a graphql query and a handlebars template, and then using an OpenAI API key to index the data.
- Entity resolution can be performed across different languages, as demonstrated by the example of comparing records in English and German. This is a significant advantage over traditional entity resolution tools.