Examples

Prerequisites

Before running any examples first install dependencies and set the DATABASE_URL environment variable:

npm i
export DATABASE_URL={YOUR DATABASE URL}

Optionally, configure a .env file containing a DATABASE_URL variable.

Semantic Search

This is a basic example to perform semantic search on a collection of documents. Embeddings are created using intfloat/e5-small-v2 model. The results are semantically similar documemts to the query. Finally, the collection is archived.

Question Answering

This is an example to find documents relevant to a question from the collection of documents. The query is passed to vector search to retrieve documents that match closely in the embeddings space. A score is returned with each of the search result.

Question Answering using Instructore Model

In this example, we will use hknlp/instructor-base model to build text embeddings instead of the default intfloat/e5-small-v2 model.

Extractive Question Answering

In this example, we will show how to use vector_recall result as a context to a HuggingFace question answering model. We will use Builtins.transform() to run the model on the database.

Summarizing Question Answering

This is an example to find documents relevant to a question from the collection of documents and then summarize those documents.

Webpack

This is an example of how to use webpack with the SDK