Quickstart

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In this tutorial, you'll learn how to upload your first data table and train your first engine.

Step 1: Get the data from Github

We'll use the movielens dataset enriched with data from IMDb and build a semantic search model.

Get the dataset here - Download movielens.jsonl

Step 2: Upload the data as a new table

1. Open your Shaped dashboard
2. Click Tables in the leftpane
3. Click Add new table in the top right

Step 3: Configure a new engine

1. In the leftpane, go to Engines
2. Click Upload a new engine in the top right
3. Paste the following YAML into the query editor:

version: v2

name: movielens_movie_recommendation_v2

data: 
  # specify a Shaped table to represent the items in your catalog
  item_dataset: 
    name: movies_2018

  # define how to index your data
  index:
    # specify a model to use to generate embeddings
    embeddings:
        - name: text_embedding
          encoder:
              type: hugging_face
              model_name: sentence-transformers/all-MiniLM-L6-v2
              item_fields:
                - movie_title

Step 4: Query your engine

1. In the leftpane, go to the Query section
2. Copy the query below into the left-hand side
3. Press Run

query:
  type: rank_items
  retrieve:
    name: similar_items
    embedding_ref: als
    type: item_similarity
    query_encoder:
      input_item_id: $param.item_id
      type: item_attribute_pooling
params:
  item_id: db1234