Skip to content

Multi-lingual Search

Getting Started

  1. Clone the examples repository.
  2. Run Marqo:

    docker rm -f marqo
    docker pull marqoai/marqo:latest
    docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
    For mode detailed instructions, check the getting started guide.

  3. Follow the instructions below or see the original code and article here.


This example uses the MultiEURLEX dataset.
Log from running:
Took 45 minutes on ml.g4dn.2xlarge
# change this to 'cpu' if the machine you are running Marqo on doesn't have a
# Nvidia GPU
DEVICE = "cuda"

# import marqo:
from marqo import Client

# import the huggingface datasets package:
from datasets import load_dataset

# import other python packages
import datetime
import json
import pprint
import logging

# this will be the name of the index:
INDEX_NAME = "my-multilingual-index"

# this helps us see information about the HTTP requests

# Create a new Marqo client:
mq = Client("http://localhost:8882")

def build_index():
    # Load the datasets. For this example we're just using the English and
    # Deutsch validation splits:
    dataset_en = load_dataset('multi_eurlex', 'en', split="validation")
    dataset_de = load_dataset('multi_eurlex', 'de', split="validation")

    # record the start time:
    t0 =

    # Create the index. The model we're using is multilingual:
    mq.create_index(index_name=INDEX_NAME, model='stsb-xlm-r-multilingual')

    # Let's break up large documents to make it easier to search:
    MAX_TEXT_LENGTH = 100000

    for ds, lang in [(dataset_en, "en"), (dataset_de, "de")]:
        num_docs_in_dataset = len(ds)

        for ii, doc in enumerate(ds):
            dumped = json.dumps(doc)
            # we'll set the doc ID to be the document's hash
            doc_id = str(hash(dumped))

            text_length = len(doc['text'])
            split_size = MAX_TEXT_LENGTH//2
            # break up the text of large documents:
            if text_length > MAX_TEXT_LENGTH:
                text_splits = [doc['text'][i: i + split_size] for i in range(0, text_length, split_size)]
                text_splits = [doc['text']]

            for i, sub_doc in enumerate(text_splits):
                # if a document is broken up, add the text's index to the end of the document:
                qualified_id = f"{doc_id}.{i}" if len(text_splits) > 1 else doc_id
                # create a dict to be posted
                to_post = dict(
                    [(k, v) if k != "labels" else (k, str(v)) for k, v in doc. items() if k != 'text']
                    + [("_id", qualified_id), ("language", lang), ('text', sub_doc)]
                print(f"doc number {ii} out of {num_docs_in_dataset} docs in dataset {lang}. "
                    f"_id: {qualified_id}, celex_id: {doc['celex_id']}, "
                    f"json to send size: {len(json.dumps(to_post))}")
                # Index the document. The device is set to 'cuda' to take
                # advantage of the machine's GPU. If you don't have a GPU,
                # change this argument to 'cpu'.
                # We set auto_refresh to False which is optimal for indexing
                # a lot of documents.
                    documents=[to_post], device=DEVICE, auto_refresh=False,
                    tensor_fields=['text', 'language']
    t1 =
    print(f"finished indexing. Started at {t0}. Finished at {t1}. Took {t1 - t0}")

def search(q):
    # Set searchable_attributes to 'text', which ensures that Marqo just
    # searches the 'text' field
    result = mq.index(INDEX_NAME).search(q=q, searchable_attributes=['text'])
    # Just print out the highlights, which makes the output easier to read
    for res in result["hits"]:

# After you finishing indexing, comment out the following line to prevent going through
# the whole indexing process again.

# Replace 'my_search_query' with whatever text you want to search. In English or Deutsch!
my_search_query = "Laws about the fishing industry"