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Tutorial: Classifying Documents & Queries by Language


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Last Updated: September 13, 2024

This tutorial uses Haystack 2.0. To learn more, read the Haystack 2.0 announcement or visit the Haystack 2.0 Documentation.

Overview

In a gobalized society with over 7,000 human languages spoken worldwide today, handling multilingual input is a common use case for NLP applications.

Good news: Haystack has a DocumentLanguageClassifier built in. This component detects the language a document was written in. This functionality lets you create branches in your Haystack pipelines, granting the flexibility to add different processing steps for each language. For example, you could use a LLM that performs better in German to answer German queries. Or, you could fetch only French restaurant reviews for your French users.

In this tutorial, you’ll take a text samples from hotel reviews, written in different languages. The text samples will be made into Haystack documents and classified by language. Then each document will be written to a language-specific DocumentStore. To validate that the language detection is working correctly, you’ll filter the document stores to display their contents.

In the last section, you’ll build a multi-lingual RAG pipeline. The language of a question is detected, and only documents in that language are used to generate the answer. For this section, the TextLanguageRouter will come in handy.

Preparing the Colab Environment

Installing Haystack

%%bash

pip install haystack-ai
pip install langdetect

Enabling Telemetry

Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See Telemetry for more details.

from haystack.telemetry import tutorial_running

tutorial_running(32)

Write Documents Into InMemoryDocumentStore

The following indexing pipeline writes French and English documents into their own InMemoryDocumentStores based on language.

Import the modules you’ll need. Then instantiate a list of Haystack Documents that are snippets of hotel reviews in various languages.

from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.classifiers import DocumentLanguageClassifier
from haystack.components.routers import MetadataRouter
from haystack.components.writers import DocumentWriter


documents = [
    Document(
        content="Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bouchons d'oreilles fournis !)"
    ),
    Document(
        content="El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre de los clérigos. Está situado en una zona de marcha, así que si vais en fin de semana , habrá ruido, aunque a nosotros no nos molestaba para dormir"
    ),
    Document(
        content="The keypad with a code is convenient and the location is convenient. Basically everything else, very noisy, wi-fi didn't work, check-in person didn't explain anything about facilities, shower head was broken, there's no cleaning and everything else one may need is charged."
    ),
    Document(
        content="It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I N G** the appartement presents itself as a elegant and as a place to relax, very wrong place to relax - you cannot sleep in this appartement, even the beds are vibrating from the bass of the clubs in the same building - you get ear plugs from the hotel -> now I understand why -> I missed a trip as it was so loud and I could not hear the alarm next day due to the ear plugs.- there is a green light indicating 'emergency exit' just above the bed, which shines very bright at night - during the arrival process, you felt the urge of the agent to leave as soon as possible. - try to go to 'RVA clerigos appartements' -> same price, super quiet, beautiful, city center and very nice staff (not an agency)- you are basically sleeping next to the fridge, which makes a lot of noise, when the compressor is running -> had to switch it off - but then had no cool food and drinks. - the bed was somehow broken down - the wooden part behind the bed was almost falling appart and some hooks were broken before- when the neighbour room is cooking you hear the fan very loud. I initially thought that I somehow activated the kitchen fan"
    ),
    Document(content="Un peu salé surtout le sol. Manque de service et de souplesse"),
    Document(
        content="Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, très sympathique. Je pense refaire un séjour parmi vous, après le confinement, tout était parfait, surtout leur gentillesse, aucune chaude négative. Je n'ai rien à redire de négative, Ils étaient a notre écoute, un gentil message tout les matins, pour nous demander si nous avions besoins de renseignement et savoir si tout allait bien pendant notre séjour."
    ),
    Document(
        content="Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Todo reformado y nuevo. Te traen un estupendo desayuno todas las mañanas al apartamento. Solo que se puede escuchar algo de ruido de la calle a primeras horas de la noche. Es un zona de ocio nocturno. Pero respetan los horarios."
    ),
]

Each language gets its own DocumentStore.

en_document_store = InMemoryDocumentStore()
fr_document_store = InMemoryDocumentStore()
es_document_store = InMemoryDocumentStore()

The DocumentLanguageClassifier takes a list of languages. The MetadataRouter needs a dictionary of rules. These rules specify which node to route a document to (in this case, which language-specific DocumentWriter), based on the document’s metadata.

The keys of the dictionary are the names of the output connections, and the values are dictionaries that follow the format of filtering expressions in Haystack..

language_classifier = DocumentLanguageClassifier(languages=["en", "fr", "es"])
router_rules = {"en": {"field": "meta.language", "operator": "==", "value": "en"}, 
                "fr": {"field": "meta.language", "operator": "==", "value": "fe"}, 
                "es": {"field": "meta.language", "operator": "==", "value": "es"}}
router = MetadataRouter(rules=router_rules)

en_writer = DocumentWriter(document_store=en_document_store)
fr_writer = DocumentWriter(document_store=fr_document_store)
es_writer = DocumentWriter(document_store=es_document_store)

Now that all the components have been created, instantiate the Pipeline. Add the components to the pipeline. Connect the outputs of one component to the input of the following component.

indexing_pipeline = Pipeline()
indexing_pipeline.add_component(instance=language_classifier, name="language_classifier")
indexing_pipeline.add_component(instance=router, name="router")
indexing_pipeline.add_component(instance=en_writer, name="en_writer")
indexing_pipeline.add_component(instance=fr_writer, name="fr_writer")
indexing_pipeline.add_component(instance=es_writer, name="es_writer")


indexing_pipeline.connect("language_classifier", "router")
indexing_pipeline.connect("router.en", "en_writer")
indexing_pipeline.connect("router.fr", "fr_writer")
indexing_pipeline.connect("router.es", "es_writer")

Draw a diagram of the pipeline to see what the graph looks like.

indexing_pipeline.draw("indexing_pipeline.png")

Run the pipeline and it will tell you how many documents were written in each language. Voila!

indexing_pipeline.run(data={"language_classifier": {"documents": documents}})

Check the Contents of Your Document Stores

You can check the contents of your document stores. Each one should only contain documents in the correct language.

print("English documents: ", en_document_store.filter_documents())
print("French documents: ", fr_document_store.filter_documents())
print("Spanish documents: ", es_document_store.filter_documents())

(Optional) Create a Multi-Lingual RAG pipeline

To build a multi-lingual RAG pipeline, you can use the TextLanguageRouter to detect the language of the query. Then, fetch documents in that same language from the correct DocumentStore.

In order to do this you’ll need an OpenAI access token, although this approach would also work with any other generator Haystack supports.

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")

Let’s assume that all these reviews we put in our document stores earlier are for the same accommodation. A RAG pipeline will let you query for information about that apartment, in the language you choose.

Import the components you’ll need for a RAG pipeline. Write a prompt that will be passed to our LLM, along with the relevant documents.

from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.joiners import DocumentJoiner
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.components.routers import TextLanguageRouter

prompt_template = """
You will be provided with reviews for an accommodation.
Answer the question concisely based solely on the given reviews.
Reviews:
  {% for doc in documents %}
    {{ doc.content }}
  {% endfor %}
Question: {{ query}}
Answer:
"""

Build the Pipeline

Create a new Pipeline. Add the following components:

  • TextLanguageRouter
  • InMemoryBM25Retriever. You’ll need a retriever per language, since each language has its own DocumentStore.
  • DocumentJoiner
  • PromptBuilder
  • OpenAIGenerator

Note: The BM25Retriever essentially does keyword matching, which isn’t as accurate as other search methods. In order to make the LLM responses more precise, you could refacctor your piplines to use an EmbeddingRetriever which performs vector search over the documents.

rag_pipeline = Pipeline()
rag_pipeline.add_component(instance=TextLanguageRouter(["en", "fr", "es"]), name="router")
rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=en_document_store), name="en_retriever")
rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=fr_document_store), name="fr_retriever")
rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=es_document_store), name="es_retriever")
rag_pipeline.add_component(instance=DocumentJoiner(), name="joiner")
rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")


rag_pipeline.connect("router.en", "en_retriever.query")
rag_pipeline.connect("router.fr", "fr_retriever.query")
rag_pipeline.connect("router.es", "es_retriever.query")
rag_pipeline.connect("en_retriever", "joiner")
rag_pipeline.connect("fr_retriever", "joiner")
rag_pipeline.connect("es_retriever", "joiner")
rag_pipeline.connect("joiner.documents", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

You can draw this pipeline and compare the architecture to the indexing_pipeline diagram we created earlier.

rag_pipeline.draw("rag_pipeline.png")

Try it out by asking a question.

en_question = "Is this apartment conveniently located?"

result = rag_pipeline.run({"router": {"text": en_question}, "prompt_builder": {"query": en_question}})

print(result["llm"]["replies"][0])

How does the pipeline perform en español?

es_question = "¿El desayuno es genial?"

result = rag_pipeline.run({"router": {"text": es_question}, "prompt_builder": {"query": es_question}})

print(result["llm"]["replies"][0])

What’s next

If you’ve been following along, now you know how to incorporate language detection into query and indexing Haystack piplines. Go forth and build the international application of your dreams. 🗺️

If you liked this tutorial, there’s more to learn about Haystack 2.0:

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