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Showcasing Different Approaches for Implementing Multilingual RAG
Latest   Machine Learning

Showcasing Different Approaches for Implementing Multilingual RAG

Last Updated on January 15, 2025 by Editorial Team

Author(s): Michael Ghaly

Originally published on Towards AI.

Image Generated by Microsoft Copilot

Retrieval-Augmented Generation (RAG)

Large language models inherently possess a significant body of factual relational knowledge [1]. However, these models still exhibit limitations in their ability to expand and manipulate this knowledge. Consequently, these models, while impressive, often suffer from hallucinations, outdated information, and opaque and untraceable reasoning processes.

Retrieval-augmented generation (RAG) is a fascinating hybrid framework that merges the generative prowess of large language models with the precision of non-parametric knowledge bases [2]. As someone deeply interested in the evolution of AI, I find it particularly compelling because it addresses some of the critical limitations of standalone language models. By leveraging RAG, we can significantly enhance the accuracy and reliability of generated content, especially in knowledge-intensive tasks, ensuring continuous knowledge updates, and being able to incorporate domain-specific information seamlessly.

The figure shows a typical RAG flow. In the document ingestion phase (marked in purple), the documents are loaded, split into chunks, embedded in a high-dimensional vector space, and stored in a vector database. When a query is submitted, a semantic similarity search is performed to obtain the relevant chunks, which are then given to the language model along with the original query for the answer generation.

Multilingual RAG: Bridging Language Barriers

Multilingual RAG leverages the inherent capabilities of large language models to support multiple languages, making it possible to build applications that facilitate natural and fluid interactions in a user’s preferred language. This not only improves user experience and accessibility but also ensures engaging conversations without the hindrance of language barriers.

This article reports the possible approaches for implementing a multilingual RAG system based on the application’s requirements.

Making Multilingual Chat Sessions a Reality

This first section presumes a monolingual knowledge base and aims to support multilingualism solely by enabling users to submit queries and receive answers in their preferred language. In this scenario, two primary approaches come to mind:

1. Multilingual Embedding Models: These models are pre-trained to handle multiple languages, enabling them to retrieve relevant information from a monolingual knowledge base regardless of the query’s language. This approach is straightforward as it maintains the same RAG architecture. However, it’s important to note that multilingual models may not perform as well as language-specific models. From my experience, this approach is a great starting point for those looking to implement multilingual capabilities quickly, though one might need to trade off some performance.

2. Query Translation: This method involves translating the user’s query into the language of the documents in the knowledge base, using a language-specific embedding model to retrieve the relevant information, and then translating the response back into the user’s preferred language. This approach relies heavily on high-quality machine translation models to preserve the accuracy and nuance of the original query and response. Alternatively, the translation task could also be assigned to the language model.

The figure illustrates how a translation module would be added in a RAG flow.

The following code snippets showcase these methods by creating two ChromaDB monolingual collections: one with the English-specific embedding model Mpnet and the other with its multilingual variant.

import chromadb
import chromadb.utils.embedding_functions as embedding_functions

# Dummy documents for testing purposes
lst_monolingual_documents = [
"The sky is blue.",
"Trees provide shade.",

"Birds sing in the morning.",
"Cats purr when they are happy.",

"Coffee is a popular morning beverage.",
"Exercise is good for your health."
]

# Define the embedding functions
monolingual_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="all-mpnet-base-v2")
multilingual_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="paraphrase-multilingual-mpnet-base-v2")

# Create the collections
chroma_client = chromadb.Client()

collection_monolingual_kb_monolingual_ef = chroma_client.create_collection(
name="monolingual_kb_monolingual_ef",
embedding_function=monolingual_ef
)
collection_monolingual_kb_multilingual_ef = chroma_client.create_collection(
name="monolingual_kb_multilingual_ef",
embedding_function=multilingual_ef
)

# Populating the collections
collection_monolingual_kb_monolingual_ef.add(
documents=lst_monolingual_documents,
ids=[f"id_{int_index}"
for int_index in range(len(lst_monolingual_documents))]
)
collection_monolingual_kb_multilingual_ef.add(
documents=lst_monolingual_documents,
ids=[f"id_{int_index}"
for int_index in range(len(lst_monolingual_documents))]
)

This section defines two natural language queries: one in English and the other in Italian. The goal is to support retrieval for both language on a knowledge base that contains all-English documents.

str_english_query = "What are the benefits of trees?"
str_italian_query = "Quali sono i benefici degli alberi?"

# Retrieval with the English Query and the monolingual embedding model
lst_english_query_retrieval = collection_monolingual_kb_monolingual_ef.query(
query_texts=str_english_query, n_results=1)["documents"]

# Retrieval with the Italian Query and the monolingual embedding model
lst_italian_query_retrieval = collection_monolingual_kb_monolingual_ef.query(
query_texts=str_italian_query, n_results=1)["documents"]

print(f"Retrieved documents for the English query: {lst_english_query_retrieval}")
print(f"Retrieved documents for the Italian query: {lst_italian_query_retrieval}")
Retrieved documents for the English query: [['Trees provide shade.']]
Retrieved documents for the Italian query: [['Coffee is a popular morning beverage.']]

As expected, retrieval for the English query was correct and Mpnet struggled to match the Italian query. However, its multilingual variant is able to match the correct document as shown below:

# Retrieval with the Italian Query and the multilingual embedding model
lst_italian_query_retrieval = collection_monolingual_kb_multilingual_ef.query(
query_texts=str_italian_query, n_results=1)["documents"]

print(f"Retrieved documents for the Italian query: {lst_italian_query_retrieval}")
Retrieved documents for the Italian query: [['Trees provide shade.']]

Alternatively, the Italian query can be translated to English and the monolingual embedding model can then be used to obtain the same result.

# Translate the Italian query into English
str_translated_query = translate(str_italian_query)

# Retrieval with the translated query and the monolingual embedding model
lst_italian_query_retrieval = collection_monolingual_kb_monolingual_ef.query(
query_texts=str_translated_query, n_results=1)["documents"]

print(f"Original Italian Query:\t{str_italian_query}")
print(f"Translated Query:\t{str_translated_query}")
print(f"\nRetrieved documents for the translated query: {lst_italian_query_retrieval}")
Original Italian Query: Quali sono i benefici degli alberi?
Translated Query: What are the benefits of trees?

Retrieved documents for the translated query: [['Trees provide shade.']]

Multilingual Knowledge Base: A Step Further

On the other hand, supporting multilingual knowledge bases adds another layer of complexity, as it involves ensuring that the system can handle retrieval of documents in multiple languages, but it is entirely achievable with the right approach. Here are three methods to consider and their code snippets:

# Dummy documents for testing purposes
lst_multilingual_documents = [
"The sky is blue.",
"Gli alberi forniscono ombra.",

"Gli uccelli cantano al mattino.",
"Cats purr when they are happy.",

"Il caffè è una bevanda popolare al mattino.",
"Exercise is good for your health."
]

# Create the collections
collection_multilingual_kb_monolingual_ef = chroma_client.create_collection(
name="multilingual_kb_monolingual_ef",
embedding_function=monolingual_ef
)
collection_multilingual_kb_multilingual_ef = chroma_client.create_collection(
name="multilingual_kb_multilingual_ef",
embedding_function=multilingual_ef
)

# Populating the collections
collection_multilingual_kb_monolingual_ef.add(
documents=lst_multilingual_documents,
ids=[f"id_{int_index}"
for int_index in range(len(lst_multilingual_documents))]
)

collection_multilingual_kb_multilingual_ef.add(
documents=lst_multilingual_documents,
ids=[f"id_{int_index}"
for int_index in range(len(lst_multilingual_documents))]
)
  • Multilingual Embedding Models: Similar to their use in chat sessions, these models can be employed to index and retrieve documents in multiple languages. This approach allows for a unified model to handle queries and documents across different languages, which helps maintain the original RAG architecture without the need for additional translation steps.
str_english_query = "What are the benefits of trees?"
str_italian_query = "Quali sono i benefici degli alberi?"

# Retrieval with the English Query and the monolingual embedding model
lst_english_query_retrieval = collection_multilingual_kb_monolingual_ef.query(
query_texts=str_english_query, n_results=1)["documents"]

# Retrieval with the Italian Query and the monolingual embedding model
lst_italian_query_retrieval = collection_multilingual_kb_monolingual_ef.query(
query_texts=str_italian_query, n_results=1)["documents"]

print(f"Retrieved documents for the English query: {lst_english_query_retrieval}")
print(f"Retrieved documents for the Italian query: {lst_italian_query_retrieval}")
Retrieved documents for the English query: [['Exercise is good for your health.']]
Retrieved documents for the Italian query: [['Gli alberi forniscono ombra.']]

As shown in this section, the retrieval for the English query failed, even with an English embedding model, due to the document being in Italian. Meanwhile, the Italian query matched the correct document despite the embedding model not being Italian.

The issue is resolved once a multilingual embedding model is used for the English query.

# Retrieval with the English Query and the multilingual embedding model
lst_english_query_retrieval = collection_multilingual_kb_multilingual_ef.query(
query_texts=str_italian_query, n_results=1)["documents"]

print(f"Retrieved documents for the English query: {lst_english_query_retrieval}")
Retrieved documents for the English query: [['Gli alberi forniscono ombra.']]
  • Query Translation: Also similarly as before, this approach involves translating a query into each language present in the knowledge base, retrieving relevant information for each translation, and then merging the results. This method is more resource-intensive because it first requires translating each query into all the languages present in the knowledge base documents. Despite this increased computational demands, this approach ensures comprehensive retrieval across all languages. Additionally, it can help compensate for situations where the multilingual model alone is not performing well enough, enhancing the overall retrieval quality.
lst_languages_in_kb = ["English", "Italian"]
str_spanish_query = "¿Cuáles son los beneficios de los árboles?"

lst_translated_query_retrieval = []
for str_language in lst_languages_in_kb:
str_translated_query = translate(str_spanish_query, str_language=str_language)
lst_translated_query_retrieval.append(
collection_multilingual_kb_multilingual_ef.query(
query_texts=str_translated_query, n_results=1)["documents"]
)

# Fuse the individual results using Reciprocal Rank Fusion
lst_translated_query_retrieval = rrf(lst_translated_query_retrieval)
print(f"Retrieved documents for the Spanish query: {lst_translated_query_retrieval}")
Retrieved documents for the Spanish query: [['Gli alberi forniscono ombra.']]
  • Chunks Translation: This approach translates the entire knowledge base into a single desired language during ingestion. Although this requires significant upfront work, as all the documents in the knowledge base must be translated in the ingestion phase, it streamlines the retrieval process by effectively simplifying the problem to a monolingual knowledge base.
lst_translated_documents = []
for str_document in lst_multilingual_documents:
lst_translated_documents.append(translate(str_document))

collection_translated_monolingual_kb = chroma_client.create_collection(
name="multilingual_kb_multilingual_ef",
embedding_function=multilingual_ef
)

collection_translated_monolingual_kb.add(
documents=lst_translated_documents,
ids=[f"id_{int_index}"
for int_index in range(len(lst_multilingual_documents))]
)

Conclusion

In conclusion, exploring these options for implementing a multilingual RAG system highlights the flexibility and adaptability of the RAG framework. By sharing these insights, I hope to provide a clearer understanding of the potential paths one can take when venturing into the world of RAG. Each approach has its unique advantages and challenges, and the best choice ultimately depends on the specific requirements and constraints of the project. Whether you’re just starting or looking to refine an existing system, I encourage you to experiment with these methods and find the one that best suits your needs.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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} } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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