Why Multilingual?

Use cases:

• Global customer support
• Cross-language research
• International knowledge bases
• Multilingual chatbots

Top Multilingual Models (Nov 2025)

mE5-large (Microsoft):

• 100+ languages
• 1024 dimensions
• Best performance/cost

multilingual-e5-large-instruct:

• Instruction-tuned
• Query/passage optimization
• SOTA on MIRACL benchmark

LaBSE (Google):

• 109 languages
• 768 dimensions
• Excellent for similar languages

Basic Implementation

DEVELOPERpython
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')

# English query, French documents
query_en = "What is machine learning?"
docs_fr = [
    "L'apprentissage automatique est une branche de l'IA",
    "Les réseaux de neurones sont utilisés en ML"
]

# Embed with instruction prefix
query_emb = model.encode(f"query: {query_en}")
doc_embs = model.encode([f"passage: {doc}" for doc in docs_fr])

# Compute similarity
from sklearn.metrics.pairwise import cosine_similarity
scores = cosine_similarity([query_emb], doc_embs)[0]

Cross-Lingual Retrieval

Search in any language, retrieve in any language:

DEVELOPERpython
# Query in English, documents in multiple languages
query = "How to bake bread?"

documents = {
    "en": "To bake bread, mix flour, water, yeast...",
    "fr": "Pour faire du pain, mélanger farine, eau, levure...",
    "es": "Para hacer pan, mezclar harina, agua, levadura...",
    "de": "Um Brot zu backen, Mehl, Wasser, Hefe mischen..."
}

# Encode all with same model
query_emb = model.encode(f"query: {query}")
doc_embs = {
    lang: model.encode(f"passage: {text}")
    for lang, text in documents.items()
}

# All documents are comparable in the same vector space

Language Detection + Routing

DEVELOPERpython
from langdetect import detect

def multilingual_rag(query):
    # Detect query language
    lang = detect(query)

    # Route to language-specific index (optional optimization)
    if lang in ['en', 'fr', 'de']:
        index = f"docs_{lang}"
    else:
        index = "docs_multilingual"

    # Search
    results = vector_db.search(
        collection_name=index,
        query_vector=model.encode(f"query: {query}")
    )

    return results

Translation Fallback

For rare languages, translate first:

DEVELOPERpython
from transformers import pipeline

translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")

def translate_then_search(query, source_lang, target_lang='en'):
    # Translate query to common language
    if source_lang != target_lang:
        translated = translator(
            query,
            src_lang=source_lang,
            tgt_lang=target_lang
        )[0]['translation_text']
    else:
        translated = query

    # Search in translated space
    results = vector_search(translated)

    return results

Qdrant with Language Filtering

DEVELOPERpython
from qdrant_client import QdrantClient
from qdrant_client.models import Filter, FieldCondition, MatchValue

client = QdrantClient("localhost", port=6333)

# Index documents with language metadata
client.upsert(
    collection_name="multilingual_docs",
    points=[{
        "id": 1,
        "vector": embedding,
        "payload": {
            "text": "...",
            "language": "fr"
        }
    }]
)

# Search with language filter
results = client.search(
    collection_name="multilingual_docs",
    query_vector=query_embedding,
    query_filter=Filter(
        must=[
            FieldCondition(
                key="language",
                match=MatchValue(value="fr")
            )
        ]
    )
)

Evaluation Across Languages

DEVELOPERpython
# MIRACL benchmark (Multilingual Information Retrieval)
from mteb import MTEB

model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')

evaluation = MTEB(tasks=["MIRACL"])
results = evaluation.run(model, output_folder="results/")

print(f"Average nDCG@10: {results['MIRACL']['ndcg@10']}")

Multilingual embeddings unlock global RAG. Use mE5 for best results in November 2025.