Multilingual search applications need to understand queries and documents written in different languages. A user might search in English while the relevant document is written in Hindi, Spanish, Arabic, or another language.
Traditional keyword-based search can struggle with this situation. A search for "employee leave policy" may not find a document titled "कर्मचारी अवकाश नीति" because the words are different, even though the meaning is similar.
Embedding models help solve this problem by converting text into numerical vectors that represent semantic information. A search system can compare these vectors to identify documents with similar meanings, even when the wording or language differs.
However, not every embedding model performs equally well across languages. Choosing the wrong model can produce poor search results, especially for less commonly supported languages, mixed-language queries, and domain-specific terminology.
This article explains how to evaluate embedding models and select an appropriate option for a multilingual search application.
What Are Text Embeddings?
A text embedding is a numerical representation of text. An embedding model converts a sentence, paragraph, or document into a vector containing numerical values.
For example, consider these two sentences:
The employee can apply for annual leave.
कर्मचारी वार्षिक छुट्टी के लिए आवेदन कर सकता है।
The sentences use different languages but communicate a similar idea.
A multilingual embedding model attempts to represent their meaning in a shared vector space. This allows a search application to identify semantic relationships between them.
A simplified workflow looks like this:
English Document ─────┐
|
Hindi Document ───────┼──> Embedding Model
|
Spanish Document ─────┘
|
v
Numerical Vectors
|
v
Vector Database
|
v
Similarity Search
When a user submits a query, the application generates its embedding and compares it with the stored document embeddings.
The database returns documents whose vectors are closest to the query vector according to the selected similarity metric.
Why Multilingual Embedding Selection Matters
A model that performs well on English search does not automatically perform well in every language.
Multilingual applications introduce several challenges.
Different Language Structures
Languages have different grammar, word order, sentence structures, and writing systems.
For example, English generally follows a subject-verb-object structure, while Hindi commonly uses a subject-object-verb structure.
An embedding model must capture semantic meaning despite these differences.
Cross-Language Search
Users may search in one language and expect results in another.
For example:
Query: How can I reset my password?
Document: पासवर्ड रीसेट करने की प्रक्रिया
A suitable model should recognize that these texts describe related information.
Mixed-Language Content
Many applications contain documents that combine multiple languages.
An Indian customer support platform, for example, may receive messages such as:
Mera account login nahi ho raha hai.
Please help me reset my password.
This type of language mixing is often called code-switching.
A model that handles individual languages well may still struggle with mixed-language sentences.
Domain-Specific Terminology
Technical, medical, financial, and legal applications use specialized vocabulary.
A model may understand general conversation but fail to distinguish between similar technical terms or domain-specific meanings.
Therefore, language coverage alone is not enough to select an embedding model.
Identify Your Multilingual Search Requirements
Before comparing models, define how the application will use search.
Different applications have different requirements.
Application | Important Embedding Capability |
|---|
Customer support search | Cross-language semantic matching |
Enterprise document search | Long-document retrieval and language coverage |
E-commerce search | Product terminology and mixed-language queries |
Technical documentation | Technical vocabulary and semantic precision |
Legal document search | Domain-specific terminology and contextual relevance |
Knowledge base search | Multilingual retrieval across document collections |
Also consider the languages your users actually use.
For example, an application serving users in India might need English, Hindi, Bengali, Marathi, Tamil, and Telugu support. An application serving European customers may require a different language combination.
Do not assume that a model supporting many languages will perform equally well in every one of them.
Compare Embedding Models Using Practical Evaluation Criteria
Several factors should influence model selection.
1. Language Coverage
Check whether the model supports the languages used by your application.
Look beyond the total number of supported languages. Review whether the model has been evaluated on your target languages and whether it supports cross-language retrieval.
A model may support Hindi and English independently but still perform inconsistently when an English query must retrieve Hindi documents.
2. Cross-Lingual Retrieval Quality
This is one of the most important criteria for multilingual search.
Test whether a query in one language can retrieve relevant documents written in another language.
For example:
Search Query | Expected Document |
|---|
How to apply for leave? | कर्मचारी अवकाश आवेदन प्रक्रिया |
पासवर्ड कैसे बदलें? | How to change your password |
Factura de pago | Payment invoice instructions |
The expected result should be based on meaning, not identical words.
3. Context and Document Length
Some models are designed for short sentences, while others support longer inputs.
If your application indexes long documents, check the model's maximum input length and how it handles content beyond that limit.
Long documents may need to be divided into smaller chunks before generating embeddings.
4. Vector Dimensions
Embedding models produce vectors with different dimensions.
For example, one model might produce 384-dimensional vectors, while another produces 768 or 1,536 dimensions.
Higher dimensions do not automatically mean better retrieval quality.
Vector dimensions affect storage requirements, indexing performance, and computational costs. Compare retrieval quality alongside these operational factors.
5. Latency and Cost
Embedding generation happens during document ingestion and user search.
Evaluate both workloads:
- Document embedding cost during indexing.
- Query embedding cost during search.
If documents are updated frequently, ingestion cost may become significant. If the application receives many searches per second, query latency and throughput may matter more.
6. Data Privacy and Deployment Options
Some embedding services process text through external APIs. Others can run locally or inside controlled infrastructure.
Review where data is processed, how requests are retained, and what privacy controls are available.
For confidential enterprise documents, deployment and data-handling requirements may influence the decision more than small differences in benchmark scores.
Evaluate Models With a Multilingual Test Dataset
Do not select an embedding model only by reading its documentation or comparing vector dimensions.
Create a test dataset using actual search scenarios from your application.
For example, an employee knowledge portal might contain these documents:
Document 1:
Employees can apply for annual leave through the HR portal.
Document 2:
कर्मचारी एचआर पोर्टल के माध्यम से वार्षिक अवकाश के लिए आवेदन कर सकते हैं।
Document 3:
Employees can reset their account password through the security page.
Document 4:
कर्मचारी सुरक्षा पृष्ठ से अपना पासवर्ड रीसेट कर सकते हैं।
Now create queries in different languages:
How can I apply for annual leave?
वार्षिक छुट्टी के लिए आवेदन कैसे करें?
How do I reset my account password?
पासवर्ड कैसे रीसेट करें?
For each query, record which documents should be retrieved.
The dataset should include:
- Queries and documents in the same language.
- Queries and documents in different languages.
- Mixed-language queries.
- Short and long search queries.
- Domain-specific terminology.
- Similar but unrelated documents.
- Common spelling variations and transliterations.
The final category is particularly important for languages where users frequently type native-language words using the Latin alphabet.
Measure Retrieval Quality
Use retrieval metrics to compare candidate embedding models.
Recall@K
Recall@K measures whether relevant documents appear within the top K search results.
For example, if there are four relevant documents and the top five results contain three of them:
[
Recall@5 = \frac{3}{4} = 0.75
]
A higher recall indicates that the search system is finding more of the relevant documents.
Precision@K
Precision@K measures how many of the top K results are relevant.
If a search returns five documents and three are relevant:
[
Precision@5 = \frac{3}{5} = 0.60
]
Precision is important when users expect the first few results to be accurate.
Mean Reciprocal Rank
Mean Reciprocal Rank (MRR) evaluates the position of the first relevant result.
If the first relevant document appears at position two:
[
RR = \frac{1}{2} = 0.5
]
MRR averages this reciprocal rank across multiple queries.
This metric is useful when the first relevant result is particularly important.
Evaluate by Language
Do not calculate only one overall score.
Report retrieval performance separately for each language and query type. An overall average can hide poor performance in a language used by a smaller group of customers.
Generate Embeddings With a Consistent Model
Once a model is selected, use the same embedding model and compatible preprocessing for both document indexing and query processing.
The following Python example demonstrates the basic workflow using a generic embedding client interface:
from typing import List
def generate_embeddings(
texts: List[str],
embedding_client
):
response = embedding_client.embed(
texts=texts,
model="multilingual-embedding-model"
)
return response.embeddings
documents = [
"Employees can apply for annual leave.",
"कर्मचारी वार्षिक अवकाश के लिए आवेदन कर सकते हैं।"
]
vectors = generate_embeddings(
documents,
embedding_client
)
print("Generated vectors:", len(vectors))
The client and model name are illustrative. The actual method, authentication, response structure, and model identifier depend on the embedding provider.
During indexing, store the vector alongside the document ID and relevant metadata.
For a PostgreSQL application using the pgvector extension, a simplified table could look like this:
CREATE TABLE knowledge_documents (
id BIGSERIAL PRIMARY KEY,
content TEXT NOT NULL,
language_code VARCHAR(10),
embedding VECTOR(768),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
The vector dimension must match the output dimension of the selected model. The value 768 is an example, not a universal requirement.
When the model changes, the vector dimension or embedding distribution may also change. Treat model migrations as a data migration rather than simply replacing the model name in the application configuration.
Improve Results With Hybrid Search
Embedding-based search is useful for semantic matching, but it does not always handle exact identifiers and specialized terms well.
Consider this query:
Search for error code ERR-5021.
A semantic search model may return documents about general server errors rather than the exact error code.
Hybrid search combines vector similarity with keyword-based retrieval.
A typical workflow is:
- Execute semantic vector search.
- Execute keyword or full-text search.
- Combine the result sets.
- Apply a ranking strategy.
- Return the most relevant documents.
This approach can improve search quality when documents contain product codes, technical identifiers, names, or exact phrases.
A reranking model can also evaluate the retrieved candidates and reorder them based on query-document relevance.
However, additional retrieval and reranking stages increase processing time and infrastructure cost. Evaluate whether the improvement justifies the added complexity.
Common Mistakes to Avoid
Selecting a Model Based Only on Language Count
A model supporting many languages may not perform well on every language pair. Evaluate actual retrieval quality for your target users.
Using Only Translated English Test Queries
Automatically translating a small English dataset does not represent real multilingual usage. Include native-language queries, regional terminology, spelling variations, and code-switching.
Ignoring Document Chunking
Large documents may exceed model input limits or contain multiple unrelated topics. Use a chunking strategy that preserves meaningful context.
Comparing Models With Different Test Datasets
Model comparisons are unreliable when each model receives different queries or relevance judgments. Use the same dataset and retrieval configuration.
Mixing Embeddings From Different Models
Vectors generated by different models generally do not share the same representation space. Do not compare them directly without a compatible migration or alignment strategy.
Ignoring Operational Requirements
A model with strong retrieval quality may still be unsuitable if its latency, cost, privacy controls, or deployment requirements do not match the application.
Best Practices for Production Multilingual Search
A production implementation should follow a repeatable evaluation and deployment process:
- Identify the languages and language combinations used by customers.
- Build a representative multilingual query-document dataset.
- Compare models using identical retrieval conditions.
- Measure Recall@K, Precision@K, and MRR.
- Review retrieval quality separately for each language.
- Evaluate latency, throughput, storage, and cost.
- Use consistent preprocessing for documents and queries.
- Add hybrid search where exact keyword matching is important.
- Version the embedding model and its configuration.
- Monitor search quality and collect feedback after deployment.
When changing embedding models, generate a new set of document vectors and validate them before switching production traffic. Keeping the old index available during migration provides a safer rollback path.
Summary
Choosing an embedding model for multilingual search requires more than checking how many languages it supports.
The model must retrieve relevant information across language boundaries, handle real user queries, and perform consistently with the terminology and document types used by the application.
Developers should evaluate candidate models using representative multilingual datasets and retrieval metrics such as Recall@K, Precision@K, and MRR. They should also consider vector dimensions, document length, latency, cost, privacy, and deployment requirements.
Hybrid search can further improve results when semantic matching needs to work alongside exact keyword retrieval.
The most suitable embedding model is not necessarily the largest or the one with the highest advertised language coverage. It is the model that delivers reliable retrieval quality for the application's actual users while meeting its operational requirements.
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