Does Haystack support multi-lingual search and retrieval?

Yes, Haystack supports multi-lingual search and retrieval through its flexible architecture and integration with language-aware components. The framework allows developers to build pipelines that handle documents and queries in multiple languages by leveraging language-specific models, document stores, and preprocessing tools. This capability is critical for applications serving global users or analyzing cross-lingual data, and Haystack provides the building blocks to implement it effectively.

Haystack achieves multi-lingual support primarily through its compatibility with language-specific embedding models and document stores. For example, you can use multilingual transformer models like sentence-transformers/all-mpnet-base-v2 or xlm-roberta-base, which generate embeddings that capture semantic meaning across languages. These embeddings enable similarity comparisons between queries and documents in different languages. On the document store side, Elasticsearch (a supported backend) offers language-specific analyzers and tokenization rules, allowing proper indexing of text in languages like German, Chinese, or Arabic. Developers can configure these analyzers during index setup to handle language-specific nuances like compound words or character-based scripts.

A practical implementation might involve a pipeline that ingests documents in multiple languages, processes them with language detection or translation components, and uses a multilingual retriever. For instance, you could:

1. Use a language detection model (e.g., langdetect library) to categorize documents by language during indexing.
2. Apply language-specific text cleaning or stemming using Elasticsearch analyzers.
3. Retrieve results using a cross-lingual retriever like EmbeddingRetriever with a multilingual model, which can match an English query to Spanish documents. For question answering, models like deepset/xlm-roberta-large-qa-multi can extract answers from multilingual documents even when the query language differs. While Haystack doesn’t handle translation natively, you could integrate translation APIs or models (e.g., Helsinki-NLP translators on Hugging Face) into the pipeline to normalize content to a single language if needed. Developers should test combinations of models and analyzers for their target languages, as performance can vary based on training data coverage and linguistic differences.