To create a multilingual search engine with Haystack, you’ll need to combine multilingual embeddings, language-aware preprocessing, and a retrieval pipeline that handles cross-lingual queries. Haystack’s modular design allows you to integrate components like document stores, retrievers, and language models to support multiple languages. The core idea is to map text in different languages into a shared embedding space so that semantically similar content—regardless of language—is clustered together for accurate retrieval.
First, choose a multilingual embedding model. Models like sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 or sentence-transformers/distiluse-base-multilingual-cased-v2 are trained to encode text in multiple languages into a shared vector space. These models allow queries in one language (e.g., French) to match documents in another (e.g., English) if their meanings align. In Haystack, you can use the EmbeddingRetriever with one of these models to index documents. For example, when indexing, the retriever converts each document’s text into an embedding, which is stored in a vector database like FAISS or Elasticsearch. During searches, the query is embedded using the same model, and the system retrieves documents with the closest embeddings.
Next, handle language detection and preprocessing. If your documents aren’t already labeled with their language, use a library like langdetect to detect and tag them during indexing. This step ensures you can apply language-specific rules (e.g., tokenization) if needed. However, multilingual transformer models often handle raw text effectively, so minimal preprocessing (like lowercasing or removing special characters) may suffice. For queries, detect the input language dynamically or let users specify it. If your search engine needs to return results in a specific language, add a filter to the pipeline. For instance, you could use a FilterRetriever to exclude documents in unwanted languages after the initial multilingual retrieval.
Finally, design the pipeline for scalability. A basic setup might include a DocumentStore (e.g., FAISS for fast vector search), the EmbeddingRetriever with a multilingual model, and optional components like a Translator to convert queries or results into a target language. For example, if a user searches in Spanish but wants results in English, you could translate the query to English before embedding it, or translate retrieved English documents to Spanish post-retrieval. Test the system with mixed-language datasets to ensure robustness. If performance varies across languages, consider fine-tuning the embedding model on domain-specific multilingual data or adding a reranking step with a cross-lingual model like BM25 for hybrid retrieval.