How does Haystack support cross-lingual retrieval?

Haystack supports cross-lingual retrieval by leveraging multilingual embedding models and translation components within its search pipelines. The framework enables developers to build systems that can process queries in one language and retrieve relevant documents in another. This is achieved through three main mechanisms: multilingual embeddings for semantic understanding, translation tools to bridge language gaps, and flexible pipelines that combine these components. By integrating these features, Haystack simplifies the creation of search systems that operate across languages.

The core of Haystack’s cross-lingual capability lies in its use of multilingual text embedding models, such as sentence-transformers or OpenAI embeddings. These models are trained on data from multiple languages, allowing them to map text in different languages into a shared semantic space. For example, a query in English and a document in Spanish can be converted into vectors that are close to each other if their meanings align. Developers can use Haystack’s EmbeddingRetriever with models like paraphrase-multilingual-MiniLM-L12-v2 to index and search multilingual documents. When a query is issued, the retriever compares its embedding against the indexed documents, regardless of language, enabling matches across linguistic boundaries.

To further enhance cross-lingual workflows, Haystack supports translation components that can be integrated into pipelines. For instance, a TransformersTranslator node can translate a user’s query into a target language before retrieval, or translate retrieved documents back into the user’s language. Developers can also use third-party translation APIs for this step. Additionally, Haystack’s ExtractiveQAPipeline can combine translation with question answering—for example, translating a German query to English, retrieving English documents, and then translating the answer back to German. This modularity allows developers to choose the best approach for their use case, balancing speed, cost, and accuracy. By combining multilingual embeddings with translation tools, Haystack provides a robust foundation for building cross-lingual search systems.