Our company operates in 12 different countries, and we need our AI search engine to handle queries in multiple languages. Does Haystack support multi-lingual embedding models like mBERT or XLM-RoBERTa natively? Also, how does the framework handle cross-language retrieval where the query is in Spanish but the source document is in English?
3 answers
Haystack is excellent for multi-lingual setups because it integrates seamlessly with the Hugging Face ecosystem. You can simply initialize your EmbeddingRetriever with a multi-lingual model like paraphrase-multilingual-mpnet-base-v2. This model maps different languages into the same vector space, enabling cross-lingual retrieval out of the box. In a project I managed in 2023, we allowed users to ask questions in Italian and get answers from our English-heavy knowledge base. The framework handles the translation of the query into a vector that matches the English documents perfectly without needing a separate translation step.
That sounds powerful, but how do you handle the "Reader" or "Generator" part of the pipeline? Even if the retrieval works across languages, won't the final answer still be in the language of the document unless you use a specific multi-lingual LLM for the generation phase?
Yes, the multi-lingual support is built-in through the retriever components. It’s quite efficient and scales well across different document stores like Milvus or FAISS.
Agreed! The ability to use any Hugging Face model makes Haystack the most versatile choice for global AI search engines currently on the market.
Good catch, Ryan. To solve that, you need to use a model like GPT-4 or a multi-lingual Llama variant in your PromptNode. You can instruct the prompt to "Always answer in the language of the user's question, regardless of the document's language." Haystack’s flexibility with prompt templates makes this very easy to implement. We used this exact strategy for a global HR portal and it worked like a charm for our non-English speaking employees.