To set up LlamaIndex for multi-language document retrieval, start by configuring the framework to handle documents in multiple languages. This involves selecting a suitable embedding model that supports multilingual text, such as SentenceTransformers’ “paraphrase-multilingual” models or OpenAI’s text-embedding-3-small. These models map text in various languages into a shared vector space, enabling cross-language similarity comparisons. For example, you might initialize a Hugging Face embedding model with embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") and set it in LlamaIndex’s ServiceContext. Pair this with a vector database like Chroma or FAISS, which can store and query embeddings efficiently.
Next, preprocess your documents to handle language-specific nuances. Use text splitters that work across languages, such as LlamaIndex’s SentenceSplitter or LangChain’s RecursiveCharacterTextSplitter configured with language-agnostic separators like newlines or periods. For instance, splitting text with splitter = RecursiveCharacterTextSplitter(["\n\n", "\n", "。", ".", " "]) accommodates English, Chinese, and many European languages. If your documents mix languages, add metadata (e.g., language: "es") during ingestion to enable filtering later. For OCR-based PDFs or scanned documents, use tools like Tesseract OCR with language packs to extract text accurately.
Finally, optimize retrieval by combining semantic and keyword-based search. Use LlamaIndex’s VectorIndexRetriever for semantic similarity and a keyword-based retriever like BM25 for hybrid search. For example, create a QueryEngine with a RouterQueryEngine that selects between language-specific indices using metadata. If queries and documents use different languages, integrate translation APIs (e.g., Google Translate) to map queries to document languages before retrieval. For instance, translate a French query to English using translator.translate(query_text, dest="en") before searching an English document index. Test with multilingual benchmarks like MrTyDi to validate performance across languages.