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.
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