---
last_updated: 2026-07-20
tags: [embedding, multilingual-e5, local, farsi, insurance, rag]
source: import-knowledge
---

# intfloat/multilingual-e5-large

## Overview

- **Type**: Local
- **Provider**: intfloat
- **Parameters**: ~560M (24-layer, xlm-roberta-large init)
- **VRAM (fp16)**: ~1.1 GB
- **Embedding dimensions**: 1024
- **Max input tokens**: 512
- **Language coverage**: 100 languages including Farsi
- **Serving platform**: sentence-transformers, TEI, Ollama (available)
- **License**: MIT
- **Model card**: https://huggingface.co/intfloat/multilingual-e5-large

## Pros

- Most widely used multilingual embedding model
- Strong community support and documentation
- 100 languages including Farsi
- MIT license
- Available on Ollama

## Cons

- **512 token max context** — requires aggressive chunking for insurance documents
- No ColBERT/sparse support
- Requires `"query: "` / `"passage: "` prefixes for optimal retrieval
- Benchmarked on Mr. TyDi but Farsi is NOT one of the 11 Mr. TyDi languages

## ⚠️ 512 Token Limit

Insurance policy clauses and FAQ answers can easily exceed 512 tokens. You will need to chunk aggressively. Consider BGE-M3 (8K context) or gte-multilingual-base (8K context) instead.

## API Test — Ollama

```bash
ollama pull multilingual-e5-large

curl -s http://localhost:11434/api/embeddings   -d '{
    "model": "multilingual-e5-large",
    "prompt": "query: بیمه نامه شخص ثالث چیست؟"
  }' | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d["embedding"])}')"
```

**⚠️ Important**: Ollama may not auto-add the `"query: "` prefix. You must add it manually in the prompt for optimal retrieval quality.

## API Test — Python

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("intfloat/multilingual-e5-large")

# ⚠️ Must add "query: " prefix for queries, "passage: " for documents
query_embedding = model.encode("query: بیمه نامه شخص ثالث چیست؟")
doc_embedding = model.encode("passage: بیمه نامه شخص ثالث شامل پوشش خسارات مالی و جانی است.")

print(f"Query dims: {len(query_embedding)}, Doc dims: {len(doc_embedding)}")
# Expected: Query dims: 1024, Doc dims: 1024
```

## TODO

- [ ] Benchmark on MIRACL-Farsi subset
- [ ] Test with aggressive chunking (max 500 tokens)
- [ ] Compare with BGE-M3 on Farsi retrieval quality