Scalable PDF Retrieval with Cohere Embed 5 and Qdrant Asymmetric Quantization

Cohere’s Embed 5 Pro and Fast models support multilingual text and image embeddings, enabling direct retrieval of PDF page images without OCR, which can lose information from charts, tables, and complex layouts. Testing Embed 5 Pro across five public English ViDoRe V3 document-retrieval corpora found that its single dense vector per page achieved a mean nDCG@10 of 65.8, outperforming several late-interaction visual models while remaining compatible with standard HNSW indexing. The post describes a Qdrant-based production approach that combines Embed 5’s Matryoshka representation learning, one-bit binary storage for document vectors, eight-bit asymmetric query quantization, and full-vector reranking of the top 20 candidates. In the reported experiments, storing only 512 binary dimensions per page required 64 bytes of RAM per vector while retaining 99.2% of full 2,048-dimensional float32 ranking quality, offering a substantial memory reduction for large collections. For 10 million PDF pages, the estimated search-vector footprint falls from about 76 GB for full Embed 5 vectors to roughly 610 MB in RAM, though full vectors remain on disk for reranking. The post also notes that Cohere Rerank 5 can improve OCR-based text retrieval pipelines, raising mean nDCG@10 from 66.8 to 73.2 in its benchmark when reranking the top 20 results.

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