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generall
2025-01-13 21:59:24 +01:00
parent 78d832ef36
commit fa3feef964
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qdrant-landing/content/documentation/advanced-tutorials/pdf-retrieval-at-scale.md
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@@ -283,7 +283,7 @@ After indexing PDF documents, we can move on to querying them using our two-stag
query = "Lee Harvey Oswald's involvement in the JFK assassination" query = "Lee Harvey Oswald's involvement in the JFK assassination"
processed_queries = model_processor.process_queries([query]).to(model.device) processed_queries = model_processor.process_queries([query]).to(model.device)
# Resulting query embedding is a tensor of shape (2, 128) # Resulting query embedding is a tensor of shape (22, 128)
query_embedding = model(**processed_queries)[0] query_embedding = model(**processed_queries)[0]
``` ```