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Search with Neural Reranking
Semantic search followed by cross-encoder reranking and formatted output.
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Pipeline Stages
1
initial_search
filter
feature_searchView parameters
{
"searches": [
{
"feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1",
"query": {
"input_mode": "text",
"value": "{{INPUT.query}}"
},
"top_k": 100
}
],
"final_top_k": 50
}2
rerank
sort
rerankView parameters
{
"inference_name": "baai_bge_reranker_v2_m3",
"query": "{{INPUT.query}}",
"document_field": "content",
"top_k": 10,
"batch_size": 32
}3
format_output
apply
json_transformView parameters
{
"template": "{\"id\": \"{{DOC.document_id}}\", \"content\": \"{{DOC.content}}\", \"score\": {{DOC.scores.rerank}}}",
"fail_on_error": false
}Input Schema
querytext
required
Search query text
e.g. how to train neural networks
Use Cases
- Question answering
- Conversational search
- High-precision retrieval
Requirements
- Collection must have text embeddings indexed
- Reranker inference service must be available
Tags
reranking
cross-encoder
advanced
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