Enriched Search
RAG-Prepared Search
Optimized pipeline to prepare search results for LLM context windows. Includes reranking and context formatting.
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Pipeline Stages
1
semantic_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": 30
}2
rerank
sort
rerankView parameters
{
"inference_name": "baai_bge_reranker_v2_m3",
"query": "{{INPUT.query}}",
"document_field": "content",
"top_k": 10
}3
rag_prepare
apply
rag_prepareView parameters
{
"max_tokens": 4000,
"overlap_tokens": 100,
"include_metadata": true,
"format": "markdown"
}Input Schema
querytext
required
Query for RAG context
Use Cases
- LLM context preparation
- Chatbot backends
- AI assistant integration
- Context window optimization
Requirements
- Collection must have text embeddings indexed
- Reranker service must be available
Tags
rag
context
llm
preparation
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