Retriever Stages

Build powerful search pipelines by composing modular stages

24 stages available

Filter

Feature Search

Search and filter documents by vector similarity using feature embeddings

feature_search

Filter

Attribute Filter

Filter documents by metadata attribute values using boolean logic

attribute_filter

Filter

Llm Filter

Filter documents using LLM-based semantic evaluation

llm_filter

Filter

Query Expand

Generate query variations with LLM and fuse search results via RRF

query_expand

Sort

Sort Relevance

Sort documents by relevance score

sort_relevance

Sort

Sort Attribute

Sort documents by any metadata field value

sort_attribute

Sort

Mmr

Reorder results using Maximal Marginal Relevance for diversity

mmr

Sort

Rerank

Rerank documents using cross-encoder models for accurate relevance

rerank

Reduce

Aggregate

Compute aggregations (COUNT, SUM, AVG, etc.) on pipeline results

aggregate

Reduce

Group By

Group documents by field value for decompose/recompose workflows

group_by

Reduce

Cluster

Cluster documents by embedding similarity to discover themes

cluster

Reduce

Sample

Sample a subset of documents using random or stratified sampling

sample

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How Retriever Pipelines Work

Combine stages to build sophisticated search and retrieval pipelines. Each stage type serves a specific purpose in the data flow.

1. Search

Retrieve documents from collections using semantic search, keyword search, or hybrid approaches.

2. Filter

Remove documents that don't match criteria using attribute filters, score thresholds, or LLM evaluation.

3. Rank

Reorder documents by relevance using sorting, cross-encoders, or LLM-based reranking.

4. Generate

Create responses from retrieved documents using RAG, summarization, or custom LLM generation.