Retrievers

Retrievers combine the features your extractors produce into multi-stage search pipelines.

31 stages available

Schedule Demo Read Documentation

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

Agent Search

LLM-driven multi-step retrieval with iterative reasoning and tool orchestration

agent_search

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

Sort

Score Normalize

Rescale document scores to a common range for consistent comparison

score_normalize

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

1 / 3

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.

Feature Extractors

Recipes