Reranker Module

Document reranking implementations for improved search relevance

Reranker Module

The Reranker module provides concrete implementations for reranking search results to improve relevance.

Import

from praisonai.adapters import LLMReranker
from praisonai.adapters.rerankers import CrossEncoderReranker, CohereReranker

Quick Example

from praisonai.adapters import LLMReranker

# Create LLM-based reranker
reranker = LLMReranker(model="gpt-4o-mini")

# Rerank documents
documents = ["Python is a language", "Java is compiled", "Python uses indentation"]
results = reranker.rerank(
    query="What is Python?",
    documents=documents,
    top_k=2
)

for r in results:
    print(f"Score: {r.score:.3f} - {r.text}")

Features

• LLM-based relevance scoring with any model
• Cross-encoder neural reranking
• Cohere Rerank API integration
• Batch processing for efficiency
• Async support

Classes

LLMReranker

Uses an LLM to score document relevance to a query.

from praisonai.adapters import LLMReranker

reranker = LLMReranker(
    model="gpt-4o-mini",
    batch_size=5
)
results = reranker.rerank("query", documents)

Parameters:

Parameter	Type	Default	Description
llm	Any	None	Custom LLM instance (Agent)
model	str	"gpt-4o-mini"	Model for scoring
batch_size	int	5	Documents per batch

How it works:

1. Prompts the LLM to rate relevance on a 0-10 scale
2. Normalizes scores to 0-1 range
3. Sorts by score descending

CrossEncoderReranker

Uses sentence-transformers cross-encoder for accurate relevance scoring.

from praisonai.adapters.rerankers import CrossEncoderReranker

reranker = CrossEncoderReranker(
    model_name="cross-encoder/ms-marco-MiniLM-L-6-v2"
)
results = reranker.rerank("query", documents, top_k=5)

Parameters:

Parameter	Type	Default	Description
model_name	str	"cross-encoder/ms-marco-MiniLM-L-6-v2"	Cross-encoder model

Requires sentence-transformers package: pip install sentence-transformers

CohereReranker

Uses Cohere’s rerank API for high-quality reranking.

from praisonai.adapters.rerankers import CohereReranker

reranker = CohereReranker(
    api_key="your-api-key",  # Or set COHERE_API_KEY env
    model="rerank-english-v3.0"
)
results = reranker.rerank("query", documents, top_k=5)

Parameters:

Parameter	Type	Default	Description
api_key	str	COHERE_API_KEY env	Cohere API key
model	str	"rerank-english-v3.0"	Rerank model

Requires cohere package: pip install cohere

Methods

rerank(query, documents, top_k=None)

Rerank documents by relevance to query.

Parameters:

• query (str): Search query
• documents (List[str]): Documents to rerank
• top_k (int, optional): Number of results to return

Returns: List[RerankResult] - Reranked documents with scores

arerank(query, documents, top_k=None)

Async version of rerank (calls sync internally).

Example: Full RAG Pipeline

from praisonai.adapters import (
    ChromaVectorStore, 
    BasicRetriever, 
    LLMReranker
)

# Setup
store = ChromaVectorStore(namespace="docs")
retriever = BasicRetriever(
    vector_store=store,
    embedding_fn=get_embedding,
    top_k=20  # Retrieve more for reranking
)
reranker = LLMReranker(model="gpt-4o-mini")

# Retrieve
query = "How to deploy Python apps?"
results = retriever.retrieve(query)

# Rerank
documents = [r.text for r in results]
reranked = reranker.rerank(query, documents, top_k=5)

# Use top results
for r in reranked:
    print(f"Score: {r.score:.3f}")
    print(f"Text: {r.text[:100]}...")

Example: Using with Agent

from praisonai.adapters import LLMReranker
from praisonaiagents import Agent

# Use an agent for reranking
agent = Agent(
    instructions="You are a relevance scoring assistant",
    llm="gpt-4o-mini"
)

reranker = LLMReranker(llm=agent)
results = reranker.rerank("Python basics", documents)

Reranker Selection Guide

Use Case	Recommended
Best accuracy	CohereReranker
Local/offline	CrossEncoderReranker
Already using LLM	LLMReranker
Fast, no dependencies	Skip reranking

Environment Variables

# For LLM reranker (uses litellm)
export OPENAI_API_KEY=sk-xxx

# For Cohere reranker
export COHERE_API_KEY=xxx

CLI Usage

# LLM reranker
praisonai knowledge query "What is Python?" --reranker llm

# Cross-encoder reranker
praisonai knowledge query "What is Python?" --reranker cross_encoder

# Cohere reranker
praisonai knowledge query "What is Python?" --reranker cohere

Related

• Retrieval Module - Retrieve documents
• Vector Store Module - Store documents