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Documentation Index

Fetch the complete documentation index at: https://docs.praison.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Reranker Module

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

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Import

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

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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}")

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Features

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

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Classes

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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:
ParameterTypeDefaultDescription
llmAnyNoneCustom LLM instance (Agent)
modelstr"gpt-4o-mini"Model for scoring
batch_sizeint5Documents 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

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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:
ParameterTypeDefaultDescription
model_namestr"cross-encoder/ms-marco-MiniLM-L-6-v2"Cross-encoder model
Requires sentence-transformers package: pip install sentence-transformers

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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:
ParameterTypeDefaultDescription
api_keystrCOHERE_API_KEY envCohere API key
modelstr"rerank-english-v3.0"Rerank model
Requires cohere package: pip install cohere

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Methods

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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

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arerank(query, documents, top_k=None)

Async version of rerank (calls sync internally).

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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]}...")

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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)

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Reranker Selection Guide

Use CaseRecommended
Best accuracyCohereReranker
Local/offlineCrossEncoderReranker
Already using LLMLLMReranker
Fast, no dependenciesSkip reranking

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Environment Variables

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

# For Cohere reranker
export COHERE_API_KEY=xxx

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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