Optimizing reranker performance is crucial for maintaining fast search response times while improving result quality. This guide covers best practices for different reranker types.

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General Optimization Principles

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Candidate Set Size

The number of candidates sent to the reranker significantly impacts performance: 
# Optimal candidate sizes for different rerankers
config_map = {
    "cohere": {"initial_candidates": 100, "top_n": 10},
    "sentence_transformer": {"initial_candidates": 50, "top_n": 10},
    "huggingface": {"initial_candidates": 30, "top_n": 5},
    "llm_reranker": {"initial_candidates": 20, "top_n": 5}
}

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Batching Strategy

Process multiple queries efficiently: 
# Configure for batch processing
config = {
    "reranker": {
        "provider": "sentence_transformer",
        "config": {
            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
            "batch_size": 16,  # Process multiple candidates at once
            "top_n": 10
        }
    }
}

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Provider-Specific Optimizations

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Cohere Optimization

# Optimized Cohere configuration
config = {
    "reranker": {
        "provider": "cohere",
        "config": {
            "model": "rerank-english-v3.0",
            "top_n": 10,
            "max_chunks_per_doc": 10,  # Limit chunk processing
            "return_documents": False   # Reduce response size
        }
    }
}
Best Practices:
  • Use v3.0 models for better speed/accuracy balance
  • Limit candidates to 100 or fewer
  • Cache API responses when possible
  • Monitor API rate limits

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Sentence Transformer Optimization

# Performance-optimized configuration
config = {
    "reranker": {
        "provider": "sentence_transformer",
        "config": {
            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
            "device": "cuda",  # Use GPU when available
            "batch_size": 32,
            "top_n": 10,
            "max_length": 512  # Limit input length
        }
    }
}
Device Optimization: 
import torch

# Auto-detect best device
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"

config = {
    "reranker": {
        "provider": "sentence_transformer",
        "config": {
            "device": device,
            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2"
        }
    }
}

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Hugging Face Optimization

# Optimized for Hugging Face models
config = {
    "reranker": {
        "provider": "huggingface",
        "config": {
            "model": "BAAI/bge-reranker-base",
            "use_fp16": True,  # Half precision for speed
            "max_length": 512,
            "batch_size": 8,
            "top_n": 10
        }
    }
}

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LLM Reranker Optimization

# Optimized LLM reranker configuration
config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
            "llm": {
                "provider": "openai",
                "config": {
                    "model": "gpt-3.5-turbo",  # Faster than gpt-4
                    "temperature": 0,  # Deterministic results
                    "max_tokens": 500  # Limit response length
                }
            },
            "batch_ranking": True,  # Rank multiple at once
            "top_n": 5,  # Fewer results for faster processing
            "timeout": 10  # Request timeout
        }
    }
}

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Performance Monitoring

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Latency Tracking

import time
from mem0 import Memory

def measure_reranker_performance(config, queries, user_id):
    memory = Memory.from_config(config)

    latencies = []
    for query in queries:
        start_time = time.time()
        results = memory.search(query, user_id=user_id)
        latency = time.time() - start_time
        latencies.append(latency)

    return {
        "avg_latency": sum(latencies) / len(latencies),
        "max_latency": max(latencies),
        "min_latency": min(latencies)
    }

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Memory Usage Monitoring

import psutil
import os

def monitor_memory_usage():
    process = psutil.Process(os.getpid())
    return {
        "memory_mb": process.memory_info().rss / 1024 / 1024,
        "memory_percent": process.memory_percent()
    }

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Caching Strategies

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Result Caching

from functools import lru_cache
import hashlib

class CachedReranker:
    def __init__(self, config):
        self.memory = Memory.from_config(config)
        self.cache_size = 1000

    @lru_cache(maxsize=1000)
    def search_cached(self, query_hash, user_id):
        return self.memory.search(query, user_id=user_id)

    def search(self, query, user_id):
        query_hash = hashlib.md5(f"{query}_{user_id}".encode()).hexdigest()
        return self.search_cached(query_hash, user_id)

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Model Caching

# Pre-load models to avoid initialization overhead
config = {
    "reranker": {
        "provider": "sentence_transformer",
        "config": {
            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
            "cache_folder": "/path/to/model/cache",
            "device": "cuda"
        }
    }
}

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Parallel Processing

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Async Configuration

import asyncio
from mem0 import Memory

async def parallel_search(config, queries, user_id):
    memory = Memory.from_config(config)

    # Process multiple queries concurrently
    tasks = [
        memory.search_async(query, user_id=user_id)
        for query in queries
    ]

    results = await asyncio.gather(*tasks)
    return results

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Hardware Optimization

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GPU Configuration

# Optimize for GPU usage
import torch

if torch.cuda.is_available():
    torch.cuda.set_per_process_memory_fraction(0.8)  # Reserve GPU memory

config = {
    "reranker": {
        "provider": "sentence_transformer",
        "config": {
            "device": "cuda",
            "model": "cross-encoder/ms-marco-electra-base",
            "batch_size": 64,  # Larger batch for GPU
            "fp16": True  # Half precision
        }
    }
}

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CPU Optimization

import torch

# Optimize CPU threading
torch.set_num_threads(4)  # Adjust based on your CPU

config = {
    "reranker": {
        "provider": "sentence_transformer",
        "config": {
            "device": "cpu",
            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
            "num_workers": 4  # Parallel processing
        }
    }
}

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Benchmarking Different Configurations

def benchmark_rerankers():
    configs = [
        {"provider": "cohere", "model": "rerank-english-v3.0"},
        {"provider": "sentence_transformer", "model": "cross-encoder/ms-marco-MiniLM-L-6-v2"},
        {"provider": "huggingface", "model": "BAAI/bge-reranker-base"}
    ]

    test_queries = ["sample query 1", "sample query 2", "sample query 3"]

    results = {}
    for config in configs:
        provider = config["provider"]
        performance = measure_reranker_performance(
            {"reranker": {"provider": provider, "config": config}},
            test_queries,
            "test_user"
        )
        results[provider] = performance

    return results

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Production Best Practices

  1. Model Selection: Choose the right balance of speed vs. accuracy
  2. Resource Allocation: Monitor CPU/GPU usage and memory consumption
  3. Error Handling: Implement fallbacks for reranker failures
  4. Load Balancing: Distribute reranking load across multiple instances
  5. Monitoring: Track latency, throughput, and error rates
  6. Caching: Cache frequent queries and model predictions
  7. Batch Processing: Group similar queries for efficient processing