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Update doc for rerank
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@ -22,14 +22,14 @@ from lightrag import LightRAG, QueryParam
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from lightrag.rerank import custom_rerank, RerankModel
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# Method 1: Using a custom rerank function with all settings included
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async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
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async def my_rerank_func(query: str, documents: list, top_n: int = None, **kwargs):
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return await custom_rerank(
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query=query,
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documents=documents,
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model="BAAI/bge-reranker-v2-m3",
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base_url="https://api.your-provider.com/v1/rerank",
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api_key="your_api_key_here",
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top_k=top_k or 10, # Handle top_k within the function
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top_n=top_n or 10, # Handle top_n within the function
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**kwargs
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)
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@ -95,7 +95,7 @@ result = await custom_rerank(
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model="BAAI/bge-reranker-v2-m3",
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base_url="https://api.your-provider.com/v1/rerank",
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api_key="your_api_key_here",
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top_k=10
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top_n=10
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)
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```
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@ -109,7 +109,7 @@ result = await jina_rerank(
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documents=documents,
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model="BAAI/bge-reranker-v2-m3",
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api_key="your_jina_api_key",
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top_k=10
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top_n=10
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)
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```
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@ -123,7 +123,7 @@ result = await cohere_rerank(
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documents=documents,
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model="rerank-english-v2.0",
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api_key="your_cohere_api_key",
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top_k=10
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top_n=10
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)
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```
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@ -141,7 +141,7 @@ Reranking is automatically applied at these key retrieval stages:
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `enable_rerank` | bool | False | Enable/disable reranking |
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| `rerank_model_func` | callable | None | Custom rerank function containing all configurations (model, API keys, top_k, etc.) |
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| `rerank_model_func` | callable | None | Custom rerank function containing all configurations (model, API keys, top_n, etc.) |
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## Example Usage
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@ -154,14 +154,14 @@ from lightrag.llm.openai import gpt_4o_mini_complete, openai_embedding
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from lightrag.rerank import jina_rerank
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async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
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async def my_rerank_func(query: str, documents: list, top_n: int = None, **kwargs):
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"""Custom rerank function with all settings included"""
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return await jina_rerank(
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query=query,
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documents=documents,
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model="BAAI/bge-reranker-v2-m3",
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api_key="your_jina_api_key_here",
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top_k=top_k or 10, # Default top_k if not provided
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top_n=top_n or 10, # Default top_n if not provided
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**kwargs
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)
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@ -186,7 +186,7 @@ async def main():
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# Query with rerank (automatically applied)
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result = await rag.aquery(
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"Your question here",
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param=QueryParam(enable_rerank=True) # This top_k is passed to rerank function
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param=QueryParam(enable_rerank=True) # This top_n is passed to rerank function
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)
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print(result)
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@ -212,7 +212,7 @@ async def test_rerank():
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model="BAAI/bge-reranker-v2-m3",
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base_url="https://api.your-provider.com/v1/rerank",
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api_key="your_api_key_here",
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top_k=2
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top_n=2
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)
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for doc in reranked:
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@ -221,11 +221,11 @@ async def test_rerank():
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## Best Practices
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1. **Self-Contained Functions**: Include all necessary configurations (API keys, models, top_k handling) within your rerank function
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1. **Self-Contained Functions**: Include all necessary configurations (API keys, models, top_n handling) within your rerank function
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2. **Performance**: Use reranking selectively for better performance vs. quality tradeoff
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3. **API Limits**: Monitor API usage and implement rate limiting within your rerank function
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4. **Fallback**: Always handle rerank failures gracefully (returns original results)
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5. **Top-k Handling**: Handle top_k parameter appropriately within your rerank function
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5. **Top-n Handling**: Handle top_n parameter appropriately within your rerank function
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6. **Cost Management**: Consider rerank API costs in your budget planning
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## Troubleshooting
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