async def test_reranking_strategy(self, mock_providers, mock_search_results):
        """Test reranking search results before including in RAG context."""
        # Define a reranker function
        def rerank_results(search_results, query):
            # This would use a model in real implementation
            # Here we'll just simulate reranking with a simple heuristic

            # Create a copy to avoid modifying the original
            reranked_results = {"chunk_search_results": []}

            # Apply a mock reranking logic
            for result in search_results["chunk_search_results"]:
                # Create a copy of the result
                new_result = result.copy()

                # Adjust score based on whether it contains keywords from query
                keywords = ["ethics", "aristotle", "philosophy"]
                score_adjustment = sum(0.1 for keyword in keywords
                                      if keyword.lower() in new_result["text"].lower())

                new_result["score"] = min(0.99, result.get("score", 0.5) + score_adjustment)
                new_result["reranked"] = True

                reranked_results["chunk_search_results"].append(new_result)

            # Sort by adjusted score
            reranked_results["chunk_search_results"].sort(
                key=lambda x: x.get("score", 0),
                reverse=True
            )

            return reranked_results

        # Mock RAG processor with reranking
        class RerankedRAGProcessor:
            def __init__(self, providers):
                self.providers = providers
                self.prompt_builder = MagicMock()
                self.prompt_builder.build_prompt = AsyncMock(
                    return_value=[
                        {"role": "system", "content": "System prompt with reranked context"},
                        {"role": "user", "content": "What did Aristotle say about ethics?"}
                    ]
                )

            async def generate_with_reranking(self, query, search_results):
                # Rerank the search results
                reranked_results = rerank_results(search_results, query)

                # Build prompt with reranked results
                messages = await self.prompt_builder.build_prompt(
                    query=query,
                    search_results=reranked_results
                )

                # Generate response
                response = await self.providers.llm.aget_completion(messages=messages)
                return response["choices"][0]["message"]["content"]

        # Create processor
        processor = RerankedRAGProcessor(mock_providers)

        # Generate response with reranking
        query = "What did Aristotle say about ethics?"
        response = await processor.generate_with_reranking(query, mock_search_results)

        # Verify the LLM was called
        mock_providers.llm.aget_completion.assert_called_once()

        # Check the response
        assert response == "LLM generated response"
Test reranking search results before including in RAG context.
py/tests/unit/retrieval/test_rag_processing.py