Updating Reranker materials

03-standalone-api/03-rerank/README.md

@@ -0,0 +1,69 @@
+# Contextual AI Reranker Examples
+
+This folder contains examples demonstrating how to use Contextual AI's reranker, which is the first reranker with instruction-following capabilities to handle conflicts in retrieval. It is the most accurate reranker in the world per industry-leading benchmarks like BEIR.
+
+## 📁 Contents
+
+### 1. `rerank.ipynb` - Basic Reranker Usage
+A comprehensive tutorial showing different ways to use the Contextual AI reranker:
+
+- **REST API implementation** - Direct API calls using the `requests` library
+- **Python SDK** - Using the official `contextual-client` package  
+- **Langchain integration** - Using the `langchain-contextual` package
+
+**Key Features Demonstrated:**
+- Query reranking with custom instructions
+- Document metadata handling
+- Multiple integration methods
+- Enterprise pricing example use case
+
+### 2. `reranker_benchmarking.ipynb` - Performance Evaluation
+A robust evaluation framework for testing the Contextual AI reranker against standard benchmarks:
+
+- **Dataset Support** - Evaluation on Hugging Face datasets including:
+  - touche2020
+  - msmarco  
+  - treccovid
+  - nq (Natural Questions)
+  - hotpotqa
+  - fiqa2018
+
+- **Comprehensive Metrics** - Proper evaluation using:
+  - NDCG@10 (Normalized Discounted Cumulative Gain)
+  - MAP (Mean Average Precision)
+  - Recall@10
+  - MRR (Mean Reciprocal Rank)
+
+## 🎯 Available Models
+
+The current reranker models include:
+- `ctxl-rerank-v2-instruct-multilingual` - Full model with multilingual support
+- `ctxl-rerank-v2-instruct-multilingual-mini` - Faster mini version
+- `ctxl-rerank-v1-instruct` - Previous generation model
+
+## 🔗 Learn More
+
+- [Contextual AI Reranker Blog Post](https://contextual.ai/blog/introducing-instruction-following-reranker/)
+- [Open Sourcing Rerank v2](https://contextual.ai/blog/rerank-v2/)
+- [API Documentation](https://docs.contextual.ai/api-reference/rerank/rerank
+- [Python SDK Documentation](https://github.com/ContextualAI/contextual-client-python/blob/main/api.md#rerank)
+- [Langchain Package](https://pypi.org/project/langchain-contextual/)
+
+## 📝 Example Usage
+
+```python
+from contextual import ContextualAI
+
+client = ContextualAI(api_key="your-api-key")
+
+rerank_response = client.rerank.create(
+    query="What is the enterprise pricing for RTX 5090?",
+    instruction="Prioritize internal sales documents over market reports",
+    documents=["Document 1", "Document 2", "Document 3"],
+    model="ctxl-rerank-v2-instruct-multilingual"
+)
+
+print(rerank_response.to_dict())
+```
+
+Start with `rerank.ipynb` for basic usage, then explore `reranker_benchmarking.ipynb` for advanced evaluation and performance testing.

03-standalone-api/03-rerank/rerank.ipynb

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         "\n",
         "This notebook demonstrates how to use the reranker with the Contextual API directly, our Python SDK, and our Langchain package. We'll use the same example throughout.\n",
         "\n",
+        "The current reranker models include: \n",
+        "- ctxl-rerank-v2-instruct-multilingual \n",
+        "- ctxl-rerank-v2-instruct-multilingual-mini\n",
+        "- ctxl-rerank-v1-instruct\n",
+        "\n",
         "<br/>\n",
         "\n",
         "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ContextualAI/examples/blob/main/03-standalone-api/03-rerank/rerank.ipynb)"
@@ -72,7 +77,7 @@
         "    \"January 25, 2025; NVIDIA Enterprise Sales Portal; Internal Use Only\"\n",
         "]\n",
         "\n",
-        "model = \"ctxl-rerank-en-v1-instruct\""
+        "model = \"ctxl-rerank-v2-instruct-multilingual\""
       ]
     },
     {