Fix paper link, add project page and sample usage

README.md

@@ -2,6 +2,7 @@
 language:
 - en
 - zh
+license: apache-2.0
 pipeline_tag: feature-extraction
 tags:
 - embedding
@@ -9,12 +10,11 @@ tags:
 - long-context
 - rag
 - qwen
-license: apache-2.0
 ---
 
 # EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory
 
-🔗 **[GitHub Repository](https://github.com/MiG-NJU/EvoEmbedding)** | 📚 **[Training Dataset](https://huggingface.co/datasets/MiG-NJU/EvoTrain-180K)** | 📑 **[Paper (https://arxiv.org/abs/2606.21649)]()**
+🔗 **[Project Page](https://clare-nie.github.io/EvoEmbedding/)** | 🐙 **[GitHub Repository](https://github.com/MiG-NJU/EvoEmbedding)** | 📚 **[Training Dataset](https://huggingface.co/datasets/MiG-NJU/EvoTrain-180K)** | 📄 **[Paper](https://arxiv.org/abs/2606.21649)**
 
 **EvoEmbedding** is a novel embedding model designed for long-context and dynamic retrieval scenarios. Unlike static embedding models that chunk text in isolation, EvoEmbedding maintains a continuously updated **Latent Memory Queue**. This allows it to capture temporal dynamics and generate *context-aware, evolvable embeddings* for precise retrieval in agentic workflows and long-conversations.
 
@@ -28,6 +28,50 @@ We provide EvoEmbedding in three sizes based on the Qwen architecture:
 | **EvoEmbedding-2B** | 2B | Qwen3.5-2B | [MiG-NJU/EvoEmbedding-2B](https://huggingface.co/MiG-NJU/EvoEmbedding-2B) |
 | **EvoEmbedding-4B** | 4B | Qwen3-4B | [MiG-NJU/EvoEmbedding-4B](https://huggingface.co/MiG-NJU/EvoEmbedding-4B) |
 
+## 🚀 Quick Start
+
+To use EvoEmbedding, first clone the [GitHub Repository](https://github.com/MiG-NJU/EvoEmbedding) to access the required `model` client implementation.
+
+### As an Embedding Model
+
+```python
+from model.client import EvoEmbeddingClient
+
+client = EvoEmbeddingClient()
+
+messages = [
+    {"role": "user", "content": "I visited Paris in April."},
+    {"role": "assistant", "content": "Noted."},
+    {"role": "user", "content": "I bought a new laptop yesterday."},
+    {"role": "assistant", "content": "Got it."},
+    {"role": "user", "content": "Where did I travel in spring?"},
+]
+
+embeddings = client.encode_messages(messages)
+```
+
+The `messages` input preserves the original dialogue order. `encode_messages` returns normalized embeddings for the history turns and the final query.
+
+### As a Reranker
+
+```python
+candidates = [
+    "I visited Paris in April.",
+    "I bought a new laptop yesterday.",
+    "The meeting was moved to Friday.",
+]
+query = "Where did I travel in spring?"
+
+ranked_candidates, ranked_indices = client.rerank(
+    query,
+    candidates,
+    top_k=1,
+    return_indices=True,
+)
+```
+
+The reranker takes a direct list of candidate strings and returns them in relevance order.
+
 ## 📚 Citation
 
 If you find this model or our methodology useful, please cite our paper: