Update README.md

README.md

@@ -52,4 +52,55 @@ Each row in the dataset contains the following fields:
 + **url** (string): The source URL where the document was retrieved from.
 
 ## How to use this dataset?
-You can use this dataset together with its embeddings to build an offline search engine.
+You can use this dataset together with its embeddings to build an offline search engine. Below is a pseduo code for **demonstration only** (for production use, consider [Faiss-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU)).
+```python
+# download index before
+# huggingface-cli download OpenResearcher/OpenResearcher-Corpus --repo-type=dataset --include="qwen3-embedding-8b/*" --local-dir ./indexes
+import glob
+import pickle
+import faiss
+import numpy as np
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+
+# 1. Load corpus
+corpus = load_dataset("OpenResearcher/OpenResearcher-Corpus", split="train")
+docid_to_doc = {str(doc["docid"]): doc for doc in corpus}
+
+# 2. Load all embedding shards from OpenResearcher-Indexes
+index_files = sorted(glob.glob("path/to/indexes/*.pkl"))
+all_embeddings = []
+all_lookup = []
+
+for file_path in index_files:
+    with open(file_path, "rb") as f:
+        embeddings, lookup = pickle.load(f)
+        all_embeddings.append(embeddings)
+        all_lookup.extend(lookup)
+
+all_embeddings = np.vstack(all_embeddings).astype(np.float32)
+faiss.normalize_L2(all_embeddings)  # Normalize for cosine similarity
+
+# 3. Build FAISS index
+index = faiss.IndexFlatIP(all_embeddings.shape[1])
+index.add(all_embeddings)
+
+# 4. Load model and encode query
+model = SentenceTransformer("Qwen/Qwen3-Embedding-8B")
+query = "What is machine learning?"
+query_embedding = model.encode([query], prompt_name="query")
+
+# 5. Search in FAISS
+scores, indices = index.search(query_embedding, k=5)
+
+# 6. Print results
+for idx, score in zip(indices[0], scores[0]):
+    docid = str(all_lookup[idx])
+    doc = docid_to_doc.get(docid)
+    if doc:
+        print(f"Score: {score:.4f}")
+        print(f"URL: {doc['url']}")
+        print(f"Text: {doc['text'][:200]}...\n")
+```
+
+