update readme

README.md

@@ -47,3 +47,162 @@ configs:
         path: "data/tr/*.parquet"
 license: apache-2.0
 ---
+# Wikipedia Embeddings with BGE-M3
+
+This dataset contains embeddings from the
+[June 2024 Wikipedia dump](https://dumps.wikimedia.org/wikidatawiki/20240601/)
+for the 11 most popular languages.
+
+The embeddings are generated with the multilingual
+[BGE-M3](https://huggingface.co/BAAI/bge-m3) model.
+
+The dataset consists of Wikipedia articles split into paragraphs,
+and embedded with the aforementioned model.
+
+To enhance search quality, the paragraphs are prefixed with their
+respective article titles before embedding.
+
+Additionally, paragraphs containing fewer than 100 characters,
+which tend to have low information density, are excluded from the dataset.
+
+The dataset contains approximately 144 million vector embeddings in total.
+
+| Language   | Config Name | Embeddings  |
+|------------|-------------|-------------|
+| English    | en          | 47_018_430  |
+| German     | de          | 20_213_669  |
+| French     | fr          | 18_324_060  |
+| Russian    | ru          | 13_618_886  |
+| Spanish    | es          | 13_194_999  |
+| Italian    | it          | 10_092_524  |
+| Japanese   | ja          | 7_769_997   |
+| Portuguese | pt          | 5_948_941   |
+| Farsi      | fa          | 2_598_251   |
+| Chinese    | zh          | 3_306_397   |
+| Turkish    | tr          | 2_051_157   |
+| **Total**  |             | 144_137_311 | 
+
+## Loading Dataset
+
+You can load the entire dataset for a language as follows.
+Please note that for some languages, the download size may be quite large.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Upstash/wikipedia-2024-06-bge-m3", "en", split="train")
+```
+
+Alternatively, you can stream portions of the dataset as needed.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Upstash/wikipedia-2024-06-bge-m3", "en", split="train", streaming=True)
+
+for data in dataset:
+    data_id = data["id"]
+    url = data["url"]
+    title = data["title"]
+    text = data["text"]
+    embedding = data["embedding"]
+    # Do some work
+    break
+```
+
+## Using Dataset
+
+One potential use case for the dataset is enabling similarity search
+by integrating it with a vector database.
+
+In fact, we have developed a vector database that allows you to search
+through the Wikipedia articles. Additionally, it includes a
+[RAG (Retrieval-Augmented Generation)](https://github.com/upstash/rag-chat) chatbot,
+which enables you to interact with a chatbot enhanced by the dataset.
+
+For more details, see this [blog post](https://upstash.com/blog/indexing-wikipedia),
+and be sure to check out the
+[search engine and chatbot](https://wikipedia-semantic-search.vercel.app) yourself.
+
+For reference, here is a rough estimation of how to implement semantic search
+functionality using this dataset and Upstash Vector.
+
+```python
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from upstash_vector import Index
+
+# You can create Upstash Vector with dimension set to 1024,
+# and similarity search function to dot product.
+index = Index(
+    url="https://upward-lion-77104-eu1-vector.upstash.io",
+    token="ABUFMHVwd2FyZC1saW9uLTc3MTA0LWV1MWFkbWluWVdSaE5HRm1NREl0TWpObU15MDBZbUl6TFdKaVpUWXRNRGMwTWpVd01qQXpaR1Jq",
+)
+
+vectors = []
+batch_size = 200
+
+dataset = load_dataset(
+    "Upstash/wikipedia-2024-06-bge-m3", "en", split="train", streaming=True
+)
+
+for data in dataset:
+    data_id = data["id"]
+    url = data["url"]
+    title = data["title"]
+    text = data["text"]
+    embedding = data["embedding"]
+
+    metadata = {
+        "url": url,
+        "title": title,
+    }
+
+    vector = (
+        data_id,  # Unique vector id
+        embedding,  # Vector embedding
+        metadata,  # Optional, JSON-like metadata
+        text,  # Optional, unstructured text data
+    )
+    vectors.append(vector)
+
+    if len(vectors) == batch_size:
+        break
+
+# Upload embeddings into Upstash Vector in batches
+index.upsert(
+    vectors=vectors,
+    namespace="en",
+)
+
+# Create the query vector
+transformer = SentenceTransformer(
+    "BAAI/bge-m3",
+    device="cuda",
+    revision="babcf60cae0a1f438d7ade582983d4ba462303c2",
+)
+
+query = "Which state has the nickname Yellowhammer State?"
+query_vector = transformer.encode(
+    sentences=query,
+    show_progress_bar=False,
+    normalize_embeddings=True,
+)
+
+results = index.query(
+    vector=query_vector,
+    top_k=2,
+    include_metadata=True,
+    include_data=True,
+    namespace="en",
+)
+
+# Query results are sorted in descending order of similarity
+for result in results:
+    print(result.id)  # Unique vector id
+    print(result.score)  # Similarity score to the query vector
+    print(result.metadata)  # Metadata associated with vector
+    print(result.data)  # Unstructured data associated with vector
+    print("---")
+```
+