README

README.md

@@ -42,9 +42,69 @@ Or you can also stream it without downloading it before:
 from datasets import load_dataset
 docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-l",  split="train", streaming=True)
 for doc in docs:
-	doc_id = doc['_id']
-	title = doc['title']
-    header = doc['header']
+	doc_id = j['docid']
+    url = doc['url']
 	text = doc['text']
-	emb = doc['emb']
+	emb = doc['embedding']
 ```
+
+
+Note, The full dataset corpus is ~ 620GB so it will take a while to download and may not fit on some devices/ 
+
+## Search
+A full search example (on the first 1,000 paragraphs):
+```python
+from datasets import load_dataset
+import torch
+from transformers import AutoModel, AutoTokenizer
+import numpy as np
+
+
+top_k = 100
+docs_stream = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-l",split="train", streaming=True)
+
+docs = []
+doc_embeddings = []
+
+for doc in docs_stream:
+    docs.append(doc)
+    doc_embeddings.append(doc['embedding'])
+    if len(docs) >= top_k:
+        break
+
+doc_embeddings = np.asarray(doc_embeddings)
+
+tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-l')
+model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-l', add_pooling_layer=False)
+model.eval()
+
+query_prefix = 'Represent this sentence for searching relevant passages: '
+queries  = ['how do you clean smoke off walls']
+queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
+query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
+
+# Compute token embeddings
+with torch.no_grad():
+    query_embeddings = model(**query_tokens)[0][:, 0]
+
+
+# normalize embeddings
+query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
+doc_embeddings = torch.nn.functional.normalize(doc_embeddings, p=2, dim=1)
+
+# Compute dot score between query embedding and document embeddings
+dot_scores = np.matmul(query_embeddings, doc_embeddings.transpose())[0]
+top_k_hits = np.argpartition(dot_scores, -top_k)[-top_k:].tolist()
+
+# Sort top_k_hits by dot score
+top_k_hits.sort(key=lambda x: dot_scores[x], reverse=True)
+
+# Print results
+print("Query:", queries[0])
+for doc_id in top_k_hits:
+    print(docs[doc_id]['doc_id'])
+    print(docs[doc_id]['text'])
+    print(docs[doc_id]['url'], "\n")
+```
+
+