wip ann indexes doc

docs/src/ann_indexes.md

@@ -0,0 +1,37 @@
+# ANN (Approximate Nearest Neighbor) Indexes
+
+In order to make vector search faster, you can create an index over your vector data. Vector indexes are faster but
+not as accurate as exhaustive search. LanceDB provide many parameters to fine tune the size of the index, the speed
+of queries and the accuracy for results
+
+## Creating an ANN Index
+
+This is how you can create a vector index:
+
+```python
+import lancedb
+import numpy as np
+uri = "~/.lancedb"
+db = lancedb.connect(uri)
+
+# Create 10,000 sample vectors
+data = [{"vector": row, "item": f"item {i}"}
+   for i, row in enumerate(np.random.random((10_000, 768)).astype('float32'))]
+
+# Add the vectors to a table
+tbl = db.create_table("my_vectors", data=data)
+
+# Create and train the index - you need to have enough data in the table for an effective training step
+tbl.create_index(num_partitions=256,num_sub_vectors=96)
+```
+
+Since `create_index` has a training, it can take a couple of minutes to finish for large tables. You can control index
+creation by providing the following parameters:
+- **num_partitions**: The number of partitions of the index. A higher number leads to better search quality, but it makes index 
+generation slower.
+- **num_partitions**: The number of subvectors (M) that will be created during Product Quantization (PQ). A larger number makes
+search more accurate, but also makes the index larger and slower to build. 
+
+## Querying an ANN Index
+
+TODO