lancedb_local_vector_table_creation_and_similarity_search.py

1import lancedb
2import pandas as pd
3import numpy as np
4
5# 1. Connect to a local database (creates the directory if it doesn't exist)
6db = lancedb.connect("./lancedb")
7
8# 2. Prepare some sample data
9data = [
10    {"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7, "item": "apple"},
11    {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1, "item": "banana"},
12]
13
14# 3. Create a table from the data
15tbl = db.create_table("my_table", data=data, mode="overwrite")
16
17# 4. Add more data (using a pandas DataFrame for efficiency)
18df = pd.DataFrame({
19    "vector": [np.array([0.5, 0.5]), np.array([1.5, 1.5])],
20    "lat": [34.1, 23.0],
21    "long": [-118.3, -82.0],
22    "item": ["orange", "guava"]
23})
24tbl.add(df)
25
26# 5. Perform a similarity search
27query_vector = [0.1, 1.9]
28results = tbl.search(query_vector).limit(2).to_pandas()
29
30print(results)