test

metagpt/document_store/lancedb_store.py

@@ -8,6 +8,7 @@
 import lancedb
 import pyarrow as pa
 import pandas as pd
+import shutil, os
 
 
 class LanceStore:
@@ -24,7 +25,7 @@ class LanceStore:
         schema = schema.remove_metadata()
         schema = schema.remove(len(schema) - 1)
 
-        self.table = self.db.create_table(self.name, schema)
+        self.table = self.db.create_table(self.name, schema=schema)
 
     def search(self, query, n_results=2, metric="L2", nprobes=20, **kwargs):
         # This assumes query is a vector embedding
@@ -85,3 +86,10 @@ class LanceStore:
             return self.table.delete(f"id = '{_id}'")
         else:
             return self.table.delete(f"id = {_id}")
+
+    def drop(self, name):
+        # This function drops a table, if it exists.
+
+        path = os.path.join(self.db.uri, name + '.lance')
+        if os.path.exists(path):
+            shutil.rmtree(path)

tests/metagpt/document_store/test_lancedb_store.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2023/8/9 15:42
+@Author  : unkn-wn (Leon Yee)
+@File    : test_lancedb_store.py
+"""
+from metagpt.document_store.lancedb_store import LanceStore
+import random
+
+def test_lance_store():
+
+    # This simply establishes the connection to the database, so we can drop the table if it exists
+    store = LanceStore('test')
+
+    store.drop('test')
+
+    store.create_table(['vector', 'id', 'meta', 'meta2'])
+
+    store.write(data=[[random.random() for _ in range(100)] for _ in range(2)],
+            metadatas=[{"source": "google-docs"}, {"source": "notion"}],
+            ids=["doc1", "doc2"])
+
+    store.add(data=[random.random() for _ in range(100)], metadatas={"source": "notion"}, ids="doc3")
+
+    result = store.search([random.random() for _ in range(100)], n_results=3)
+    print(result)
+    assert(len(result) > 0)