Add tutorial notebook

Convert contextualization and embeddings functionality.
And use it with converted notebook for video search

python/lancedb/context.py

@@ -0,0 +1,61 @@
+#  Copyright 2023 LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import pandas as pd
+
+
+def contextualize(raw_df):
+    return Contextualizer(raw_df)
+
+
+class Contextualizer:
+    def __init__(self, raw_df):
+        self._text_col = None
+        self._groupby = None
+        self._stride = None
+        self._window = None
+        self._raw_df = raw_df
+
+    def window(self, window):
+        self._window = window
+        return self
+
+    def stride(self, stride):
+        self._stride = stride
+        return self
+
+    def groupby(self, groupby):
+        self._groupby = groupby
+        return self
+
+    def text_col(self, text_col):
+        self._text_col = text_col
+        return self
+
+    def to_df(self):
+        def process_group(grp):
+            # For each video, create the text rolling window
+            text = grp[self._text_col].values
+            contexts = grp.iloc[: -self._window : self._stride, :].copy()
+            contexts[self._text_col] = [
+                " ".join(text[start_i : start_i + self._window])
+                for start_i in range(0, len(grp) - self._window, self._stride)
+            ]
+            return contexts
+
+        if self._groupby is None:
+            return process_group(self._raw_df)
+        # concat result from all groups
+        return pd.concat(
+            [process_group(grp) for _, grp in self._raw_df.groupby(self._groupby)]
+        )