lancedb integration update (#11490)

* updated example, pkg breakage due to refactor errors fixed

* typos

* nb clean

* output restored

* formatting

* typo

* integration update

* error fix

* pytest fixes

llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/llama_index/vector_stores/lancedb/base.py

@@ -4,6 +4,7 @@ import logging
 from typing import Any, List, Optional
 
 import numpy as np
+from llama_index.core.bridge.pydantic import PrivateAttr
 from llama_index.core.schema import (
     BaseNode,
     MetadataMode,
@@ -13,7 +14,7 @@ from llama_index.core.schema import (
 )
 from llama_index.core.vector_stores.types import (
     MetadataFilters,
-    VectorStore,
+    BasePydanticVectorStore,
     VectorStoreQuery,
     VectorStoreQueryResult,
 )
@@ -54,7 +55,7 @@ def _to_llama_similarities(results: DataFrame) -> List[float]:
     return normalized_similarities.tolist()
 
 
-class LanceDBVectorStore(VectorStore):
+class LanceDBVectorStore(BasePydanticVectorStore):
     """
     The LanceDB Vector Store.
 
@@ -84,10 +85,18 @@ class LanceDBVectorStore(VectorStore):
 
     stores_text = True
     flat_metadata: bool = True
+    _connection: Any = PrivateAttr()
+    uri: Optional[str]
+    table_name: Optional[str]
+    vector_column_name: Optional[str]
+    nprobes: Optional[int]
+    refine_factor: Optional[int]
+    text_key: Optional[str]
+    doc_id_key: Optional[str]
 
     def __init__(
         self,
-        uri: str,
+        uri: Optional[str],
         table_name: str = "vectors",
         vector_column_name: str = "vector",
         nprobes: int = 20,
@@ -97,19 +106,48 @@ class LanceDBVectorStore(VectorStore):
         **kwargs: Any,
     ) -> None:
         """Init params."""
-        self.connection = lancedb.connect(uri)
-        self.uri = uri
-        self.table_name = table_name
-        self.vector_column_name = vector_column_name
-        self.nprobes = nprobes
-        self.text_key = text_key
-        self.doc_id_key = doc_id_key
-        self.refine_factor = refine_factor
+        self._connection = lancedb.connect(uri)
+        super().__init__(
+            uri=uri,
+            table_name=table_name,
+            vector_column_name=vector_column_name,
+            nprobes=nprobes,
+            refine_factor=refine_factor,
+            text_key=text_key,
+            doc_id_key=doc_id_key,
+            **kwargs,
+        )
 
     @property
     def client(self) -> None:
         """Get client."""
-        return
+        return self._connection
+
+    @classmethod
+    def from_params(
+        cls,
+        uri: Optional[str],
+        table_name: str = "vectors",
+        vector_column_name: str = "vector",
+        nprobes: int = 20,
+        refine_factor: Optional[int] = None,
+        text_key: str = DEFAULT_TEXT_KEY,
+        doc_id_key: str = DEFAULT_DOC_ID_KEY,
+        **kwargs: Any,
+    ) -> "LanceDBVectorStore":
+        """Create instance from params."""
+        _connection_ = cls._connection
+        return cls(
+            _connection=_connection_,
+            uri=uri,
+            table_name=table_name,
+            vector_column_name=vector_column_name,
+            nprobes=nprobes,
+            refine_factor=refine_factor,
+            text_key=text_key,
+            doc_id_key=doc_id_key,
+            **kwargs,
+        )
 
     def add(
         self,
@@ -132,11 +170,11 @@ class LanceDBVectorStore(VectorStore):
             data.append(append_data)
             ids.append(node.node_id)
 
-        if self.table_name in self.connection.table_names():
-            tbl = self.connection.open_table(self.table_name)
+        if self.table_name in self._connection.table_names():
+            tbl = self._connection.open_table(self.table_name)
             tbl.add(data)
         else:
-            self.connection.create_table(self.table_name, data)
+            self._connection.create_table(self.table_name, data)
         return ids
 
     def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
@@ -147,7 +185,7 @@ class LanceDBVectorStore(VectorStore):
             ref_doc_id (str): The doc_id of the document to delete.
 
         """
-        table = self.connection.open_table(self.table_name)
+        table = self._connection.open_table(self.table_name)
         table.delete('document_id = "' + ref_doc_id + '"')
 
     def query(
@@ -167,7 +205,7 @@ class LanceDBVectorStore(VectorStore):
         else:
             where = kwargs.pop("where", None)
 
-        table = self.connection.open_table(self.table_name)
+        table = self._connection.open_table(self.table_name)
         lance_query = (
             table.search(
                 query=query.query_embedding,

llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/tests/test_vector_stores_lancedb.py

-from llama_index.core.vector_stores.types import VectorStore
+from llama_index.core.vector_stores.types import BasePydanticVectorStore
 from llama_index.vector_stores.lancedb import LanceDBVectorStore
 
 
 def test_class():
     names_of_base_classes = [b.__name__ for b in LanceDBVectorStore.__mro__]
-    assert VectorStore.__name__ in names_of_base_classes
+    assert BasePydanticVectorStore.__name__ in names_of_base_classes