[langchain-db2/fix]: Fixed providing client instance object to _create_table function (#92)

* fix providing client instance object to _create_table function

* change type style to snake case, init cursor outside try except block

Author: MateuszOssGit

libs/langchain-db2/langchain_db2/db2vs.py

@@ -74,8 +74,8 @@ def wrapper(*args: Any, **kwargs: Any) -> Any:
 
 
 def _table_exists(client: Connection, table_name: str) -> bool:
+    cursor = client.cursor()
     try:
-        cursor = client.cursor()
         cursor.execute(f"SELECT COUNT(*) FROM {table_name}")
     except Exception as ex:
         if "SQL0204N" in str(ex):
@@ -239,7 +239,7 @@ def __init__(
             self.table_name = table_name
             self.distance_strategy = distance_strategy
             self.params = params
-            _create_table(client, table_name, embedding_dim)
+            _create_table(self.client, self.table_name, embedding_dim)
         except ibm_db_dbi.DatabaseError as db_err:
             logger.exception(f"Database error occurred while create table: {db_err}")
             raise RuntimeError(
@@ -375,7 +375,7 @@ def add_texts(
         if not metadatas:
             metadatas = [{} for _ in texts]
 
-        embeddingLen = self.get_embedding_dimension()
+        embedding_len = self.get_embedding_dimension()
         docs: List[Tuple[Any, Any, Any, Any]]
         docs = [
             (id_, f"{embedding}", json.dumps(metadata), text)
@@ -386,7 +386,7 @@ def add_texts(
 
         SQL_INSERT = (
             f"INSERT INTO {self.table_name} (id, embedding, metadata, text) "
-            f"VALUES (?, VECTOR(?, {embeddingLen}, FLOAT32), SYSTOOLS.JSON2BSON(?), ?)"
+            f"VALUES (?, VECTOR(?, {embedding_len}, FLOAT32), SYSTOOLS.JSON2BSON(?), ?)"
         )
 
         cursor = self.client.cursor()
@@ -455,13 +455,13 @@ def similarity_search_by_vector_with_relevance_scores(
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         docs_and_scores = []
-        embeddingLen = self.get_embedding_dimension()
+        embedding_len = self.get_embedding_dimension()
 
         query = f"""
         SELECT id,
           text,
           SYSTOOLS.BSON2JSON(metadata),
-          vector_distance(embedding, VECTOR('{embedding}', {embeddingLen}, FLOAT32),
+          vector_distance(embedding, VECTOR('{embedding}', {embedding_len}, FLOAT32),
           {_get_distance_function(self.distance_strategy)}) as distance
         FROM {self.table_name}
         ORDER BY distance
@@ -509,13 +509,13 @@ def similarity_search_by_vector_returning_embeddings(
         **kwargs: Any,
     ) -> List[Tuple[Document, float, np.ndarray]]:
         documents = []
-        embeddingLen = self.get_embedding_dimension()
+        embedding_len = self.get_embedding_dimension()
 
         query = f"""
         SELECT id,
           text,
           SYSTOOLS.BSON2JSON(metadata),
-          vector_distance(embedding, VECTOR('{embedding}', {embeddingLen}, FLOAT32),
+          vector_distance(embedding, VECTOR('{embedding}', {embedding_len}, FLOAT32),
           {_get_distance_function(self.distance_strategy)}) as distance,
           embedding
         FROM {self.table_name}