Fixed bug in AnalyticDB Vector Store caused by upgrade SQLAlchemy version (#6736)

langchain/vectorstores/analyticdb.py

@@ -80,34 +80,34 @@ class AnalyticDB(VectorStore):
             extend_existing=True,
         )
         with self.engine.connect() as conn:
-            # Create the table
-            Base.metadata.create_all(conn)
+            with conn.begin():
+                # Create the table
+                Base.metadata.create_all(conn)
 
-            # Check if the index exists
-            index_name = f"{self.collection_name}_embedding_idx"
-            index_query = text(
-                f"""
-                SELECT 1
-                FROM pg_indexes
-                WHERE indexname = '{index_name}';
-            """
-            )
-            result = conn.execute(index_query).scalar()
-
-            # Create the index if it doesn't exist
-            if not result:
-                index_statement = text(
+                # Check if the index exists
+                index_name = f"{self.collection_name}_embedding_idx"
+                index_query = text(
                     f"""
-                    CREATE INDEX {index_name}
-                    ON {self.collection_name} USING ann(embedding)
-                    WITH (
-                        "dim" = {self.embedding_dimension},
-                        "hnsw_m" = 100
-                    );
+                    SELECT 1
+                    FROM pg_indexes
+                    WHERE indexname = '{index_name}';
                 """
                 )
-                conn.execute(index_statement)
-            conn.commit()
+                result = conn.execute(index_query).scalar()
+
+                # Create the index if it doesn't exist
+                if not result:
+                    index_statement = text(
+                        f"""
+                        CREATE INDEX {index_name}
+                        ON {self.collection_name} USING ann(embedding)
+                        WITH (
+                            "dim" = {self.embedding_dimension},
+                            "hnsw_m" = 100
+                        );
+                    """
+                    )
+                    conn.execute(index_statement)
 
     def create_collection(self) -> None:
         if self.pre_delete_collection:
@@ -118,8 +118,8 @@ class AnalyticDB(VectorStore):
         self.logger.debug("Trying to delete collection")
         drop_statement = text(f"DROP TABLE IF EXISTS {self.collection_name};")
         with self.engine.connect() as conn:
-            conn.execute(drop_statement)
-            conn.commit()
+            with conn.begin():
+                conn.execute(drop_statement)
 
     def add_texts(
         self,
@@ -160,30 +160,28 @@ class AnalyticDB(VectorStore):
 
         chunks_table_data = []
         with self.engine.connect() as conn:
-            for document, metadata, chunk_id, embedding in zip(
-                texts, metadatas, ids, embeddings
-            ):
-                chunks_table_data.append(
-                    {
-                        "id": chunk_id,
-                        "embedding": embedding,
-                        "document": document,
-                        "metadata": metadata,
-                    }
-                )
+            with conn.begin():
+                for document, metadata, chunk_id, embedding in zip(
+                    texts, metadatas, ids, embeddings
+                ):
+                    chunks_table_data.append(
+                        {
+                            "id": chunk_id,
+                            "embedding": embedding,
+                            "document": document,
+                            "metadata": metadata,
+                        }
+                    )
 
-                # Execute the batch insert when the batch size is reached
-                if len(chunks_table_data) == batch_size:
+                    # Execute the batch insert when the batch size is reached
+                    if len(chunks_table_data) == batch_size:
+                        conn.execute(insert(chunks_table).values(chunks_table_data))
+                        # Clear the chunks_table_data list for the next batch
+                        chunks_table_data.clear()
+
+                # Insert any remaining records that didn't make up a full batch
+                if chunks_table_data:
                     conn.execute(insert(chunks_table).values(chunks_table_data))
-                    # Clear the chunks_table_data list for the next batch
-                    chunks_table_data.clear()
-
-            # Insert any remaining records that didn't make up a full batch
-            if chunks_table_data:
-                conn.execute(insert(chunks_table).values(chunks_table_data))
-
-            # Commit the transaction only once after all records have been inserted
-            conn.commit()
 
         return ids
 
@@ -333,9 +331,9 @@ class AnalyticDB(VectorStore):
     ) -> AnalyticDB:
         """
         Return VectorStore initialized from texts and embeddings.
-        Postgres connection string is required
+        Postgres Connection string is required
         Either pass it as a parameter
-        or set the PGVECTOR_CONNECTION_STRING environment variable.
+        or set the PG_CONNECTION_STRING environment variable.
         """
 
         connection_string = cls.get_connection_string(kwargs)
@@ -363,7 +361,7 @@ class AnalyticDB(VectorStore):
             raise ValueError(
                 "Postgres connection string is required"
                 "Either pass it as a parameter"
-                "or set the PGVECTOR_CONNECTION_STRING environment variable."
+                "or set the PG_CONNECTION_STRING environment variable."
             )
 
         return connection_string
@@ -381,9 +379,9 @@ class AnalyticDB(VectorStore):
     ) -> AnalyticDB:
         """
         Return VectorStore initialized from documents and embeddings.
-        Postgres connection string is required
+        Postgres Connection string is required
         Either pass it as a parameter
-        or set the PGVECTOR_CONNECTION_STRING environment variable.
+        or set the PG_CONNECTION_STRING environment variable.
         """
 
         texts = [d.page_content for d in documents]