code: def test_dummy_embedding(self, mock_handler):
      self.run_sql("create database proj")
      # create  the model
      self.run_sql(
          """
              CREATE MODEL proj.test_dummy_embedding
              PREDICT embeddings_output_column
              USING
                  engine='langchain_embedding',
                  class = 'FakeEmbeddings',
                  size = 512,
                  input_columns = ['content']
              """
      )
  
      self.wait_predictor("proj", "test_dummy_embedding")
  
      # predictions
      # one line
      ret = self.run_sql(
          """
              SELECT * FROM proj.test_dummy_embedding
              WHERE content='hello'
              """
      )
      assert "content" in ret.columns
      assert "embeddings_output_column" in ret.columns
      # the embeddings should be a list of 512 floats
      assert len(ret["embeddings_output_column"][0]) == 512
  
      # multiple lines
      # insert data
      df = pd.DataFrame(
          [
              ["hello"],
              ["world"],
              ["foo"],
              ["bar"],
          ],
          columns=["content"],
      )
      self.set_handler(mock_handler, name="pg", tables={"df": df})
  
      # query
      ret = self.run_sql(
          """
              SELECT * FROM proj.test_dummy_embedding
              JOIN pg.df
              """
      )
  
      assert "content" in ret.columns
      assert "embeddings_output_column" in ret.columns
      assert ret.shape[0] == 4
  
file_path: mindsdb/tests/unit/ml_handlers/test_langchain_embedding.py
imports: import os
  import time
  from unittest.mock import patch
  import pandas
  import pytest
  from mindsdb_sql import parse_sql
  from ..executor_test_base import BaseExecutorTest
label_desc: Use image node_id 3 for calling the TestLangchainEmbedding obj's underlying member method code with example usage: obj.test_dummy_embedding(mock_handler) without return types