code: def test_custom_vector_db(self):
      try:
          import lancedb
      except ImportError:
          return
      from autogen.agentchat.contrib.retrieve_user_proxy_agent import (
          RetrieveUserProxyAgent,
      )
  
      db_path = "/tmp/lancedb"
  
      def create_lancedb():
          db = lancedb.connect(db_path)
          data = [
              {
                  "vector": [1.1, 1.2],
                  "id": 1,
                  "documents": "This is a test document spark",
              },
              {
                  "vector": [0.2, 1.8],
                  "id": 2,
                  "documents": "This is another test document",
              },
              {
                  "vector": [0.1, 0.3],
                  "id": 3,
                  "documents": "This is a third test document spark",
              },
              {
                  "vector": [0.5, 0.7],
                  "id": 4,
                  "documents": "This is a fourth test document",
              },
              {
                  "vector": [2.1, 1.3],
                  "id": 5,
                  "documents": "This is a fifth test document spark",
              },
              {
                  "vector": [5.1, 8.3],
                  "id": 6,
                  "documents": "This is a sixth test document",
              },
          ]
          try:
              db.create_table("my_table", data)
          except OSError:
              pass
  
      class MyRetrieveUserProxyAgent(RetrieveUserProxyAgent):
          def query_vector_db(
              self,
              query_texts,
              n_results=10,
              search_string="",
          ):
              if query_texts:
                  vector = [0.1, 0.3]
              db = lancedb.connect(db_path)
              table = db.open_table("my_table")
              query = (
                  table.search(vector)
                  .where(f"documents LIKE '%{search_string}%'")
                  .limit(n_results)
                  .to_df()
              )
              return {
                  "ids": [query["id"].tolist()],
                  "documents": [query["documents"].tolist()],
              }
  
          def retrieve_docs(
              self,
              problem: str,
              n_results: int = 20,
              search_string: str = "",
          ):
              results = self.query_vector_db(
                  query_texts=[problem],
                  n_results=n_results,
                  search_string=search_string,
              )
  
              self._results = results
              print("doc_ids: ", results["ids"])
  
      ragragproxyagent = MyRetrieveUserProxyAgent(
          name="ragproxyagent",
          human_input_mode="NEVER",
          max_consecutive_auto_reply=2,
          retrieve_config={
              "task": "qa",
              "chunk_token_size": 2000,
              "client": "__",
              "embedding_model": "all-mpnet-base-v2",
          },
      )
  
      create_lancedb()
      ragragproxyagent.retrieve_docs(
          "This is a test document spark",
          n_results=10,
          search_string="spark",
      )
      assert ragragproxyagent._results["ids"] == [[3, 1, 5]]
  
file_path: autogen/test/test_retrieve_utils.py
imports: import pytest
  import os
label_desc: Use image node_id 9 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_custom_vector_db() and returns: dict