code: from langchain.document_loaders import TextLoader
  from langchain.embeddings.openai import OpenAIEmbeddings
  from langchain.text_splitter import CharacterTextSplitter
  from langchain.vectorstores import LanceDB
  
  # embedding_model = HuggingFaceEmbeddings(model_name = "moka-ai/m3e-base")
  
  from langchain.embeddings import LocalAIEmbeddings
  
  openai_api_base_address = "http://172.23.115.108:20000/v1"
  
  # 这个可以工作，使用服务化embedding模型
  embedding_model=LocalAIEmbeddings(openai_api_key = "aaabbbcccdddeeefffedddsfasdfasdf",  
                              openai_api_base = openai_api_base_address,
                              model = "vicuna-13b-v1.5")
  
  import lancedb
  
  db = lancedb.connect("/tmp/lancedb")
  table = db.create_table(
      "my_table",
      data=[
          {
              "vector": embedding_model.embed_query("Hello World"),
              "text": "Hello World",
              "id": "1",
          }
      ],
      mode="overwrite",
  )
  
  # Load the document, split it into chunks, embed each chunk and load it into the vector store.
  raw_documents = TextLoader('./LangChainStudy/demo_text.txt').load()
  text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
  documents = text_splitter.split_documents(raw_documents)
  db = LanceDB.from_documents(documents, embedding_model, connection=table)
  
  query = "how to customize your text splitter"
  docs = db.similarity_search(query)
  print(docs[0].page_content)
apis: lancedb.connect
extract_api: [((439, 577), 'langchain.embeddings.LocalAIEmbeddings', 'LocalAIEmbeddings', ([], {'openai_api_key': '"""aaabbbcccdddeeefffedddsfasdfasdf"""', 'openai_api_base': 'openai_api_base_address', 'model': '"""vicuna-13b-v1.5"""'}), "(openai_api_key='aaabbbcccdddeeefffedddsfasdfasdf',\n    openai_api_base=openai_api_base_address, model='vicuna-13b-v1.5')\n", (456, 577), False, 'from langchain.embeddings import LocalAIEmbeddings\n'), ((660, 691), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (675, 691), False, 'import lancedb\n'), ((1099, 1154), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(1000)', 'chunk_overlap': '(0)'}), '(chunk_size=1000, chunk_overlap=0)\n', (1120, 1154), False, 'from langchain.text_splitter import CharacterTextSplitter\n'), ((1217, 1285), 'langchain.vectorstores.LanceDB.from_documents', 'LanceDB.from_documents', (['documents', 'embedding_model'], {'connection': 'table'}), '(documents, embedding_model, connection=table)\n', (1239, 1285), False, 'from langchain.vectorstores import LanceDB\n'), ((1031, 1075), 'langchain.document_loaders.TextLoader', 'TextLoader', (['"""./LangChainStudy/demo_text.txt"""'], {}), "('./LangChainStudy/demo_text.txt')\n", (1041, 1075), False, 'from langchain.document_loaders import TextLoader\n')]