code: from typing import Any, Callable
  from pandas import DataFrame
  
  from exact_rag.config import EmbeddingType, Embeddings, DatabaseType, Databases
  
  from langchain_openai import OpenAIEmbeddings
  from langchain_community.embeddings import OllamaEmbeddings
  from langchain.vectorstores.chroma import Chroma
  from langchain.vectorstores.elasticsearch import ElasticsearchStore
  from langchain.indexes import SQLRecordManager
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain_community.document_loaders import DataFrameLoader
  from langchain.indexes import index
  from langchain.chains import RetrievalQA
  from langchain_openai import ChatOpenAI
  from langchain_community.llms.ollama import Ollama
  
  
  class Caller:
      def __init__(
          self,
          callable: Callable[..., Any],
          arg_swap: dict[str, Any] | None = None,
          accept_only: list[str] | None = None,
      ):
          self._callable = callable
          self._arg_swap = arg_swap
          self._accept_only = accept_only
  
      def __call__(self, **args):
          if self._accept_only:
              args = {
                  key: value for (key, value) in args.items() if key in self._accept_only
              }
  
          if self._arg_swap:
              args = {
                  self._arg_swap.get(arg, arg): value for (arg, value) in args.items()
              }
  
          return self._callable(**args)
  
  
  embeddings = {
      EmbeddingType.openai: Caller(OpenAIEmbeddings, accept_only=["api_key"]),
      EmbeddingType.ollama: Caller(OllamaEmbeddings, accept_only=["model"]),
  }
  
  dbs = {
      DatabaseType.chroma: Caller(
          Chroma,
          {"embedding": "embedding_function"},
          accept_only=["embedding", "persist_directory", "collection_name"],
      ),
      DatabaseType.elastic: Caller(
          ElasticsearchStore,
          {"collection_name": "index_name", "url": "es_url"},
          accept_only=[
              "embedding",
              "url",
              "collection_name",
              "distance_strategy",
              "strategy",
          ],
      ),
  }
  
  chats = {
      EmbeddingType.openai: Caller(
          ChatOpenAI,
          {
              "api_key": "openai_api_key",
              "chat_model_name": "model_name",
              "chat_temperature": "temperature",
          },
          accept_only=["chat_model_name", "chat_temperature", "api_key"],
      ),
      EmbeddingType.ollama: Caller(
          Ollama,
          accept_only=["model"],
      ),
  }
  
  
  class DataEmbedding:
      def __init__(self, embedding_model: Embeddings, database_model: Databases):
          embedding_type = embedding_model.type
          self._embedding = embeddings[embedding_type](**embedding_model.model_dump())
          print("Embedding initialized.")
  
          database_type = database_model.type
          self._vectorstore = dbs[database_type](
              embedding=self._embedding,
              **database_model.model_dump(),
          )
          print("Vectorstore initialized.")
  
          self._record_manager = SQLRecordManager(
              database_model.sql_namespace,
              db_url=f"sqlite:///{database_model.sql_url}",
          )
          print("Record manager initialized.")
  
          self._record_manager.create_schema()
          print("    schema created.")
  
          self._splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
              chunk_size=database_model.splitter_chunk_size,
              chunk_overlap=database_model.splitter_chunk_overlap,
          )
          print("Splitter initialized.")
  
          self._qa = RetrievalQA.from_chain_type(
              llm=chats[embedding_type](**embedding_model.model_dump()),
              chain_type="stuff",
              retriever=self._vectorstore.as_retriever(
                  search_type=embedding_model.search_type,
                  search_kwargs={
                      "k": embedding_model.search_k,
                      "fetch_k": embedding_model.search_fetch_k,
                  },
              ),
          )
          print("Chat initialized.")
  
      def load(self, text: str):
          id_key = "hash"
          content_name = "text"
          dataframe = DataFrame().from_dict([{id_key: hash(text), content_name: text}])
          loader = DataFrameLoader(dataframe, page_content_column=content_name)
          data = loader.load()
          documents = self._splitter.split_documents(data)
          index(
              documents,
              self._record_manager,
              self._vectorstore,
              cleanup="incremental",
              source_id_key=id_key,
          )
  
      def chat(self, query: str):
          self.load(query)
          return self._qa.invoke({"query": query})
  
apis: langchain_community.document_loaders.DataFrameLoader langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder langchain.indexes.SQLRecordManager langchain.indexes.index
extract_api: [((2955, 3052), 'langchain.indexes.SQLRecordManager', 'SQLRecordManager', (['database_model.sql_namespace'], {'db_url': 'f"""sqlite:///{database_model.sql_url}"""'}), "(database_model.sql_namespace, db_url=\n    f'sqlite:///{database_model.sql_url}')\n", (2971, 3052), False, 'from langchain.indexes import SQLRecordManager\n'), ((3237, 3399), 'langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder', 'RecursiveCharacterTextSplitter.from_tiktoken_encoder', ([], {'chunk_size': 'database_model.splitter_chunk_size', 'chunk_overlap': 'database_model.splitter_chunk_overlap'}), '(chunk_size=\n    database_model.splitter_chunk_size, chunk_overlap=database_model.\n    splitter_chunk_overlap)\n', (3289, 3399), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter\n'), ((4141, 4201), 'langchain_community.document_loaders.DataFrameLoader', 'DataFrameLoader', (['dataframe'], {'page_content_column': 'content_name'}), '(dataframe, page_content_column=content_name)\n', (4156, 4201), False, 'from langchain_community.document_loaders import DataFrameLoader\n'), ((4296, 4403), 'langchain.indexes.index', 'index', (['documents', 'self._record_manager', 'self._vectorstore'], {'cleanup': '"""incremental"""', 'source_id_key': 'id_key'}), "(documents, self._record_manager, self._vectorstore, cleanup=\n    'incremental', source_id_key=id_key)\n", (4301, 4403), False, 'from langchain.indexes import index\n'), ((4058, 4069), 'pandas.DataFrame', 'DataFrame', ([], {}), '()\n', (4067, 4069), False, 'from pandas import DataFrame\n')]