code: import lancedb
  import uuid
  from datetime import datetime
  from tqdm import tqdm
  from typing import Optional, List, Iterator, Dict
  
  from memgpt.config import MemGPTConfig
  from memgpt.connectors.storage import StorageConnector, TableType
  from memgpt.config import AgentConfig, MemGPTConfig
  from memgpt.constants import MEMGPT_DIR
  from memgpt.utils import printd
  from memgpt.data_types import Record, Message, Passage, Source
  
  from datetime import datetime
  
  from lancedb.pydantic import Vector, LanceModel
  
  """ Initial implementation - not complete """
  
  
  def get_db_model(table_name: str, table_type: TableType):
      config = MemGPTConfig.load()
  
      if table_type == TableType.ARCHIVAL_MEMORY or table_type == TableType.PASSAGES:
          # create schema for archival memory
          class PassageModel(LanceModel):
              """Defines data model for storing Passages (consisting of text, embedding)"""
  
              id: uuid.UUID
              user_id: str
              text: str
              doc_id: str
              agent_id: str
              data_source: str
              embedding: Vector(config.embedding_dim)
              metadata_: Dict
  
              def __repr__(self):
                  return f"<Passage(passage_id='{self.id}', text='{self.text}', embedding='{self.embedding})>"
  
              def to_record(self):
                  return Passage(
                      text=self.text,
                      embedding=self.embedding,
                      doc_id=self.doc_id,
                      user_id=self.user_id,
                      id=self.id,
                      data_source=self.data_source,
                      agent_id=self.agent_id,
                      metadata=self.metadata_,
                  )
  
          return PassageModel
      elif table_type == TableType.RECALL_MEMORY:
  
          class MessageModel(LanceModel):
              """Defines data model for storing Message objects"""
  
              __abstract__ = True  # this line is necessary
  
              # Assuming message_id is the primary key
              id: uuid.UUID
              user_id: str
              agent_id: str
  
              # openai info
              role: str
              text: str
              model: str
              user: str
  
              # function info
              function_name: str
              function_args: str
              function_response: str
  
              embedding = Vector(config.embedding_dim)
  
              # Add a datetime column, with default value as the current time
              created_at = datetime
  
              def __repr__(self):
                  return f"<Message(message_id='{self.id}', text='{self.text}', embedding='{self.embedding})>"
  
              def to_record(self):
                  return Message(
                      user_id=self.user_id,
                      agent_id=self.agent_id,
                      role=self.role,
                      user=self.user,
                      text=self.text,
                      model=self.model,
                      function_name=self.function_name,
                      function_args=self.function_args,
                      function_response=self.function_response,
                      embedding=self.embedding,
                      created_at=self.created_at,
                      id=self.id,
                  )
  
          """Create database model for table_name"""
          return MessageModel
      elif table_type == TableType.DATA_SOURCES:
  
          class SourceModel(LanceModel):
              """Defines data model for storing Passages (consisting of text, embedding)"""
  
              # Assuming passage_id is the primary key
              id: uuid.UUID
              user_id: str
              name: str
              created_at: datetime
  
              def __repr__(self):
                  return f"<Source(passage_id='{self.id}', name='{self.name}')>"
  
              def to_record(self):
                  return Source(id=self.id, user_id=self.user_id, name=self.name, created_at=self.created_at)
  
          """Create database model for table_name"""
          return SourceModel
      else:
          raise ValueError(f"Table type {table_type} not implemented")
  
  
  class LanceDBConnector(StorageConnector):
      """Storage via LanceDB"""
  
      # TODO: this should probably eventually be moved into a parent DB class
  
      def __init__(self, name: Optional[str] = None, agent_config: Optional[AgentConfig] = None):
          # TODO
          pass
  
      def generate_where_filter(self, filters: Dict) -> str:
          where_filters = []
          for key, value in filters.items():
              where_filters.append(f"{key}={value}")
          return where_filters.join(" AND ")
  
      @abstractmethod
      def get_all_paginated(self, filters: Optional[Dict] = {}, page_size: Optional[int] = 1000) -> Iterator[List[Record]]:
          # TODO
          pass
  
      @abstractmethod
      def get_all(self, filters: Optional[Dict] = {}, limit=10) -> List[Record]:
          # TODO
          pass
  
      @abstractmethod
      def get(self, id: str) -> Optional[Record]:
          # TODO
          pass
  
      @abstractmethod
      def size(self, filters: Optional[Dict] = {}) -> int:
          # TODO
          pass
  
      @abstractmethod
      def insert(self, record: Record):
          # TODO
          pass
  
      @abstractmethod
      def insert_many(self, records: List[Record], show_progress=False):
          # TODO
          pass
  
      @abstractmethod
      def query(self, query: str, query_vec: List[float], top_k: int = 10, filters: Optional[Dict] = {}) -> List[Record]:
          # TODO
          pass
  
      @abstractmethod
      def query_date(self, start_date, end_date):
          # TODO
          pass
  
      @abstractmethod
      def query_text(self, query):
          # TODO
          pass
  
      @abstractmethod
      def delete_table(self):
          # TODO
          pass
  
      @abstractmethod
      def delete(self, filters: Optional[Dict] = {}):
          # TODO
          pass
  
      @abstractmethod
      def save(self):
          # TODO
          pass
  
apis: lancedb.pydantic.Vector
extract_api: [((622, 641), 'memgpt.config.MemGPTConfig.load', 'MemGPTConfig.load', ([], {}), '()\n', (639, 641), False, 'from memgpt.config import AgentConfig, MemGPTConfig\n'), ((1077, 1105), 'lancedb.pydantic.Vector', 'Vector', (['config.embedding_dim'], {}), '(config.embedding_dim)\n', (1083, 1105), False, 'from lancedb.pydantic import Vector, LanceModel\n'), ((1333, 1523), 'memgpt.data_types.Passage', 'Passage', ([], {'text': 'self.text', 'embedding': 'self.embedding', 'doc_id': 'self.doc_id', 'user_id': 'self.user_id', 'id': 'self.id', 'data_source': 'self.data_source', 'agent_id': 'self.agent_id', 'metadata': 'self.metadata_'}), '(text=self.text, embedding=self.embedding, doc_id=self.doc_id,\n    user_id=self.user_id, id=self.id, data_source=self.data_source,\n    agent_id=self.agent_id, metadata=self.metadata_)\n', (1340, 1523), False, 'from memgpt.data_types import Record, Message, Passage, Source\n'), ((2335, 2363), 'lancedb.pydantic.Vector', 'Vector', (['config.embedding_dim'], {}), '(config.embedding_dim)\n', (2341, 2363), False, 'from lancedb.pydantic import Vector, LanceModel\n'), ((2674, 2989), 'memgpt.data_types.Message', 'Message', ([], {'user_id': 'self.user_id', 'agent_id': 'self.agent_id', 'role': 'self.role', 'user': 'self.user', 'text': 'self.text', 'model': 'self.model', 'function_name': 'self.function_name', 'function_args': 'self.function_args', 'function_response': 'self.function_response', 'embedding': 'self.embedding', 'created_at': 'self.created_at', 'id': 'self.id'}), '(user_id=self.user_id, agent_id=self.agent_id, role=self.role, user=\n    self.user, text=self.text, model=self.model, function_name=self.\n    function_name, function_args=self.function_args, function_response=self\n    .function_response, embedding=self.embedding, created_at=self.\n    created_at, id=self.id)\n', (2681, 2989), False, 'from memgpt.data_types import Record, Message, Passage, Source\n'), ((3815, 3904), 'memgpt.data_types.Source', 'Source', ([], {'id': 'self.id', 'user_id': 'self.user_id', 'name': 'self.name', 'created_at': 'self.created_at'}), '(id=self.id, user_id=self.user_id, name=self.name, created_at=self.\n    created_at)\n', (3821, 3904), False, 'from memgpt.data_types import Record, Message, Passage, Source\n')]