class_docstr: This handler handles connection and execution of the PostgreSQL with pgvector extension statements.
class_docstr_tok: ["This","handler","handles","connection","and","execution","of","the","PostgreSQL","with","pgvector","extension","statements","."]
docstr: given inputs, build string query
docstr_tok: ["given","inputs",",","build","string","query"]
code: def _build_select_query(
      self,
      table_name: str,
      columns: List[str] = None,
      conditions: List[FilterCondition] = None,
      limit: int = None,
      offset: int = None,
  ) -> str:
      """
      given inputs, build string query
      """
      limit_clause = f"LIMIT {limit}" if limit else ""
      offset_clause = f"OFFSET {offset}" if offset else ""
  
      # translate filter conditions to dictionary
      filter_conditions = self._translate_conditions(conditions)
  
      # check if search vector is in filter conditions
      embedding_search = filter_conditions.get("embeddings", None)
  
      # given filter conditions, construct where clause
      where_clause = self._construct_where_clause(filter_conditions)
  
      # construct full after from clause, where clause + offset clause + limit clause
      after_from_clause = self._construct_full_after_from_clause(
          where_clause, offset_clause, limit_clause
      )
  
      if columns is None:
          targets = "*"
      else:
          targets = ", ".join(columns)
  
      if filter_conditions:
          if embedding_search:
              # if search vector, return similar rows, apply other filters after if any
              search_vector = filter_conditions["embeddings"]["value"][
                  0
              ]
              filter_conditions.pop("embeddings")
              return f"SELECT {targets} FROM {table_name} ORDER BY embeddings <=> '{search_vector}' {after_from_clause}"
          else:
              # if filter conditions, return filtered rows
              return f"SELECT {targets} FROM {table_name} {after_from_clause}"
      else:
          # if no filter conditions, return all rows
          return (
              f"SELECT {targets} FROM {table_name} {after_from_clause}"
          )
  
imports: from collections import OrderedDict
  from typing import List, Union
  import pandas
  import psycopg
  from mindsdb_sql import ASTNode, Parameter, Identifier, Update, BinaryOperation
  from pgvector.psycopg import register_vector
  from mindsdb.integrations.handlers.postgres_handler.postgres_handler import PostgresHandler
  from mindsdb.integrations.libs.const import HANDLER_CONNECTION_ARG_TYPE
  from mindsdb.integrations.libs.response import RESPONSE_TYPE
  from mindsdb.integrations.libs.response import HandlerResponse
  from mindsdb.integrations.libs.vectordatabase_handler import FilterCondition, VectorStoreHandler
  from mindsdb.utilities import log
  from mindsdb.utilities.profiler import profiler
label_desc: Use image node_id 6 for calling the PgVectorHandler obj's underlying member method code with example usage: obj._build_select_query(table_name, columns, conditions, limit, offset) and returns: str, targets, str, table_name, str, after_from_clause, str, targets, str, table_name, str, search_vector, str, after_from_clause, str, targets, str, table_name, str, after_from_clause