original_docstring: 
          Search the vec embeddings in Faiss and then lookup the metadata in PSQL
  
          :param docs: `Document` with `.embedding` the same shape as the
              `Documents` stored in the `FaissSearcher`. The ids of the `Documents`
              stored in `FaissSearcher` need to exist in the `PostgreSQLStorage`.
              Otherwise you will not get back the original metadata.
          :param parameters: dictionary to define the ``traversal_paths``. This will
              override the default parameters set at init.
  
          :return: The `FaissSearcher` attaches matches to the `Documents` sent as inputs,
              with the id of the match, and its embedding. Then, the `PostgreSQLStorage`
              retrieves the full metadata (original text or image blob) and attaches
              those to the Document. You receive back the full Document.
  
          
docstring: Search the vec embeddings in Faiss and then lookup the metadata in PSQL
docstring_tokens: Search the vec embeddings in Faiss and then lookup the metadata in PSQL
code: def search(self, docs: 'DocumentArray', parameters: Optional[Dict] = {}, **kwargs):
          """
          Search the vec embeddings in Faiss and then lookup the metadata in PSQL
  
          :param docs: `Document` with `.embedding` the same shape as the
              `Documents` stored in the `FaissSearcher`. The ids of the `Documents`
              stored in `FaissSearcher` need to exist in the `PostgreSQLStorage`.
              Otherwise you will not get back the original metadata.
          :param parameters: dictionary to define the ``traversal_paths``. This will
              override the default parameters set at init.
  
          :return: The `FaissSearcher` attaches matches to the `Documents` sent as inputs,
              with the id of the match, and its embedding. Then, the `PostgreSQLStorage`
              retrieves the full metadata (original text or image blob) and attaches
              those to the Document. You receive back the full Document.
  
          """
          if self._kv_indexer and self._vec_indexer:
              self._vec_indexer.search(docs, parameters)
              kv_parameters = copy.deepcopy(parameters)
              kv_parameters['traversal_paths'] = [
                  path + 'm' for path in kv_parameters.get('traversal_paths', ['r'])
              ]
              self._kv_indexer.search(docs, kv_parameters)
          else:
              self.logger.warning('Indexers have not been initialized. Empty results')
              return
code_tokens: def search ( self , docs : 'DocumentArray' , parameters : Optional [ Dict ] = { } , ** kwargs ) : if self . _kv_indexer and self . _vec_indexer : self . _vec_indexer . search ( docs , parameters ) kv_parameters = copy . deepcopy ( parameters ) kv_parameters [ 'traversal_paths' ] = [ path + 'm' for path in kv_parameters . get ( 'traversal_paths' , [ 'r' ] ) ] self . _kv_indexer . search ( docs , kv_parameters ) else : self . logger . warning ( 'Indexers have not been initialized. Empty results' ) return
short_docstring: Search the vec embeddings in Faiss and then lookup the metadata in PSQL
short_docstring_tokens: Search the vec embeddings in Faiss and then lookup the metadata in PSQL
comment: """
          Search the vec embeddings in Faiss and then lookup the metadata in PSQL
  
          :param docs: `Document` with `.embedding` the same shape as the
              `Documents` stored in the `FaissSearcher`. The ids of the `Documents`
              stored in `FaissSearcher` need to exist in the `PostgreSQLStorage`.
              Otherwise you will not get back the original metadata.
          :param parameters: dictionary to define the ``traversal_paths``. This will
              override the default parameters set at init.
  
          :return: The `FaissSearcher` attaches matches to the `Documents` sent as inputs,
              with the id of the match, and its embedding. Then, the `PostgreSQLStorage`
              retrieves the full metadata (original text or image blob) and attaches
              those to the Document. You receive back the full Document.
  
          """