Settings.embed_model = FastEmbedEmbedding()
splitter = SemanticSplitterNodeParser(embed_model= Settings.embed_model)

# default mode of building the index
response_synthesizer = get_response_synthesizer(
    response_mode="tree_summarize", use_async=True
)
doc_summary_index = DocumentSummaryIndex.from_documents(
    docs,
    transformations=[splitter],
    response_synthesizer=response_synthesizer,
    show_progress=True,
)
retriever = DocumentSummaryIndexRetriever(
    doc_summary_index,
)

response_synthesizer = get_response_synthesizer(response_mode="tree_summarize")
retriever_query_engine = RetrieverQueryEngine(
    retriever=retriever,
    response_synthesizer=response_synthesizer,
)

File document_summary\retrievers.py:98, in DocumentSummaryIndexLLMRetriever._retrieve(self, query_bundle)
      raw_response = self._llm.predict(
          self._choice_select_prompt,
         context_str=fmt_batch_str,
          query_str=query_str,
     97 )
---> 98 raw_choices, relevances = self._parse_choice_select_answer_fn(
     99     raw_response, len(summary_nodes)
    1 )
     choice_idxs = [choice - 1 for choice in raw_choices]
     choice_summary_ids = [summary_ids_batch[ci] for ci in choice_idxs]

File llama_index\core\indices\utils.py:104, in default_parse_choice_select_answer_fn(answer, num_choices, raise_error)
          else:
              raise ValueError(
                 f"Invalid answer line: {answer_line}. "
                "Answer line must be of the form: "
    102             "answer_num: <int>, answer_relevance: <float>"
    103         )
--> 104 answer_num = int(line_tokens[0].split(":")[1].strip())
    105 if answer_num > num_choices:
        continue

IndexError: list index out of range