def process_retriever_component_fn(self, user_query: str):
        """Transform the output of the sentence_retriver"""

        logger.info("Sentence Retriever Output processing...")
        sentence_retriever = self.index.as_retriever(similarity_top_k=5)

        nodes = sentence_retriever.retrieve(user_query)

        with open("first_nodes.txt", mode="w") as f:
            for node in nodes:
                f.write(str(node) + "\n")
        # Create a QueryBundle from the user query
        query_bundle = QueryBundle(query_str=user_query)

        logger.info("Relevant Node Retrieved...")
        logger.info("Starting the reranking process...")
        postprocessor = LLMRerank(top_n=3, llm=self.llm, choice_batch_size=1)
        reranked_nodes = postprocessor.postprocess_nodes(
            nodes=nodes, query_bundle=query_bundle
        )
        contexts = ""
        with open("second_nodes.txt", mode="w") as f:
            for reranked_node in reranked_nodes:
                f.write(str(reranked_node) + "\n")
                contexts += str(reranked_node) + "\n"
        return contexts

File "/usr/local/lib/python3.8/dist-packages/llama_index/core/postprocessor/types.py", line 56, in postprocess_nodes
return self._postprocess_nodes(nodes, query_bundle)
File "/usr/local/lib/python3.8/dist-packages/llama_index/core/instrumentation/dispatcher.py", line 230, in wrapper
result = func(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/llama_index/core/postprocessor/llm_rerank.py", line 99, in _postprocess_nodes
raw_choices, relevances = self._parse_choice_select_answer_fn(
File "/usr/local/lib/python3.8/dist-packages/llama_index/core/indices/utils.py", line 104, in default_parse_choice_select_answer_fn
answer_num = int(line_tokens[0].split(":")[1].strip())
IndexError: list index out of range

fastapi
uvicorn
pydantic
loguru
llama-index-experimental
llama-index-embeddings-huggingface
llama-index-llms-ollama
llama-index-finetuning
llama-index-readers-file
sqlalchemy

    def get_query_pipeline(self):
        """Create & Return the Query Pipeline of database generation"""

        qp = QP(
            modules={
                "input": InputComponent(),
                "process_retriever": self.process_retriever_component,
                "table_creation_prompt": self.table_creation_prompt,
                "llm1": self.llm1,
                "python_output_parser": self.python_parser_component,
            },
            verbose=True,
        )

        qp.add_link("input", "process_retriever")
        qp.add_link("input", "table_creation_prompt", dest_key="query_str")
        qp.add_link(
            "process_retriever", "table_creation_prompt", dest_key="retrieved_nodes"
        )

        qp.add_chain(["table_creation_prompt", "llm1", "python_output_parser"])

        return qp

    def get_query_pipeline(self):
        """Create & Return the Query Pipeline of database generation"""

        qp = QP(
            modules={
                "input": InputComponent(),
                "process_retriever": self.process_retriever_component,
                "table_creation_prompt": self.table_creation_prompt,
                "llm1": self.llm1,
                "python_output_parser": self.python_parser_component,
                "table_insert_prompt": self.table_insert_prompt,
                "llm2": self.llm1,
                "python_output_parser1": self.python_parser_component,
            },
            verbose=True,
        )

        qp.add_link("input", "process_retriever")
        qp.add_link("input", "table_creation_prompt", dest_key="query_str")
        qp.add_link(
            "process_retriever", "table_creation_prompt", dest_key="retrieved_nodes"
        )

        qp.add_chain(["table_creation_prompt", "llm1", "python_output_parser"])
        
        ...
        return qp

    instruction_str = (
        "1. Convert the query to executable Python code using Pandas.\n"
        "2. The code should represent a solution to the query.\n"
        "3. PRINT ONLY THE EXPRESSION.\n"
    )

    pandas_prompt_str = (
        "You are working with a pandas dataframe in Python.\n"
        "The name of the dataframe is `df`.\n"
        "Here is a list of columns from the dataframe `df` with explanations for each column.\n"
        "{columns_explain}\n\n"
        "Here's are some relevant example rows (values in the same order as columns above)\n"
        "{relevant_node}\n\n"
        "Follow these instructions:\n"
        "{instruction_str}\n"
        "Query: {query_str}\n\n"
    )

if not os.path.exists("vector_index"):
        logger.info("CSVReader working..")
        reader = CSVReader(concat_rows=False)
        nodes = reader.load_data(file=Path(path))
        logger.info("CSVReader Done..")

        logger.info("Vectorizing..")
        index = VectorStoreIndex(nodes, show_progress=True)
        logger.info("Vectorizing done..")
        logger.info("Storing..")
        index.storage_context.persist("vector_index")
        logger.info("Storing done..")
    else:
        logger.info("Loading from the storage..")
        storage_context = StorageContext.from_defaults(persist_dir="vector_index")
        index = load_index_from_storage(storage_context)