from llama_index.core import (    SimpleDirectoryReader,    VectorStoreIndex,    StorageContext,    Settings,    get_response_synthesizer)from llama_index.core.query_engine import RetrieverQueryEngine, TransformQueryEnginefrom llama_index.core.node_parser import SentenceSplitterfrom llama_index.core.schema import TextNode, MetadataModefrom llama_index.vector_stores.qdrant import QdrantVectorStorefrom llama_index.embeddings.ollama import OllamaEmbeddingfrom llama_index.llms.ollama import Ollamafrom llama_index.core.retrievers import VectorIndexRetrieverfrom llama_index.core.indices.query.query_transform import HyDEQueryTransformimport qdrant_clientimport logging

logging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__)
# load the local data directory and chunk the data for further processingdocs = SimpleDirectoryReader(input_dir="data", required_exts=[".pdf"]).load_data(show_progress=True)text_parser = SentenceSplitter(chunk_size=512, chunk_overlap=100)
text_chunks = []doc_ids = []nodes = []

# Create a local Qdrant vector storelogger.info("initializing the vector store related objects")client = qdrant_client.QdrantClient(host="localhost", port=6333)vector_store = QdrantVectorStore(client=client, collection_name="research_papers"）

# local vector embeddings modellogger.info("initializing the OllamaEmbedding")embed_model = OllamaEmbedding(model_name='mxbai-embed-large', base_url='<http://localhost:11434>')logger.info("initializing the global settings")Settings.embed_model = embed_modelSettings.llm = Ollama(model="llama3", base_url='<http://localhost:11434>')Settings.transformations = [text_parser]

logger.info("enumerating docs")for doc_idx, doc in enumerate(docs):    curr_text_chunks = text_parser.split_text(doc.text)    text_chunks.extend(curr_text_chunks)    doc_ids.extend([doc_idx] * len(curr_text_chunks))
logger.info("enumerating text_chunks")for idx, text_chunk in enumerate(text_chunks):    node = TextNode(text=text_chunk)    src_doc = docs[doc_ids[idx]]    node.metadata = src_doc.metadata    nodes.append(node)
logger.info("enumerating nodes")for node in nodes:    node_embedding = embed_model.get_text_embedding(        node.get_content(metadata_mode=MetadataMode.ALL)    )    node.embedding = node_embedding
logger.info("initializing the storage context")storage_context = StorageContext.from_defaults(vector_store=vector_store)logger.info("indexing the nodes in VectorStoreIndex")index = VectorStoreIndex(    nodes=nodes,    storage_context=storage_context,    transformations=Settings.transformations,)
logger.info("initializing the VectorIndexRetriever with top_k as 5")vector_retriever = VectorIndexRetriever(index=index, similarity_top_k=5)response_synthesizer = get_response_synthesizer()logger.info("creating the RetrieverQueryEngine instance")vector_query_engine = RetrieverQueryEngine(    retriever=vector_retriever,    response_synthesizer=response_synthesizer,)logger.info("creating the HyDEQueryTransform instance")hyde = HyDEQueryTransform(include_original=True)hyde_query_engine = TransformQueryEngine(vector_query_engine, hyde)
logger.info("retrieving the response to the query")response = hyde_query_engine.query(    str_or_query_bundle="what are all the data sets used in the experiment and told in the paper")print(response)
client.close()