code: import qdrant_client
  from llama_index.llms import Ollama
  from llama_index import (
      VectorStoreIndex,
      ServiceContext,
  )
  from llama_index.vector_stores.qdrant import QdrantVectorStore
  
  # re-initialize the vector store
  client = qdrant_client.QdrantClient(
      path="./qdrant_data"
  )
  vector_store = QdrantVectorStore(client=client, collection_name="tweets")
  
  # get the LLM again
  llm = Ollama(model="mistral")
  service_context = ServiceContext.from_defaults(llm=llm,embed_model="local")
  # load the index from the vector store
  index = VectorStoreIndex.from_vector_store(vector_store=vector_store,service_context=service_context)
  
  
  def rag_pipline(query):
          if query is not None:
              query_engine = index.as_query_engine(similarity_top_k=20)
              response = query_engine.query(query)
              return response
          else:
              return "i am sorry. i cannot answer you for this due to some error in data"
apis: llama_index.vector_stores.qdrant.QdrantVectorStore llama_index.VectorStoreIndex.from_vector_store llama_index.ServiceContext.from_defaults llama_index.llms.Ollama
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