VectorStoreIndex over QdrantVectorStore

Completion

Implement a function create_index_from_nodes that builds a VectorStoreIndex over a QdrantVectorStore, following the pattern in build_index_from_nodes from the repository source. Your function must:

Accept a list of pre-embedded TextNode objects and an embed_model.
Use the centralized Qdrant client (via _get_qdrant_client()) if the backend is "qdrant"; otherwise fall back to a plain in-memory VectorStoreIndex.
Create a QdrantVectorStore with index_doc_id=True and text_key="text" so that Qdrant acts as the single source of truth.
Create a StorageContext from the vector store and build the VectorStoreIndex with it, ensuring LlamaIndex never re-embeds.
Use the provided collection_name variable (already computed) for the Qdrant collection.

The starter code has three # TODO: blanks at the critical wiring points. Fill them in.

Your code

import os
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore

# Assume these are defined elsewhere in the codebase (as in the source)
from .memory_common import _get_qdrant_client, vector_store_backend

def create_index_from_nodes(nodes, embed_model, *, collection_name="my_collection"):
    """
    Builds a VectorStoreIndex over QdrantVectorStore, following the same
    pattern as build_index_from_nodes. Falls back to in-memory if backend is memory.
    """
    # TODO 1: Get the Qdrant client based on backend decision.
    # Use _get_qdrant_client() if vector_store_backend() == "qdrant", else None.
    client = None  # replace this line
    
    if client is None:
        # Fallback to plain in-memory index
        return VectorStoreIndex(nodes, embed_model=embed_model)
    
    # TODO 2: Create the QdrantVectorStore with correct arguments.
    # Must set index_doc_id=True and text_key="text".
    vector_store = None  # replace this line
    
    # TODO 3: Create StorageContext from the vector store and build the index.
    # Return the VectorStoreIndex built with nodes, storage_context, embed_model.
    storage_context = None  # replace this line
    return None  # replace this line

Sources

roadmap-kg/kg/memory_common.py:931-972
roadmap-kg/kg/memory_common.py:975-990
roadmap-kg/kg/memory_common.py:836-872