fix(docs): handle collection not found error on RAG tutorial when qdrant is selected as vectorStore (langchain-ai#32099)

In [Rag Part 1
Tutorial](https://python.langchain.com/docs/tutorials/rag/), when QDrant
vector store is selected, the sample code does not work
It fails with error  `ValueError: Collection test not found`

So, this fix is creating that collection and ensuring its dimension size
is matching the selection the embedding size of the selected LLM Model

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: 3 people

docs/src/theme/VectorStoreTabs.js

@@ -77,7 +77,7 @@ export default function VectorStoreTabs(props) {
         {
             value: "Qdrant",
             label: "Qdrant",
-            text: `from langchain_qdrant import QdrantVectorStore\nfrom qdrant_client import QdrantClient\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\nclient = QdrantClient(":memory:")\n${vectorStoreVarName} = QdrantVectorStore(\n    client=client,\n    collection_name="test",\n    embedding=embeddings,\n)`,
+            text: `from qdrant_client.models import Distance, VectorParams\nfrom langchain_qdrant import QdrantVectorStore\nfrom qdrant_client import QdrantClient\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\nclient = QdrantClient(":memory:")\n\nvector_size = len(embeddings.embed_query("sample text"))\n\nif not client.collection_exists("test"):\n    client.create_collection(\n        collection_name="test",\n        vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE)\n    )\n${vectorStoreVarName} = QdrantVectorStore(\n    client=client,\n    collection_name="test",\n    embedding=embeddings,\n)`,
             packageName: "langchain-qdrant",
             default: false,
         },