Construire un assistant local IA pour les compétitions Kaggle avec Qdrant RAG et Ollama

• •Informations de connexion au serveur Qdrant

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        "content": "## Step 2. Summarise and Vectorise Document Contents\n[Learn more about using the Qdrant VectorStore](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant)\n\nCapturing the document into our vector store is intended for a technique we'll use later known as Retrieval Augumented Generation or \"RAG\" for short. For our scenario, this allows our LLM to retrieve context more efficiently which produces better respsonses."
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