RAGAgent

The plan includes RAGAgent to query unstructured text and synthesize answers with vLLM. RAG Agent uses Qdrant for semantic vector search of documents RAG Agent uses Ollama embeddings via nomic-embed-text for document retrieval RAG Agent generates answers using the qwen3-coder-next LLM via Ollama RAGAgent corresponds to the rag agent implementation RAGAgent corresponds to the rag agent implementation RAG Agent uses Qdrant for semantic vector search of documents RAG Agent uses Ollama embeddings via nomic-embed-text for document retrieval RAG Agent generates answers using the qwen3-coder-next LLM via Ollama RAGAgent corresponds to the rag agent implementation RAGAgent corresponds to the rag agent implementation RAG Agent uses Qdrant for semantic vector search of documents RAG Agent uses Ollama embeddings via nomic-embed-text for document retrieval RAG Agent generates answers using the qwen3-coder-next LLM via Ollama RAGAgent corresponds to the rag agent implementation RAGAgent corresponds to the rag agent implementation The RAGAgent is part of Document RAG to perform retrieval and answer synthesis. RAG Agent integrates with Qdrant for vector storage and semantic search. RAG Agent uses Ollama to generate embeddings and answers based on semantic search results. RAG Agent employs the nomic-embed-text embedding model for creating document embeddings. RAG Agent realizes the Natural Language Querying capability by semantic search and answer generation over documents. Lightweight Implementation uses RAG Agent for document question answering without heavy dependencies. RAGAgent optionally searches unstructured text in the data analysis process. RAGAgent uses LlamaIndex to chunk PDFs and generate embeddings. RAGAgent stores embeddings in the Qdrant vector database for retrieval.