Build a Production RAG Application with LlamaIndex and Qdrant
Document ingestion, hybrid search, reranking, and evaluation with LlamaIndex
Build a Production RAG Application with LlamaIndex and Qdrant
Document ingestion, hybrid search, reranking, and evaluation with LlamaIndex
Complete guide to building a production RAG application using LlamaIndex for orchestration, Qdrant for vector storage, and comprehensive evaluation with LlamaIndex evaluation modules.
LlamaIndex provides higher-level abstractions for RAG than LangChain. Setup: pip install llama-index llama-index-vector-stores-qdrant llama-index-embeddings-openai. Document ingestion pipeline: from llama_index.core import VectorStoreIndex, SimpleDirectoryReader; documents = SimpleDirectoryReader("./docs").load_data(); index = VectorStoreIndex.from_documents(documents, embed_model=OpenAIEmbedding(model="text-embedding-3-small")). Qdrant vector store: from llama_index.vector_stores.qdrant import QdrantVectorStore; client = QdrantClient(url="http://localhost:6333"); vector_store = QdrantVectorStore(client=client, collection_name="docs"); storage_context = StorageContext.from_defaults(vector_store=vector_store); index = VectorStoreIndex.from_documents(documents, storage_context=storage_context). Advanced retrieval: HybridRetriever combining dense + sparse, RouterQueryEngine for routing to specialized indices based on query type, SubQuestionQueryEngine for decomposing complex questions. Query engine: query_engine = index.as_query_engine(similarity_top_k=5, response_mode="tree_summarize"); response = query_engine.query("How does the refund policy work?"). Evaluation: from llama_index.core.evaluation import FaithfulnessEvaluator, RelevancyEvaluator; automated pipeline testing against ground truth QA pairs.
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