{"id":"6b8edbf0-d498-47f2-ba17-b29033f75ae8","slug":"spillwavesolutions-developing-llamaindex-systems","name":"developing-llamaindex-systems","description":"Production-grade agentic system development with LlamaIndex in Python. Covers semantic ingestion (SemanticSplitterNodeParser, CodeSplitter, IngestionPipeline), retrieval strategies (BM25Retriever, hybrid search, alpha weighting), PropertyGraphIndex with graph stores (Neo4j), context RAG (RouterQueryEngine, SubQuestionQueryEngine, LLMRerank), agentic orchestration (ReAct, Workflows, FunctionTool), and observability (Arize Phoenix). Use when asked to \"build a LlamaIndex agent\", \"set up semantic chunking\", \"index source code\", \"implement hybrid search\", \"create a knowledge graph with LlamaIndex\", \"implement query routing\", \"debug RAG pipeline\", \"add Phoenix observability\", or \"create an event-driven workflow\". 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Triggers on \"PropertyGraphIndex\", \"SemanticSplitterNodeParser\", \"CodeSplitter\", \"BM25Retriever\", \"hybrid search\", \"ReAct agent\", \"Workflow pattern\", \"LLMRerank\", \"Text-to-Cypher\".","source":"GITHUB_OPENCLEW","sourceId":"github:1124319393","repository":"https://github.com/SpillwaveSolutions/developing-llamaindex-systems","documentation":"https://www.xpersona.co/agent/spillwavesolutions-developing-llamaindex-systems","protocols":["OPENCLEW"],"languages":["typescript"],"install":{"command":"git clone https://github.com/SpillwaveSolutions/developing-llamaindex-systems.git","ecosystem":"git"},"examples":[{"kind":"example","language":"bash","snippet":"pip install llama-index-core>=0.10.0 llama-index-llms-openai llama-index-embeddings-openai arize-phoenix"},{"kind":"example","language":"python","snippet":"from llama_index.core import SimpleDirectoryReader\nfrom llama_index.core.node_parser import SemanticSplitterNodeParser\nfrom llama_index.embeddings.openai import OpenAIEmbedding\n\nembed_model = OpenAIEmbedding(model_name=\"text-embedding-3-small\")\nsplitter = SemanticSplitterNodeParser(\n    buffer_size=1,\n    breakpoint_percentile_threshold=95,\n    embed_model=embed_model\n)\n\ndocs = SimpleDirectoryReader(input_files=[\"data.pdf\"]).load_data()\nnodes = splitter.get_nodes_from_documents(docs)"}]}}