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Magnus Hedemark
3e86349539
Comprehensive skill covering: - Core architecture (7 primitives, Settings, data flow) - RAG strategies (basic through advanced with hybrid retrieval, reranking) - Multi-agent orchestration (AgentWorkflow, handoff bug fix) - Event-driven workflows (durable execution, checkpoint/resume) - Production deployment (llama-deploy, debugging, observability) - PropertyGraphIndex (knowledge graphs, hybrid retrieval) - Evaluation and span-attached observability - Integration ecosystem (vector stores, LlamaHub, LlamaParse) 9 reference files, 4 templates, 1 verification script. MIT licensed. 100% AI agent portable (no platform-specific content). Signed-off-by: Magnus Hedemark <magnus919@pm.me>
32 lines
829 B
Python
32 lines
829 B
Python
#!/usr/bin/env python3
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"""
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Minimal RAG pipeline using LlamaIndex.
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Loads documents from a directory, builds a vector index, and answers queries.
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"""
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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from llama_index.llms.openai import OpenAI
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from llama_index.core import Settings
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# --- Configuration ---
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Settings.llm = OpenAI(model="gpt-4o-mini")
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DATA_DIR = "./data"
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# --- Load ---
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documents = SimpleDirectoryReader(DATA_DIR).load_data()
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# --- Index ---
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index = VectorStoreIndex.from_documents(documents)
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# --- Query ---
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query_engine = index.as_query_engine(
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similarity_top_k=5,
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)
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response = query_engine.query("What does this data say about your question?")
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print(f"Answer: {response}")
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# Show sources
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for source in response.source_nodes:
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print(f" [{source.score:.3f}] {source.text[:100]}...")
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