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pythonintermediate

LlamaIndex Document Query Engine

Index and query documents with LlamaIndex's VectorStoreIndex for fast semantic search.

python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

Settings.llm = OpenAI(model='gpt-4o-mini')
Settings.embed_model = OpenAIEmbedding(model='text-embedding-3-small')

documents = SimpleDirectoryReader('./docs').load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist('./index_store')

query_engine = index.as_query_engine(similarity_top_k=5)

response = query_engine.query('What are the key findings?')
print(response)
for node in response.source_nodes:
    print(f'  Score: {node.score:.3f} | {node.text[:100]}...')

Use Cases

  • document RAG
  • enterprise search
  • knowledge Q&A

Tags

#llamaindex#rag#query-engine#python

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