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Build LlamaIndex Vector Query Engine with Gemini 2.5 Flash
Linked challenge: MAS Regulatory Intelligence & Asset Screening Agent with LlamaIndex
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implementation8 lines3 sectionsNo variables0 checklist items
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.gemini import Gemini
from llama_index.embeddings.gemini import GeminiEmbedding
Settings.llm = Gemini(model="models/gemini-2.5-flash")
Settings.embed_model = GeminiEmbedding(model_name="models/text-embedding-004")
documents = SimpleDirectoryReader("./mas_docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)