Galeria: Pipeline de RAG & LLMs
Demonstração de uma arquitetura completa de Recuperação Aumentada por Geração (Retrieval-Augmented Generation), conectando clientes, vetores, modelos e bancos relacionais.
Demonstração Visual
Código de Demonstração
Disponível em examples/rag_pipeline_manim.py:
from manim import Scene
import animaflow as af
class RAGFlowAnimation(Scene):
def construct(self):
flow = af.Flow(title="Production RAG System Architecture")
user = flow.add_node("User Query", subtitle="Prompt / Context")
embed = flow.add_node("Embedding Engine", subtitle="text-embedding-3")
vdb = flow.add_node("Vector Database", subtitle="Qdrant / Milvus")
llm = flow.add_node("LLM Inference", subtitle="Claude / GPT-4")
resp = flow.add_node("Response Output", subtitle="Streaming Token")
flow.auto_layout_layers(
layers=[[user], [embed], [vdb], [llm], [resp]],
h_gap=1.5
)
flow.connect(user, embed, label="embed_query()")
flow.connect(embed, vdb, label="similarity_search()")
flow.connect(vdb, llm, label="inject_context()")
flow.connect(llm, resp, label="stream_response()")
flow.timeline.reveal_sequence(delay=0.2)
flow.timeline.send_packet(from_node=user, to_node=embed)
flow.timeline.send_packet(from_node=embed, to_node=vdb)
flow.timeline.highlight_node(vdb)
flow.timeline.send_packet(from_node=vdb, to_node=llm)
flow.timeline.stream_packets(from_node=llm, to_node=resp, count=6, duration=2.5)
flow.render_manim(self)