RAG — chat with your documents

Load documents and the agent answers grounded in them. Load → chunk → embed → store → retrieve → inject, all built in.

from neuroagent import Agent

agent = Agent(provider="openai", rag=True)

agent.load_document("handbook.pdf")     # PDF/DOCX/TXT/CSV/MD/HTML/JSON (PDF/DOCX need [rag])
agent.load_text("Our refund window is 30 days.")

print(agent.ask("Summarize chapter 5 of the handbook.").content)
print(agent.ask("What is our refund window?").content)

# Better diversity in retrieved context with MMR re-ranking:
agent = Agent(provider="openai", rag=True, rag_k=6, rag_rerank=True)

Custom embedder or vector store

Build a KnowledgeBase and pass it in — the chat model and embedder can differ:

from neuroagent import Agent, KnowledgeBase
from neuroagent.providers.openai import OpenAIProvider
from neuroagent.rag import InMemoryVectorStore

kb = KnowledgeBase(embedder=OpenAIProvider(), store=InMemoryVectorStore(), chunk_size=800)
await kb.aadd_path("manual.pdf")
agent = Agent(provider="anthropic", rag=kb)