القائمة
Use when the user wants a cuga agent to ingest, search, or answer questions from documents (PDF/DOCX/XLSX/PPTX/HTML/Markdown/images) - RAG / knowledge base features.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Use when the user wants to govern agent behavior with a cuga policy - block/redirect an intent, add step-by-step playbook guidance, require approval before a tool runs, enhance a tool's description, or reshape agent output format.
Use when the user wants to write Python code that creates or invokes a CugaAgent or CugaSupervisor, e.g. "build an agent with cuga", "how do I call agent.invoke", "set up a multi-agent supervisor".
Use when the user wants a running CugaAgent to load a new capability at runtime via cuga's own skill system, e.g. "add a skill to my agent", "create a .cuga/skills SKILL.md", "how does load_skill work".
Use when the user wants to install, launch, or build something with the cuga agent framework (pip package `cuga`), or asks "how do I use cuga" / "how do I build an agent with cuga" — routes to the right cuga-harness-kit skill instead of guessing.
Use when the user wants to install cuga, start the demo/web UI, or asks what `cuga start` does or which service/mode to run.
Use when the user wants to give a cuga agent a new capability via a Python function, REST/OpenAPI service, or MCP server, e.g. "add a tool", "connect this API to cuga", "register an MCP server".
استنادا إلى تصنيف SOC المهني
| name | cuga-knowledge-rag |
| description | Use when the user wants a cuga agent to ingest, search, or answer questions from documents (PDF/DOCX/XLSX/PPTX/HTML/Markdown/images) - RAG / knowledge base features. |
cuga has a built-in knowledge base: local vector store + Docling for parsing/normalizing documents before chunking and embedding, so ingestion stays self-contained with no external document service.
Knowledge is enabled by default (enable_knowledge=True); the SDK auto-injects knowledge tools/awareness so the agent knows what's available and how to search it.
uv run cuga start demo_knowledge
Full walkthrough with sample docs: docs/examples/knowledge_demo/ in a cuga-agent checkout.
from cuga import CugaAgent
import asyncio
agent = CugaAgent(enable_knowledge=True)
async def main():
await agent.knowledge.ingest("/path/to/quarterly_report.pdf")
result = await agent.invoke("What does the report say about Q4 revenue?")
print(result.answer) # agent searches the knowledge base automatically
results = await agent.knowledge.search("Q4 revenue figures")
for r in results:
print(f"{r['filename']} (page {r['page']}): {r['text'][:100]}")
docs = await agent.knowledge.list_documents()
await agent.aclose()
asyncio.run(main())
# Session-scoped: temporary, tied to one conversation thread
await agent.knowledge.ingest("/path/to/file.pdf", scope="session", thread_id="user-session-123")
results = await agent.knowledge.search("query", scope="session", thread_id="user-session-123")
# Agent-scoped (default): permanent, shared across conversations
await agent.knowledge.ingest("/path/to/file.pdf", scope="agent")
Use session scope for per-conversation uploads that shouldn't leak between users; use agent scope for a shared reference corpus.
agent = CugaAgent(tools=[my_tools], enable_knowledge=False)
PDF, DOCX, XLSX, PPTX, HTML, Markdown, images, and more (via Docling). Embedding provider (fastembed default/local, huggingface, openai, ollama, openrouter) plus model/batch/concurrency are set under [knowledge.embeddings] in settings.toml or via --embeddings-* CLI flags. Switching provider/model invalidates existing vectors (different dimensionality) — the manage UI (cuga start manager) surfaces a "re-index recommended" banner when that happens.
Full provider matrix: https://docs.cuga.dev/docs/sdk/knowledge/