Agentic Rag With Reasoning

1"""
2Agentic Rag With Reasoning
3=============================
4
5Demonstrates agentic RAG with reranking and explicit reasoning tools.
6"""
7
8import asyncio
9
10from kern.agent import Agent
11from kern.knowledge.embedder.cohere import CohereEmbedder
12from kern.knowledge.knowledge import Knowledge
13from kern.knowledge.reranker.cohere import CohereReranker
14from kern.models.openai import OpenAIResponses
15from kern.tools.reasoning import ReasoningTools
16from kern.vectordb.lancedb import LanceDb, SearchType
17
18# ---------------------------------------------------------------------------
19# Setup
20# ---------------------------------------------------------------------------
21knowledge = Knowledge(
22    # Use LanceDB as the vector database, store embeddings in the `agno_docs` table
23    vector_db=LanceDb(
24        uri="tmp/lancedb",
25        table_name="agno_docs",
26        search_type=SearchType.hybrid,
27        embedder=CohereEmbedder(id="embed-v4.0"),
28        reranker=CohereReranker(model="rerank-v3.5"),
29    ),
30)
31
32# ---------------------------------------------------------------------------
33# Create Agent
34# ---------------------------------------------------------------------------
35agent = Agent(
36    model=OpenAIResponses(id="gpt-5.2"),
37    # Agentic RAG is enabled by default when `knowledge` is provided to the Agent.
38    knowledge=knowledge,
39    # search_knowledge=True gives the Agent the ability to search on demand
40    # search_knowledge is True by default
41    search_knowledge=True,
42    tools=[ReasoningTools(add_instructions=True)],
43    instructions=[
44        "Include sources in your response.",
45        "Always search your knowledge before answering the question.",
46    ],
47    markdown=True,
48)
49
50# ---------------------------------------------------------------------------
51# Run Agent
52# ---------------------------------------------------------------------------
53if __name__ == "__main__":
54    asyncio.run(
55        knowledge.ainsert_many(urls=["https://kern.ndx.rocks/basics/agents/overview.md"])
56    )
57    agent.print_response(
58        "What are Agents?",
59        stream=True,
60        show_full_reasoning=True,
61    )

1# Clone and setup repo
2git clone https://github.com/kern-ai/kern.git
3cd kern/cookbook/02_agents/07_knowledge
4
5# Create and activate virtual environment
6./scripts/demo_setup.sh
7source .venvs/demo/bin/activate
8
9python agentic_rag_with_reasoning.py