Capabilities

haiku.rag provides two native Pydantic AI capabilities:

Both capabilities are deferred by default. An agent initially sees only their descriptions and the standard load_capability tool. Instructions and tools enter the model context only when the model loads a capability.

Compose an agent

from pydantic_ai import Agent
from haiku.rag.capabilities.rag import create_capability

rag = create_capability(db_path="my.lancedb")
agent = Agent("openai:gpt-5", capabilities=[rag])

result = await agent.run("What does the knowledge base say about X?")
print(result.output)

Attach both capabilities when an agent should choose between retrieval and computation:

from haiku.rag.capabilities.analysis import create_capability as analysis
from haiku.rag.capabilities.rag import create_capability as rag

agent = Agent(
    "openai:gpt-5",
    capabilities=[rag(db_path="my.lancedb"), analysis(db_path="my.lancedb")],
)

State

Capabilities use a plain state: dict[str, Any] attribute on agent dependencies when one is available. RAG state lives under "rag"; analysis state lives under "analysis". This keeps state independent of any transport or UI protocol.

Applications serving AG-UI should adapt the agent with Pydantic AI's AGUIAdapter. Native model and tool events require no haiku.rag-specific bridge.

Database path

Both factories resolve their database in this order:

1. The db_path argument.
2. HAIKU_RAG_DB.
3. config.storage.data_dir / "haiku.rag.lancedb".