RAG Capability

RAGCapability adds grounded document search and citations to a Pydantic AI agent. It is deferred by default, so its instructions and tools do not consume model context until loaded.

Tools

The distinct rag_ prefix lets this capability coexist with analysis and other search providers.

Create and compose

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 safety equipment does the manual require?")
print(result.output)

create_capability accepts db_path, config, defer_loading, request_limit, and vision. Set defer_loading=False for a dedicated RAG agent where routing is unnecessary. The default request limit is 20 model requests per question; set request_limit=None to disable it. vision controls whether picture results are attached to search returns as images and should reflect the model the hosting agent runs; it defaults to the configured QA model's vision flag.

When the limit is reached, only the RAG capability's tools are removed. The model gets one more turn to answer from evidence already gathered, while unrelated agent and capability tools remain available. A new agent run starts a fresh limit, so multi-turn chat does not consume one shared budget.

State

When agent dependencies expose a state dictionary, the capability maintains a RAGState under "rag":

class RAGState(BaseModel):
    citation_index: dict[str, Citation]
    citations: list[str]
    document_filter: str | None
    searches: dict[str, list[SearchResult]]

document_filter persists between runs. Current citations and searches reset for each run, while the citation index remains available to the host application.

State is ordinary application state; the capability does not depend on AG-UI. An AG-UI application can expose it using Pydantic AI's standard adapter.

Context management

Large RAG tool results from earlier user turns are replaced with a short marker before model requests. Tool-call pairing and current-turn evidence are retained. This prevents long conversations from repeatedly sending old retrieved content.

Domain context and vision

prompts.domain_preamble is prepended to the packaged capability instructions. When the capability's vision gate is on (by default, when the configured QA model has vision: true), picture results are attached to search returns as BinaryContent.

See Search and question answering and picture processing.