c137305468
The request-limit notice said only the cite tool remained available, but chat registers rag and analysis in one agent, so exhausting analysis claimed rag_search was gone too. Scoped to the capability's own tools. The cite window was counted over every model request once loaded, so turns spent on another capability expired it before the model was ever placed where citing was the obvious move. Count only requests whose preceding response called one of this capability's tools; engagement is also the only thing that can loop, which is all the bound guards against. Also: _count_tool_traffic returns a named tuple rather than four bare ints, and counts failures only for this capability's tools, so host-tool retries and output-validation retries no longer read as its failures.
55 lines
3 KiB
Markdown
55 lines
3 KiB
Markdown
# RAG Capability
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`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.
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## Tools
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| Tool | Purpose |
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|---|---|
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| `rag_search(query, limit?)` | Hybrid vector and full-text search with context expansion. |
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| `rag_cite(chunk_ids)` | Register exact result chunk IDs as answer citations. |
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The distinct `rag_` prefix lets this capability coexist with analysis and other search providers.
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## Create and compose
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```python
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from pydantic_ai import Agent
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from haiku.rag.capabilities.rag import create_capability
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rag = create_capability(db_path="my.lancedb")
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agent = Agent("openai:gpt-5", capabilities=[rag])
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result = await agent.run("What safety equipment does the manual require?")
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print(result.output)
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```
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`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.
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When the limit is reached, `rag_search` is removed while `rag_cite` remains for two further requests that call a RAG tool, so the model can register citations before answering from evidence already gathered. Requests spent on other capabilities do not count against that window. 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.
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## State
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When agent dependencies expose a `state` dictionary, the capability maintains a `RAGState` under `"rag"`:
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```python
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class RAGState(BaseModel):
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citation_index: dict[str, Citation]
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citations: list[str]
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document_filter: str | None
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searches: dict[str, list[SearchResult]]
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```
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`document_filter` persists between runs. Current citations and searches reset for each run, while the citation index remains available to the host application.
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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.
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## Context management
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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.
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## Domain context and vision
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`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`.
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See [Search and question answering](../configuration/qa.md) and [picture processing](../configuration/processing.md#picture-handling).
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