Files
deepseek-harness/packages/llm/llm-pi-ai/README.md
Tianyi Cui 0ebb86e70f Implement mandatory app-attribution headers per the RFC
dsh-llm owns the vocabulary (attribution.ts): AppIdentity with the version
read from the package manifest, userAgent(), and attributionHeaders(target,
identity) over a closed AttributionTarget union ('generic' | 'openrouter').
Both adapters send the headers on every provider request — llm-deepseek in
its fetch headers, llm-pi-ai through pi-ai's StreamOptions.headers — behind
an explicit attributionTarget config (never inferred from baseURL), with
mock-server tests asserting exact wire arrival and the absence of the
OpenRouter set by default.

The RFC moves to implemented/ amended with the settled identity (the
deepseek-harness token, the DeepSeek Harness title, the planned
deepseek-ai/deepseek-harness-sdk URL behind a FIXME until that repo exists)
and the explicit-config OpenRouter decision.
2026-07-04 18:14:42 +08:00

44 lines
3.2 KiB
Markdown

# @deepseek-ai/dsh-llm-pi-ai
DeepSeek adapter for the harness LLM seam backed by [`@earendil-works/pi-ai`](https://www.npmjs.com/package/@earendil-works/pi-ai) (the LLM library behind the pi agent).
## Why a second adapter exists
`@deepseek-ai/dsh-llm-deepseek` already talks to the same endpoint. This package is its **design-verification twin**: same models, same wire protocol, completely different internals — a unified LLM library with its own event vocabulary versus hand-rolled fetch/SSE. Anything the harness `StreamChunk` protocol cannot express for BOTH implementations is a core-vocabulary bug. The differences it exercised on purpose:
- pi-ai hands tool-call `arguments` around as **parsed objects**; the harness keeps raw JSON strings. The adapter patches replay payloads back to the original raw strings before sending them, and re-stringifies parsed output tool calls at `block-end`.
- pi-ai reports failures as **in-stream error events** (it never throws mid-stream); these map to `finish {kind:'error'|'aborted'}` chunks — the protocol's other sanctioned error path besides throwing (which llm-deepseek uses).
- pi-ai folds reasoning tokens into `usage.output`; there is no separate reasoning count to map.
- pi-ai's options omit some DeepSeek/OpenAI-compatible details; the adapter uses its `onPayload` hook to preserve the harness contract (`stop`, per-tool `strict`, omitted reasoning effort, raw replayed tool arguments).
## Config
Same shape as llm-deepseek (one-line swap in cordis.yml), with pi-ai's thinking-level vocabulary:
```yaml
- id: llm
name: '@deepseek-ai/dsh-llm-pi-ai'
config:
apiKey: !!js process.env.DEEPSEEK_API_KEY
baseURL: !!js process.env.DEEPSEEK_BASE_URL
models: [deepseek-v4-flash, deepseek-v4-pro]
reasoning: high # off | high | xhigh (xhigh → wire 'max')
attributionTarget: openrouter # optional; generic | openrouter — omitted ⇒ generic
```
## App attribution
Every request carries the shared attribution headers from dsh-llm's `attributionHeaders()`, passed through pi-ai's `headers` stream option (pi-ai merges caller headers last, so they always reach the wire — the unit suite asserts arrival on the mock server, same as llm-deepseek). `attributionTarget: openrouter` adds OpenRouter's documented set (`HTTP-Referer`, `X-OpenRouter-Title`, `X-OpenRouter-Categories`) and is explicit config only — never inferred from `baseURL`. See [dsh-llm § App attribution](../llm/README.md#app-attribution-attributionts).
## Dependency weight
pi-ai declares the openai/anthropic/google/mistral/AWS SDKs as install-time dependencies. They are lazy-loaded — only the openai SDK actually loads for this adapter — but they do land in `node_modules`. Accepted for a package whose purpose is design verification.
## Limitations
Same MVP contract as llm-deepseek: `prefill` throws `UNSUPPORTED`, images are not representable, `tool_choice` is not mapped.
## Testing
Unit suites run against a local `node:http` mock SSE server (pi-ai's openai SDK happily talks to any base URL). Real-API coverage in `tests/adapter.e2e.ts` (`pnpm run test:e2e`, key-gated): V4 Flash + V4 Pro across all exposed reasoning levels (off/high/xhigh), the thinking+tools round trip, and a cross-adapter structural-equivalence check against llm-deepseek.