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Tianyi Cui d02e9f1bd6 Reorganize packages into a modular hierarchy

Move the 18 flat packages/<name> packages into role-grouped dirs:
core/, llm/, bash/, session-persistence/, ui/, support/. Group dirs are
pure containers; each package keeps its @deepseek-ai/dsh-* name.

Collapse the per-package tsconfig paths maps (base + typecheck) into one
@deepseek-ai/dsh-* wildcard with a candidate per group, and derive the
publint list from the hierarchy. Update all depth-coupled globs/configs
(workspace, tsdown, vitest, eslint, knip, tsconfig includes/refs,
per-package tsconfigs, generators, doc-script scopes, type-equiv manifest)
and the cross-package/script relative imports in tests.

Fix doc-typecheck's workspacePaths() to parse tsconfig JSONC via the
TypeScript API instead of a regex comment-strip, which corrupted the
new wildcard `/*/` path candidates.

WIP: doc cross-links and package/RFC docs still to update.

2026-06-20 22:55:20 +08:00

2.6 KiB

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@deepseek-ai/dsh-llm-pi-ai

DeepSeek adapter for the harness LLM seam backed by @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:

- 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')

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.

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