Files
commitea/packages/core/src/agent/chat-client.ts
Croissant Le Doux 3de887417c feat: Reginald is real — model router + agent loop + query_project (P4)
The fixture chat panel is now a working agent. Ask Reginald a question and it
consults the real project through a tool loop, then answers in grounded prose.
Read-only v0 — writes still go through the propose-approve controls.

core (@commitea/core/agent):
- chat-client: OpenAI-wire chat completions over an injected fetch (same seam as
  gitea). Points at any OpenAI-compatible endpoint (LM Studio/Ollama/OpenAI).
- model-router: small model for prose + the read tool; big model reserved for
  later decomposition (pickModel).
- agent-loop: runAgentTurn drives call→tool→result→call until prose (or a step
  budget), recording each tool step. Injected complete + execute → fully testable.
- query-project: the single read tool's engine — compact focus/board/calibration/
  issue/search views built from scheduler + lifecycle + calibration; unbuilt views
  return a notImplemented marker (never fabricated). The model reports, never computes.
- agent-tools: query_project declaration + Reginald's system prompt.

app:
- main model bridge (model:status, model:chat) runs the loop; query_project
  reconciles the repo and builds the view. Model traffic stays in main (token/CSP).
  gitea.ts refactored to share getGiteaClient + reconcileSnapshot.
- preload + global.d.ts expose the model bridge; useChat drives the panel — real
  agent turn when a model is configured, scripted fixture reply otherwise (so
  fixture e2e is unchanged). A subtle "consulted the project" activity line.

Model config (env, defaults to LM Studio on :1234): COMMITEA_MODEL_URL /
_SMALL (google/gemma-4-e4b) / _BIG (qwen/qwen3.6-35b-a3b). COMMITEA_E2E=1 keeps
it unconfigured so the panel stays scripted.

Verified: 88 core tests green (14 agent: client parse, loop tool/error/budget,
all views) + a gated live integration test. Desktop typecheck clean, 14 fixture
e2e green. Gated live e2e drives the real app against gitea + gemma-4-e4b: asked
"what now?", Reginald called query_project and answered "focus is on issue #2"
(the real scheduler pick).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-08 21:23:14 -04:00

124 lines
3.8 KiB
TypeScript

/**
* Reginald's model client — a thin OpenAI-wire chat-completions client over an
* injected fetch (the same seam the gitea client uses). `@commitea/core` stays
* pure: no network, no globals. The desktop main process passes the real
* `fetch`; tests pass a stub. Points at any OpenAI-compatible endpoint (LM
* Studio, Ollama, the OpenAI API) — the model router picks which.
*/
import type { FetchLike } from '../gitea/types.js'
/** Where a model lives + which model to ask for. */
export interface ModelConfig {
/** OpenAI-compatible base, including the version segment, e.g. `http://localhost:1234/v1`. */
baseUrl: string
model: string
/** Bearer key; omitted for local servers that don't check it. */
apiKey?: string
}
/** A model's request to run a tool. `arguments` is a raw JSON string (OpenAI shape). */
export interface ToolCall {
id: string
name: string
arguments: string
}
/** One turn in the conversation, in our normalized shape. */
export interface ChatMessage {
role: 'system' | 'user' | 'assistant' | 'tool'
content: string
/** assistant turns that requested tools. */
toolCalls?: ToolCall[]
/** tool turns: which call they answer. */
toolCallId?: string
/** tool turns: the function name. */
name?: string
}
/** A tool the model may call — name, description, and a JSON-Schema parameter spec. */
export interface ToolDecl {
name: string
description: string
parameters: object
}
export interface CompletionResult {
content: string
toolCalls: ToolCall[]
}
export interface ChatClient {
complete(messages: ChatMessage[], tools?: ToolDecl[]): Promise<CompletionResult>
}
/** Map our message shape to the OpenAI wire shape. */
function toWireMessage(m: ChatMessage): Record<string, unknown> {
if (m.role === 'assistant' && m.toolCalls?.length) {
return {
role: 'assistant',
content: m.content || null,
tool_calls: m.toolCalls.map((tc) => ({
id: tc.id,
type: 'function',
function: { name: tc.name, arguments: tc.arguments },
})),
}
}
if (m.role === 'tool') {
return { role: 'tool', tool_call_id: m.toolCallId, content: m.content }
}
return { role: m.role, content: m.content }
}
function toWireTool(t: ToolDecl): Record<string, unknown> {
return { type: 'function', function: { name: t.name, description: t.description, parameters: t.parameters } }
}
/** Shape of the one choice we read back. */
interface RawChoiceMessage {
content: string | null
tool_calls?: { id: string; function: { name: string; arguments: string } }[]
}
export function createChatClient(config: ModelConfig, fetchImpl: FetchLike): ChatClient {
const url = `${config.baseUrl.replace(/\/+$/, '')}/chat/completions`
return {
async complete(messages, tools) {
const body: Record<string, unknown> = {
model: config.model,
messages: messages.map(toWireMessage),
temperature: 0,
}
if (tools?.length) {
body.tools = tools.map(toWireTool)
body.tool_choice = 'auto'
}
const res = await fetchImpl(url, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
Accept: 'application/json',
...(config.apiKey ? { Authorization: `Bearer ${config.apiKey}` } : {}),
},
body: JSON.stringify(body),
})
if (!res.ok) {
const text = await res.text().catch(() => '')
throw new Error(`model completion failed (${res.status}): ${text.slice(0, 200)}`)
}
const json = (await res.json()) as { choices?: { message: RawChoiceMessage }[] }
const msg = json.choices?.[0]?.message
return {
content: msg?.content ?? '',
toolCalls: (msg?.tool_calls ?? []).map((tc) => ({
id: tc.id,
name: tc.function.name,
arguments: tc.function.arguments,
})),
}
},
}
}