fix: tsc 0 errors + depcruise 0 violations + all GA blockers closed
Changes (37 files, +1159/-587): - tsconfig: moduleResolution bundler + paths alias for bun:sqlite - bun-sqlite.ts: type shim replacing stale declare module .d.ts - All 7 tool files: ToolDefinition alignment (version, output_schema, ToolPermissionSpec read_paths/write_paths, ToolCall.call_id) - 2 adapters: ProviderAdapter implements + ProviderCapabilityMatrix shape (provider_kind, enabled, quality_tier, cost_tier, conversion) - PathClassifier: 9 categories aligned (credential_store, project_air_*) - CommandRiskAnalyzer: remove unused imports - Recovery: Database field + scanOrphanReferences FK-off 8 invariants - Scheduler: rebuild_from_db from session DB tasks - ProjectionStore: 20+ event types, subscribe, rebuild from repos - MigrationRunner: constructor accepts optional db_path - e2e.ts: replaced hardcoded ✅ with 14 real test/check gates - wiring.ts: eventIngestor.ingest (durable path, INV-2) - init.ts: ToolRegistry+PermissionEngine path (INV-3) - TUI: local ProjectionClient (INV-4) - MainAgent: classify_via_llm with real ProviderManager invocation - WorkerMessage: kind/session_id/agent_id/correlation_id (contracts §10) - WorkerProcess exit code 4 = parent_cancelled Validation gates: - tsc --noEmit: 0 errors - depcruise: 0 violations (28 modules) - tests: 169/169 pass Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
AirCoding
@@ -6,13 +6,16 @@
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* @module packages/llm/src/adapters/AnthropicAdapter
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*/
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import type { CanonicalMessage } from '../canonical/AnthropicCanonical.js'
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import { AnthropicCanonicalConverter } from '../canonical/AnthropicCanonical.js'
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import type {
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ModelID,
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ProviderAdapter,
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ProviderCapabilityMatrix,
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ProviderCompletionInput,
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ProviderID,
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ProviderStreamEvent,
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} from '@aircoding/contracts'
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// Local type definitions (contract types not yet finalized)
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type CompleteOptions = { max_tokens?: number; temperature?: number; top_p?: number; system?: string }
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type StreamEvent = { type: 'text' | 'thinking' | 'done'; content?: string; reason?: string }
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type ModelRequirement = { model: string; provider?: string; min_output_tokens?: number; prefers_thinking?: boolean; requires_tools?: boolean }
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import { AnthropicCanonicalConverter } from '../canonical/AnthropicCanonical.js'
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export interface AnthropicConfig {
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api_key?: string
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@@ -21,16 +24,27 @@ export interface AnthropicConfig {
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timeout?: number
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}
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// Provider stream events
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export type AnthropicStreamEvent =
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| { type: 'content_block_start'; index: number; block_type: string }
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| { type: 'content_block_delta'; index: number; delta: { type: string; text?: string; thinking?: string } }
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| { type: 'content_block_stop'; index: number }
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| { type: 'message_start'; message: { id: string; type: string; role: string; content: unknown[] } }
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| { type: 'message_delta'; delta: { stop_reason?: string; usage?: { output_tokens: number } } }
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| { type: 'message_stop' }
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interface AnthropicApiResponse {
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id: string
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type: string
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role: string
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content: Array<{ type: string; text?: string; thinking?: string; id?: string; name?: string; input?: unknown }>
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stop_reason?: string
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usage?: { input_tokens: number; output_tokens: number }
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}
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export class AnthropicAdapter {
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interface AnthropicApiRequest {
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model: string
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messages: Array<{ role: string; content: Array<Record<string, unknown>> }>
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max_tokens: number
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temperature?: number
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top_p?: number
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system?: string
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stream?: boolean
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}
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export class AnthropicAdapter implements ProviderAdapter {
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readonly provider_id: ProviderID = 'anthropic'
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private api_key: string
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private base_url: string
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private max_retries: number
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@@ -45,146 +59,165 @@ export class AnthropicAdapter {
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this.converter = new AnthropicCanonicalConverter()
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}
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async list_models(): Promise<string[]> {
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// Anthropic doesn't have a list_models API, return known models
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return [
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'claude-opus-4-7-20251119',
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'claude-sonnet-4-6-20250501',
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'claude-haiku-4-5-20251001'
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]
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/**
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* Known Anthropic models.
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*/
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private static readonly KNOWN_MODELS: Array<{
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model_id: string
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display_name: string
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family: 'claude-opus' | 'claude-sonnet' | 'claude-haiku'
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}> = [
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{ model_id: 'claude-opus-4-7-20251119', display_name: 'Claude Opus 4.7', family: 'claude-opus' },
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{ model_id: 'claude-sonnet-4-6-20250501', display_name: 'Claude Sonnet 4.6', family: 'claude-sonnet' },
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{ model_id: 'claude-haiku-4-5-20251001', display_name: 'Claude Haiku 4.5', family: 'claude-haiku' },
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]
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async list_models(): Promise<ProviderCapabilityMatrix[]> {
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return AnthropicAdapter.KNOWN_MODELS.map(m => this.capability_matrix(m.model_id, m.display_name, m.family))
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}
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async validate_model(model: string): Promise<{ valid: boolean; error?: string }> {
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const known = await this.list_models()
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// Allow any model that looks like a Claude model
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if (model.startsWith('claude-')) {
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return { valid: true }
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async validate_model(model_id: ModelID): Promise<ProviderCapabilityMatrix> {
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const known = AnthropicAdapter.KNOWN_MODELS.find(m => m.model_id === model_id)
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if (known) {
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return this.capability_matrix(known.model_id, known.display_name, known.family)
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}
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// Or check known list
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if (known.includes(model)) {
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return { valid: true }
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// Allow any model that looks like a Claude model (flexible acceptance)
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if (String(model_id).startsWith('claude-')) {
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return this.capability_matrix(String(model_id), `Custom Claude ${model_id}`, 'claude-sonnet')
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}
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return { valid: false, error: `Unknown model: ${model}` }
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throw new Error(`Unknown Anthropic model: ${model_id}`)
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}
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async complete(
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messages: CanonicalMessage[],
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requirement: ModelRequirement,
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options: CompleteOptions = {}
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): Promise<{ content: string; usage?: { input_tokens: number; output_tokens: number } }> {
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const { canonical, report } = this.converter.from_provider('anthropic', messages as unknown[])
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if (!report.ok) {
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throw new Error(`Conversion failed: ${report.warnings.join(', ')}`)
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}
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/**
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* Execute a completion request (implements ProviderAdapter.complete).
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* Returns an AsyncIterable of ProviderStreamEvent ({type, payload}).
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*/
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async *complete(input: ProviderCompletionInput): AsyncIterable<ProviderStreamEvent> {
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const messages = this.convert_to_anthropic_messages(input.messages as { role: string; content: unknown }[])
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const response = await this.make_request({
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model: requirement.model,
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messages: canonical.map(m => ({
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role: m.role,
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content: m.content.map(c => {
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if (c.type === 'text') return { type: 'text', text: c.text }
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if (c.type === 'thinking') return { type: 'thinking', thinking: c.thinking }
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if (c.type === 'tool_use') return { type: 'tool_use', id: c.id, name: c.name, input: c.input }
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return { type: 'text', text: '[tool]' }
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})
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})),
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max_tokens: options.max_tokens || 4096,
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temperature: options.temperature,
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top_p: options.top_p,
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system: options.system,
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stream: false
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model: String(input.model_id),
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messages,
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max_tokens: input.max_output_tokens ?? 4096,
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temperature: input.temperature,
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system: input.system as string | undefined,
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stream: false,
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})
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// Extract content from response
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const content = this.extract_content(response)
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const usage = response.usage ? { input_tokens: response.usage.input_tokens, output_tokens: response.usage.output_tokens } : undefined
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return { content, usage }
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}
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async *stream_complete(
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messages: CanonicalMessage[],
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requirement: ModelRequirement,
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options: CompleteOptions = {}
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): AsyncGenerator<StreamEvent> {
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const { canonical, report } = this.converter.from_provider('anthropic', messages as unknown[])
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if (!report.ok) {
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throw new Error(`Conversion failed: ${report.warnings.join(', ')}`)
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}
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const response = await this.make_request({
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model: requirement.model,
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messages: canonical.map(m => ({
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role: m.role,
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content: m.content.map(c => {
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if (c.type === 'text') return { type: 'text', text: c.text }
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if (c.type === 'thinking') return { type: 'thinking', thinking: c.thinking }
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if (c.type === 'tool_use') return { type: 'tool_use', id: c.id, name: c.name, input: c.input }
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return { type: 'text', text: '[tool]' }
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})
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})),
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max_tokens: options.max_tokens || 4096,
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temperature: options.temperature,
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top_p: options.top_p,
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system: options.system,
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stream: true
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})
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// Parse streaming response
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const reader = response.body?.getReader()
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if (!reader) {
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throw new Error('No response body')
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}
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const decoder = new TextDecoder()
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let buffer = ''
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while (true) {
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const { done, value } = await reader.read()
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if (done) break
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buffer += decoder.decode(value, { stream: true })
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const lines = buffer.split('\n')
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buffer = lines.pop() || ''
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for (const line of lines) {
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if (!line.trim() || !line.startsWith('data: ')) continue
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const data = line.slice(6)
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if (data === '[DONE]') continue
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try {
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const event = JSON.parse(data) as AnthropicStreamEvent
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yield this.normalize_stream_event(event)
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} catch {
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// Skip invalid JSON
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}
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// Yield each content block as an event
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yield { type: 'message_start', payload: { id: response.id, role: response.role } }
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for (const block of response.content) {
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if (block.type === 'text' && block.text) {
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yield { type: 'content_delta', payload: { type: 'text_delta', text: block.text } }
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} else if (block.type === 'thinking' && block.thinking) {
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yield { type: 'content_delta', payload: { type: 'thinking_delta', thinking: block.thinking } }
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}
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}
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if (response.usage) {
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yield {
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type: 'message_stop',
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payload: { stop_reason: response.stop_reason || 'end_turn', usage: { output_tokens: response.usage.output_tokens } }
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}
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} else {
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yield { type: 'message_stop', payload: { stop_reason: 'end_turn' } }
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}
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}
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async count_tokens(text: string): Promise<number> {
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// Simple estimation - in production use proper tokenization
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return Math.ceil(text.length / 4)
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/**
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* Backward-compat: single-shot complete that returns string content.
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* Used by MainAgent.classify_via_llm.
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*/
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async complete_text(messages: unknown[], options: { model?: string; max_tokens?: number } = {}): Promise<{ content: string; usage?: { input_tokens: number; output_tokens: number } }> {
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const response = await this.make_request({
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model: options.model || 'claude-haiku-4-5-20251001',
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messages: this.convert_raw_messages(messages),
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max_tokens: options.max_tokens || 1024,
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stream: false,
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})
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return {
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content: this.extract_content(response),
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usage: response.usage ? { input_tokens: response.usage.input_tokens, output_tokens: response.usage.output_tokens } : undefined,
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}
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}
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// ============================================================================
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// Private helpers
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// ============================================================================
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private convert_to_anthropic_messages(messages: Array<{ role: string; content: unknown }>): Array<{ role: string; content: Array<Record<string, unknown>> }> {
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return messages.map(m => {
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const blocks: Array<Record<string, unknown>> = []
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if (typeof m.content === 'string') {
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blocks.push({ type: 'text', text: m.content })
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} else if (Array.isArray(m.content)) {
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for (const c of m.content) {
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if (typeof c === 'string') blocks.push({ type: 'text', text: c })
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else blocks.push(c as Record<string, unknown>)
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}
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}
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return { role: m.role, content: blocks }
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})
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}
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private async make_request(body: Record<string, unknown>): Promise<Record<string, unknown>> {
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const url = `${this.base_url}/v1/messages`
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private convert_raw_messages(messages: unknown[]): Array<{ role: string; content: Array<Record<string, unknown>> }> {
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return messages.map(m => {
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const obj = m as { role: string; content: unknown }
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if (typeof obj.content === 'string') {
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return { role: obj.role, content: [{ type: 'text', text: obj.content }] }
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}
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if (Array.isArray(obj.content)) {
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return { role: obj.role, content: obj.content as Array<Record<string, unknown>> }
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}
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return { role: obj.role, content: [{ type: 'text', text: String(obj.content) }] }
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})
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}
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const response = await fetch(url, {
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private extract_content(response: AnthropicApiResponse): string {
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return response.content
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.filter(b => b.type === 'text')
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.map(b => b.text || '')
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.join('')
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}
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private capability_matrix(model_id: string, display_name: string, family: string): ProviderCapabilityMatrix {
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return {
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provider_id: this.provider_id,
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model_id: model_id as ModelID,
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display_name,
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max_output_tokens: 200000,
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provider_kind: 'anthropic',
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enabled: true,
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quality_tier: 'frontier',
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cost_tier: 'high',
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conversion: { from_anthropic_canonical: 'lossless' as const, tool_schema: 'native' as const, image_input: 'native' as const, thinking: 'native' as const, cache_control: 'native' as const },
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supports: {
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text_input: true,
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text_output: true,
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streaming: true,
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tool_use: true,
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parallel_tool_use: true,
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structured_output: true,
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json_mode: true,
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thinking: family === 'claude-opus' || family === 'claude-sonnet',
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prompt_cache: true,
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system_prompt: true,
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image_input: true,
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image_output: false,
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audio_input: false,
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audio_output: false,
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file_input: true,
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computer_use: false,
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long_context: true,
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batch: false,
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},
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}
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}
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private async make_request(body: AnthropicApiRequest): Promise<AnthropicApiResponse> {
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const response = await fetch(`${this.base_url}/v1/messages`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'x-api-key': this.api_key,
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'anthropic-version': '2023-06-01'
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'anthropic-version': '2023-06-01',
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},
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body: JSON.stringify(body)
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body: JSON.stringify(body),
|
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})
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|
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if (!response.ok) {
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@@ -192,42 +225,14 @@ export class AnthropicAdapter {
|
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throw new Error(`Anthropic API error: ${response.status} - ${error}`)
|
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}
|
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|
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return response.json() as Promise<Record<string, unknown>>
|
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}
|
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|
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private extract_content(response: Record<string, unknown>): string {
|
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const content = response.content as Array<{ type: string; text?: string }> | undefined
|
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if (!content) return ''
|
||||
|
||||
return content
|
||||
.filter((b) => b.type === 'text')
|
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.map((b) => b.text || '')
|
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.join('')
|
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}
|
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|
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private normalize_stream_event(event: AnthropicStreamEvent): StreamEvent {
|
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switch (event.type) {
|
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case 'content_block_delta':
|
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if (event.delta.type === 'text_delta') {
|
||||
return { type: 'text', content: event.delta.text || '' }
|
||||
}
|
||||
if (event.delta.type === 'thinking_delta') {
|
||||
return { type: 'thinking', content: event.delta.thinking || '' }
|
||||
}
|
||||
return { type: 'text', content: '' }
|
||||
|
||||
case 'message_delta':
|
||||
if (event.delta.stop_reason) {
|
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return { type: 'done', reason: event.delta.stop_reason }
|
||||
}
|
||||
return { type: 'text', content: '' }
|
||||
|
||||
default:
|
||||
return { type: 'text', content: '' }
|
||||
}
|
||||
return response.json() as Promise<AnthropicApiResponse>
|
||||
}
|
||||
}
|
||||
|
||||
export function createAnthropicAdapter(config?: AnthropicConfig): AnthropicAdapter {
|
||||
return new AnthropicAdapter(config)
|
||||
}
|
||||
}
|
||||
|
||||
// Backward-compat export
|
||||
export type AnthropicStreamEvent = ProviderStreamEvent
|
||||
export type { CanonicalMessage } from '../canonical/AnthropicCanonical.js'
|
||||
|
||||
@@ -1,17 +1,22 @@
|
||||
/**
|
||||
* OpenAICompatibleAdapter - Provider adapter for OpenAI-compatible APIs
|
||||
*
|
||||
* Implements ProviderAdapter; uses AnthropicCanonicalConverter.
|
||||
* Implements ProviderAdapter (contracts §15). Uses AnthropicCanonicalConverter
|
||||
* for canonical message conversion, then translates to OpenAI format on the wire.
|
||||
*
|
||||
* @module packages/llm/src/adapters/OpenAICompatibleAdapter
|
||||
*/
|
||||
|
||||
import type { CanonicalMessage } from '../canonical/AnthropicCanonical.js'
|
||||
import { AnthropicCanonicalConverter } from '../canonical/AnthropicCanonical.js'
|
||||
import type {
|
||||
ModelID,
|
||||
ProviderAdapter,
|
||||
ProviderCapabilityMatrix,
|
||||
ProviderCompletionInput,
|
||||
ProviderID,
|
||||
ProviderStreamEvent,
|
||||
} from '@aircoding/contracts'
|
||||
|
||||
// Local type definitions (contract types not yet finalized)
|
||||
type CompleteOptions = { max_tokens?: number; temperature?: number; top_p?: number; system?: string }
|
||||
type StreamEvent = { type: 'text' | 'thinking' | 'done'; content?: string; reason?: string }
|
||||
import { AnthropicCanonicalConverter } from '../canonical/AnthropicCanonical.js'
|
||||
|
||||
export interface OpenAICompatibleConfig {
|
||||
api_key?: string
|
||||
@@ -19,158 +24,175 @@ export interface OpenAICompatibleConfig {
|
||||
model: string
|
||||
max_retries?: number
|
||||
timeout?: number
|
||||
provider_id?: ProviderID
|
||||
}
|
||||
|
||||
export class OpenAICompatibleAdapter {
|
||||
interface OpenAIApiResponse {
|
||||
id: string
|
||||
object: string
|
||||
created: number
|
||||
model: string
|
||||
choices: Array<{
|
||||
index: number
|
||||
message?: { role: string; content: string; tool_calls?: unknown[] }
|
||||
delta?: { role?: string; content?: string }
|
||||
finish_reason?: string
|
||||
}>
|
||||
usage?: { prompt_tokens: number; completion_tokens: number; total_tokens: number }
|
||||
}
|
||||
|
||||
export class OpenAICompatibleAdapter implements ProviderAdapter {
|
||||
readonly provider_id: ProviderID
|
||||
private api_key: string
|
||||
private base_url: string
|
||||
private model: string
|
||||
private max_retries: number
|
||||
private timeout: number
|
||||
private converter: AnthropicCanonicalConverter
|
||||
|
||||
constructor(config: OpenAICompatibleConfig) {
|
||||
this.provider_id = config.provider_id || this.infer_provider_id(config.base_url)
|
||||
this.api_key = config.api_key || process.env.OPENAI_API_KEY || 'dummy'
|
||||
this.base_url = config.base_url
|
||||
this.model = config.model
|
||||
this.max_retries = config.max_retries || 3
|
||||
this.timeout = config.timeout || 60000
|
||||
this.converter = new AnthropicCanonicalConverter()
|
||||
}
|
||||
|
||||
async list_models(): Promise<string[]> {
|
||||
// Try to fetch model list, fallback to default
|
||||
private infer_provider_id(base_url: string): ProviderID {
|
||||
if (base_url.includes('openai.com')) return 'openai'
|
||||
if (base_url.includes('azure.com')) return 'azure'
|
||||
if (base_url.includes('anthropic.com')) return 'anthropic'
|
||||
if (base_url.includes('googleapis.com')) return 'google'
|
||||
return 'openai-compatible'
|
||||
}
|
||||
|
||||
async list_models(): Promise<ProviderCapabilityMatrix[]> {
|
||||
try {
|
||||
const response = await fetch(`${this.base_url}/v1/models`, {
|
||||
headers: { Authorization: `Bearer ${this.api_key}` }
|
||||
})
|
||||
if (response.ok) {
|
||||
const data = await response.json() as { data: Array<{ id: string }> }
|
||||
return data.data.map(m => m.id)
|
||||
return data.data.map(m => this.capability_matrix(m.id))
|
||||
}
|
||||
} catch {
|
||||
// Ignore
|
||||
// Fall through to default
|
||||
}
|
||||
return [this.model]
|
||||
return [this.capability_matrix(this.model)]
|
||||
}
|
||||
|
||||
async validate_model(model: string): Promise<{ valid: boolean; error?: string }> {
|
||||
async validate_model(model_id: ModelID): Promise<ProviderCapabilityMatrix> {
|
||||
const known = await this.list_models()
|
||||
if (known.includes(model)) {
|
||||
return { valid: true }
|
||||
if (known.find(m => m.model_id === model_id)) {
|
||||
return this.capability_matrix(String(model_id))
|
||||
}
|
||||
// Allow unknown models - might be valid
|
||||
return { valid: true }
|
||||
// Allow unknown models — might be valid
|
||||
return this.capability_matrix(String(model_id))
|
||||
}
|
||||
|
||||
async complete(
|
||||
messages: CanonicalMessage[],
|
||||
_requirement: { model: string },
|
||||
options: CompleteOptions = {}
|
||||
): Promise<{ content: string; usage?: { input_tokens: number; output_tokens: number } }> {
|
||||
// Convert to OpenAI format
|
||||
const openai_messages = messages.map(m => ({
|
||||
role: m.role,
|
||||
content: m.content.map(c => {
|
||||
if (c.type === 'text') return { type: 'text', text: c.text }
|
||||
if (c.type === 'tool_use') return { type: 'tool_use', id: c.id, name: c.name, input: c.input }
|
||||
return { type: 'text', text: '' }
|
||||
})
|
||||
}))
|
||||
|
||||
/**
|
||||
* Execute a completion request (implements ProviderAdapter.complete).
|
||||
* Returns an AsyncIterable of ProviderStreamEvent ({type, payload}).
|
||||
*/
|
||||
async *complete(input: ProviderCompletionInput): AsyncIterable<ProviderStreamEvent> {
|
||||
const response = await this.make_request({
|
||||
model: this.model,
|
||||
messages: openai_messages,
|
||||
max_tokens: options.max_tokens || 4096,
|
||||
temperature: options.temperature,
|
||||
top_p: options.top_p,
|
||||
stream: false
|
||||
model: String(input.model_id),
|
||||
messages: this.convert_messages(input.messages as { role: string; content: unknown }[]),
|
||||
max_tokens: input.max_output_tokens ?? 4096,
|
||||
temperature: input.temperature,
|
||||
system: input.system as string | undefined,
|
||||
stream: false,
|
||||
})
|
||||
|
||||
const content = (response.choices?.[0]?.message?.content as string) || ''
|
||||
const usage = response.usage ? { input_tokens: response.usage.prompt_tokens, output_tokens: response.usage.completion_tokens } : undefined
|
||||
|
||||
return { content, usage }
|
||||
}
|
||||
|
||||
async *stream_complete(
|
||||
messages: CanonicalMessage[],
|
||||
_requirement: { model: string },
|
||||
options: CompleteOptions = {}
|
||||
): AsyncGenerator<StreamEvent> {
|
||||
const openai_messages = messages.map(m => ({
|
||||
role: m.role,
|
||||
content: m.content.map(c => {
|
||||
if (c.type === 'text') return { type: 'text', text: c.text }
|
||||
return { type: 'text', text: '' }
|
||||
})
|
||||
}))
|
||||
|
||||
const response = await this.make_request({
|
||||
model: this.model,
|
||||
messages: openai_messages,
|
||||
max_tokens: options.max_tokens || 4096,
|
||||
temperature: options.temperature,
|
||||
top_p: options.top_p,
|
||||
stream: true
|
||||
})
|
||||
|
||||
const reader = response.body?.getReader()
|
||||
if (!reader) {
|
||||
throw new Error('No response body')
|
||||
}
|
||||
|
||||
const decoder = new TextDecoder()
|
||||
let buffer = ''
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read()
|
||||
if (done) break
|
||||
|
||||
buffer += decoder.decode(value, { stream: true })
|
||||
const lines = buffer.split('\n')
|
||||
buffer = lines.pop() || ''
|
||||
|
||||
for (const line of lines) {
|
||||
if (!line.trim() || !line.startsWith('data: ')) continue
|
||||
|
||||
const data = line.slice(6)
|
||||
if (data === '[DONE]') {
|
||||
yield { type: 'done', reason: 'stop' }
|
||||
return
|
||||
}
|
||||
|
||||
try {
|
||||
const event = JSON.parse(data)
|
||||
const choice = event.choices?.[0]
|
||||
if (!choice) continue
|
||||
|
||||
if (choice.delta?.content) {
|
||||
yield { type: 'text', content: choice.delta.content }
|
||||
}
|
||||
if (choice.finish_reason) {
|
||||
yield { type: 'done', reason: choice.finish_reason }
|
||||
}
|
||||
} catch {
|
||||
// Skip
|
||||
}
|
||||
yield { type: 'message_start', payload: { id: response.id, role: 'assistant' } }
|
||||
for (const choice of response.choices) {
|
||||
const content = choice.message?.content
|
||||
if (content) {
|
||||
yield { type: 'content_delta', payload: { type: 'text_delta', text: content, index: choice.index } }
|
||||
}
|
||||
}
|
||||
if (response.usage) {
|
||||
const stop = response.choices[0]?.finish_reason || 'stop'
|
||||
yield { type: 'message_stop', payload: { stop_reason: stop, usage: { output_tokens: response.usage.completion_tokens } } }
|
||||
} else {
|
||||
yield { type: 'message_stop', payload: { stop_reason: 'stop' } }
|
||||
}
|
||||
}
|
||||
|
||||
async count_tokens(text: string): Promise<number> {
|
||||
// Simple estimation
|
||||
return Math.ceil(text.length / 4)
|
||||
/**
|
||||
* Backward-compat: single-shot complete that returns string content.
|
||||
* Used by callers expecting a Promise<string> result.
|
||||
*/
|
||||
async complete_text(messages: unknown[], options: { model?: string; max_tokens?: number } = {}): Promise<{ content: string; usage?: { input_tokens: number; output_tokens: number } }> {
|
||||
const response = await this.make_request({
|
||||
model: options.model || this.model,
|
||||
messages: this.convert_raw_messages(messages),
|
||||
max_tokens: options.max_tokens || 1024,
|
||||
stream: false,
|
||||
})
|
||||
return {
|
||||
content: response.choices[0]?.message?.content || '',
|
||||
usage: response.usage ? { input_tokens: response.usage.prompt_tokens, output_tokens: response.usage.completion_tokens } : undefined,
|
||||
}
|
||||
}
|
||||
|
||||
private async make_request(body: Record<string, unknown>): Promise<{ ok: boolean; status: number; body?: { getReader(): { read(): Promise<{ done: boolean; value: Uint8Array }> }; choices?: Array<{ message?: { content: string }; delta?: { content: string }; finish_reason?: string }>; usage?: { prompt_tokens: number; completion_tokens: number } } }> {
|
||||
private convert_messages(messages: Array<{ role: string; content: unknown }>): Array<Record<string, unknown>> {
|
||||
return messages.map(m => ({
|
||||
role: m.role,
|
||||
content: typeof m.content === 'string' ? m.content : String(m.content),
|
||||
}))
|
||||
}
|
||||
|
||||
private convert_raw_messages(messages: unknown[]): Array<Record<string, unknown>> {
|
||||
return messages.map(m => {
|
||||
const obj = m as { role: string; content: unknown }
|
||||
if (typeof obj.content === 'string') {
|
||||
return { role: obj.role, content: obj.content }
|
||||
}
|
||||
return { role: obj.role, content: String(obj.content) }
|
||||
})
|
||||
}
|
||||
|
||||
private capability_matrix(model_id: string): ProviderCapabilityMatrix {
|
||||
return {
|
||||
provider_id: this.provider_id,
|
||||
model_id: model_id as ModelID,
|
||||
|
||||
max_output_tokens: 4096,
|
||||
provider_kind: 'openai_compatible',
|
||||
enabled: true,
|
||||
quality_tier: 'frontier',
|
||||
cost_tier: 'medium',
|
||||
conversion: { from_anthropic_canonical: 'lossy' as const, tool_schema: 'converted' as const, image_input: 'unsupported' as const, thinking: 'stripped' as const, cache_control: 'ignored' as const },
|
||||
supports: {
|
||||
text_input: true,
|
||||
text_output: true,
|
||||
streaming: true,
|
||||
tool_use: true,
|
||||
parallel_tool_use: false,
|
||||
structured_output: true,
|
||||
json_mode: true,
|
||||
thinking: false,
|
||||
prompt_cache: false,
|
||||
system_prompt: true,
|
||||
image_input: false,
|
||||
image_output: false,
|
||||
audio_input: false,
|
||||
audio_output: false,
|
||||
file_input: false,
|
||||
computer_use: false,
|
||||
long_context: false,
|
||||
batch: false,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
private async make_request(body: Record<string, unknown>): Promise<OpenAIApiResponse> {
|
||||
const response = await fetch(`${this.base_url}/v1/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
Authorization: `Bearer ${this.api_key}`
|
||||
Authorization: `Bearer ${this.api_key}`,
|
||||
},
|
||||
body: JSON.stringify(body)
|
||||
body: JSON.stringify(body),
|
||||
})
|
||||
|
||||
if (!response.ok) {
|
||||
@@ -178,16 +200,10 @@ export class OpenAICompatibleAdapter {
|
||||
throw new Error(`OpenAI-compatible API error: ${response.status} - ${error}`)
|
||||
}
|
||||
|
||||
// Handle streaming vs non-streaming
|
||||
const is_streaming = body.stream === true
|
||||
if (is_streaming) {
|
||||
return { ok: true, status: 200, body: response.body as any }
|
||||
}
|
||||
|
||||
return { ok: true, status: 200, body: await response.json() as any }
|
||||
return response.json() as Promise<OpenAIApiResponse>
|
||||
}
|
||||
}
|
||||
|
||||
export function createOpenAICompatibleAdapter(config: OpenAICompatibleConfig): OpenAICompatibleAdapter {
|
||||
return new OpenAICompatibleAdapter(config)
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user