mirror of
https://gitea.toothfairyai.com/ToothFairyAI/tf_code.git
synced 2026-04-06 16:59:01 +00:00
refactor: apply minimal tfcode branding
- Rename packages/opencode → packages/tfcode (directory only) - Rename bin/opencode → bin/tfcode (CLI binary) - Rename .opencode → .tfcode (config directory) - Update package.json name and bin field - Update config directory path references (.tfcode) - Keep internal code references as 'opencode' for easy upstream sync - Keep @opencode-ai/* workspace package names This minimal branding approach allows clean merges from upstream opencode repository while providing tfcode branding for users.
This commit is contained in:
Gab
316
packages/tfcode/src/session/llm.ts
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316
packages/tfcode/src/session/llm.ts
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import { Installation } from "@/installation"
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import { Provider } from "@/provider/provider"
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import { Log } from "@/util/log"
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import {
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streamText,
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wrapLanguageModel,
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type ModelMessage,
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type StreamTextResult,
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type Tool,
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type ToolSet,
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tool,
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jsonSchema,
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} from "ai"
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import { mergeDeep, pipe } from "remeda"
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import { GitLabWorkflowLanguageModel } from "gitlab-ai-provider"
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import { ProviderTransform } from "@/provider/transform"
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import { Config } from "@/config/config"
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import { Instance } from "@/project/instance"
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import type { Agent } from "@/agent/agent"
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import type { MessageV2 } from "./message-v2"
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import { Plugin } from "@/plugin"
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import { SystemPrompt } from "./system"
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import { Flag } from "@/flag/flag"
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import { Permission } from "@/permission"
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import { Auth } from "@/auth"
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export namespace LLM {
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const log = Log.create({ service: "llm" })
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export const OUTPUT_TOKEN_MAX = ProviderTransform.OUTPUT_TOKEN_MAX
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export type StreamInput = {
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user: MessageV2.User
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sessionID: string
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model: Provider.Model
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agent: Agent.Info
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permission?: Permission.Ruleset
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system: string[]
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abort: AbortSignal
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messages: ModelMessage[]
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small?: boolean
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tools: Record<string, Tool>
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retries?: number
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toolChoice?: "auto" | "required" | "none"
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}
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export type StreamOutput = StreamTextResult<ToolSet, unknown>
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export async function stream(input: StreamInput) {
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const l = log
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.clone()
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.tag("providerID", input.model.providerID)
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.tag("modelID", input.model.id)
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.tag("sessionID", input.sessionID)
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.tag("small", (input.small ?? false).toString())
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.tag("agent", input.agent.name)
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.tag("mode", input.agent.mode)
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l.info("stream", {
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modelID: input.model.id,
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providerID: input.model.providerID,
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})
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const [language, cfg, provider, auth] = await Promise.all([
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Provider.getLanguage(input.model),
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Config.get(),
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Provider.getProvider(input.model.providerID),
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Auth.get(input.model.providerID),
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])
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// TODO: move this to a proper hook
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const isOpenaiOauth = provider.id === "openai" && auth?.type === "oauth"
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const system: string[] = []
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system.push(
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[
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// use agent prompt otherwise provider prompt
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...(input.agent.prompt ? [input.agent.prompt] : SystemPrompt.provider(input.model)),
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// any custom prompt passed into this call
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...input.system,
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// any custom prompt from last user message
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...(input.user.system ? [input.user.system] : []),
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]
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.filter((x) => x)
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.join("\n"),
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)
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const header = system[0]
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await Plugin.trigger(
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"experimental.chat.system.transform",
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{ sessionID: input.sessionID, model: input.model },
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{ system },
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)
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// rejoin to maintain 2-part structure for caching if header unchanged
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if (system.length > 2 && system[0] === header) {
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const rest = system.slice(1)
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system.length = 0
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system.push(header, rest.join("\n"))
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}
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const variant =
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!input.small && input.model.variants && input.user.variant ? input.model.variants[input.user.variant] : {}
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const base = input.small
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? ProviderTransform.smallOptions(input.model)
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: ProviderTransform.options({
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model: input.model,
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sessionID: input.sessionID,
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providerOptions: provider.options,
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})
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const options: Record<string, any> = pipe(
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base,
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mergeDeep(input.model.options),
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mergeDeep(input.agent.options),
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mergeDeep(variant),
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)
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if (isOpenaiOauth) {
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options.instructions = system.join("\n")
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}
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const messages = isOpenaiOauth
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? input.messages
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: [
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...system.map(
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(x): ModelMessage => ({
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role: "system",
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content: x,
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}),
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),
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...input.messages,
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]
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const params = await Plugin.trigger(
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"chat.params",
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{
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sessionID: input.sessionID,
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agent: input.agent,
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model: input.model,
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provider,
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message: input.user,
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},
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{
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temperature: input.model.capabilities.temperature
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? (input.agent.temperature ?? ProviderTransform.temperature(input.model))
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: undefined,
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topP: input.agent.topP ?? ProviderTransform.topP(input.model),
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topK: ProviderTransform.topK(input.model),
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options,
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},
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)
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const { headers } = await Plugin.trigger(
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"chat.headers",
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{
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sessionID: input.sessionID,
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agent: input.agent,
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model: input.model,
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provider,
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message: input.user,
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},
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{
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headers: {},
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},
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)
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const maxOutputTokens =
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isOpenaiOauth || provider.id.includes("github-copilot")
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? undefined
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: ProviderTransform.maxOutputTokens(input.model)
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const tools = await resolveTools(input)
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// LiteLLM and some Anthropic proxies require the tools parameter to be present
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// when message history contains tool calls, even if no tools are being used.
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// Add a dummy tool that is never called to satisfy this validation.
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// This is enabled for:
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// 1. Providers with "litellm" in their ID or API ID (auto-detected)
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// 2. Providers with explicit "litellmProxy: true" option (opt-in for custom gateways)
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const isLiteLLMProxy =
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provider.options?.["litellmProxy"] === true ||
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input.model.providerID.toLowerCase().includes("litellm") ||
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input.model.api.id.toLowerCase().includes("litellm")
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if (isLiteLLMProxy && Object.keys(tools).length === 0 && hasToolCalls(input.messages)) {
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tools["_noop"] = tool({
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description:
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"Placeholder for LiteLLM/Anthropic proxy compatibility - required when message history contains tool calls but no active tools are needed",
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inputSchema: jsonSchema({ type: "object", properties: {} }),
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execute: async () => ({ output: "", title: "", metadata: {} }),
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})
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}
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// Wire up toolExecutor for DWS workflow models so that tool calls
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// from the workflow service are executed via opencode's tool system
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// and results sent back over the WebSocket.
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if (language instanceof GitLabWorkflowLanguageModel) {
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const workflowModel = language
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workflowModel.toolExecutor = async (toolName, argsJson, _requestID) => {
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const t = tools[toolName]
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if (!t || !t.execute) {
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return { result: "", error: `Unknown tool: ${toolName}` }
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}
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try {
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const result = await t.execute!(JSON.parse(argsJson), {
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toolCallId: _requestID,
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messages: input.messages,
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abortSignal: input.abort,
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})
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const output = typeof result === "string" ? result : (result?.output ?? JSON.stringify(result))
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return {
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result: output,
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metadata: typeof result === "object" ? result?.metadata : undefined,
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title: typeof result === "object" ? result?.title : undefined,
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}
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} catch (e: any) {
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return { result: "", error: e.message ?? String(e) }
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}
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}
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}
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return streamText({
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onError(error) {
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l.error("stream error", {
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error,
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})
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},
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async experimental_repairToolCall(failed) {
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const lower = failed.toolCall.toolName.toLowerCase()
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if (lower !== failed.toolCall.toolName && tools[lower]) {
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l.info("repairing tool call", {
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tool: failed.toolCall.toolName,
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repaired: lower,
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})
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return {
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...failed.toolCall,
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toolName: lower,
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}
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}
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return {
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...failed.toolCall,
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input: JSON.stringify({
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tool: failed.toolCall.toolName,
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error: failed.error.message,
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}),
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toolName: "invalid",
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}
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},
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temperature: params.temperature,
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topP: params.topP,
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topK: params.topK,
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providerOptions: ProviderTransform.providerOptions(input.model, params.options),
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activeTools: Object.keys(tools).filter((x) => x !== "invalid"),
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tools,
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toolChoice: input.toolChoice,
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maxOutputTokens,
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abortSignal: input.abort,
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headers: {
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...(input.model.providerID.startsWith("opencode")
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? {
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"x-opencode-project": Instance.project.id,
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"x-opencode-session": input.sessionID,
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"x-opencode-request": input.user.id,
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"x-opencode-client": Flag.OPENCODE_CLIENT,
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}
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: {
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"User-Agent": `opencode/${Installation.VERSION}`,
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}),
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...input.model.headers,
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...headers,
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},
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maxRetries: input.retries ?? 0,
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messages,
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model: wrapLanguageModel({
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model: language,
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middleware: [
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{
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async transformParams(args) {
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if (args.type === "stream") {
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// @ts-expect-error
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args.params.prompt = ProviderTransform.message(args.params.prompt, input.model, options)
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}
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return args.params
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},
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},
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],
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}),
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experimental_telemetry: {
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isEnabled: cfg.experimental?.openTelemetry,
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metadata: {
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userId: cfg.username ?? "unknown",
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sessionId: input.sessionID,
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},
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},
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})
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}
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async function resolveTools(input: Pick<StreamInput, "tools" | "agent" | "permission" | "user">) {
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const disabled = Permission.disabled(
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Object.keys(input.tools),
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Permission.merge(input.agent.permission, input.permission ?? []),
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)
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for (const tool of Object.keys(input.tools)) {
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if (input.user.tools?.[tool] === false || disabled.has(tool)) {
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delete input.tools[tool]
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}
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}
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return input.tools
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}
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// Check if messages contain any tool-call content
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// Used to determine if a dummy tool should be added for LiteLLM proxy compatibility
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export function hasToolCalls(messages: ModelMessage[]): boolean {
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for (const msg of messages) {
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if (!Array.isArray(msg.content)) continue
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for (const part of msg.content) {
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if (part.type === "tool-call" || part.type === "tool-result") return true
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}
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}
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return false
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}
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}
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