Files
famapp/src/modules/agent/server/run.ts
T
ginnoir c8db5475d3 feat(agent): add voice input and photo attachments to assistant
Wire mic through Whisper-compatible transcriptions on LLM_BASE_URL.

Photos upload to MinIO and reach the vision model as base64 image_url parts.
2026-07-05 02:01:48 -05:00

128 lines
4.0 KiB
TypeScript

import { createLlmClient, type ChatMessage, type LlmClient } from "@/lib/llm";
import { buildVisionContentParts, textFromMessageContent } from "@/lib/llm/content";
import { AGENT_SYSTEM_PROMPT, AGENT_TOOLS } from "../tools";
import type { ClientChatMessage } from "../messages";
import { describeToolActivity } from "../tool-labels";
import { createApiToolExecutor, type ToolExecutor } from "../tool-executor";
import { resolveAssistantImageDataUrls } from "./resolve-images";
import { thinkingLabel, type AgentProgressEvent } from "./progress";
const MAX_TOOL_ROUNDS = 8;
export type AgentToolCallSummary = {
name: string;
status: number;
};
export type AgentChatResult = {
message: ClientChatMessage;
toolCalls: AgentToolCallSummary[];
};
export type AgentProgressHandler = (event: AgentProgressEvent) => void;
async function toLlmUserMessage(message: ClientChatMessage): Promise<ChatMessage> {
if (message.role === "assistant") {
return { role: "assistant", content: message.content };
}
const imageUrls =
message.attachments?.filter((attachment) => attachment.type === "image").map((a) => a.url) ??
[];
if (imageUrls.length === 0) {
return { role: "user", content: message.content };
}
const dataUrls = await resolveAssistantImageDataUrls(imageUrls);
return {
role: "user",
content: buildVisionContentParts(message.content, dataUrls),
};
}
export async function runAgentChat(options: {
messages: ClientChatMessage[];
request: Request;
systemPrompt?: string;
llm?: LlmClient;
executeTool?: ToolExecutor;
onProgress?: AgentProgressHandler;
}): Promise<AgentChatResult> {
const llm = options.llm ?? createLlmClient();
const executeTool = options.executeTool ?? createApiToolExecutor(options.request);
const onProgress = options.onProgress;
const systemPrompt = options.systemPrompt ?? AGENT_SYSTEM_PROMPT;
const userMessages = await Promise.all(
options.messages.map((message) => toLlmUserMessage(message)),
);
const transcript: ChatMessage[] = [{ role: "system", content: systemPrompt }, ...userMessages];
const toolCalls: AgentToolCallSummary[] = [];
for (let round = 0; round < MAX_TOOL_ROUNDS; round += 1) {
onProgress?.({ type: "thinking", label: thinkingLabel(round), round });
const completion = await llm.chatCompletion({
messages: transcript,
tools: AGENT_TOOLS,
});
const assistantMessage = completion.message;
transcript.push(assistantMessage);
if (!assistantMessage.tool_calls?.length) {
onProgress?.({ type: "responding", label: "Writing a reply…" });
return {
message: {
role: "assistant",
content:
textFromMessageContent(assistantMessage.content).trim() ||
"I couldn't generate a response.",
},
toolCalls,
};
}
for (const toolCall of assistantMessage.tool_calls) {
const label = describeToolActivity(toolCall.function.name, toolCall.function.arguments);
onProgress?.({ type: "tool", name: toolCall.function.name, label });
let result: string;
let status: number;
try {
result = await executeTool(toolCall.function.name, toolCall.function.arguments);
const parsed = JSON.parse(result) as { status?: number };
status = typeof parsed.status === "number" ? parsed.status : 200;
} catch (err) {
status = 500;
result = JSON.stringify({
status: 500,
body: { error: err instanceof Error ? err.message : "Tool execution failed" },
});
}
toolCalls.push({ name: toolCall.function.name, status });
transcript.push({
role: "tool",
tool_call_id: toolCall.id,
name: toolCall.function.name,
content: result,
});
}
}
onProgress?.({ type: "responding", label: "Wrapping up…" });
return {
message: {
role: "assistant",
content: "I hit the tool-call limit for this request. Please try a simpler question.",
},
toolCalls,
};
}