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feat: complete overhaul of AI Assist with premium WOW level UI and stable preview engine

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Gemini AI
2025-12-28 11:22:19 +04:00
Unverified
parent 820e740b9d
commit d6c2986df8
9 changed files with 856 additions and 449 deletions
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29
lib/services/model-adapter.ts
View File

@@ -269,6 +269,35 @@ export class ModelAdapter {
return this.callWithFallback((service) => service.generateAIAssist(options, model), providers);
}
async generateAIAssistStream(
options: {
messages: AIAssistMessage[];
currentAgent: string;
onChunk: (chunk: string) => void;
signal?: AbortSignal;
},
provider?: ModelProvider,
model?: string
): Promise<APIResponse<void>> {
const fallback = this.buildFallbackProviders(this.preferredProvider, "qwen", "ollama", "zai");
const providers: ModelProvider[] = provider ? [provider] : fallback;
// For now we don't handle fallback for streaming strictly, just use first available
const activeProvider = providers[0];
let service: any;
switch (activeProvider) {
case "qwen": service = this.qwenService; break;
case "ollama": service = this.ollamaService; break;
case "zai": service = this.zaiService; break;
}
if (!service || !service.generateAIAssistStream) {
return { success: false, error: "Streaming not supported for this provider" };
}
return await service.generateAIAssistStream(options, model);
}
async chatCompletion(
messages: ChatMessage[],

91
lib/services/ollama-cloud.ts
View File

@@ -730,6 +730,97 @@ Perform a DEEP 360° competitive intelligence analysis and generate 5-7 strategi
return await this.chatCompletion(chatMessages, model || this.getAvailableModels()[0]);
}
async generateAIAssistStream(
options: {
messages: AIAssistMessage[];
currentAgent: string;
onChunk: (chunk: string) => void;
signal?: AbortSignal;
},
model?: string
): Promise<APIResponse<void>> {
try {
// ... existing prompt logic ...
const systemPrompt = `You are "AI Assist", the master orchestrator.
Your goal is to provide intelligent conversational support and switch to specialized agents.
CANVAS MODE (CRITICAL):
When the user asks to "build", "design", "create", or "write code", you MUST use the [PREVIEW] tag.
Inside [PREVIEW], output ONLY the actual functional code (HTML/Tailwind, Javascript, etc.).
Do NOT explain what the code does inside the bubble if you are generating a preview.
The user wants to see it WORKING in the Canvas immediately.
STRICT OUTPUT FORMAT:
[AGENT:id] - Optional: switch to content, seo, smm, pm, code, design, web, app.
[PREVIEW:type:language]
ACTUAL_FUNCTIONAL_CODE_OR_DATA
[/PREVIEW]
Optional conversational text (keep it brief).
Example for a mockup:
[AGENT:design]
[PREVIEW:design:html]
<div class="bg-blue-500 p-10">...</div>
[/PREVIEW]`;
const messages: ChatMessage[] = [
{ role: "system", content: systemPrompt },
...options.messages.map(m => ({
role: m.role as "user" | "assistant" | "system",
content: m.content
}))
];
const response = await fetch(LOCAL_CHAT_URL, {
method: "POST",
headers: this.getHeaders({ "Content-Type": "application/json" }),
signal: options.signal,
body: JSON.stringify({
model: model || this.getAvailableModels()[0],
messages,
stream: true,
}),
});
if (!response.ok) {
throw new Error("Stream request failed");
}
const reader = response.body?.getReader();
if (!reader) throw new Error("No reader");
const decoder = new TextDecoder();
let buffer = "";
while (true) {
const { done, value } = await reader.read();
if (done) break;
const chunk = decoder.decode(value, { stream: true });
buffer += chunk;
const lines = buffer.split("\n");
buffer = lines.pop() || "";
for (const line of lines) {
if (!line.trim()) continue;
try {
const data = JSON.parse(line);
if (data.message?.content) {
options.onChunk(data.message.content);
}
} catch (e) {
console.error("Error parsing stream line", e);
}
}
}
return { success: true, data: undefined };
} catch (error) {
return { success: false, error: error instanceof Error ? error.message : "Stream failed" };
}
}
}
export default OllamaCloudService;

101
lib/services/qwen-oauth.ts
View File

@@ -1006,6 +1006,107 @@ Perform analysis based on provided instructions.`,
return await this.chatCompletion(chatMessages, model || this.getAvailableModels()[0]);
}
async generateAIAssistStream(
options: {
messages: AIAssistMessage[];
currentAgent: string;
onChunk: (chunk: string) => void;
signal?: AbortSignal;
},
model?: string
): Promise<APIResponse<void>> {
try {
// ... existing prompt logic ...
const systemPrompt = `You are "AI Assist".
Your goal is to provide intelligent support with a "Canvas" experience.
CANVAS MODE (CRITICAL):
When building or designing, you MUST use the [PREVIEW] tag.
Inside [PREVIEW], output ONLY the actual code (HTML/Tailwind etc).
The user wants to see it WORKING in the Canvas immediately.
STRICT OUTPUT FORMAT:
[AGENT:id] - Optional: content, seo, smm, pm, code, design, web, app.
[PREVIEW:type:language]
ACTUAL_FUNCTIONAL_CODE
[/PREVIEW]
Optional brief text.`;
const messages: ChatMessage[] = [
{ role: "system", content: systemPrompt },
...options.messages.map(m => ({
role: m.role as "user" | "assistant" | "system",
content: m.content
}))
];
const endpoint = "/tools/promptarch/api/qwen/chat";
const headers: Record<string, string> = {
"Content-Type": "application/json",
};
const tokenInfo = this.getTokenInfo();
if (tokenInfo?.accessToken) {
headers["Authorization"] = `Bearer ${tokenInfo.accessToken}`;
} else if (this.apiKey) {
headers["Authorization"] = `Bearer ${this.apiKey}`;
}
const response = await fetch(endpoint, {
method: "POST",
headers,
signal: options.signal,
body: JSON.stringify({
model: model || this.getAvailableModels()[0],
messages,
stream: true,
}),
});
if (!response.ok) {
throw new Error("Stream request failed");
}
const reader = response.body?.getReader();
if (!reader) throw new Error("No reader");
const decoder = new TextDecoder();
let buffer = "";
while (true) {
const { done, value } = await reader.read();
if (done) break;
const chunk = decoder.decode(value, { stream: true });
buffer += chunk;
const lines = buffer.split("\n");
buffer = lines.pop() || "";
for (const line of lines) {
const trimmedLine = line.trim();
if (!trimmedLine || !trimmedLine.startsWith("data:")) continue;
const dataStr = trimmedLine.replace(/^data:\s*/, "");
if (dataStr === "[DONE]") break;
try {
const data = JSON.parse(dataStr);
if (data.choices?.[0]?.delta?.content) {
options.onChunk(data.choices[0].delta.content);
}
} catch (e) {
// Ignore parse errors for incomplete lines
}
}
}
return { success: true, data: undefined };
} catch (error) {
return { success: false, error: error instanceof Error ? error.message : "Stream failed" };
}
}
async listModels(): Promise<APIResponse<string[]>> {
const models = [
"coder-model",

82
lib/services/zai-plan.ts
View File

@@ -799,6 +799,88 @@ MISSION: Perform a DEEP 360° competitive intelligence analysis and generate 5-7
return await this.chatCompletion(chatMessages, model || this.getAvailableModels()[0]);
}
async generateAIAssistStream(
options: {
messages: AIAssistMessage[];
currentAgent: string;
onChunk: (chunk: string) => void;
signal?: AbortSignal;
},
model?: string
): Promise<APIResponse<void>> {
try {
if (!this.config.apiKey) {
throw new Error("API key is required.");
}
// ... existing prompt logic ...
const systemPrompt = `You are "AI Assist".
Your goal is to provide a "Canvas" experience.
CANVAS MODE (CRITICAL):
When building or designing, you MUST use the [PREVIEW] tag.
Inside [PREVIEW], output ONLY the actual code (HTML/Tailwind etc).
The user wants to see it WORKING in the Canvas immediately.
STRICT OUTPUT FORMAT:
[AGENT:id] - Optional switch.
[PREVIEW:type:language]
ACTUAL_FUNCTIONAL_CODE
[/PREVIEW]
Optional brief text.`;
const messages: ChatMessage[] = [
{ role: "system", content: systemPrompt },
...options.messages.map(m => ({
role: m.role as "user" | "assistant" | "system",
content: m.content
}))
];
const endpoint = this.config.codingEndpoint; // AI Assist often involves coding
const response = await fetch(`${endpoint}/chat/completions`, {
method: "POST",
headers: this.getHeaders(),
signal: options.signal,
body: JSON.stringify({
model: model || this.getAvailableModels()[0],
messages,
stream: true,
}),
});
if (!response.ok) {
throw new Error(`Stream failed: ${response.statusText}`);
}
const reader = response.body?.getReader();
if (!reader) throw new Error("No reader");
const decoder = new TextDecoder();
while (true) {
const { done, value } = await reader.read();
if (done) break;
const chunk = decoder.decode(value, { stream: true });
const lines = chunk.split("\n");
for (const line of lines) {
if (!line.trim() || !line.startsWith("data:")) continue;
const dataStr = line.replace(/^data:\s*/, "");
if (dataStr === "[DONE]") break;
try {
const data = JSON.parse(dataStr);
const content = data.choices?.[0]?.delta?.content || data.output?.choices?.[0]?.delta?.content;
if (content) options.onChunk(content);
} catch (e) { }
}
}
return { success: true, data: undefined };
} catch (error) {
return { success: false, error: error instanceof Error ? error.message : "Stream failed" };
}
}
}
export default ZaiPlanService;