Thanks for your reply.
This is my code:
import { NextResponse } from 'next/server';
import OpenAI from 'openai';
import { logger, LOGLEVELS } from "@/lib/logger";
import { decrypt } from "@/utils/encryption";
const tools: OpenAI.ChatCompletionTool[] = [
{
type: "function",
function: {
name: "performVectorSearch",
description: "Perform a vector similarity search to find relevant documents for a given query.",
parameters: {
type: "object",
properties: {
text: {
type: "string",
description: "The query text to search for.",
},
},
required: ["text"],
},
},
},
];
export async function POST(request: Request) {
const { messages, preset, shouldStream } = await request.json();
if (!messages || !Array.isArray(messages)) {
return NextResponse.json(
{ error: 'Invalid messages format' },
{ status: 400 }
);
}
const openai = new OpenAI({
baseURL: '',
apiKey: api_key
defaultHeaders: {'Content-Type': 'application/json'}
});
try {
// Non-streaming mode
const completion = await openai.chat.completions.create({
messages: messages.map(msg => ({
role: msg.role,
content: msg.content
})),
model: model,
temperature: parseFloat(temperature),
max_tokens: parseInt(max_tokens),
tools: tools,
tool_choice: "auto", // Let the model decide whether to use the tool
});
console.log("performVectorSearch called 1Response", completion);
// Handle tool calls
const responseMessage = completion.choices[0].message;
console.log("performVectorSearch called 2Response", responseMessage);
if (responseMessage.tool_calls) {
const toolCall = responseMessage.tool_calls[0];
const { name, arguments: args } = toolCall.function;
console.log("performVectorSearch called", name);
if (name === "performVectorSearch") {
const { text } = JSON.parse(args);
console.log("performVectorSearch called", text);
const context = await performVectorSearch(text); // Call the tool
// Send the tool result back to the model
const secondResponse = await openai.chat.completions.create({
messages: [
...messages,
responseMessage,
{
role: "tool",
name: "performVectorSearch",
content: context,
},
],
model: model,
temperature: parseFloat(temperature),
max_tokens: parseInt(max_tokens),
});
console.log("performVectorSearch called secondResponse", secondResponse.choices[0].message.content);
return NextResponse.json({
text: secondResponse.choices[0].message.content,
});
}
}
// If no tool is called, return the model's response directly
if (!responseMessage.content)
throw new Error('No response content from API');
return NextResponse.json({
text: responseMessage.content,
});
} catch (error) {
const errorMessage =
error instanceof Error ? error.message : "Failed to generate chat response";
logger.log(
LOGLEVELS.ERROR,
`Error in /api/chat route: ${errorMessage}`
);
return NextResponse.json(
{ error: errorMessage },
{ status: 500 }
);
}
}
async function performVectorSearch(text: string): Promise<string> {
const embedding = await generateEmbedding(text);
if (!embedding) return "";
const response = await fetch(`${APIURL}/api/search`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({ embedding }),
});
const data = await response.json();
if (!response.ok) {
console.error("Search API error:", data.error || response.status);
return "";
}
if (!data.searchResults || data.searchResults.length === 0) {
console.log("No search results found for the query");
return "";
}
const context = data.searchResults.map((doc) => doc.content).join("\n\n");
// return context;
console.log(context)
return `Context for: ${context}`;
}
async function generateEmbedding(content: string): Promise<number[]> {
const response = await fetch(`${APIURL}/api/embedding`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({ content }),
});
const data = await response.json();
if (!response.ok) {
console.error("Embedding API error:", data.error || response.status);
return null;
}
if (!data.embedding) {
console.error("No embedding received from server");
return null;
}
return data.embedding;
}
Prompt:
You are Chris, a professional sales representative of my company. Your role is to naturally and engagingly assist users with my company product and policies.
### 🔹 **Your Style & Approach:**
**Be friendly & conversational.** Use contractions, avoid robotic wording.
**Keep answers simple, engaging, and direct.**
**Stick to company policies.** Never answer unrelated questions (e.g., sports, movies, history).
**If the user keeps asking off-topic questions, stay polite but firm.**
I’m not sure what’s going wrong—it’s not working as expected on my end.
Could you help me identify the issue?
Do I need to make any changes to my code or the prompt?
