Building Conversational AI Models with Oxlo

说明与用途

使用 Oxlo.ai 和 Llama 3.3 70B 构建客户支持聊天机器人，处理订单查询和退货政策，降低人工工单量。

问题

Create a customer support chatbot that handles order lookups, returns, and policy questions without generating repetitive tickets for e-commerce and SaaS teams.

结果

The chatbot successfully answers order status and return policy questions, demonstrating tool calling with Oxlo.ai’s Llama 3.3 70B model and reducing manual support tickets.

工作流

• 1. Install Python 3.10+ and OpenAI SDK. 2. Obtain an Oxlo.ai API key. 3. Initialize the Oxlo.ai client using the OpenAI-compatible endpoint. 4. Define a concise system prompt describing the agent’s role, tone, and available tools. 5. Create a simple conversation history list that includes the system prompt. 6. Define a mock order database and a lookup_order function. 7. Specify the function schema for tool calling. 8. Build the SupportAgent class: send user messages, handle tool calls, execute lookup_order locally, and produce final natural‑language reply. 9. Test the agent with sample queries for order lookup and return policy. 10. (Optional) Deploy the agent via FastAPI or switch to a different model for multilingual/vision needs.

证据

I will walk through the exact code I shipped, using Oxlo.ai's OpenAI-compatible API and Llama 3.3 70B for fast, capable reasoning.

来源

Oxlo.ai supports function calling on Llama 3.3 70B, which makes this straightforward.

来源