Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
通过与 OpenAI 兼容的函数调用、工具 schema 以及 /chat/completions API，让 Venice 聊天模型调用你应用中的工具和函数。
tools 数组来定义工具：
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, such as San Francisco, CA",
}
},
"required": ["location"],
},
},
}
]
response = client.chat.completions.create(
model="zai-org-glm-5",
messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
tools=tools,
)
print(response.choices[0].message.tool_calls)
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.VENICE_API_KEY,
baseURL: "https://api.venice.ai/api/v1",
});
const tools = [
{
type: "function",
function: {
name: "get_weather",
description: "Get the current weather in a location",
parameters: {
type: "object",
properties: {
location: {
type: "string",
description: "City and state, such as San Francisco, CA",
},
},
required: ["location"],
},
},
},
];
const response = await client.chat.completions.create({
model: "zai-org-glm-5",
messages: [{ role: "user", content: "What is the weather in San Francisco?" }],
tools,
});
console.log(response.choices[0].message.tool_calls);
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org-glm-5",
"messages": [
{"role": "user", "content": "What is the weather in San Francisco?"}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, such as San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
}'
message.tool_calls，解析其参数，执行你的应用函数，然后把结果作为 tool 消息发回。
import json
message = response.choices[0].message
tool_call = message.tool_calls[0]
arguments = json.loads(tool_call.function.arguments)
weather = get_weather(arguments["location"])
follow_up = client.chat.completions.create(
model="zai-org-glm-5",
messages=[
{"role": "user", "content": "What is the weather in San Francisco?"},
message.model_dump(),
{
"role": "tool",
"tool_call_id": tool_call.id,
"content": json.dumps(weather),
},
],
tools=tools,
)
print(follow_up.choices[0].message.content)
supportsFunctionCalling 能力的模型。