Venice API

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-5-1",
    "messages": [{"role": "user", "content": "What are the latest developments in AI?"}],
    "venice_parameters": {
      "enable_web_search": "auto"
    }
  }'

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.VENICE_API_KEY!,
  baseURL: "https://api.venice.ai/api/v1",
});

const completion = await client.chat.completions.create({
  model: "zai-org-glm-5-1",
  messages: [{ role: "user", content: "What are the latest developments in AI?" }],
  // @ts-expect-error - Venice-specific parameter
  venice_parameters: {
    enable_web_search: "auto",
  },
});

console.log(completion.choices[0].message.content);

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["VENICE_API_KEY"],
    base_url="https://api.venice.ai/api/v1",
)

response = client.chat.completions.create(
    model="zai-org-glm-5-1",
    messages=[{"role": "user", "content": "What are the latest developments in AI?"}],
    extra_body={
        "venice_parameters": {
            "enable_web_search": "auto",
        }
    },
)

print(response.choices[0].message.content)

# Alternative: Parameter direkt an die Modell-ID anhängen
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-5-1:enable_web_search=on&enable_web_citations=true",
    "messages": [{"role": "user", "content": "What are the latest developments in AI?"}]
  }'

curl https://api.venice.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai-gpt-55",
    "messages": [
      {"role": "user", "content": "Summarize this post in five bullets: https://venice.ai/blog/how-to-use-venice-api"}
    ],
    "venice_parameters": {
      "enable_web_scraping": true
    }
  }'

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.VENICE_API_KEY!,
  baseURL: "https://api.venice.ai/api/v1",
});

const response = await client.chat.completions.create({
  model: "openai-gpt-55",
  messages: [
    {
      role: "user",
      content:
        "Summarize this post in five bullets: https://venice.ai/blog/how-to-use-venice-api",
    },
  ],
  // @ts-expect-error - Venice-specific parameter
  venice_parameters: {
    enable_web_scraping: true,
  },
});

console.log(response.choices[0].message.content);

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["VENICE_API_KEY"],
    base_url="https://api.venice.ai/api/v1",
)

response = client.chat.completions.create(
    model="openai-gpt-55",
    messages=[
        {
            "role": "user",
            "content": "Summarize this post in five bullets: https://venice.ai/blog/how-to-use-venice-api",
        }
    ],
    extra_body={
        "venice_parameters": {
            "enable_web_scraping": True,
        }
    },
)

print(response.choices[0].message.content)

# Eine lokale Datei als base64-Data-URL kodieren und dann inline senden
FILE_B64=$(base64 q3-report.pdf | tr -d '\n')

curl https://api.venice.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"openai-gpt-55\",
    \"messages\": [
      {
        \"role\": \"user\",
        \"content\": [
          {\"type\": \"text\", \"text\": \"Summarize this report in five bullets and list the main risks.\"},
          {\"type\": \"file\", \"file\": {\"filename\": \"q3-report.pdf\", \"file_data\": \"data:application/pdf;base64,${FILE_B64}\"}}
        ]
      }
    ]
  }"

import OpenAI from "openai";
import { readFile } from "node:fs/promises";

const client = new OpenAI({
  apiKey: process.env.VENICE_API_KEY!,
  baseURL: "https://api.venice.ai/api/v1",
});

const pdf = await readFile("q3-report.pdf");
const fileData = `data:application/pdf;base64,${pdf.toString("base64")}`;

const response = await client.chat.completions.create({
  model: "openai-gpt-55",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Summarize this report in five bullets and list the main risks." },
        // @ts-expect-error - Venice file input block
        { type: "file", file: { filename: "q3-report.pdf", file_data: fileData } },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

import base64
import os
from pathlib import Path
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["VENICE_API_KEY"],
    base_url="https://api.venice.ai/api/v1",
)

path = Path("q3-report.pdf")
file_data = "data:application/pdf;base64," + base64.b64encode(path.read_bytes()).decode("utf-8")

response = client.chat.completions.create(
    model="openai-gpt-55",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Summarize this report in five bullets and list the main risks."},
                {"type": "file", "file": {"filename": "q3-report.pdf", "file_data": file_data}},
            ],
        }
    ],
)

print(response.choices[0].message.content)

curl https://api.venice.ai/api/v1/crypto/rpc/ethereum-mainnet \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "eth_blockNumber",
    "params": [],
    "id": 1
  }'

const response = await fetch(
  "https://api.venice.ai/api/v1/crypto/rpc/base-mainnet",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify([
      { jsonrpc: "2.0", method: "eth_chainId", params: [], id: 1 },
      { jsonrpc: "2.0", method: "eth_blockNumber", params: [], id: 2 },
    ]),
  }
);

const results = await response.json();
console.log(results);

import os
import requests

response = requests.post(
    "https://api.venice.ai/api/v1/crypto/rpc/ethereum-mainnet",
    headers={
        "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
        "Content-Type": "application/json",
    },
    json={
        "jsonrpc": "2.0",
        "method": "eth_getBalance",
        "params": ["0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045", "latest"],
        "id": 1,
    },
)

print(response.json())