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"
    }
  }'

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
    }
  }'

# Encode a local file as a base64 data URL, then send it inline
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}\"}}
        ]
      }
    ]
  }"

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
  }'