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0b9dc2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | # -*- coding: utf-8 -*-
"""Example of OpenAI Responses API model multimodal (vision) calls using
DataBlock."""
import asyncio
import base64
import os
from pathlib import Path
from _utils import stream_and_collect
from agentscope.message import (
Msg,
TextBlock,
DataBlock,
URLSource,
Base64Source,
)
from agentscope.model import OpenAIResponseModel
from agentscope.credential import OpenAICredential
# A publicly accessible test image (a simple cat photo)
TEST_IMAGE_URL = (
"https://help-static-aliyun-doc.aliyuncs.com/file-manage"
"-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
)
async def example_image_url() -> None:
"""Call gpt-4.1 (Responses API) with an image URL and ask what is in
the image."""
model = OpenAIResponseModel(
credential=OpenAICredential(
api_key=os.environ["OPENAI_API_KEY"],
),
model="gpt-4.1",
stream=True,
context_size=1_047_576,
)
image_block = DataBlock(
source=URLSource(
url=TEST_IMAGE_URL,
media_type="image/jpeg",
),
)
msgs = [
Msg(
name="user",
content=[
TextBlock(
text="What animal is in this image? Describe it briefly.",
),
image_block,
],
role="user",
),
]
print("=== Multimodal Call (Image URL) ===")
await stream_and_collect(await model(msgs))
def _build_model() -> OpenAIResponseModel:
return OpenAIResponseModel(
credential=OpenAICredential(api_key=os.environ["OPENAI_API_KEY"]),
model="gpt-4.1",
stream=True,
context_size=1_047_576,
)
async def example_image_local_path() -> None:
"""Call gpt-4.1 (Responses API) with a local image using a ``file://`` URL.
The formatter reads the file from disk and converts it to a base64 data
URI.
"""
model = _build_model()
abs_path = str(Path(__file__).parent / "test.jpeg")
msgs = [
Msg(
name="user",
content=[
TextBlock(
text="What is happening in this image? Describe it "
"briefly.",
),
DataBlock(
source=URLSource(
url=f"file://{abs_path}",
media_type="image/jpeg",
),
),
],
role="user",
),
]
print("=== Local Path Call (file://) ===")
await stream_and_collect(await model(msgs))
async def example_image_base64() -> None:
"""Call gpt-4.1 (Responses API) with a local image using explicit base64.
Use ``Base64Source`` when you already have the binary data in memory or
want full control over the encoding step.
"""
model = _build_model()
with open(Path(__file__).parent / "test.jpeg", "rb") as f:
data = base64.b64encode(f.read()).decode("utf-8")
msgs = [
Msg(
name="user",
content=[
TextBlock(
text="What is happening in this image? Describe it "
"briefly.",
),
DataBlock(
source=Base64Source(data=data, media_type="image/jpeg"),
),
],
role="user",
),
]
print("=== Explicit Base64 Call ===")
await stream_and_collect(await model(msgs))
if __name__ == "__main__":
asyncio.run(example_image_url())
asyncio.run(example_image_local_path())
asyncio.run(example_image_base64())
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