vision: images are the primary channel for multimodal models (digest off by default); digest is the text-only fallback; --digest/--no-digest to override
This commit is contained in:
opencode
@@ -7044,3 +7044,116 @@
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[26877820ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[26882430ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[26888780ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27030792ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27035327ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27037817ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27042485ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27048292ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27068723ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27123436ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27129079ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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[27132854ms] [ERROR] WebSocket connection to 'ws://127.0.0.1:8001/' failed: Error in connection establishment: net::ERR_CONNECTION_REFUSED @ http://127.0.0.1:8000/:40
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+11
-9
@@ -103,17 +103,19 @@ many tool steps the model may chain per request (0 = one action per turn).
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### Real vision
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By default the model gets frames as a color-grid digest (works for any
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text-only model). If the model can actually see images (gemma3/gemma4,
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qwen2.5-vl, gpt-4o-mini, ...), pass the frames as real images:
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Vision is the **primary channel**: when the model is multimodal, `vision`
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tool results attach the actual camera frame as an image to the conversation —
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the model sees the crate, the wall, the glowing beacon. The color-grid
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digest is only the **fallback** for text-only models:
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||||
- auto-detected for local ollama (`/api/show` capabilities) — nothing to do
|
||||
- otherwise force it: `--vision` (or `--no-vision` to disable)
|
||||
- multimodal model (auto-detected for local ollama via `/api/show`;
|
||||
otherwise `--vision`): frame as image, digest off
|
||||
- text-only model: digest on automatically; `--digest` forces the digest to
|
||||
be included even alongside images, `--no-digest` disables it everywhere
|
||||
|
||||
With vision ON, `vision` tool results attach the actual camera frame as an
|
||||
image to the conversation — the model sees the crate, the wall, the glowing
|
||||
beacon, and orients itself. Verified locally: gemma4:12b completed a beacon
|
||||
mission on its first attempt using look_at/move navigation.
|
||||
Verified locally: gemma4:12b sees frames and completed a beacon mission on
|
||||
its first attempt; gemma4:e2b describes what it sees (crates, beacon) and
|
||||
orients itself from the image.
|
||||
|
||||
## Real-time mode
|
||||
|
||||
|
||||
@@ -63,11 +63,14 @@ async def chat_loop(client: AICCClient, manifest, args: argparse.Namespace) -> i
|
||||
system_prompt=CHAT_MISSION,
|
||||
log=lambda role, msg: print(f" [{role}] {msg}"),
|
||||
multimodal=args.vision,
|
||||
digest=args.digest,
|
||||
)
|
||||
auto_steps = args.auto_steps
|
||||
print(f"[chat] model: {model} (endpoint {args.base_url})")
|
||||
if controller.multimodal:
|
||||
print("[chat] vision: ON — the model sees the actual camera frames")
|
||||
else:
|
||||
print("[chat] vision: text-only model — frames are sent as a color-grid digest")
|
||||
print("[chat] type your commands; /help for the command list; /exit to quit\n")
|
||||
|
||||
async def cmd_state() -> None:
|
||||
@@ -422,6 +425,12 @@ def main() -> int:
|
||||
default=None,
|
||||
help="pass real camera frames to the model as images (auto-detected for local ollama)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--digest",
|
||||
action=argparse.BooleanOptionalAction,
|
||||
default=None,
|
||||
help="always include the color-grid digest alongside images (off by default for multimodal models)",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
try:
|
||||
return asyncio.run(run(args))
|
||||
|
||||
@@ -140,6 +140,7 @@ async def run_llm_agent(
|
||||
max_steps: int,
|
||||
recorder: FrameRecorder | None = None,
|
||||
vision: bool | None = None,
|
||||
digest: bool | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Autonomous LLM run: controller + nudge/correct loop (see llm_agent)."""
|
||||
from testbed.llm_agent import LLMController, run_llm_agent_loop
|
||||
@@ -152,6 +153,7 @@ async def run_llm_agent(
|
||||
model=model,
|
||||
log=lambda role, msg: print(f"[{role}] {msg}"),
|
||||
multimodal=vision,
|
||||
digest=digest,
|
||||
)
|
||||
try:
|
||||
return await run_llm_agent_loop(
|
||||
@@ -310,6 +312,7 @@ async def run_demo(args: argparse.Namespace) -> dict[str, Any]:
|
||||
max_steps=args.max_steps,
|
||||
recorder=recorder,
|
||||
vision=args.vision,
|
||||
digest=args.digest,
|
||||
)
|
||||
elif args.agent == "scripted":
|
||||
summary = await run_scripted_agent(
|
||||
@@ -331,6 +334,7 @@ async def run_demo(args: argparse.Namespace) -> dict[str, Any]:
|
||||
max_steps=llm_steps,
|
||||
recorder=recorder,
|
||||
vision=args.vision,
|
||||
digest=args.digest,
|
||||
)
|
||||
if summary.get("interacted"):
|
||||
return summary
|
||||
@@ -398,6 +402,12 @@ def main() -> int:
|
||||
default=None,
|
||||
help="pass real camera frames to the model as images (auto-detected for local ollama)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--digest",
|
||||
action=argparse.BooleanOptionalAction,
|
||||
default=None,
|
||||
help="always include the color-grid digest alongside images (off by default for multimodal models)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--frame",
|
||||
default="demo_final_frame.png",
|
||||
|
||||
+21
-10
@@ -229,6 +229,7 @@ class LLMController:
|
||||
system_prompt: str = MISSION,
|
||||
log: LogFn | None = None,
|
||||
multimodal: bool | None = None,
|
||||
digest: bool | None = None,
|
||||
):
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
@@ -237,6 +238,9 @@ class LLMController:
|
||||
if multimodal is None:
|
||||
multimodal = detect_multimodal(base_url, model)
|
||||
self.multimodal = multimodal
|
||||
# The color-grid digest is the fallback channel for text-only models.
|
||||
# For multimodal models it is off unless explicitly requested.
|
||||
self.include_digest = (not multimodal) if digest is None else digest
|
||||
self.ac = AsyncOpenAI(base_url=base_url, api_key=api_key)
|
||||
self.tools = to_openai_tools(manifest.tools)
|
||||
self.messages: list[dict[str, Any]] = [
|
||||
@@ -344,9 +348,10 @@ class LLMController:
|
||||
digest = None
|
||||
extra = ""
|
||||
if name == "vision" and isinstance(output.get("png_b64"), str):
|
||||
digest = vision_digest(output["png_b64"])
|
||||
extra = "\n" + digest
|
||||
output = dict(output, png_b64="<binary, decoded for digest>")
|
||||
if self.include_digest:
|
||||
digest = vision_digest(outcome.output["png_b64"])
|
||||
extra = "\n" + digest
|
||||
self.log("bridge", f"ok {json.dumps(output)[:500]}{extra}")
|
||||
if name == "world_query":
|
||||
b = output.get("beacon")
|
||||
@@ -358,13 +363,14 @@ class LLMController:
|
||||
if name == "vision" and isinstance(outcome.output.get("png_b64"), str):
|
||||
frame_b64 = outcome.output["png_b64"]
|
||||
if self.multimodal and frame_b64 is not None:
|
||||
# Real vision: hand the model the actual frame as an image
|
||||
# (data URI), not as base64 text. Keep the digest too —
|
||||
# it is a cheap textual anchor for the model.
|
||||
content = (hint + "\n" if hint else "") + (
|
||||
# Primary channel: hand the model the actual frame as an
|
||||
# image (data URI). The digest is optional and off by
|
||||
# default for multimodal models.
|
||||
frame_note = (
|
||||
f"Camera frame ({output['width']}x{output['height']}, "
|
||||
f"tick {output.get('tick')}) attached as an image.{extra}"
|
||||
f"tick {output.get('tick')}) attached as an image."
|
||||
)
|
||||
content = (hint + "\n" if hint else "") + frame_note + extra
|
||||
self.messages.append(
|
||||
{"role": "tool", "tool_call_id": tc.id, "content": content}
|
||||
)
|
||||
@@ -386,9 +392,14 @@ class LLMController:
|
||||
}
|
||||
)
|
||||
else:
|
||||
content = json.dumps(outcome.output) + extra
|
||||
if hint:
|
||||
content = hint + "\n" + content
|
||||
# Fallback channel: the digest (plus frame metadata) for
|
||||
# text-only models.
|
||||
content = (
|
||||
(hint + "\n" if hint else "")
|
||||
+ f"Camera frame ({output['width']}x{output['height']}, "
|
||||
+ f"tick {output.get('tick')})."
|
||||
+ extra
|
||||
)
|
||||
self.messages.append(
|
||||
{"role": "tool", "tool_call_id": tc.id, "content": content}
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user