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some bugfixes

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This commit is contained in:
teknium
2025-10-15 18:07:06 +00:00
parent 8d256779d8
commit de9c0edc51
4 changed files with 80 additions and 35 deletions
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32
batch_runner.py
View File

@@ -86,13 +86,35 @@ def _extract_tool_stats(messages: List[Dict[str, Any]]) -> Dict[str, Dict[str, i
# Determine if tool call was successful
is_success = True
try:
# Try to parse as JSON and check for error field
# Try to parse as JSON and check for actual error values
content_json = json.loads(content) if isinstance(content, str) else content
if isinstance(content_json, dict) and "error" in content_json:
is_success = False
if isinstance(content_json, dict):
# Check if error field exists AND has a non-null value
if "error" in content_json and content_json["error"] is not None:
is_success = False
# Special handling for terminal tool responses
# Terminal wraps its response in a "content" field
if "content" in content_json and isinstance(content_json["content"], dict):
inner_content = content_json["content"]
# Check for actual error (non-null error field or non-zero exit code)
has_error = (inner_content.get("error") is not None or
inner_content.get("exit_code", 0) != 0)
if has_error:
is_success = False
# Check for "success": false pattern used by some tools
if content_json.get("success") is False:
is_success = False
except:
# If not JSON, check if content contains error indicators
if not content or "error" in content.lower():
# If not JSON, check if content is empty or explicitly states an error
# Note: We avoid simple substring matching to prevent false positives
if not content:
is_success = False
# Only mark as failure if it explicitly starts with "Error:" or "ERROR:"
elif content.strip().lower().startswith("error:"):
is_success = False
# Update success/failure count

9
run_agent.py
View File

@@ -99,10 +99,11 @@ class AIAgent:
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
# Also set OpenAI client logging to debug
logging.getLogger('openai').setLevel(logging.DEBUG)
logging.getLogger('httpx').setLevel(logging.DEBUG)
print("🔍 Verbose logging enabled")
# Keep OpenAI and httpx at INFO level to avoid massive base64 logs
# Even in verbose mode, we don't want to see full request/response bodies
logging.getLogger('openai').setLevel(logging.INFO)
logging.getLogger('httpx').setLevel(logging.WARNING)
print("🔍 Verbose logging enabled (OpenAI/httpx request bodies suppressed)")
else:
# Set logging to INFO level for important messages only
logging.basicConfig(

12
run_datagen_images.sh Normal file
View File

@@ -0,0 +1,12 @@
python batch_runner.py \
--dataset_file="hermes-agent-imagen-data/hermes_agent_imagen_eval.jsonl" \
--batch_size=10 \
--run_name="imagen_eval_gpt5" \
--distribution="image_gen" \
--model="gpt-5" \
--base_url="https://api.openai.com/v1" \
--api_key="${OPENAI_API_KEY}" \
--num_workers=4 \
--max_turns=5 \
--verbose \
--ephemeral_system_prompt="When generating an image for the user view the image by using the vision_analyze tool to ensure it is what the user wanted. If it isn't feel free to retry a few times. If none are perfect, choose the best option that is the closest match, and explain its imperfections. If the image generation tool fails, try again a few times. If the vision analyze tool fails, provide the image to the user and explain it is your best effort attempt."

62
tools/vision_tools.py
View File

@@ -33,10 +33,10 @@ import asyncio
import uuid
import datetime
import base64
import requests
from pathlib import Path
from typing import Dict, Any, Optional
from openai import AsyncOpenAI
import httpx # Use httpx for async HTTP requests
# Initialize Nous Research API client for vision processing
nous_client = AsyncOpenAI(
@@ -131,9 +131,9 @@ def _validate_image_url(url: str) -> bool:
return True # Allow all HTTP/HTTPS URLs for flexibility
def _download_image(image_url: str, destination: Path) -> Path:
async def _download_image(image_url: str, destination: Path) -> Path:
"""
Download an image from a URL to a local destination.
Download an image from a URL to a local destination (async).
Args:
image_url (str): The URL of the image to download
@@ -148,16 +148,17 @@ def _download_image(image_url: str, destination: Path) -> Path:
# Create parent directories if they don't exist
destination.parent.mkdir(parents=True, exist_ok=True)
# Download the image with appropriate headers
response = requests.get(
image_url,
timeout=30,
headers={"User-Agent": "hermes-agent-vision/1.0"},
)
response.raise_for_status()
# Download the image with appropriate headers using async httpx
async with httpx.AsyncClient(timeout=30.0) as client:
response = await client.get(
image_url,
headers={"User-Agent": "hermes-agent-vision/1.0"},
)
response.raise_for_status()
# Save the image content
destination.write_bytes(response.content)
# Save the image content
destination.write_bytes(response.content)
return destination
@@ -249,20 +250,21 @@ async def vision_analyze_tool(
debug_call_data = {
"parameters": {
"image_url": image_url,
"user_prompt": user_prompt,
"user_prompt": user_prompt[:200] + "..." if len(user_prompt) > 200 else user_prompt,
"model": model
},
"error": None,
"success": False,
"analysis_length": 0,
"model_used": model
"model_used": model,
"image_size_bytes": 0
}
temp_image_path = None
try:
print(f"🔍 Analyzing image from URL: {image_url[:60]}{'...' if len(image_url) > 60 else ''}")
print(f"📝 User prompt: {user_prompt[:100]}{'...' if len(user_prompt) > 100 else ''}")
print(f"🔍 Analyzing image from URL: {image_url[:60]}{'...' if len(image_url) > 60 else ''}", flush=True)
print(f"📝 User prompt: {user_prompt[:100]}{'...' if len(user_prompt) > 100 else ''}", flush=True)
# Validate image URL
if not _validate_image_url(image_url):
@@ -273,17 +275,25 @@ async def vision_analyze_tool(
raise ValueError("NOUS_API_KEY environment variable not set")
# Download the image to a temporary location
print(f"⬇️ Downloading image from URL...")
print(f"⬇️ Downloading image from URL...", flush=True)
temp_dir = Path("./temp_vision_images")
temp_image_path = temp_dir / f"temp_image_{uuid.uuid4()}.jpg"
_download_image(image_url, temp_image_path)
print(f"✅ Image downloaded successfully")
await _download_image(image_url, temp_image_path)
# Get image file size for logging
image_size_bytes = temp_image_path.stat().st_size
image_size_kb = image_size_bytes / 1024
print(f"✅ Image downloaded successfully ({image_size_kb:.1f} KB)", flush=True)
# Convert image to base64 data URL
print(f"🔄 Converting image to base64...")
print(f"🔄 Converting image to base64...", flush=True)
image_data_url = _image_to_base64_data_url(temp_image_path)
print(f"✅ Image converted to base64 ({len(image_data_url)} characters)")
# Calculate size in KB for better readability
data_size_kb = len(image_data_url) / 1024
print(f"✅ Image converted to base64 ({data_size_kb:.1f} KB)", flush=True)
debug_call_data["image_size_bytes"] = image_size_bytes
# Use the prompt as provided (model_tools.py now handles full description formatting)
comprehensive_prompt = user_prompt
@@ -307,7 +317,7 @@ async def vision_analyze_tool(
}
]
print(f"🧠 Processing image with {model}...")
print(f"🧠 Processing image with {model}...", flush=True)
# Call the vision API
response = await nous_client.chat.completions.create(
@@ -321,7 +331,7 @@ async def vision_analyze_tool(
analysis = response.choices[0].message.content.strip()
analysis_length = len(analysis)
print(f"✅ Image analysis completed ({analysis_length} characters)")
print(f"✅ Image analysis completed ({analysis_length} characters)", flush=True)
# Prepare successful response
result = {
@@ -340,7 +350,7 @@ async def vision_analyze_tool(
except Exception as e:
error_msg = f"Error analyzing image: {str(e)}"
print(f"❌ {error_msg}")
print(f"❌ {error_msg}", flush=True)
# Prepare error response
result = {
@@ -359,9 +369,9 @@ async def vision_analyze_tool(
if temp_image_path and temp_image_path.exists():
try:
temp_image_path.unlink()
print(f"🧹 Cleaned up temporary image file")
print(f"🧹 Cleaned up temporary image file", flush=True)
except Exception as cleanup_error:
print(f"⚠️ Warning: Could not delete temporary file: {cleanup_error}")
print(f"⚠️ Warning: Could not delete temporary file: {cleanup_error}", flush=True)
def check_nous_api_key() -> bool: