From c7fa4447b831e2320b7d07e451bbef63dbf28cf3 Mon Sep 17 00:00:00 2001
From: Teknium <127238744+teknium1@users.noreply.github.com>
Date: Sat, 6 Sep 2025 22:07:38 -0700
Subject: [PATCH] cleanup
---
model_tools.py | 67 ++++++++++++++------------------------------------
test_run.sh | 25 ++++++++++++++++---
2 files changed, 40 insertions(+), 52 deletions(-)
diff --git a/model_tools.py b/model_tools.py
index 835867424..08b073953 100644
--- a/model_tools.py
+++ b/model_tools.py
@@ -8,22 +8,28 @@ for defining tools and executing function calls.
Currently supports:
- Web tools (search, extract, crawl) from web_tools.py
+- Terminal tools (command execution with interactive sessions) from terminal_tool.py
+- Vision tools (image analysis) from vision_tools.py
+- Mixture of Agents tools (collaborative multi-model reasoning) from mixture_of_agents_tool.py
+- Image generation tools (text-to-image with upscaling) from image_generation_tool.py
Usage:
from model_tools import get_tool_definitions, handle_function_call
- # Get tool definitions for model API
+ # Get all available tool definitions for model API
tools = get_tool_definitions()
+ # Get specific toolsets
+ web_tools = get_tool_definitions(enabled_toolsets=['web_tools'])
+
# Handle function calls from model
- result = handle_function_call("web_search_tool", {"query": "Python", "limit": 3})
+ result = handle_function_call("web_search", {"query": "Python", "limit": 3})
"""
import json
import asyncio
from typing import Dict, Any, List
-# Import toolsets
from web_tools import web_search_tool, web_extract_tool, web_crawl_tool, check_firecrawl_api_key
from terminal_tool import terminal_tool, check_hecate_requirements, TERMINAL_TOOL_DESCRIPTION
from vision_tools import vision_analyze_tool, check_vision_requirements
@@ -75,11 +81,6 @@ def get_web_tool_definitions() -> List[Dict[str, Any]]:
"items": {"type": "string"},
"description": "List of URLs to extract content from (max 5 URLs per call)",
"maxItems": 5
- },
- "format": {
- "type": "string",
- "enum": ["markdown", "html"],
- "description": "Desired output format for extracted content (optional)"
}
},
"required": ["urls"]
@@ -101,12 +102,6 @@ def get_web_tool_definitions() -> List[Dict[str, Any]]:
"instructions": {
"type": "string",
"description": "Specific instructions for what to crawl/extract using AI intelligence (e.g., 'Find pricing information', 'Get documentation pages', 'Extract contact details')"
- },
- "depth": {
- "type": "string",
- "enum": ["basic", "advanced"],
- "description": "Depth of extraction - 'basic' for surface content, 'advanced' for deeper analysis (default: basic)",
- "default": "basic"
}
},
"required": ["url"]
@@ -185,12 +180,7 @@ def get_vision_tool_definitions() -> List[Dict[str, Any]]:
},
"question": {
"type": "string",
- "description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question. Examples: 'What text can you read?', 'What architectural style is this?', 'Describe the mood and emotions', 'What safety hazards do you see?'"
- },
- "model": {
- "type": "string",
- "description": "The vision model to use for analysis (optional, default: gemini-2.5-flash)",
- "default": "gemini-2.5-flash"
+ "description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question."
}
},
"required": ["image_url", "question"]
@@ -212,7 +202,7 @@ def get_moa_tool_definitions() -> List[Dict[str, Any]]:
"type": "function",
"function": {
"name": "mixture_of_agents",
- "description": "Process extremely difficult problems requiring intense reasoning using the Mixture-of-Agents methodology. This tool leverages multiple frontier language models to collaboratively solve complex tasks that single models struggle with. Uses a fixed 2-layer architecture: reference models (claude-opus-4, gemini-2.5-pro, o4-mini, deepseek-r1) generate diverse responses, then an aggregator synthesizes the best solution. Best for: complex mathematical proofs, advanced coding problems, multi-step analytical reasoning, precise and complex STEM problems, algorithm design, and problems requiring diverse domain expertise.",
+ "description": "Process extremely difficult problems requiring intense reasoning using a Mixture-of-Agents. This tool leverages multiple frontier language models to collaboratively solve complex tasks that single models struggle with. Uses a fixed 2-layer architecture: reference models generate diverse responses, then an aggregator synthesizes the best solution. Best for: complex mathematical proofs, advanced coding problems, multi-step analytical reasoning, precise and complex STEM problems, algorithm design, and problems requiring diverse domain expertise.",
"parameters": {
"type": "object",
"properties": {
@@ -240,13 +230,13 @@ def get_image_tool_definitions() -> List[Dict[str, Any]]:
"type": "function",
"function": {
"name": "image_generate",
- "description": "Generate high-quality images from text prompts using FAL.ai's FLUX.1 Krea model with automatic 2x upscaling. Creates detailed, artistic images that are automatically enhanced for superior quality. Returns a single upscaled image URL that can be displayed using ![](\"{URL}\")
tags.",
+ "description": "Generate high-quality images from text prompts using FLUX Krea model with automatic 2x upscaling. Creates detailed, artistic images that are automatically enhanced for superior quality. Returns a single upscaled image URL that can be displayed using tags.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
- "description": "The text prompt describing the desired image. Be detailed and descriptive for best results."
+ "description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"image_size": {
"type": "string",
@@ -291,10 +281,6 @@ def get_all_tool_names() -> List[str]:
if check_image_generation_requirements():
tool_names.extend(["image_generate"])
- # Future toolsets can be added here:
- # if check_file_tools():
- # tool_names.extend(["file_read", "file_write"])
-
return tool_names
@@ -316,7 +302,6 @@ def get_toolset_for_tool(tool_name: str) -> str:
"vision_analyze": "vision_tools",
"mixture_of_agents": "moa_tools",
"image_generate": "image_tools"
- # Future tools can be added here
}
return toolset_mapping.get(tool_name, "unknown")
@@ -400,8 +385,6 @@ def get_tool_definitions(
"vision_tools": get_vision_tool_definitions() if check_vision_requirements() else [],
"moa_tools": get_moa_tool_definitions() if check_moa_requirements() else [],
"image_tools": get_image_tool_definitions() if check_image_generation_requirements() else []
- # Future toolsets can be added here:
- # "file_tools": get_file_tool_definitions() if check_file_tools() else [],
}
# HIGHEST PRIORITY: enabled_tools (overrides everything)
@@ -487,16 +470,14 @@ def handle_web_function_call(function_name: str, function_args: Dict[str, Any])
urls = function_args.get("urls", [])
# Limit URLs to prevent abuse
urls = urls[:5] if isinstance(urls, list) else []
- format = function_args.get("format")
# Run async function in event loop
- return asyncio.run(web_extract_tool(urls, format))
+ return asyncio.run(web_extract_tool(urls, "markdown"))
elif function_name == "web_crawl":
url = function_args.get("url", "")
instructions = function_args.get("instructions")
- depth = function_args.get("depth", "basic")
# Run async function in event loop
- return asyncio.run(web_crawl_tool(url, instructions, depth))
+ return asyncio.run(web_crawl_tool(url, instructions, "basic"))
else:
return json.dumps({"error": f"Unknown web function: {function_name}"})
@@ -518,7 +499,7 @@ def handle_terminal_function_call(function_name: str, function_args: Dict[str, A
background = function_args.get("background", False)
idle_threshold = function_args.get("idle_threshold", 5.0)
timeout = function_args.get("timeout")
- # Session management is handled internally - don't pass session_id from model
+
return terminal_tool(command, input_keys, None, background, idle_threshold, timeout)
else:
@@ -539,13 +520,11 @@ def handle_vision_function_call(function_name: str, function_args: Dict[str, Any
if function_name == "vision_analyze":
image_url = function_args.get("image_url", "")
question = function_args.get("question", "")
- model = function_args.get("model", "gemini-2.5-flash")
-
- # Automatically prepend full description request to user's question
- full_prompt = f"Fully describe and explain everything about this image\n\n{question}"
+
+ full_prompt = f"Fully describe and explain everything about this image, then answer the following question:\n\n{question}"
# Run async function in event loop
- return asyncio.run(vision_analyze_tool(image_url, full_prompt, model))
+ return asyncio.run(vision_analyze_tool(image_url, full_prompt, "gemini-2.5-flash"))
else:
return json.dumps({"error": f"Unknown vision function: {function_name}"})
@@ -592,7 +571,6 @@ def handle_image_function_call(function_name: str, function_args: Dict[str, Any]
if not prompt:
return json.dumps({"success": False, "image": None})
- # Extract only the exposed parameters
image_size = function_args.get("image_size", "landscape_16_9")
# Use fixed internal defaults for all other parameters (not exposed to model)
@@ -662,12 +640,6 @@ def handle_function_call(function_name: str, function_args: Dict[str, Any]) -> s
elif function_name in ["image_generate"]:
return handle_image_function_call(function_name, function_args)
- # Future toolsets can be routed here:
- # elif function_name in ["file_read_tool", "file_write_tool"]:
- # return handle_file_function_call(function_name, function_args)
- # elif function_name in ["code_execute_tool", "code_analyze_tool"]:
- # return handle_code_function_call(function_name, function_args)
-
else:
error_msg = f"Unknown function: {function_name}"
print(f"❌ {error_msg}")
@@ -716,7 +688,6 @@ def get_available_toolsets() -> Dict[str, Dict[str, Any]]:
"description": "Generate high-quality images from text prompts using FAL.ai's FLUX.1 Krea model with automatic 2x upscaling for enhanced quality",
"requirements": ["FAL_KEY environment variable", "fal-client package"]
}
- # Future toolsets can be added here
}
return toolsets
diff --git a/test_run.sh b/test_run.sh
index 73be27b9f..54856eeb0 100644
--- a/test_run.sh
+++ b/test_run.sh
@@ -1,12 +1,29 @@
+#!/bin/bash
+
+# Check if a prompt argument was provided
+if [ $# -eq 0 ]; then
+ echo "Error: Please provide a prompt as an argument"
+ echo "Usage: $0 \"your prompt here\""
+ exit 1
+fi
+
+# Get the prompt from the first argument
+PROMPT="$1"
+
+# Set debug mode for web tools
export WEB_TOOLS_DEBUG=true
+# Run the agent with the provided prompt
python run_agent.py \
- --query "Tell me about this animal pictured: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQi1nkrYXY-ijQv5aCxkwooyg2roNFxj0ewJA&s" \
+ --query "$PROMPT" \
--max_turns 30 \
- --model claude-sonnet-4-20250514 \
- --base_url https://api.anthropic.com/v1/ \
+# --model claude-sonnet-4-20250514 \
+# --base_url https://api.anthropic.com/v1/ \
+ --model hermes-4-70B \
+ --base_url http://bore.pub:8292/v1 \
--api_key $ANTHROPIC_API_KEY \
- --enabled_toolsets=vision_tools
+ --save_trajectories
+ #--enabled_toolsets=vision_tools
#Possible Toolsets:
#web_tools