nodes/lum3on-comfyui_LLM_Polymath
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added settings for gemini

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benjamin
2025-03-30 18:19:42 +02:00
parent 5b8efe66ea
commit 9065d2df5e
1 changed files with 38 additions and 28 deletions
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polymath.py
+38 -28
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@@ -154,10 +154,8 @@ class OllamaAPI:
payload["temperature"] = options.get("temperature", 0.8)
payload["top_p"] = options.get("top_p", 0.95)
payload["top_k"] = options.get("top_k", 40)
payload["num_predict"] = options.get("max_tokens", -1)
payload["num_predict"] = options.get("max_output_tokens", -1)
payload["seed"] = options.get("seed", 42)
payload["repeat_penalty"] = options.get("repeat_penalty", 1.1)
payload["repeat_last_n"] = options.get("repeat_last_n", 64)
payload["keep_alive"] = options.get("ollama_keep_alive", 5)
response = requests.post(url, json=payload)
@@ -178,11 +176,7 @@ class PolymathSettings:
"temperature": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.05}),
"top_p": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.05}),
"top_k": ("INT", {"default": 40, "min": 0, "max": 100}),
"max_tokens": ("INT", {"default": 1024, "min": -1, "max": 8192}), # -1 might mean 'unlimited' or 'default'
"presence_penalty": ("FLOAT", {"default": 0.0, "min": -2.0, "max": 2.0, "step": 0.1}),
"frequency_penalty": ("FLOAT", {"default": 0.0, "min": -2.0, "max": 2.0, "step": 0.1}),
"repeat_penalty": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 2.0, "step": 0.05}),
"repeat_last_n": ("INT", {"default": 64, "min": 0, "max": 2048}),
"max_output_tokens": ("INT", {"default": 1024, "min": -1, "max": 65536}),
"response_format_json": ("BOOLEAN", {"default": False}),
"ollama_keep_alive": ("INT", {"default": 5, "min": 1, "max": 10}),
"request_timeout": ("INT", {"default": 120, "min": 0, "max": 600}),
@@ -224,7 +218,7 @@ class Polymath:
"multiline": True
}
),
"seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffff, "step": 1}),
"seed": ("INT", {"default": 42, "min": 0, "max": 0xfffffff, "step": 1}),
},
"optional": {
"llm_settings": ("LLM_SETTINGS",),
@@ -439,8 +433,8 @@ class Polymath:
"stream": False,
"temperature": settings.get("temperature", 0.8),
"top_p": settings.get("top_p", 0.95),
"max_tokens": settings.get("max_tokens", 1024),
"seed": settings.get("seed", 42),
"max_tokens": settings.get("max_output_tokens", 1024),
"seed": seed,
}
stop_seq = settings.get("stop_sequences", "")
if stop_seq:
@@ -575,29 +569,39 @@ class Polymath:
# Initialize the Gemini client (using your Gemini API key)
client = genai.Client(api_key=api_key_gemini)
system_instruction = None
if custom_instruction != "None":
try:
with open(os.path.join(custom_instructions_directory, f"{custom_instruction}.txt"), 'r') as f:
custom_instr = f.read()
instr_path = os.path.join(custom_instructions_directory, f"{custom_instruction}.txt")
with open(instr_path, 'r', encoding='utf-8') as f:
system_instruction = f.read()
except Exception as e:
print("Error reading custom instruction file:", e)
custom_instr = ""
else:
custom_instr = ""
print(f"Warning: Error reading custom instruction file {instr_path}: {e}")
prompt_text = (system_instruction + "\n\n" + prompt).strip() if system_instruction else prompt
# Combine custom instructions (if any) with the prompt.
prompt_text = (custom_instr + "\n" + prompt).strip() if custom_instr else prompt
# Build contents (history + current prompt + images) - (keep as is)
contents = []
# Add chat history if keep_context is True (convert to Gemini format)
if keep_context and Polymath.chat_history:
for msg in Polymath.chat_history:
role = msg.get("role")
content = msg.get("content")
gemini_role = "user" if role == "user" else "model"
# Handle content structure
if isinstance(content, list): # Already structured (e.g., multimodal parts)
contents.append({"role": gemini_role, "parts": content})
elif isinstance(content, str): # Simple text content
contents.append({"role": gemini_role, "parts": [{"text": content}]})
# Build the contents for the generation call.
# If a base64 image is provided, decode it into a PIL Image and pass a list [prompt_text, pil_image]
if b64:
images = []
for img_str in b64:
image_bytes = base64.b64decode(img_str)
pil_image = Image.open(BytesIO(image_bytes))
images.append(pil_image)
contents = [prompt_text] + images
contents = [prompt_text, images]
else:
contents = prompt_text
@@ -609,10 +613,14 @@ class Polymath:
config=types.GenerateContentConfig(
response_modalities=["Text", "Image"],
seed=seed,
max_output_tokens=settings.get("max_output_tokens", 1024),
temperature=settings.get("temperature", 0.8),
top_p=settings.get("top_p", 0.95),
top_k=settings.get("top_k", 40)
),
)
except Exception as e:
if "only supports text output" in str(e):
if "support" in str(e):
if console_log:
print("Gemini model does not support image output. Falling back to text-only.")
response = client.models.generate_content(
@@ -621,6 +629,10 @@ class Polymath:
config=types.GenerateContentConfig(
response_modalities=["Text"],
seed=seed,
max_output_tokens=settings.get("max_output_tokens", 1024),
temperature=settings.get("temperature", 0.8),
top_p=settings.get("top_p", 0.95),
top_k=settings.get("top_k", 40)
),
)
else:
@@ -652,10 +664,8 @@ class Polymath:
"temperature": settings.get("temperature", 0.8),
"top_p": settings.get("top_p", 0.95),
"top_k": settings.get("top_k", 40),
"num_predict": settings.get("max_tokens", -1), # Map max_tokens
"seed": settings.get("seed", 42),
"repeat_penalty": settings.get("repeat_penalty", 1.1),
"repeat_last_n": settings.get("repeat_last_n", 64),
"num_predict": settings.get("max_output_tokens", -1), # Map max_tokens
"seed": seed,
"keep_alive": settings.get("ollama_keep_alive", 5)
}
stop_seq = settings.get("stop_sequences", "")