nodes/RCAKangle-ComfyUI_LLM_Embeder
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Add multi-provider base_url support

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Kangle
2026-01-30 23:12:07 +00:00
parent 2a74e194e9
commit 3103f14720
3 changed files with 177 additions and 8 deletions
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README.md
+28 -6
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@@ -5,6 +5,8 @@ Local LLM chat nodes for ComfyUI, with a clean handoff path to downstream prompt
## Features
- Multi-turn chat with Ollama `/api/chat`
- Optional Hugging Face Inference API support via LLM Config
- OpenAI-compatible chat support (OpenAI / DeepSeek / Qwen) via custom `base_url`
- Anthropic Claude support via custom `base_url`
- Session memory by `session_id` (in-memory only)
- Optional `system_prompt`
- Scrollable history in a modal viewer
@@ -39,13 +41,15 @@ Behavior:
Outputs a `LLM_CONFIG` struct that can be shared across Chat nodes.
Fields:
- `provider`: `ollama` or `huggingface`
- `base_url`: Ollama base URL
- `model_name`: Ollama model name or Hugging Face model id
- `provider`: `ollama`, `huggingface`, `openai`, `deepseek`, `qwen`, `claude`
- `base_url`: provider base URL (see sections below)
- `model_name`: model name or model id (provider specific)
- `temperature`
- `top_p`
- `max_new_tokens`
- `hf_token`: Hugging Face API token (recommended)
- `hf_api_url`: optional override for Hugging Face Inference API endpoint
- `api_key`: API key for OpenAI-compatible or Anthropic providers
### Chat History Viewer
Shows `readable_history` and provides an "Open History" modal for full scroll.
@@ -58,6 +62,23 @@ Notes:
- Many public models still require an HF token.
- Chat history is converted into a single prompt before the API call.
## OpenAI-Compatible API (OpenAI / DeepSeek / Qwen)
When `provider=openai|deepseek|qwen`, the Chat node will call:
`{base_url}/chat/completions`
Notes:
- Use an OpenAI-compatible base URL for your provider.
- Set `api_key` in LLM Config.
- `model_name` should be the provider's model id.
## Anthropic Claude API
When `provider=claude`, the Chat node will call:
`{base_url}/v1/messages`
Notes:
- Use an Anthropic-compatible base URL.
- Set `api_key` in LLM Config.
## Install
1) Place this folder under `ComfyUI/custom_nodes/`
2) Restart ComfyUI
@@ -68,9 +89,10 @@ Notes:
## Usage
1) Create `Chat (Ollama)` and set `base_url` / `model_name`
2) Set `action=send`, click Execute to chat
3) When satisfied, set `action=deliver_to_optimizer` and Execute
4) Use `assistant_response` output to feed your optimizer node
2) (Optional) Add `LLM Config` to select provider and set `api_key`
3) Set `action=send`, click Execute to chat
4) When satisfied, set `action=deliver_to_optimizer` and Execute
5) Use `assistant_response` output to feed your optimizer node
## Screenshot
![ComfyUI node graph example](LLM_embeder_nodes.png)
chat_optimize_nodes.py
+143 -2
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@@ -119,6 +119,121 @@ def _call_hf_inference(
return generated_text.strip()
def _build_openai_compatible_url(base_url: str) -> str:
base = (base_url or "").rstrip("/")
if base.endswith("/chat/completions"):
return base
return base + "/chat/completions"
def _call_openai_compatible_chat(
base_url: str,
model: str,
api_key: str,
messages: List[Dict[str, str]],
temperature: Optional[float] = None,
top_p: Optional[float] = None,
max_new_tokens: Optional[int] = None,
timeout: int = REQUEST_TIMEOUT,
) -> str:
url = _build_openai_compatible_url(base_url)
headers = _build_headers()
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
payload: Dict[str, object] = {
"model": model,
"messages": messages,
"stream": False,
}
if temperature is not None:
payload["temperature"] = temperature
if top_p is not None:
payload["top_p"] = top_p
if max_new_tokens is not None:
payload["max_tokens"] = int(max_new_tokens)
resp = requests.post(url, headers=headers, data=json.dumps(payload), timeout=timeout)
resp.raise_for_status()
data = resp.json()
if isinstance(data, dict) and data.get("error"):
raise RuntimeError(data.get("error"))
choices = data.get("choices") if isinstance(data, dict) else None
if isinstance(choices, list) and choices:
message = choices[0].get("message", {})
return message.get("content", "").strip()
return ""
def _split_system_messages(messages: List[Dict[str, str]]):
system_parts: List[str] = []
non_system: List[Dict[str, str]] = []
for msg in messages:
if msg.get("role") == "system":
system_parts.append(msg.get("content", ""))
else:
non_system.append(msg)
return ("\n\n".join([p for p in system_parts if p]), non_system)
def _call_anthropic_messages(
base_url: str,
model: str,
api_key: str,
messages: List[Dict[str, str]],
temperature: Optional[float] = None,
top_p: Optional[float] = None,
max_new_tokens: Optional[int] = None,
timeout: int = REQUEST_TIMEOUT,
) -> str:
base = (base_url or "").rstrip("/")
if base.endswith("/v1/messages"):
url = base
elif base.endswith("/v1"):
url = base + "/messages"
else:
url = base + "/v1/messages"
headers = _build_headers()
if api_key:
headers["x-api-key"] = api_key
headers["anthropic-version"] = "2023-06-01"
system_prompt, non_system = _split_system_messages(messages)
formatted_messages: List[Dict[str, object]] = []
for msg in non_system:
role = msg.get("role", "user")
content = msg.get("content", "")
formatted_messages.append(
{"role": role, "content": [{"type": "text", "text": content}]}
)
payload: Dict[str, object] = {
"model": model,
"messages": formatted_messages,
"max_tokens": int(max_new_tokens or 256),
}
if system_prompt:
payload["system"] = system_prompt
if temperature is not None:
payload["temperature"] = temperature
if top_p is not None:
payload["top_p"] = top_p
resp = requests.post(url, headers=headers, data=json.dumps(payload), timeout=timeout)
resp.raise_for_status()
data = resp.json()
if isinstance(data, dict) and data.get("error"):
raise RuntimeError(data.get("error"))
content = data.get("content") if isinstance(data, dict) else None
if isinstance(content, list) and content:
first = content[0]
if isinstance(first, dict):
return first.get("text", "").strip()
return ""
def _reset_history(session_id: str, system_prompt: str) -> List[Dict[str, str]]:
history: List[Dict[str, str]] = []
if system_prompt:
@@ -166,6 +281,7 @@ class ChatNode:
provider = "ollama"
hf_token = ""
hf_api_url = ""
api_key = ""
temperature = None
top_p = None
max_new_tokens = None
@@ -175,6 +291,7 @@ class ChatNode:
base_url = llm_config.get("base_url", base_url)
hf_token = llm_config.get("hf_token", "")
hf_api_url = llm_config.get("hf_api_url", "")
api_key = llm_config.get("api_key", "")
if "temperature" in llm_config:
temperature = llm_config["temperature"]
if "top_p" in llm_config:
@@ -231,6 +348,26 @@ class ChatNode:
max_new_tokens=max_new_tokens,
api_url=hf_api_url or None,
)
elif provider in ("openai", "deepseek", "qwen"):
response_text = _call_openai_compatible_chat(
base_url,
model_name,
api_key,
messages,
temperature=temperature,
top_p=top_p,
max_new_tokens=max_new_tokens,
)
elif provider == "claude":
response_text = _call_anthropic_messages(
base_url,
model_name,
api_key,
messages,
temperature=temperature,
top_p=top_p,
max_new_tokens=max_new_tokens,
)
else:
options = {}
if temperature is not None:
@@ -259,13 +396,15 @@ class LLMConfigNode:
def INPUT_TYPES(cls):
return {
"required": {
"provider": (["ollama", "huggingface"], {"default": "ollama"}),
"provider": (["ollama", "huggingface", "openai", "deepseek", "qwen", "claude"], {"default": "ollama"}),
"base_url": ("STRING", {"default": "http://127.0.0.1:11434"}),
"model_name": ("STRING", {"default": "llama3"}),
"temperature": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"top_p": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
"max_new_tokens": ("INT", {"default": 256, "min": 1, "max": 8192, "step": 1}),
"hf_token": ("STRING", {"default": ""}),
"hf_api_url": ("STRING", {"default": ""}),
"api_key": ("STRING", {"default": ""}),
}
}
@@ -274,7 +413,7 @@ class LLMConfigNode:
FUNCTION = "config"
CATEGORY = "ChatOptimize"
def config(self, provider, base_url, model_name, temperature, top_p, max_new_tokens, hf_token):
def config(self, provider, base_url, model_name, temperature, top_p, max_new_tokens, hf_token, hf_api_url, api_key):
return (
{
"provider": provider,
@@ -284,6 +423,8 @@ class LLMConfigNode:
"top_p": top_p,
"max_new_tokens": max_new_tokens,
"hf_token": hf_token,
"hf_api_url": hf_api_url,
"api_key": api_key,
},
)
web/chat_optimize.js
+6
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@@ -190,6 +190,12 @@ function getLLMConfig(node) {
const tokenW = sourceNode.widgets?.find(w => w.name === "hf_token");
if (tokenW) config.hf_token = tokenW.value;
const hfApiUrlW = sourceNode.widgets?.find(w => w.name === "hf_api_url");
if (hfApiUrlW) config.hf_api_url = hfApiUrlW.value;
const apiKeyW = sourceNode.widgets?.find(w => w.name === "api_key");
if (apiKeyW) config.api_key = apiKeyW.value;
return config;
}