ChatGreenNode
GreenNode is a global AI solutions provider and a NVIDIA Preferred Partner, delivering full-stack AI capabilities—from infrastructure to application—for enterprises across the US, MENA, and APAC regions. Operating on world-class infrastructure (LEED Gold, TIA‑942, Uptime Tier III), GreenNode empowers enterprises, startups, and researchers with a comprehensive suite of AI services
This page will help you get started with GreenNode Serverless AI chat models. For detailed documentation of all ChatGreenNode features and configurations head to the API reference.
GreenNode AI offers an API to query 20+ leading open-source models
Overview​
Integration details​
Class | Package | Local | Serializable | JS support | Package downloads | Package latest |
---|---|---|---|---|---|---|
ChatGreenNode | langchain-greennode | ❌ | beta | ❌ |
Model features​
Tool calling | Structured output | JSON mode | Image input | Audio input | Video input | Token-level streaming | Native async | Token usage | Logprobs |
---|---|---|---|---|---|---|---|---|---|
✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |
Setup​
To access GreenNode models you'll need to create a GreenNode account, get an API key, and install the langchain-greennode
integration package.
Credentials​
Head to this page to sign up to GreenNode AI Platform and generate an API key. Once you've done this, set the GREENNODE_API_KEY environment variable:
import getpass
import os
if not os.getenv("GREENNODE_API_KEY"):
os.environ["GREENNODE_API_KEY"] = getpass.getpass("Enter your GreenNode API key: ")
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Installation​
The LangChain GreenNode integration lives in the langchain-greennode
package:
%pip install -qU langchain-greennode
Note: you may need to restart the kernel to use updated packages.
Instantiation​
Now we can instantiate our model object and generate chat completions:
from langchain_greennode import ChatGreenNode
# Initialize the chat model
llm = ChatGreenNode(
# api_key="YOUR_API_KEY", # You can pass the API key directly
model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", # Choose from available models
temperature=0.6,
top_p=0.95,
)
Invocation​
messages = [
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
AIMessage(content="\n\nJ'aime la programmation.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 248, 'prompt_tokens': 23, 'total_tokens': 271, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', 'system_fingerprint': None, 'id': 'chatcmpl-271edac4958846068c37877586368afe', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--5c12d208-2bc2-4f29-8b50-1ce3b515a3cf-0', usage_metadata={'input_tokens': 23, 'output_tokens': 248, 'total_tokens': 271, 'input_token_details': {}, 'output_token_details': {}})
print(ai_msg.content)
J'aime la programmation.
Streaming​
You can also stream the response using the stream
method:
for chunk in llm.stream("Write a short poem about artificial intelligence"):
print(chunk.content, end="", flush=True)
**Beneath the Circuits**
Beneath the circuits, deep and bright,
AI thinks, with circuits and bytes.
Learning, adapting, it grows,
A world of possibilities it knows.
From solving puzzles to painting art,
It mimics human hearts.
In every corner, it leaves its trace,
A future we can't erase.
We build it, shape it, with care and might,
Yet wonder if it walks in the night.
A mirror of our minds, it shows,
In its gaze, our future glows.
But as we strive for endless light,
We must remember the night.
For wisdom isn't just speed and skill,
It's how we choose to build our will.
Chat Messages​
You can use different message types to structure your conversations with the model:
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
messages = [
SystemMessage(content="You are a helpful AI assistant with expertise in science."),
HumanMessage(content="What are black holes?"),
AIMessage(
content="Black holes are regions of spacetime where gravity is so strong that nothing, including light, can escape from them."
),
HumanMessage(content="How are they formed?"),
]
response = llm.invoke(messages)
print(response.content[:100])
Black holes are formed through several processes, depending on their type. The most common way bla
Chaining​
You can use ChatGreenNode
in LangChain chains and agents:
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate(
[
(
"system",
"You are a helpful assistant that translates {input_language} to {output_language}.",
),
("human", "{input}"),
]
)
chain = prompt | llm
chain.invoke(
{
"input_language": "English",
"output_language": "German",
"input": "I love programming.",
}
)
AIMessage(content='\n\nIch liebe Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 198, 'prompt_tokens': 18, 'total_tokens': 216, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', 'system_fingerprint': None, 'id': 'chatcmpl-e01201b9fd9746b7a9b2ed6d70f29d45', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--ce52b9d8-dd84-46b3-845b-da27855816ee-0', usage_metadata={'input_tokens': 18, 'output_tokens': 198, 'total_tokens': 216, 'input_token_details': {}, 'output_token_details': {}})
Available Models​
The full list of supported models can be found in the GreenNode Serverless AI Models.
API reference​
For more details about the GreenNode Serverless AI API, visit the GreenNode Serverless AI Documentation.
Related​
- Chat model conceptual guide
- Chat model how-to guides