ChatNetmind
This will help you getting started with Netmind chat models. For detailed documentation of all ChatNetmind features and configurations head to the API reference.
- See https://www.netmind.ai/ for an example.
Overview
Integration details
| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |
|---|---|---|---|---|---|---|
| ChatNetmind | langchain-netmind | ✅ | ❌ | ❌ |  |  |
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 Netmind models you'll need to create a/an Netmind account, get an API key, and install the langchain-netmind integration package.
Credentials
Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:
import getpass
import os
if not os.getenv("NETMIND_API_KEY"):
os.environ["NETMIND_API_KEY"] = getpass.getpass("Enter your Netmind 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["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Installation
The LangChain Netmind integration lives in the langchain-netmind package:
%pip install -qU langchain-netmind
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m24.0[0m[39;49m -> [0m[32;49m25.0.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
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_netmind import ChatNetmind
llm = ChatNetmind(
model="deepseek-ai/DeepSeek-V3",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# other params...
)
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="J'adore programmer.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 31, 'total_tokens': 44, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ca6c2010-844d-4bf6-baac-6e248491b000-0', usage_metadata={'input_tokens': 31, 'output_tokens': 13, 'total_tokens': 44, 'input_token_details': {}, 'output_token_details': {}})
print(ai_msg.content)
J'adore programmer.
Chaining
We can chain our model with a prompt template like so:
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='Ich liebe es zu programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 26, 'total_tokens': 40, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d63adcc6-53ba-4caa-9a79-78d640b39274-0', usage_metadata={'input_tokens': 26, 'output_tokens': 14, 'total_tokens': 40, 'input_token_details': {}, 'output_token_details': {}})
API reference
For detailed documentation of all ChatNetmind features and configurations head to the API reference:
Related
- Chat model conceptual guide
- Chat model how-to guides
