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Documentation Index

Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt

Use this file to discover all available pages before exploring further.

This will help you get started with Netmind chat models. For detailed documentation of all ChatNetmind features and configurations head to the API reference.

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Overview

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Integration details

ClassPackageLocalSerializableJS supportDownloadsVersion
ChatNetmindlangchain-netmind✅❌❌PyPI - DownloadsPyPI - Version

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Model features

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
✅✅✅❌❌❌✅✅✅✅

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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.

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Credentials

Head to 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: ")

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Installation

The LangChain Netmind integration lives in the langchain-netmind package: 
%pip install -qU langchain-netmind

[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: pip install -U pip
Note: you may need to restart the kernel to use updated packages.

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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...
)

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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.

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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': {}})

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API reference

For detailed documentation of all ChatNetmind features and configurations head to the API reference: