ChatNetmind

• TODO: Make sure API reference link is correct.

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

• TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.

Overview

Integration details

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• TODO: Remove JS support link if not relevant, otherwise ensure link is correct.
• TODO: Make sure API reference links are correct.

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatNetmind	langchain-netmind	✅/❌	beta/❌	✅/❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅/❌	✅/❌	✅/❌	✅/❌	✅/❌	✅/❌	✅/❌	✅/❌	✅/❌	✅/❌

Setup

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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

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Head to (TODO: link) 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

Instantiation

Now we can instantiate our model object and generate chat completions:

• TODO: Update model instantiation with relevant params.

from langchain_netmind import ChatNetmind

llm = ChatNetmind(
    model="model-name",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)

Invocation

• TODO: Run cells so output can be seen.

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

print(ai_msg.content)

Chaining

We can chain our model with a prompt template like so:

• TODO: Run cells so output can be seen.

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.",
    }
)

API Reference:ChatPromptTemplate

TODO: Any functionality specific to this model provider

E.g. creating/using finetuned models via this provider. Delete if not relevant.

API reference

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

Related

• Chat model conceptual guide
• Chat model how-to guides