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

Instantiation

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

from langchain_netmind import ChatNetmind

llm = ChatNetmind(
    model="model-name",
    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

print(ai_msg.content)

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.",
    }
)

API Reference:ChatPromptTemplate

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