ChatWatsonx

ChatWatsonx is a wrapper for IBM watsonx.ai foundation models.

The aim of these examples is to show how to communicate with watsonx.ai models using LangChain LLMs API.

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatWatsonx	❌	❌	❌

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 IBM watsonx.ai models you'll need to create an IBM watsonx.ai account, get an API key, and install the langchain-ibm integration package.

Credentials

The cell below defines the credentials required to work with watsonx Foundation Model inferencing.

Action: Provide the IBM Cloud user API key. For details, see Managing user API keys.

import os
from getpass import getpass

watsonx_api_key = getpass()
os.environ["WATSONX_APIKEY"] = watsonx_api_key

Additionally you are able to pass additional secrets as an environment variable.

import os

os.environ["WATSONX_URL"] = "your service instance url"
os.environ["WATSONX_TOKEN"] = "your token for accessing the CPD cluster"
os.environ["WATSONX_PASSWORD"] = "your password for accessing the CPD cluster"
os.environ["WATSONX_USERNAME"] = "your username for accessing the CPD cluster"
os.environ["WATSONX_INSTANCE_ID"] = "your instance_id for accessing the CPD cluster"

Installation

The LangChain IBM integration lives in the langchain-ibm package:

!pip install -qU langchain-ibm

Instantiation

You might need to adjust model parameters for different models or tasks. For details, refer to Available MetaNames.

parameters = {
    "decoding_method": "sample",
    "max_new_tokens": 100,
    "min_new_tokens": 1,
    "stop_sequences": ["."],
}

Initialize the WatsonxLLM class with the previously set parameters.

Note:

To provide context for the API call, you must pass the project_id or space_id. To get your project or space ID, open your project or space, go to the Manage tab, and click General. For more information see: Project documentation or Deployment space documentation.
Depending on the region of your provisioned service instance, use one of the urls listed in watsonx.ai API Authentication.

In this example, we’ll use the project_id and Dallas URL.

You need to specify the model_id that will be used for inferencing. You can find the list of all the available models in Supported foundation models.

from langchain_ibm import ChatWatsonx

chat = ChatWatsonx(
    model_id="ibm/granite-13b-chat-v2",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
    params=parameters,
)

Alternatively, you can use Cloud Pak for Data credentials. For details, see watsonx.ai software setup.

chat = ChatWatsonx(
    model_id="ibm/granite-13b-chat-v2",
    url="PASTE YOUR URL HERE",
    username="PASTE YOUR USERNAME HERE",
    password="PASTE YOUR PASSWORD HERE",
    instance_id="openshift",
    version="4.8",
    project_id="PASTE YOUR PROJECT_ID HERE",
    params=parameters,
)

Instead of model_id, you can also pass the deployment_id of the previously tuned model. The entire model tuning workflow is described in Working with TuneExperiment and PromptTuner.

chat = ChatWatsonx(
    deployment_id="PASTE YOUR DEPLOYMENT_ID HERE",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
    params=parameters,
)

Invocation

To obtain completions, you can call the model directly using a string prompt.

# Invocation

messages = [
    ("system", "You are a helpful assistant that translates English to French."),
    (
        "human",
        "I love you for listening to Rock.",
    ),
]

chat.invoke(messages)

AIMessage(content="Je t'aime pour écouter la Rock.", response_metadata={'token_usage': {'generated_token_count': 12, 'input_token_count': 28}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-05b305ce-5401-4a10-b557-41a4b15c7f6f-0')

# Invocation multiple chat
from langchain_core.messages import (
    HumanMessage,
    SystemMessage,
)

system_message = SystemMessage(
    content="You are a helpful assistant which telling short-info about provided topic."
)
human_message = HumanMessage(content="horse")

chat.invoke([system_message, human_message])

API Reference:HumanMessage | SystemMessage

AIMessage(content='Sure, I can help you with that! Horses are large, powerful mammals that belong to the family Equidae.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-391776ff-3b38-4768-91e8-ff64177149e5-0')

Chaining

Create ChatPromptTemplate objects which will be responsible for creating a random question.

from langchain_core.prompts import ChatPromptTemplate

system = (
    "You are a helpful assistant that translates {input_language} to {output_language}."
)
human = "{input}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

API Reference:ChatPromptTemplate

Provide a inputs and run the chain.

chain = prompt | chat
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love Python",
    }
)

AIMessage(content='Ich liebe Python.', response_metadata={'token_usage': {'generated_token_count': 5, 'input_token_count': 23}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-1b1ccf5d-0e33-46f2-a087-e2a136ba1fb7-0')

Streaming the Model output

You can stream the model output.

system_message = SystemMessage(
    content="You are a helpful assistant which telling short-info about provided topic."
)
human_message = HumanMessage(content="moon")

for chunk in chat.stream([system_message, human_message]):
    print(chunk.content, end="")

The moon is a natural satellite of the Earth, and it has been a source of fascination for humans for centuries.

Batch the Model output

You can batch the model output.

message_1 = [
    SystemMessage(
        content="You are a helpful assistant which telling short-info about provided topic."
    ),
    HumanMessage(content="cat"),
]
message_2 = [
    SystemMessage(
        content="You are a helpful assistant which telling short-info about provided topic."
    ),
    HumanMessage(content="dog"),
]

chat.batch([message_1, message_2])

[AIMessage(content='Cats are domestic animals that belong to the Felidae family.', response_metadata={'token_usage': {'generated_token_count': 13, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-71a8bd7a-a1aa-497b-9bdd-a4d6fe1d471a-0'),
 AIMessage(content='Dogs are domesticated mammals of the family Canidae, characterized by their adaptability to various environments and social structures.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-22b7a0cb-e44a-4b68-9921-872f82dcd82b-0')]

Tool calling

ChatWatsonx.bind_tools()

Please note that ChatWatsonx.bind_tools is on beta state, so right now we only support mistralai/mixtral-8x7b-instruct-v01 model.

You should also redefine max_new_tokens parameter to get the entire model response. By default max_new_tokens is set to 20.

from langchain_ibm import ChatWatsonx

parameters = {"max_new_tokens": 200}

chat = ChatWatsonx(
    model_id="mistralai/mixtral-8x7b-instruct-v01",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
    params=parameters,
)

from langchain_core.pydantic_v1 import BaseModel, Field

class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

llm_with_tools = chat.bind_tools([GetWeather])

ai_msg = llm_with_tools.invoke(
    "Which city is hotter today: LA or NY?",
)
ai_msg

AIMessage(content='', additional_kwargs={'function_call': {'type': 'function'}, 'tool_calls': [{'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{"location": "Los Angeles"}'}, 'id': None}, {'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{"location": "New York"}'}, 'id': None}]}, response_metadata={'token_usage': {'generated_token_count': 99, 'input_token_count': 320}, 'model_name': 'mistralai/mixtral-8x7b-instruct-v01', 'system_fingerprint': '', 'finish_reason': 'eos_token'}, id='run-38627104-f2ac-4edb-8390-d5425fb65979-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None}, {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}])

AIMessage.tool_calls

Notice that the AIMessage has a tool_calls attribute. This contains in a standardized ToolCall format that is model-provider agnostic.

ai_msg.tool_calls

[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None},
 {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}]

Chat model conceptual guide
Chat model how-to guides

ChatWatsonx

Overview

Integration details

Model features

Setup

Credentials

Installation

Instantiation

Invocation

Chaining

Streaming the Model output

Batch the Model output

Tool calling

ChatWatsonx.bind_tools()

AIMessage.tool_calls

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ChatWatsonx

Overview​

Integration details​

Model features​

Setup​

Credentials​

Installation​

Instantiation​

Invocation​

Chaining​

Streaming the Model output​

Batch the Model output​

Tool calling​

ChatWatsonx.bind_tools()​

AIMessage.tool_calls​

Related​

Was this page helpful?

You can also leave detailed feedback on GitHub.

Overview

Integration details

Model features

Setup

Credentials

Installation

Instantiation

Invocation

Chaining

Streaming the Model output

Batch the Model output

Tool calling

ChatWatsonx.bind_tools()

AIMessage.tool_calls

Related