GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cloud/notebooks/python_sdk/deployments/foundation_models/Use watsonx, and LangChain to make a series of calls to a language model.ipynb
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Kernel: rt241_v2

Use watsonx, and LangChain to make a series of calls to a language model

Disclaimers

Use only Projects and Spaces that are available in watsonx context.

Notebook content

This notebook contains the steps and code to demonstrate Sequential Chain using langchain integration with watsonx models.

Some familiarity with Python is helpful. This notebook uses Python 3.11.

Learning goal

The goal of this notebook is to demonstrate how to chain google/flan-ul2 and google/flan-t5-xxl models to generate a sequence of creating a random question on a given topic and an answer to that question and also to make the user friends with LangChain framework, using simple chain (LLMChain) and the extended chain (SequentialChain) with the WatsonxLLM.

This notebook contains the following parts:

Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

Create a watsonx.ai Runtime Service instance (a free plan is offered and information about how to create the instance can be found here).

Install and import the `datasets` and dependecies

In [ ]:

!pip install -U "langchain>=0.3,<0.4" | tail -n 1
!pip install -U "langchain_ibm>=0.3,<0.4" | tail -n 1

Defining the watsonx.ai credentials

This cell defines the watsonx.ai credentials required to work with watsonx Foundation Model inferencing.

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

In [2]:

import getpass
from ibm_watsonx_ai import Credentials

credentials = Credentials(
    url="https://us-south.ml.cloud.ibm.com",
    api_key=getpass.getpass("Please enter your watsonx.ai api key (hit enter): "),
)

Defining the project id

The Foundation Model requires project id that provides the context for the call. We will obtain the id from the project in which this notebook runs. Otherwise, please provide the project id.

In [3]:

import os

try:
    project_id = os.environ["PROJECT_ID"]
except KeyError:
    project_id = input("Please enter your project_id (hit enter): ")

Create an instance of APIClient with authentication details.

In [5]:

from ibm_watsonx_ai import APIClient

api_client = APIClient(credentials=credentials, project_id=project_id)

Foundation Models on `watsonx.ai`

List available models

All avaliable models are presented under TextModels class.

In [8]:

api_client.foundation_models.TextModels.show()

Out[8]:

{'MT0_XXL': 'bigscience/mt0-xxl', 'CODELLAMA_34B_INSTRUCT_HF': 'codellama/codellama-34b-instruct-hf', 'JAIS_13B_CHAT': 'core42/jais-13b-chat', 'ELYZA_JAPANESE_LLAMA_2_7B_INSTRUCT': 'elyza/elyza-japanese-llama-2-7b-instruct', 'FLAN_T5_XL': 'google/flan-t5-xl', 'FLAN_T5_XXL': 'google/flan-t5-xxl', 'FLAN_UL2': 'google/flan-ul2', 'GRANITE_13B_CHAT_V2': 'ibm/granite-13b-chat-v2', 'GRANITE_13B_INSTRUCT_V2': 'ibm/granite-13b-instruct-v2', 'GRANITE_20B_CODE_INSTRUCT': 'ibm/granite-20b-code-instruct', 'GRANITE_20B_MULTILINGUAL': 'ibm/granite-20b-multilingual', 'GRANITE_3_2B_INSTRUCT': 'ibm/granite-3-2b-instruct', 'GRANITE_3_8B_INSTRUCT': 'ibm/granite-3-8b-instruct', 'GRANITE_34B_CODE_INSTRUCT': 'ibm/granite-34b-code-instruct', 'GRANITE_3B_CODE_INSTRUCT': 'ibm/granite-3b-code-instruct', 'GRANITE_7B_LAB': 'ibm/granite-7b-lab', 'GRANITE_8B_CODE_INSTRUCT': 'ibm/granite-8b-code-instruct', 'GRANITE_8B_JAPANESE': 'ibm/granite-8b-japanese', 'GRANITE_GUARDIAN_3_2B': 'ibm/granite-guardian-3-2b', 'GRANITE_GUARDIAN_3_8B': 'ibm/granite-guardian-3-8b', 'LLAMA_2_13B_CHAT': 'meta-llama/llama-2-13b-chat', 'LLAMA_3_1_70B_INSTRUCT': 'meta-llama/llama-3-1-70b-instruct', 'LLAMA_3_1_8B_INSTRUCT': 'meta-llama/llama-3-1-8b-instruct', 'LLAMA_3_2_11B_VISION_INSTRUCT': 'meta-llama/llama-3-2-11b-vision-instruct', 'LLAMA_3_2_1B_INSTRUCT': 'meta-llama/llama-3-2-1b-instruct', 'LLAMA_3_2_3B_INSTRUCT': 'meta-llama/llama-3-2-3b-instruct', 'LLAMA_3_2_90B_VISION_INSTRUCT': 'meta-llama/llama-3-2-90b-vision-instruct', 'LLAMA_3_405B_INSTRUCT': 'meta-llama/llama-3-405b-instruct', 'LLAMA_3_70B_INSTRUCT': 'meta-llama/llama-3-70b-instruct', 'LLAMA_3_8B_INSTRUCT': 'meta-llama/llama-3-8b-instruct', 'LLAMA_GUARD_3_11B_VISION': 'meta-llama/llama-guard-3-11b-vision', 'MISTRAL_LARGE': 'mistralai/mistral-large', 'MIXTRAL_8X7B_INSTRUCT_V01': 'mistralai/mixtral-8x7b-instruct-v01', 'PIXTRAL_12B': 'mistralai/pixtral-12b', 'LLAMA2_13B_DPO_V7': 'mncai/llama2-13b-dpo-v7', 'ALLAM_1_13B_INSTRUCT': 'sdaia/allam-1-13b-instruct'}

You need to specify model_id's that will be used for inferencing:

In [9]:

model_id_1 = "google/flan-ul2"
model_id_2 = "google/flan-t5-xxl"

Defining the model parameters

You might need to adjust model parameters for different models or tasks, to do so please refer to documentation under GenTextParamsMetaNames class.

Action: If any complications please refer to the documentation.

In [10]:

from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
from ibm_watsonx_ai.foundation_models.utils.enums import DecodingMethods

parameters = {
    GenParams.DECODING_METHOD: DecodingMethods.SAMPLE.value,
    GenParams.MAX_NEW_TOKENS: 100,
    GenParams.MIN_NEW_TOKENS: 1,
    GenParams.TEMPERATURE: 0.5,
    GenParams.TOP_K: 50,
    GenParams.TOP_P: 1
}

LangChain integration

WatsonxLLM is a wrapper around watsonx.ai models that provide chain integration around the models.

Action: For more details about CustomLLM check the LangChain documentation

Initialize the `WatsonxLLM` class.

In [11]:

from langchain_ibm import WatsonxLLM

flan_ul2_llm = WatsonxLLM(
    model_id=model_id_1,
    url=credentials["url"],
    apikey=credentials["apikey"],
    project_id=project_id,
    params=parameters
    )
flan_t5_llm = WatsonxLLM(
    model_id=model_id_2,
    url=credentials["url"],
    apikey=credentials["apikey"],
    project_id=project_id
    )

You can print all set data about the WatsonxLLM object using the dict() method.

In [12]:

flan_ul2_llm.dict()

Out[12]:

{'model_id': 'google/flan-ul2',
 'deployment_id': None,
 'params': {'decoding_method': 'sample',
  'max_new_tokens': 100,
  'min_new_tokens': 1,
  'temperature': 0.5,
  'top_k': 50,
  'top_p': 1},
 'project_id': 'd2436d2e-5814-4370-bd06-1754670e7d46',
 'space_id': None,
 '_type': 'IBM watsonx.ai'}

Sequential Chain experiment

The simplest type of sequential chain is called a SequentialChain, in which each step has a single input and output and the output of one step serves as the input for the following step.

The experiment will consist in generating a random question about any topic and answer the following question.

An object called PromptTemplate assists in generating prompts using a combination of user input, additional non-static data, and a fixed template string.

In our case we would like to create two PromptTemplate objects which will be responsible for creating a random question and answering it.

In [13]:

from langchain_core.prompts import PromptTemplate

prompt_1 = PromptTemplate(
    input_variables=["topic"], 
    template="Generate a random question about {topic}: Question: "
)
prompt_2 = PromptTemplate(
    input_variables=["question"],
    template="Answer the following question: {question}",
)

We would like to add functionality around language models using LLMChain chain.

prompt_to_flan_ul2 chain formats the prompt template whose task is to generate random question, passes the formatted string to LLM and returns the LLM output.

In [ ]:

from langchain.chains import LLMChain

prompt_to_flan_ul2 = LLMChain(llm=flan_ul2_llm, prompt=prompt_1, output_key='question')

flan_to_t5 chain formats the prompt template whose task is to answer the question we got from prompt_to_flan_ul2 chain, passes the formatted string to LLM and returns the LLM output.

In [15]:

flan_to_t5 = LLMChain(llm=flan_t5_llm, prompt=prompt_2, output_key='answer')

This is the overall chain where we run prompt_to_flan_ul2 and flan_to_t5 chains in sequence.

In [16]:

from langchain.chains import SequentialChain

qa = SequentialChain(chains=[prompt_to_flan_ul2, flan_to_t5], input_variables=["topic"], output_variables=['question', 'answer'], verbose=True)

Generate random question and answer to topic.

In [17]:

qa.invoke({"topic": "life"})

Out[17]:

> Entering new SequentialChain chain...

> Finished chain.

{'topic': 'life',
 'question': 'What is the name of the largest zoo in the world?',
 'answer': 'zoo sao paulo brazil'}

Python function

Let's wrap the chain code within python function that can be used as a content of inference endpoint.

Function implementation.

Let's wrap the above chain code into function.

In [18]:

ai_params = {
    "url": credentials["url"],
    "apikey": credentials["apikey"],
    "project_id": project_id,
    "generation_parameters": parameters
}

In [19]:

def chain_text_generator(params=ai_params):
    
    from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes
    from langchain import PromptTemplate
    from langchain.chains import LLMChain, SequentialChain
    from langchain_ibm import WatsonxLLM

    url = params["url"]
    apikey = params["apikey"]
    project_id = params['project_id']
    parameters = params['generation_parameters']
    flan_ul2_llm = WatsonxLLM(model_id=ModelTypes.FLAN_UL2.value, url=url, apikey=apikey, project_id=project_id, params=parameters)
    flan_t5_llm = WatsonxLLM(model_id=ModelTypes.FLAN_T5_XXL.value, url=url, apikey=apikey, project_id=project_id)
    prompt_1 = PromptTemplate(input_variables=["topic"], template="Generate a random question about {topic}: Question: ")
    prompt_2 = PromptTemplate(input_variables=["question"], template="Answer the following question: {question}")
    prompt_to_flan_ul2 = LLMChain(llm=flan_ul2_llm, prompt=prompt_1, output_key='question')
    flan_to_t5 = LLMChain(llm=flan_t5_llm, prompt=prompt_2, output_key='answer')
    chain = SequentialChain(chains=[prompt_to_flan_ul2, flan_to_t5], input_variables=["topic"], output_variables=['question', 'answer'])

    def score(payload):
        """Generates question based on provided topic and returns the answer."""

        answer = chain.invoke({"topic": payload["input_data"][0]['values'][0][0]})
        return {'predictions': [{'fields': ['topic', 'question', 'answer'], 'values': [answer['topic'], answer['question'], answer['answer']]}]}

    return score

Test the function

It is good practice to validate the code locally first.

In [20]:

sample_payload = {
    "input_data": [
        {
            "fields": ["topic"],
            "values": [["life"]]
        }
    ]
}

inference = chain_text_generator()
inference(sample_payload)

Out[20]:

{'predictions': [{'fields': ['topic', 'question', 'answer'],
   'values': ['life',
    'What is the term for the process of a living thing gaining nutrients from dead organisms?',
    'decomposition']}]}

Custom inference endpoint

Create the online deployment of python function.

Custom software specification

Create new software specification based on default Python 3.11 environment extended by langchain package.

In [19]:

config_yml =\
"""
name: python311
channels:
  - empty
dependencies:
  - pip:
    - langchain_ibm==0.3.3
    - langchain==0.3.7
prefix: /opt/anaconda3/envs/python311
"""

with open("config.yaml", "w", encoding="utf-8") as f:
    f.write(config_yml)

In [20]:

space_id = 'PASTE YOUR SPACE ID HERE'

Now you need to store new package extension with APIClient.

In [21]:

from ibm_watsonx_ai import APIClient

client = APIClient(credentials)
client.set.default_space(space_id)
base_sw_spec_id = client.software_specifications.get_id_by_name("runtime-24.1-py3.11")
meta_prop_pkg_extn = {
    client.package_extensions.ConfigurationMetaNames.NAME: "langchain watsonx.ai env",
    client.package_extensions.ConfigurationMetaNames.DESCRIPTION: "Environment with langchain",
    client.package_extensions.ConfigurationMetaNames.TYPE: "conda_yml"
}

pkg_extn_details = client.package_extensions.store(meta_props=meta_prop_pkg_extn, file_path="config.yaml")
pkg_extn_id = client.package_extensions.get_id(pkg_extn_details)
pkg_extn_url = client.package_extensions.get_href(pkg_extn_details)

Out[21]:

Creating package extensions
SUCCESS

Create new software specification and add created package extension to it.

In [22]:

meta_prop_sw_spec = {
    client.software_specifications.ConfigurationMetaNames.NAME: "langchain watsonx.ai custom software specification",
    client.software_specifications.ConfigurationMetaNames.DESCRIPTION: "Software specification for statsmodels",
    client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION: {"guid": base_sw_spec_id}
}

sw_spec_details = client.software_specifications.store(meta_props=meta_prop_sw_spec)
sw_spec_id = client.software_specifications.get_id(sw_spec_details)
client.software_specifications.add_package_extension(sw_spec_id, pkg_extn_id)

Out[22]:

SUCCESS

'SUCCESS'

Store the function

In [23]:

meta_props = {
    client.repository.FunctionMetaNames.NAME: "SequenceChain LLM function",
    client.repository.FunctionMetaNames.SOFTWARE_SPEC_ID: sw_spec_id
}

function_details = client.repository.store_function(meta_props=meta_props, function=chain_text_generator)
function_id = client.repository.get_function_id(function_details)

Create online deployment

In [24]:

metadata = {
    client.deployments.ConfigurationMetaNames.NAME: "Deployment of LLMs chain function",
    client.deployments.ConfigurationMetaNames.ONLINE: {}
}

function_deployment = client.deployments.create(function_id, meta_props=metadata)

Out[24]:

######################################################################################

Synchronous deployment creation for id: 'd71113c7-bcc7-4adf-ba23-fe8ea3c120c6' started

######################################################################################


initializing
Note: online_url and serving_urls are deprecated and will be removed in a future release. Use inference instead.
...........
ready


-----------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_id='8d7ec29e-51d5-40ae-813b-3d37102ded54'
-----------------------------------------------------------------------------------------------

Scoring

Generate text using custom inference endpoint.

In [25]:

deployment_id = client.deployments.get_id(function_deployment)
client.deployments.score(deployment_id, sample_payload)

Out[25]:

{'predictions': [{'fields': ['topic', 'question', 'answer'],
   'values': ['life',
    'What is the name of the smallest unit of life?',
    'cell']}]}

Summary and next steps

You successfully completed this notebook!

You learned how to use Sequential Chain using custom llm WastonxLLM.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors:

Lukasz Cmielowski, PhD, Senior Technical Staff Member at watsonx.ai.

Mateusz Szewczyk, Software Engineer at watsonx.ai.

Use watsonx, and LangChain to make a series of calls to a language model

Disclaimers

Notebook content

Learning goal

Contents

Set up the environment

Install and import the `datasets` and dependecies

Defining the watsonx.ai credentials

Defining the project id

Foundation Models on `watsonx.ai`

List available models

Defining the model parameters

LangChain integration

Initialize the `WatsonxLLM` class.

Sequential Chain experiment

Python function

Function implementation.

Test the function

Custom inference endpoint

Custom software specification

Store the function

Create online deployment

Scoring

Summary and next steps

Authors:

Product

Resources

Company

Use watsonx, and LangChain to make a series of calls to a language model

Disclaimers

Notebook content

Learning goal

Contents

Set up the environment

Install and import the datasets and dependecies

Defining the watsonx.ai credentials

Defining the project id

Foundation Models on watsonx.ai

List available models

Defining the model parameters

LangChain integration

Initialize the WatsonxLLM class.

Sequential Chain experiment

Python function

Function implementation.

Test the function

Custom inference endpoint

Custom software specification

Store the function

Create online deployment

Scoring

Summary and next steps

Authors:

Install and import the `datasets` and dependecies

Foundation Models on `watsonx.ai`

Initialize the `WatsonxLLM` class.