LangChain
---------

IBM integration with LangChain is available under `IBM and LangChain integration documentation <https://python.langchain.com/docs/integrations/providers/ibm>`_.


.. autoclass:: langchain_ibm.WatsonxLLM
    :members:
    :exclude-members: Config, is_lc_serializable, lc_secrets, validate_environment


Example of using **SimpleSequentialChain**:

.. code-block:: python

    from langchain_ibm import WatsonxLLM
    from ibm_watsonx_ai import Credentials
    from ibm_watsonx_ai.foundation_models import ModelInference
    from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes, DecodingMethods
    from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams

    from langchain_core.prompts import PromptTemplate
    from langchain.chains import LLMChain, SimpleSequentialChain

    params = {
        GenParams.MAX_NEW_TOKENS: 100,
        GenParams.MIN_NEW_TOKENS: 1,
        GenParams.DECODING_METHOD: DecodingMethods.SAMPLE,
        GenParams.TEMPERATURE: 0.5,
        GenParams.TOP_K: 50,
        GenParams.TOP_P: 1
    }
    credentials = Credentials(
                      url = "https://us-south.ml.cloud.ibm.com",
                      api_key = "***********"
                     )
    project = "*****"

    pt1 = PromptTemplate(
        input_variables=["topic"],
        template="Generate a random question about {topic}: Question: ")
    pt2 = PromptTemplate(
        input_variables=["question"],
        template="Answer the following question: {question}")

    flan_ul2_model = ModelInference(
        model_id='google/flan-ul2',
        credentials=credentials,
        params=params,
        project_id=project_id)
    flan_ul2_llm = WatsonxLLM(watsonx_model=flan_ul2_model)

    flan_t5_model = ModelInference(
        model_id="google/flan-t5-xxl",
        credentials=credentials,
        project_id=project_id)
    flan_t5_llm = WatsonxLLM(watsonx_model=flan_t5_model)

    prompt_to_flan_ul2 = LLMChain(llm=flan_ul2_llm, prompt=pt1)
    flan_ul2_to_flan_t5 = LLMChain(llm=flan_t5_llm, prompt=pt2)

    qa = SimpleSequentialChain(chains=[prompt_to_flan_ul2, flan_ul2_to_flan_t5], verbose=True)
    qa.run("cat")