[Python] pydantic.ValidationError: LangChainInterface credentials instance expected when using...

I’m building a RAG application using LangChain and Watsonx AI with Streamlit. I want to create a LangChainInterface instance using my IBM Watsonx API credentials, but I’m getting the following validation error:

pydantic.error_wrappers.ValidationError: 3 validation errors for LangChainInterface credentials instance of Credentials expected (type=type_error.arbitrary_type; expected_arbitrary_type=Credentials) model_id extra fields not permitted (type=value_error.extra) project_id extra fields not permitted (type=value_error.extra)
Traceback:
File "D:\projects\UniGpt\APP\app.py", line 22, in <module>
llm = LangChainInterface(
^^^^^^^^^^^^^^^^^^^
File "C:\Users\CHAMIKA\unigpt-env\Lib\site-packages\langchain_core\load\serializable.py", line 113, in __init__
super().__init__(*args, **kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__


Screenshot of error

Here’s my code:

#import logchain dependancies
from langchain.document_loaders import PyPDFLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

#bring in steamlit for UI dev
import streamlit as st

#bring in watsonx interface
from wxai_langchain.llm import LangChainInterface, Credentials

from config import API_KEY,URL,PROJECT_ID
#setup cerdentials directly in the code
creds={
'api_key': API_KEY,
'url': URL,
}

#create llm using langchain
llm = LangChainInterface(
credentials=creds,
model_id="granite-3-3-8b-instruct",
params={
'decoding_method':'sample',
'max_new_tokens':200,
'temprature':0.5
},
project_id=PROJECT_ID
)

#function to load and index the pdf document
@st.cache_resource
def load_pdf():
pdf_name = "Resources/HandBook.pdf"
#load the pdf
loader = PyPDFLoader(pdf_name)
#split the pdf into chunks
index=VectorstoreIndexCreator(
embedding=HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2"),
text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
).from_loaders([loader])
return index

#setup the retrieval qa chain
chain=RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=load_pdf().vectorstore.as_retriever(),
input_key="question"
)


#setup the app title
st.title("Ask UniExpert")

#setup session state message variable to hold all the old message
if "messages" not in st.session_state:
st.session_state.messages = []

#display all the historical messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])

#build a prompt input template to display the prompts
prompt = st.chat_input("Enter your question:")

#if the user hits enter
if prompt:
#display the prompt
st.chat_message("user").markdown(prompt)
#store the user prompt in state
st.session_state.messages.append({"role": "user", "content": prompt})
#response from llm
response=chain.run(prompt)
#show the response
st.chat_message("assistant").markdown(response)
#store the response in state
st.session_state.messages.append(
{"role": "assistant", "content": response})


What I’ve tried:

Passing the credentials as a dictionary (as shown above). Searching for a proper Credentials class usage in the documentation.

Question:

How should I correctly create a LangChainInterface instance with Watsonx credentials without triggering pydantic.ValidationError? Should I pass the credentials differently? How can I handle model_id and project_id parameters in the current version of wxai_langchain?

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