Repository: explodinggradients/ragas
Issue: I want to use Huggingface model for test data generation, but getting error: ERROR:ragas.testset.transforms.engine:unable to apply transformation: Node 3da710f8-fc20-40ef-97bf-7f11fb7be538 has no summary_embedding
Root cause: Interface Contract Mismatch

**Describe the Feature**
As cost for test data generation is too high, thats why I want to use huggingface opensource model for test data generation. But it is not compatible with current version.

**Why is the feature important for you?**
from langchain.llms import HuggingFacePipeline
from transformers import pipeline

# Load HuggingFace model using transformers pipeline
generator = pipeline("text-generation", model="gpt2")

# Create a HuggingFacePipeline LLM instance
llm = HuggingFacePipeline(pipeline=generator)

from langchain.embeddings import HuggingFaceEmbeddings
from sentence_transformers import SentenceTransformer

# Load the HuggingFace embedding model (e.g., a sentence transformer model)
model_name = "all-MiniLM-L6-v2"  # A common model for sentence embeddings

# Create an embedding instance using HuggingFaceEmbeddings, providing the model_name
embedding = HuggingFaceEmbeddings(model_name=model_name)

from langchain_core.language_models import BaseLanguageModel
from langchain_core.embeddings import Embeddings

# make sure to wrap them with wrappers
from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper

langchain_llm = LangchainLLMWrapper(llm)
langchain_embeddings = LangchainEmbeddingsWrapper(embedding)

# ! git clone https://huggingface.co/datasets/explodinggradients/prompt-engineering-guide-papers

from langchain_community.document_loaders import DirectoryLoader
loader = DirectoryLoader("./prompt-engineering-guide-papers/", glob="*.pdf")
documents = loader.load()

for document in documents:
    document.metadata["filename"] = document.metadata["source"]

docs = [doc for doc in documents if len(doc.page_content.split()) > 5000]

from ragas.testset import TestsetGenerator

# generator with openai models
generator_llm = langchain_llm
critic_llm = langchain_llm
embeddings = langchain_embeddings

generator = TestsetGenerator.from_langchain(llm=generator_llm, embedding_model=embeddings)

# generate testset
testset = generator.generate_with_langchain_docs(documents[:2], testset_size=3)

for this code I am getting an error message as follows:

AttributeError: 'LangchainLLMWrapper' object has no attribute 'agenerate_prompt'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: 'LangchainLLMWrapper' object has no attribute 'agenerate_prompt'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: 'LangchainLLMWrapper' object has no attribute 'agenerate_prompt'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: 'headlines' property not found in this node
ERROR:ragas.testset.transforms.engine:unable to apply transformation: 'headlines' property not found in this node
ERROR:ragas.testset.transforms.engine:unable to apply transformation: 'LangchainLLMWrapper' object has no attribute 'agenerate_prompt'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: 'LangchainLLMWrapper' object has no attribute 'agenerate_prompt'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: node.property('summary') must be a string, found '<class 'NoneType'>'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: node.property('summary') must be a string, found '<class 'NoneType'>'
ERROR:ragas.testset.transforms.engine:unable to apply transformation: Node 3da710f8-fc20-40ef-97bf-7f11fb7be538 has no summary_embedding

**Additional context**

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