I am findig an error with EmbeddingFunction call in my RAG apllication

Question

This is the error I get for my LLM application-

An error occurred: Expected EmbeddingFunction.call to have the following signature: odict_keys(['self', 'input']), got odict_keys(['self', 'args', 'kwargs']) Please see https://docs.trychroma.com/embeddings for details of the EmbeddingFunction interface. Please note the recent change to the EmbeddingFunction interface: https://docs.trychroma.com/migration#migration-to-0416---november-7-2023

This is main part of the code -

try:
                    start = timeit.default_timer()
                    config = {
                    'max_new_tokens': 1024,
                    'repetition_penalty': 1.1,
                    'temperature': 0.1,
                    'top_k': 50,
                    'top_p': 0.9,
                    'stream': True,
                    'threads': int(os.cpu_count() / 2)
                    }
                    
                    llm = CTransformers(
                        model = "TheBloke/zephyr-7B-beta-GGUF",
                        model_file = "zephyr-7b-beta.Q4_0.gguf",
                        model_type="mistral",
                        lib="avx2", #for CPU use
                        **config
                    )
                    st.write("LLM Initialized:")

                    model_name = "BAAI/bge-large-en"
                    model_kwargs = {'device': 'cpu'}
                    encode_kwargs = {'normalize_embeddings': False}
                    embeddings = HuggingFaceBgeEmbeddings(
                        model_name=model_name,
                        model_kwargs=model_kwargs,
                        encode_kwargs=encode_kwargs
                    )

                    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
                    #                                   model_kwargs={"device": "cpu"})


                    
                    
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=30, length_function = len)
chunked_documents = text_splitter.split_documents(loaded_documents)
persist_directory = 'db'
# Create and persist a Chroma vector database from the chunked documents
db=Chroma.from_documents(documents=chunked_documents,embedding=embeddings,persist_directory=persist_directory)
db.persist()

retriever = db.as_retriever(search_kwargs={"k":1})

qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, verbose=True)
                    bot_response = qa(query)
                    lines = bot_response['result'].split('\n')
                    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
                    wrapped_text = '\n'.join(wrapped_lines)

for source in bot_response["source_documents"]:
     sources = source.metadata['source']
                    
end = timeit.default_timer()
st.write("Elapsed time:")
st.write(end - start)
                    
st.write("Bot Response:")
st.write(wrapped_text)
                    
 st.write(sources)
except Exception as e:
st.error(f"An error occurred: {str(e)}")

I tried changing the embbeding lioke-

embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
                    #                                   model_kwargs={"device": "cpu"})

but still the same error. Please help what is the problem here. Here is the link to the full code - https://huggingface.co/spaces/captain-awesome/Docuverse-zephyr-beta/blob/main/app.py