Skip to main content
Prerequisites: pip install "mellea[hf]", a GPU or Apple Silicon Mac recommended for acceptable inference speed. All intrinsics require a LocalHFBackend with a Granite model. Intrinsics are adapter-accelerated operations for RAG quality checks. They use LoRA/aLoRA adapters loaded directly into the HuggingFace backend — faster and more reliable than prompting a general-purpose model for these specialized micro-tasks.
Backend note: Intrinsics require LocalHFBackend with an IBM Granite model (e.g., ibm-granite/granite-4.0-micro). They do not work with Ollama, OpenAI, or other remote backends.
Set up the backend once and reuse it across intrinsic calls: 
from mellea.backends.huggingface import LocalHFBackend

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")

​
Answerability

Check whether a set of retrieved documents can answer a given question: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Document, Message
from mellea.stdlib.components.intrinsic import rag
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")
context = ChatContext().add(Message("assistant", "Hello! How can I help you?"))
question = "What is the square root of 4?"

docs_answerable = [Document("The square root of 4 is 2.")]
docs_not_answerable = [Document("The square root of 8 is approximately 2.83.")]

print(rag.check_answerability(question, docs_answerable, context, backend))   # True
print(rag.check_answerability(question, docs_not_answerable, context, backend))  # False

​
Context relevance

Assess whether a document is relevant to a question: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Document
from mellea.stdlib.components.intrinsic import rag
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")
context = ChatContext()
question = "Who is the CEO of Microsoft?"
document = Document(
    "Microsoft Corporation is an American multinational corporation "
    "headquartered in Redmond, Washington."
)

result = rag.check_context_relevance(question, document, context, backend)
print(result)  # False — the document does not mention the CEO

​
Hallucination detection

Flag sentences in an assistant response that are not grounded in the source documents: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Document, Message
from mellea.stdlib.components.intrinsic import rag
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")
context = (
    ChatContext()
    .add(Message("assistant", "Hello! How can I help you?"))
    .add(Message("user", "Tell me about yellow fish."))
)

response = "Purple bumble fish are yellow. Green bumble fish are also yellow."
documents = [
    Document(doc_id="1", text="The only type of fish that is yellow is the purple bumble fish.")
]

result = rag.flag_hallucinated_content(response, documents, context, backend)
print(result)
# Flags "Green bumble fish are also yellow." as hallucinated

​
Answer relevance rewriting

Rewrite a vague or incomplete answer to be more grounded in the source documents: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Document, Message
from mellea.stdlib.components.intrinsic import rag
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")
context = ChatContext().add(Message("user", "Who attended the meeting?"))
documents = [
    Document("Meeting attendees: Alice, Bob, Carol."),
    Document("Meeting time: 9:00 am to 11:00 am."),
]
original = "Many people attended the meeting."

result = rag.rewrite_answer_for_relevance(original, documents, context, backend)
print(result)
# A more specific, grounded answer — output will vary

​
Query rewriting

Rewrite an ambiguous user query using conversation history to improve retrieval: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Message
from mellea.stdlib.components.intrinsic import rag
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")
context = (
    ChatContext()
    .add(Message("assistant", "Welcome to pet questions!"))
    .add(Message("user", "I have two pets: a dog named Rex and a cat named Lucy."))
    .add(Message("assistant", "Rex spends a lot of time outdoors, and Lucy is always inside."))
    .add(Message("user", "Sounds good! Rex must love exploring outside."))
)
next_turn = "But is he more likely to get fleas because of that?"

result = rag.rewrite_question(next_turn, context, backend)
print(result)
# Resolves "he" to "Rex" and incorporates context about outdoor exposure

​
Citations

Find supporting sentences in source documents for a given assistant response: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Document, Message
from mellea.stdlib.components.intrinsic import rag
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")
context = ChatContext().add(
    Message("user", "How did Murdoch expand in Australia versus New Zealand?")
)
response = (
    "Murdoch expanded in Australia and New Zealand by acquiring local newspapers. "
    "I do not have information about his expansion in New Zealand after purchasing "
    "The Dominion."
)
documents = [
    Document(doc_id="1", text="Keith Rupert Murdoch was born on 11 March 1931 in Melbourne..."),
    Document(doc_id="2", text="This document has nothing to do with Rupert Murdoch."),
]

result = rag.find_citations(response, documents, context, backend)
print(result)
# Maps each response sentence to supporting document sentences

​
Direct intrinsic usage

Advanced: For custom adapter tasks, use the Intrinsic component and GraniteCommonAdapter directly. 
import mellea.stdlib.functional as mfuncs
from mellea.backends.adapters.adapter import GraniteCommonAdapter
from mellea.backends.huggingface import LocalHFBackend
from mellea.stdlib.components import Intrinsic, Message
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro")

# Register an adapter by task name
req_adapter = GraniteCommonAdapter(
    "requirement_check",
    base_model_name=backend.base_model_name,
)
backend.add_adapter(req_adapter)

ctx = ChatContext()
ctx = ctx.add(Message("user", "Hi, can you help me?"))
ctx = ctx.add(Message("assistant", "Yes! What can I help with?"))

out, _ = mfuncs.act(
    Intrinsic(
        "requirement_check",
        intrinsic_kwargs={"requirement": "The assistant is helpful."},
    ),
    ctx,
    backend,
)
print(out)  # {"requirement_likelihood": 1.0}
The Intrinsic component loads aLoRA adapters (falling back to LoRA) by task name. Output format is task-specific — requirement_check returns a likelihood score.