> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mellea.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Intrinsics

> Adapter-accelerated RAG quality checks using LoRA/aLoRA adapters with Granite models.

**Prerequisites:** `pip install "mellea[hf]"` for LocalHFBackend (GPU or Apple
Silicon Mac recommended), or `pip install mellea` for OpenAIBackend with a
[Granite Switch](../guide/glossary#granite-switch) model served via vLLM.

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 work with two backends:
>
> * **LocalHFBackend** — loads LoRA/aLoRA adapters from the catalog at runtime.
>   All intrinsics are available. Requires a GPU or Apple Silicon Mac.
> * **OpenAIBackend** — uses a Granite Switch model served via vLLM with
>   `load_embedded_adapters=True`. Only intrinsics embedded in the model are
>   available — check the model's `adapter_index.json` for the list.
>   See `docs/docs/examples/granite-switch/README.md`
>
> Intrinsics do not work with Ollama or other remote backends.

Set up the backend once and reuse it across intrinsic calls:

```python theme={null}
# Requires: mellea[hf]
# Returns: LocalHFBackend
from mellea.backends.huggingface import LocalHFBackend

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

Or, with a Granite Switch model via the OpenAI backend:

```python theme={null}
from mellea.backends.openai import OpenAIBackend
from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B_PREVIEW
from mellea.formatters import TemplateFormatter

backend = OpenAIBackend(
    model_id=IBM_GRANITE_SWITCH_4_1_3B_PREVIEW.hf_model_name,
    formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B_PREVIEW.hf_model_name),
    base_url="http://localhost:8000/v1",  # vLLM server
    api_key="EMPTY",
    load_embedded_adapters=True,
)
```

## Answerability

Check whether a set of retrieved documents can answer a given question:

```python theme={null}
# Requires: mellea[hf]
# Returns: bool
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:

```python theme={null}
# Requires: mellea[hf]
# Returns: float
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:

```python theme={null}
# Requires: mellea[hf]
# Returns: list[str]
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:

```python theme={null}
# Requires: mellea[hf]
# Returns: str
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:

```python theme={null}
# Requires: mellea[hf]
# Returns: str
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:

```python theme={null}
# Requires: mellea[hf]
# Returns: dict
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
> `CustomIntrinsicAdapter` directly.

```python theme={null}
# Requires: mellea[hf]
# Returns: dict
import mellea.stdlib.functional as mfuncs
from mellea.backends.adapters.adapter import CustomIntrinsicAdapter
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 = CustomIntrinsicAdapter(
    "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.
For OpenAI backends with Granite Switch, adapters are loaded from the model's
HuggingFace repository configuration instead of the intrinsic catalog.
Output format is task-specific — `requirement-check` returns a likelihood score.