diff --git a/docs/docs/advanced/intrinsics.md b/docs/docs/advanced/intrinsics.md index a0fc33fb8..dd1b9bb0c 100644 --- a/docs/docs/advanced/intrinsics.md +++ b/docs/docs/advanced/intrinsics.md @@ -4,17 +4,23 @@ description: "Adapter-accelerated RAG quality checks using LoRA/aLoRA adapters w # diataxis: how-to --- -**Prerequisites:** `pip install "mellea[hf]"`, a GPU or Apple Silicon Mac recommended for -acceptable inference speed. All intrinsics require a `LocalHFBackend` with a -[Granite](https://huggingface.co/ibm-granite) model. +**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 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. +> **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. +> +> Intrinsics do not work with Ollama or other remote backends. Set up the backend once and reuse it across intrinsic calls: @@ -24,6 +30,22 @@ 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 +from mellea.backends.openai import OpenAIBackend +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.formatters import TemplateFormatter + +backend = OpenAIBackend( + model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, + formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.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: @@ -208,4 +230,6 @@ 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. diff --git a/docs/docs/advanced/lora-and-alora-adapters.md b/docs/docs/advanced/lora-and-alora-adapters.md index 17eb2e428..fa0aad481 100644 --- a/docs/docs/advanced/lora-and-alora-adapters.md +++ b/docs/docs/advanced/lora-and-alora-adapters.md @@ -15,8 +15,12 @@ and use it as a requirement validator in any Mellea program. Apple Silicon Mac with sufficient VRAM for the chosen base model. Uploading requires a Hugging Face account. -> **Backend note:** Trained adapters can only be loaded into `LocalHFBackend`. They do -> not work with Ollama, OpenAI, or other remote backends. +> **Backend note:** Custom-trained adapters can only be loaded into `LocalHFBackend`. +> They do not work with Ollama, OpenAI, or other remote backends. +> +> Granite Switch models ship with pre-trained intrinsic adapters embedded in the +> model weights, which can be used via `OpenAIBackend` with +> `load_embedded_adapters=True`. See [Intrinsics](./intrinsics) for details. ## LoRA vs aLoRA diff --git a/docs/docs/examples/index.md b/docs/docs/examples/index.md index 933b34e0b..87c5e0f70 100644 --- a/docs/docs/examples/index.md +++ b/docs/docs/examples/index.md @@ -78,6 +78,7 @@ to run. | -------- | ------------- | | `aLora/` | Training aLoRA adapters for fast constraint checking; performance optimisation | | `intrinsics/` | Answer relevance, hallucination detection, citation validation, context relevance — specialised adapter-backed checks | +| `granite-switch/` | Running intrinsics via OpenAI backend with Granite Switch embedded adapters | | `sofai/` | Two-tier sampling: fast-model iteration with escalation to a slow model; cost optimisation | ### Multimodal diff --git a/docs/docs/guide/glossary.md b/docs/docs/guide/glossary.md index c86f6d464..8392a0230 100644 --- a/docs/docs/guide/glossary.md +++ b/docs/docs/guide/glossary.md @@ -27,7 +27,9 @@ See: [act() and aact()](./act-and-aact) An **Activated LoRA** (aLoRA) is a LoRA adapter dynamically loaded by `LocalHFBackend` at inference time to serve as a lightweight requirement verifier. Instead of running a full LLM call to check a requirement, the adapter is activated -on the same model weights already in memory. +on the same model weights already in memory. [Granite Switch](#granite-switch) +models embed these adapters directly in the model weights, enabling intrinsic +functions via `OpenAIBackend` without runtime adapter loading. See: [LoRA and aLoRA Adapters](../advanced/lora-and-alora-adapters) @@ -292,6 +294,20 @@ See: [Making Agents Reliable](../tutorials/04-making-agents-reliable) --- +## Granite Switch + +A Granite model variant with LoRA and aLoRA adapters pre-baked into the model +weights. When served via vLLM and accessed through `OpenAIBackend` with +`load_embedded_adapters=True`, these embedded adapters enable +[Intrinsics](../advanced/intrinsics) (RAG quality checks, requirement +validation, safety evaluation) without runtime adapter loading. Only intrinsics +embedded in the model are available — check the model's `adapter_index.json`. + +See: [Intrinsics](../advanced/intrinsics) | +[OpenAI and OpenAI-Compatible APIs](../integrations/openai) + +--- + ## KV smashing The technique of concatenating key-value attention caches from separately prefilled @@ -360,9 +376,10 @@ See: [Use Images and Vision Models](../how-to/use-images-and-vision) ## Intrinsic An `Intrinsic` is a backend-level primitive in Mellea — a structured generation -operation with special handling (e.g., constrained decoding, RAG retrieval). The -`LocalHFBackend` exposes Intrinsics directly; server backends route them through -adapter endpoints. +operation with special handling (e.g., constrained decoding, RAG retrieval). +`LocalHFBackend` exposes Intrinsics via runtime adapter loading. `OpenAIBackend` +supports Intrinsics when backed by a [Granite Switch](#granite-switch) model with +`load_embedded_adapters=True`. See: [Intrinsics](../advanced/intrinsics) diff --git a/docs/docs/integrations/huggingface.md b/docs/docs/integrations/huggingface.md index 363c77378..aaf9efc31 100644 --- a/docs/docs/integrations/huggingface.md +++ b/docs/docs/integrations/huggingface.md @@ -81,6 +81,10 @@ See [Prefix Caching and KV Blocks](../advanced/prefix-caching-and-kv-blocks) for adapters — lightweight domain-specific requirement validators that run on local GPU hardware. See the aLoRA guide for training and usage. +> **Tip:** For intrinsics without local GPU requirements, Granite Switch models +> serve pre-embedded adapters via vLLM and the OpenAI backend. See +> [Intrinsics](../advanced/intrinsics) for details. + ## Vision support Vision support for `LocalHFBackend` is model-dependent and experimental. Pass a PIL diff --git a/docs/docs/integrations/openai.md b/docs/docs/integrations/openai.md index 74fa0518b..3a570b200 100644 --- a/docs/docs/integrations/openai.md +++ b/docs/docs/integrations/openai.md @@ -236,6 +236,73 @@ m = MelleaSession( > LiteLLM provides a verified integration — see > [Backends and Configuration](../guide/backends-and-configuration). +## Intrinsics with Granite Switch + +Granite Switch models embed LoRA/aLoRA adapters directly in the model weights. +When served via vLLM, these adapters enable intrinsic functions (RAG quality +checks, safety evaluation, requirement validation) through the OpenAI-compatible +API without loading adapter weights at runtime. + +Start a vLLM server with the Granite Switch model: + +```bash +python -m vllm.entrypoints.openai.api_server \ + --model \ + --dtype bfloat16 \ + --enable-prefix-caching +``` + +Then create a backend with `load_embedded_adapters=True`: + +```python +from mellea.backends.openai import OpenAIBackend +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.formatters import TemplateFormatter + +backend = OpenAIBackend( + model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, + formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), + base_url="http://localhost:8000/v1", + api_key="EMPTY", + load_embedded_adapters=True, +) +``` + +The high-level intrinsic wrappers (`rag.check_answerability`, +`core.check_certainty`, etc.) work identically with this backend. See +[Intrinsics](../advanced/intrinsics) for the full list of available intrinsics. + +> **Note:** `load_embedded_adapters=True` downloads adapter I/O configurations +> from the model's HuggingFace repository on first use. No adapter weights are +> transferred — the adapters are already part of the model. Only intrinsics +> embedded in the model are available — check the model's `adapter_index.json` +> for the list. + +For more control, load adapters manually with `load_embedded_adapters=False`: + +```python +from mellea.backends.adapters.adapter import EmbeddedIntrinsicAdapter +from mellea.backends.openai import OpenAIBackend +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.formatters import TemplateFormatter + +backend = OpenAIBackend( + model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, + formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), + base_url="http://localhost:8000/v1", + api_key="EMPTY", + load_embedded_adapters=False, +) + +# Load a single adapter from the model's HuggingFace repo +adapters = EmbeddedIntrinsicAdapter.from_hub( + IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, + intrinsic_name="answerability", +) +for adapter in adapters: + backend.add_adapter(adapter) +``` + ## Troubleshooting ### `OPENAI_API_KEY` not set error @@ -257,4 +324,5 @@ local servers, list available models from the server's API or UI. --- **See also:** [Backends and Configuration](../guide/backends-and-configuration) | -[Enforce Structured Output](../how-to/enforce-structured-output) +[Enforce Structured Output](../how-to/enforce-structured-output) | +[Intrinsics](../advanced/intrinsics) diff --git a/docs/examples/granite-switch/README.md b/docs/examples/granite-switch/README.md new file mode 100644 index 000000000..30f4bfece --- /dev/null +++ b/docs/examples/granite-switch/README.md @@ -0,0 +1,58 @@ +# Granite Switch Examples + +This directory contains examples for running Mellea intrinsics through an +OpenAI-compatible backend using Granite Switch models. + +## What is Granite Switch? + +Granite Switch models ship with LoRA and aLoRA adapters pre-baked into the model +weights. Instead of loading adapters at runtime (as `LocalHFBackend` does), these +embedded adapters are activated via control tokens injected by the model's chat +template. Only the I/O transformation configs are downloaded — no adapter weights +are transferred. + +## Prerequisites + +1. A Granite Switch model hosted via [vLLM](https://docs.vllm.ai/): + +```bash +python -m vllm.entrypoints.openai.api_server \ + --model \ + --dtype bfloat16 \ + --enable-prefix-caching +``` + +2. `pip install mellea` + +## Available adapters + +Not all intrinsics are embedded in every Granite Switch model. Check the model's +`adapter_index.json` for the list of available adapters. The current model +includes: `answerability`, `citations`, `context_relevance`, `guardian-core`, +`hallucination_detection`, `query_clarification`, `query_rewrite`, and +`requirement-check`. + +## Files + +### answerability_openai.py + +Demonstrates `rag.check_answerability()` using `OpenAIBackend` with +`load_embedded_adapters=True` — the simplest way to use intrinsics with Granite +Switch. + +### hallucination_detection_openai.py + +Demonstrates `rag.flag_hallucinated_content()` using `OpenAIBackend` with +`load_embedded_adapters=True`. + +### manual_adapter_loading.py + +Shows how to manually load embedded adapters using +`EmbeddedIntrinsicAdapter.from_hub()` and `backend.add_adapter()`. Useful when +you only need a subset of adapters or want more control over adapter +registration. + +## Related + +- [`../intrinsics/`](../intrinsics/) — the same intrinsics using `LocalHFBackend` +- [Intrinsics documentation](../../docs/docs/advanced/intrinsics.md) diff --git a/docs/examples/granite-switch/answerability_openai.py b/docs/examples/granite-switch/answerability_openai.py new file mode 100644 index 000000000..ec89459a0 --- /dev/null +++ b/docs/examples/granite-switch/answerability_openai.py @@ -0,0 +1,56 @@ +# pytest: e2e, vllm, skip + +"""Example: running the answerability intrinsic via OpenAI backend with Granite Switch. + +Requires a vLLM server hosting a Granite Switch model. + +To start the server: + python -m vllm.entrypoints.openai.api_server \ + --model \ + --dtype bfloat16 --enable-prefix-caching + +To run this script from the root of the Mellea source tree: + uv run python docs/examples/granite-switch/answerability_openai.py +""" + +import os +import sys + +import requests + +VLLM_BASE_URL = os.environ.get("VLLM_SWITCH_TEST_BASE_URL", "http://localhost:8000") +try: + requests.get(f"{VLLM_BASE_URL}/v1/models", timeout=2) +except requests.ConnectionError: + # Detected by docs/examples/conftest.py subprocess runner as a skip. + print(f"Skipped: vLLM server not reachable at {VLLM_BASE_URL}", file=sys.stderr) + raise SystemExit(1) + +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.backends.openai import OpenAIBackend +from mellea.formatters import TemplateFormatter +from mellea.stdlib.components import Document, Message +from mellea.stdlib.components.intrinsic import rag +from mellea.stdlib.context import ChatContext + +SWITCH_MODEL_ID = IBM_GRANITE_SWITCH_4_1_3B.hf_model_name +assert SWITCH_MODEL_ID is not None + +backend = OpenAIBackend( + model_id=SWITCH_MODEL_ID, + formatter=TemplateFormatter(model_id=SWITCH_MODEL_ID), + base_url="http://localhost:8000/v1", + api_key="EMPTY", + load_embedded_adapters=True, +) + +context = ChatContext().add(Message("assistant", "Hello there, how can I help you?")) +question = "What is the square root of 4?" +documents_answerable = [Document("The square root of 4 is 2.")] +documents_unanswerable = [Document("The square root of 8 is not 2.")] + +result = rag.check_answerability(question, documents_answerable, context, backend) +print(f"Answerability (answer in docs): {result}") + +result = rag.check_answerability(question, documents_unanswerable, context, backend) +print(f"Answerability (answer NOT in docs): {result}") diff --git a/docs/examples/granite-switch/hallucination_detection_openai.py b/docs/examples/granite-switch/hallucination_detection_openai.py new file mode 100644 index 000000000..6e99c984a --- /dev/null +++ b/docs/examples/granite-switch/hallucination_detection_openai.py @@ -0,0 +1,63 @@ +# pytest: e2e, vllm, skip + +"""Example: running hallucination detection via OpenAI backend with Granite Switch. + +Requires a vLLM server hosting a Granite Switch model. + +To start the server: + python -m vllm.entrypoints.openai.api_server \ + --model \ + --dtype bfloat16 --enable-prefix-caching + +To run this script from the root of the Mellea source tree: + uv run python docs/examples/granite-switch/hallucination_detection_openai.py +""" + +import json +import os +import sys + +import requests + +VLLM_BASE_URL = os.environ.get("VLLM_SWITCH_TEST_BASE_URL", "http://localhost:8000") +try: + requests.get(f"{VLLM_BASE_URL}/v1/models", timeout=2) +except requests.ConnectionError: + print(f"Skipped: vLLM server not reachable at {VLLM_BASE_URL}", file=sys.stderr) + raise SystemExit(1) + +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.backends.openai import OpenAIBackend +from mellea.formatters import TemplateFormatter +from mellea.stdlib.components import Document, Message +from mellea.stdlib.components.intrinsic import rag +from mellea.stdlib.context import ChatContext + +SWITCH_MODEL_ID = IBM_GRANITE_SWITCH_4_1_3B.hf_model_name +assert SWITCH_MODEL_ID is not None + +backend = OpenAIBackend( + model_id=SWITCH_MODEL_ID, + formatter=TemplateFormatter(model_id=SWITCH_MODEL_ID), + base_url="http://localhost:8000/v1", + api_key="EMPTY", + load_embedded_adapters=True, +) + +context = ( + ChatContext() + .add(Message("assistant", "Hello there, how can I help you?")) + .add(Message("user", "Tell me about some yellow fish.")) +) + +assistant_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(assistant_response, documents, context, backend) +print(f"Result of hallucination check: {json.dumps(result, indent=2)}") diff --git a/docs/examples/granite-switch/manual_adapter_loading.py b/docs/examples/granite-switch/manual_adapter_loading.py new file mode 100644 index 000000000..c6f77b210 --- /dev/null +++ b/docs/examples/granite-switch/manual_adapter_loading.py @@ -0,0 +1,81 @@ +# pytest: e2e, vllm, skip + +"""Example: manually loading embedded adapters for the OpenAI backend. + +Instead of using ``load_embedded_adapters=True`` (which loads all adapters from +the model repo at init), this example shows how to create an OpenAIBackend with +``load_embedded_adapters=False`` and then manually load individual adapters using +``EmbeddedIntrinsicAdapter.from_hub()`` or ``from_model_directory()``. + +This is useful when: +- You only need a subset of the model's embedded adapters. +- You want to load adapters from a local directory rather than HuggingFace Hub. +- You want more control over adapter registration. + +Requires a vLLM server hosting a Granite Switch model. + +To start the server: + python -m vllm.entrypoints.openai.api_server \ + --model \ + --dtype bfloat16 --enable-prefix-caching + +To run this script from the root of the Mellea source tree: + uv run python docs/examples/granite-switch/manual_adapter_loading.py +""" + +import os +import sys + +import requests + +VLLM_BASE_URL = os.environ.get("VLLM_SWITCH_TEST_BASE_URL", "http://localhost:8000") +try: + requests.get(f"{VLLM_BASE_URL}/v1/models", timeout=2) +except requests.ConnectionError: + print(f"Skipped: vLLM server not reachable at {VLLM_BASE_URL}", file=sys.stderr) + raise SystemExit(1) + +from mellea.backends.adapters.adapter import EmbeddedIntrinsicAdapter +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.backends.openai import OpenAIBackend +from mellea.formatters import TemplateFormatter +from mellea.stdlib.components import Document, Message +from mellea.stdlib.components.intrinsic import rag +from mellea.stdlib.context import ChatContext + +SWITCH_MODEL_ID = IBM_GRANITE_SWITCH_4_1_3B.hf_model_name +assert SWITCH_MODEL_ID is not None + +# Create the backend WITHOUT auto-loading adapters. +backend = OpenAIBackend( + model_id=SWITCH_MODEL_ID, + formatter=TemplateFormatter(model_id=SWITCH_MODEL_ID), + base_url="http://localhost:8000/v1", + api_key="EMPTY", + load_embedded_adapters=False, +) + +# --- Option A: Load a single adapter from HuggingFace Hub --- +adapters = EmbeddedIntrinsicAdapter.from_hub( + SWITCH_MODEL_ID, intrinsic_name="answerability" +) +for adapter in adapters: + backend.add_adapter(adapter) + +# --- Option B (alternative): Load from a local model directory --- +# adapters = EmbeddedIntrinsicAdapter.from_model_directory( +# "/path/to/local/granite-switch-model", +# intrinsic_name="answerability", +# ) +# for adapter in adapters: +# backend.add_adapter(adapter) + +print(f"Registered adapters: {backend.list_adapters()}") + +# Now use the intrinsic as usual. +context = ChatContext().add(Message("assistant", "Hello there, how can I help you?")) +question = "What is the square root of 4?" +documents = [Document("The square root of 4 is 2.")] + +result = rag.check_answerability(question, documents, context, backend) +print(f"Answerability result: {result}") diff --git a/docs/examples/intrinsics/README.md b/docs/examples/intrinsics/README.md index 328ad8e3a..392ea2a27 100644 --- a/docs/examples/intrinsics/README.md +++ b/docs/examples/intrinsics/README.md @@ -56,7 +56,7 @@ Identifies sentences in conversation history and documents that most influenced - **Adapter System**: Using LoRA/aLoRA adapters for specific tasks - **RAG Evaluation**: Assessing retrieval-augmented generation quality - **Quality Metrics**: Measuring relevance, groundedness, and accuracy -- **Backend Integration**: Adding adapters to different backend types +- **Backend Integration**: Adding adapters to different backend types (LocalHFBackend with runtime adapters, OpenAIBackend with Granite Switch embedded adapters) ## Basic Usage @@ -74,13 +74,41 @@ backend.add_adapter(adapter) # Use intrinsic out, new_ctx = mfuncs.act( - Intrinsic("requirement_check", - intrinsic_kwargs={"requirement": "The assistant is helpful."}), + Intrinsic( + "requirement_check", + intrinsic_kwargs={"requirement": "The assistant is helpful."}), ctx, backend ) ``` +OpenAIBackends also support a type of embedded adapter for Granite Switch models: +```python +backend = OpenAIBackend( + model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, + load_embedded_adapters=True, # Auto-loads adapters from huggingface repo. + ... +) + +# Assumes the model has the `requirement_check` adapter embedded. +out, new_ctx = mfuncs.act( + Intrinsic( + "requirement_check", + intrinsic_kwargs={"requirement": "The assistant is helpful."}), + ctx, + backend +) +``` + +For complete runnable examples using the OpenAI backend with Granite Switch, +see [`../granite-switch/`](../granite-switch/). + +> **Note:** Not all intrinsics are embedded in every Granite Switch model. The +> current model includes: `answerability`, `citations`, `context_relevance`, +> `guardian-core`, `hallucination_detection`, `query_clarification`, +> `query_rewrite`, and `requirement-check`. Check the model's +> `adapter_index.json` for the full list. + ## Available Intrinsics - **requirement_check**: Validate requirements (used by ALoraRequirement) diff --git a/docs/examples/intrinsics/answerability.py b/docs/examples/intrinsics/answerability.py index 3afbddbd5..edf2bfaa5 100644 --- a/docs/examples/intrinsics/answerability.py +++ b/docs/examples/intrinsics/answerability.py @@ -14,6 +14,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ChatContext().add(Message("assistant", "Hello there, how can I help you?")) next_user_turn = "What is the square root of 4?" documents_answerable = [Document("The square root of 4 is 2.")] diff --git a/docs/examples/intrinsics/citations.py b/docs/examples/intrinsics/citations.py index 9b2fc1296..24499202a 100644 --- a/docs/examples/intrinsics/citations.py +++ b/docs/examples/intrinsics/citations.py @@ -16,6 +16,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ChatContext().add( Message( "user", diff --git a/docs/examples/intrinsics/context_attribution.py b/docs/examples/intrinsics/context_attribution.py index e90628fe0..006466690 100644 --- a/docs/examples/intrinsics/context_attribution.py +++ b/docs/examples/intrinsics/context_attribution.py @@ -20,6 +20,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add( diff --git a/docs/examples/intrinsics/context_relevance.py b/docs/examples/intrinsics/context_relevance.py index 32562d0ac..5dd79931a 100644 --- a/docs/examples/intrinsics/context_relevance.py +++ b/docs/examples/intrinsics/context_relevance.py @@ -14,6 +14,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ChatContext() question = "Who is the CEO of Microsoft?" document = Document( diff --git a/docs/examples/intrinsics/factuality_correction.py b/docs/examples/intrinsics/factuality_correction.py index 123ee92f4..8cc9655fe 100644 --- a/docs/examples/intrinsics/factuality_correction.py +++ b/docs/examples/intrinsics/factuality_correction.py @@ -83,6 +83,21 @@ # Create the backend. backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add(document) diff --git a/docs/examples/intrinsics/factuality_detection.py b/docs/examples/intrinsics/factuality_detection.py index 9418ff9b6..f3d682415 100644 --- a/docs/examples/intrinsics/factuality_detection.py +++ b/docs/examples/intrinsics/factuality_detection.py @@ -26,6 +26,21 @@ # Create the backend. backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add(document) diff --git a/docs/examples/intrinsics/guardian_core.py b/docs/examples/intrinsics/guardian_core.py index 40951e65c..2df898ac9 100644 --- a/docs/examples/intrinsics/guardian_core.py +++ b/docs/examples/intrinsics/guardian_core.py @@ -20,6 +20,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- # ---- Example 1: Prompt-level harm detection ---- print("--- Example 1: Prompt-level Harm Detection ---") diff --git a/docs/examples/intrinsics/hallucination_detection.py b/docs/examples/intrinsics/hallucination_detection.py index babfe789c..0d2628d27 100644 --- a/docs/examples/intrinsics/hallucination_detection.py +++ b/docs/examples/intrinsics/hallucination_detection.py @@ -16,6 +16,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add(Message("assistant", "Hello there, how can I help you?")) diff --git a/docs/examples/intrinsics/intrinsics.py b/docs/examples/intrinsics/intrinsics.py index f69f3a7b8..65a07d8a2 100644 --- a/docs/examples/intrinsics/intrinsics.py +++ b/docs/examples/intrinsics/intrinsics.py @@ -10,6 +10,21 @@ # for helper functions. backend = LocalHFBackend(model_id="ibm-granite/granite-3.3-8b-instruct") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- # Create the Adapter. IntrinsicAdapter's default to ALORAs. req_adapter = IntrinsicAdapter( diff --git a/docs/examples/intrinsics/policy_guardrails.py b/docs/examples/intrinsics/policy_guardrails.py index c9772ce70..2e508ac0b 100644 --- a/docs/examples/intrinsics/policy_guardrails.py +++ b/docs/examples/intrinsics/policy_guardrails.py @@ -18,6 +18,21 @@ # Create the backend. backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- policy_text = "hiring managers should steer away from any questions that directly seek information about protected classes—such as “how old are you,” “where are you from,” “what year did you graduate” or “what are your plans for having kids.”" diff --git a/docs/examples/intrinsics/query_clarification.py b/docs/examples/intrinsics/query_clarification.py index 8b8973e39..d9ed18adf 100644 --- a/docs/examples/intrinsics/query_clarification.py +++ b/docs/examples/intrinsics/query_clarification.py @@ -14,6 +14,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- # Example 1: Ambiguous query that needs clarification (Positive Example) print("=" * 80) diff --git a/docs/examples/intrinsics/query_rewrite.py b/docs/examples/intrinsics/query_rewrite.py index 07d42fc1a..35650dfb1 100644 --- a/docs/examples/intrinsics/query_rewrite.py +++ b/docs/examples/intrinsics/query_rewrite.py @@ -14,6 +14,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add(Message("assistant", "Welcome to pet questions!")) diff --git a/docs/examples/intrinsics/requirement_check.py b/docs/examples/intrinsics/requirement_check.py index c0d5792e5..7096d0e8b 100644 --- a/docs/examples/intrinsics/requirement_check.py +++ b/docs/examples/intrinsics/requirement_check.py @@ -41,6 +41,21 @@ requirement = "Use a professional tone." backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add(Message("user", user_text)) diff --git a/docs/examples/intrinsics/uncertainty.py b/docs/examples/intrinsics/uncertainty.py index d3318d151..fdc9c8119 100644 --- a/docs/examples/intrinsics/uncertainty.py +++ b/docs/examples/intrinsics/uncertainty.py @@ -17,6 +17,21 @@ from mellea.stdlib.context import ChatContext backend = LocalHFBackend(model_id="ibm-granite/granite-4.0-micro") +# --- Alternative: OpenAI backend with Granite Switch (requires vLLM server) --- +# Requires the adapter for this intrinsic to be embedded in the Granite Switch +# model. See docs/examples/granite-switch/ for a full runnable example. +# from mellea.backends.openai import OpenAIBackend +# from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +# from mellea.formatters import TemplateFormatter +# +# backend = OpenAIBackend( +# model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name, +# formatter=TemplateFormatter(model_id=IBM_GRANITE_SWITCH_4_1_3B.hf_model_name), +# base_url="http://localhost:8000/v1", # vLLM server URL +# api_key="EMPTY", +# load_embedded_adapters=True, +# ) +# --- End alternative --- context = ( ChatContext() .add(Message("user", "What is the square root of 4?")) diff --git a/mellea/backends/adapters/__init__.py b/mellea/backends/adapters/__init__.py index 131a1c1b1..d593a1b36 100644 --- a/mellea/backends/adapters/__init__.py +++ b/mellea/backends/adapters/__init__.py @@ -3,6 +3,7 @@ from .adapter import ( AdapterMixin, AdapterType, + EmbeddedIntrinsicAdapter, IntrinsicAdapter, LocalHFAdapter, fetch_intrinsic_metadata, @@ -12,6 +13,7 @@ __all__ = [ "AdapterMixin", "AdapterType", + "EmbeddedIntrinsicAdapter", "IntrinsicAdapter", "LocalHFAdapter", "fetch_intrinsic_metadata", diff --git a/mellea/backends/adapters/adapter.py b/mellea/backends/adapters/adapter.py index 80ac85837..739671030 100644 --- a/mellea/backends/adapters/adapter.py +++ b/mellea/backends/adapters/adapter.py @@ -304,6 +304,168 @@ def list_adapters(self) -> list[str]: ) +class EmbeddedIntrinsicAdapter(Adapter): + """Adapter for intrinsics embedded in a Granite Switch model. + + Unlike PEFT-based adapters that are loaded into the model at runtime, + embedded adapters are already baked into the model weights and activated + via control tokens injected by the model's chat template. Only the I/O + transformation config (``io.yaml``) is needed; no adapter weights are + downloaded or loaded. + + Args: + intrinsic_name (str): Name of the intrinsic (e.g. ``"answerability"``). + config (dict): Parsed I/O transformation configuration (from ``io.yaml``). + technology (str): Adapter technology in the switch model — ``"lora"`` or + ``"alora"``. Determines where the control token is placed in the + chat template (beginning of sequence for LoRA, before generation + prompt for aLoRA). + + Attributes: + intrinsic_name (str): Name of the intrinsic this adapter implements. + config (dict): Parsed I/O transformation configuration. + technology (str): ``"lora"`` or ``"alora"``. + """ + + def __init__(self, intrinsic_name: str, config: dict, technology: str = "lora"): + """Initialize an embedded intrinsic adapter with its I/O config.""" + if technology not in ("lora", "alora"): + raise ValueError( + f"technology must be 'lora' or 'alora', got '{technology}'" + ) + adapter_type = AdapterType.ALORA if technology == "alora" else AdapterType.LORA + super().__init__(intrinsic_name, adapter_type) + self.intrinsic_name = intrinsic_name + self.config = config + self.technology = technology + + @staticmethod + def from_model_directory( + model_path: str | pathlib.Path, intrinsic_name: str | None = None + ) -> list["EmbeddedIntrinsicAdapter"]: + """Load embedded adapters from a Granite Switch model directory. + + Reads ``adapter_index.json`` and the corresponding ``io_configs/*/io.yaml`` + files from the model directory. + + Args: + model_path (str | pathlib.Path): Path to a Granite Switch model + directory that contains ``adapter_index.json`` and ``io_configs/``. + intrinsic_name (str | None): If provided, only load the adapter + matching this intrinsic name. ``None`` loads all adapters. + + Returns: + list[EmbeddedIntrinsicAdapter]: One adapter per entry in the index. + + Raises: + FileNotFoundError: If ``adapter_index.json`` is missing. + ValueError: If an ``io.yaml`` file listed in the index cannot be found + or if no adapters are found. + """ + import json as _json + + model_path = pathlib.Path(model_path) + index_path = model_path / "adapter_index.json" + if not index_path.exists(): + raise FileNotFoundError(f"No adapter_index.json found at {index_path}") + + with open(index_path, encoding="utf-8") as f: + index = _json.load(f) + + adapters: list[EmbeddedIntrinsicAdapter] = [] + for entry in index.get("adapters", []): + entry_name = entry.get("adapter_name") + if entry_name is None: + continue + if intrinsic_name is not None and entry_name != intrinsic_name: + continue + io_config_rel = entry.get("io_config") + if io_config_rel is None: + continue + + io_config_path = model_path / io_config_rel + if not io_config_path.exists(): + raise ValueError( + f"io.yaml for intrinsic '{entry_name}' " + f"not found at {io_config_path}" + ) + + with open(io_config_path, encoding="utf-8") as f: + config_dict = yaml.safe_load(f) + + adapters.append( + EmbeddedIntrinsicAdapter( + intrinsic_name=entry_name, + config=config_dict, + technology=entry.get("technology", "lora"), + ) + ) + + if not adapters: + if intrinsic_name is not None: + raise ValueError( + f"No adapter found for intrinsic '{intrinsic_name}' in {model_path}" + ) + raise ValueError(f"No adapters found in {model_path}") + + return adapters + + @staticmethod + def from_hub( + repo_id: str, + revision: str = "main", + cache_dir: str | None = None, + intrinsic_name: str | None = None, + ) -> list["EmbeddedIntrinsicAdapter"]: + """Load embedded adapters from a Granite Switch model on HuggingFace Hub. + + Downloads ``adapter_index.json`` and the ``io_configs/`` directory, then + delegates to :meth:`from_model_directory`. + + Args: + repo_id (str): HuggingFace Hub repository ID + (e.g. ``"ibm-granite/granite-switch-micro"``). + revision (str): Git revision to download from. + cache_dir (str | None): Local cache directory; ``None`` for the default. + intrinsic_name (str | None): If provided, only load the adapter + matching this intrinsic name. ``None`` loads all adapters. + + Returns: + list[EmbeddedIntrinsicAdapter]: One adapter per entry in the index. + + Raises: + ImportError: If ``huggingface_hub`` is not installed. + FileNotFoundError: If ``adapter_index.json`` is missing (delegated + from :meth:`from_model_directory`). + ValueError: If no adapters are found (delegated from + :meth:`from_model_directory`). + """ + try: + import huggingface_hub + except ImportError as e: + raise ImportError( + "huggingface_hub is required to download embedded adapter configs from " + 'HuggingFace Hub. Please install it with: pip install "mellea[switch]"' + ) from e + + local_root = huggingface_hub.snapshot_download( + repo_id=repo_id, + allow_patterns=["adapter_index.json", "io_configs/**"], + cache_dir=cache_dir, + revision=revision, + ) + try: + return EmbeddedIntrinsicAdapter.from_model_directory( + local_root, intrinsic_name=intrinsic_name + ) + except ValueError as e: + if intrinsic_name is not None: + raise ValueError( + f"No adapter found for intrinsic '{intrinsic_name}' in {repo_id}" + ) from e + raise ValueError(f"No adapters found in {repo_id}") from e + + class CustomIntrinsicAdapter(IntrinsicAdapter): """Special class for users to subclass when creating custom intrinsic adapters. diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index b7c8d693e..db651864d 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -499,7 +499,7 @@ async def _generate_from_intrinsic( if len(model_options.items()) > 0: MelleaLogger.get_logger().info( - "passing in model options when generating with an adapter; some model options may be overwritten / ignored" + "passing in model options when generating with an intrinsic; only temperature and seed are kept from model options" ) linearized_ctx = ctx.view_for_generation() @@ -514,10 +514,6 @@ async def _generate_from_intrinsic( # through their rewriters. conversation: list[dict] = [] - system_prompt = model_options.get(ModelOption.SYSTEM_PROMPT, "") - if system_prompt != "": - conversation.append({"role": "system", "content": system_prompt}) - conversation.extend([message_to_openai_message(m) for m in ctx_as_message_list]) docs = messages_to_docs(ctx_as_message_list) @@ -526,9 +522,9 @@ async def _generate_from_intrinsic( if seed is not None: set_seed(seed) - if model_options.get(ModelOption.STREAM, None) is not None: - # Intrinsics don't support streaming because of their post-processing step. - raise Exception("Intrinsics do not support streaming.") + # Intrinsics don't support streaming because of their post-processing step. + if model_options.get(ModelOption.STREAM, False): + raise NotImplementedError("Intrinsics do not support streaming.") adapter = get_adapter_for_intrinsic( action.intrinsic_name, action.adapter_types, self._added_adapters @@ -540,9 +536,10 @@ async def _generate_from_intrinsic( # TODO: Code below this point is mostly specific to RagIntrinsics # It should be refactored into a specific adapter.transform() function. - assert isinstance(adapter, IntrinsicAdapter), ( - "currently Mellea only supports IntrinsicAdapters and Intrinsics" - ) + if not isinstance(adapter, IntrinsicAdapter): + raise TypeError( + f"LocalHFBackend only supports IntrinsicAdapters, got: {type(adapter).__name__}" + ) intrinsic_config = adapter.config assert intrinsic_config is not None diff --git a/mellea/backends/model_ids.py b/mellea/backends/model_ids.py index 61e163c9c..09ce73acb 100644 --- a/mellea/backends/model_ids.py +++ b/mellea/backends/model_ids.py @@ -119,6 +119,12 @@ class ModelIdentifier: hf_model_name="ibm-granite/granite-4.0-tiny-base-preview" ) +# Pre-Built Granite Switch Models +IBM_GRANITE_SWITCH_4_1_3B = ModelIdentifier( + hf_model_name="GrizleeBer/gs-test-1" # TODO: Placeholder. Change this value. +) +"""Granite Switch.""" # Document what adapters are included by default here. + ##################### #### Meta models #### ##################### diff --git a/mellea/backends/openai.py b/mellea/backends/openai.py index 759f50609..111dcf838 100644 --- a/mellea/backends/openai.py +++ b/mellea/backends/openai.py @@ -29,7 +29,7 @@ Requirement, ) from ..core.base import AbstractMelleaTool -from ..formatters import ChatFormatter, TemplateFormatter +from ..formatters import ChatFormatter, TemplateFormatter, granite as granite_formatters from ..helpers import ( ClientCache, _server_type, @@ -50,6 +50,13 @@ start_generate_span, ) from ..telemetry.context import generate_request_id, with_context +from .adapters.adapter import ( + Adapter, + AdapterMixin, + EmbeddedIntrinsicAdapter, + get_adapter_for_intrinsic, +) +from .adapters.catalog import AdapterType from .backend import FormatterBackend from .model_options import ModelOption from .tools import ( @@ -66,7 +73,7 @@ format: None = None # typing this variable in order to shadow the global format function and ensure mypy checks for errors -class OpenAIBackend(FormatterBackend): +class OpenAIBackend(FormatterBackend, AdapterMixin): """A generic OpenAI compatible backend. Args: @@ -79,6 +86,10 @@ class OpenAIBackend(FormatterBackend): model_options (dict | None): Default model options for generation requests. default_to_constraint_checking_alora (bool): If ``False``, deactivates aLoRA constraint checking; primarily for benchmarking and debugging. + load_embedded_adapters (bool): If ``True``, automatically download and register + embedded intrinsic adapters from the model specified by *model_id*. + The model ID is used as a HuggingFace Hub repo ID to load + ``adapter_index.json`` and the corresponding I/O configs. api_key (str | None): API key; falls back to ``OPENAI_API_KEY`` env var. kwargs: Additional keyword arguments forwarded to the OpenAI client. @@ -101,6 +112,7 @@ def __init__( model_options: dict | None = None, *, default_to_constraint_checking_alora: bool = True, + load_embedded_adapters: bool = False, api_key: str | None = None, **kwargs, ): @@ -202,9 +214,92 @@ def __init__( self._client_cache = ClientCache(2) + self._added_adapters: dict[str, EmbeddedIntrinsicAdapter] = {} + # Call once to create an async_client and populate the cache. _ = self._async_client + # TODO: We should change this logic once we have a better protocol for "auto-loading" + # adapters during call_intrinsic, or once we support other types of adapters for + # OpenAIBackends. + # OpenAI Backends only support embedded_adapters. + self._uses_embedded_adapters = True + if load_embedded_adapters: + self.register_embedded_adapter_model(self._model_id) + + # ------------------------------------------------------------------ + # AdapterMixin implementation + # ------------------------------------------------------------------ + + def add_adapter(self, adapter: Adapter) -> None: + """Register an adapter with this backend. + + Currently only :class:`EmbeddedIntrinsicAdapter` is supported. + + Args: + adapter: The adapter to register. + + Raises: + TypeError: If *adapter* is not an ``EmbeddedIntrinsicAdapter``. + """ + if not isinstance(adapter, EmbeddedIntrinsicAdapter): + raise TypeError( + f"OpenAIBackend currently only supports EmbeddedIntrinsicAdapter. " + f"Got: {type(adapter).__name__}" + ) + adapter.backend = self + self._added_adapters[adapter.qualified_name] = adapter + + def load_adapter(self, adapter_qualified_name: str) -> None: + """No-op for embedded adapters — weights are baked into the model.""" + MelleaLogger.get_logger().debug( + "load_adapter is a no-op for OpenAIBackends (adapter: %s)", + adapter_qualified_name, + ) + + def unload_adapter(self, adapter_qualified_name: str) -> None: + """No-op for embedded adapters — weights are baked into the model.""" + MelleaLogger.get_logger().debug( + "unload_adapter is a no-op for OpenAIBackends (adapter: %s)", + adapter_qualified_name, + ) + + def list_adapters(self) -> list[str]: + """Return qualified names of all registered adapters. + + Returns: + list[str]: Qualified adapter names. + """ + return list(self._added_adapters.keys()) + + # ------------------------------------------------------------------ + # Convenience registration helpers + # ------------------------------------------------------------------ + + def register_embedded_adapter_model( + self, source: str, *, revision: str = "main", cache_dir: str | None = None + ) -> list[str]: + """Register all embedded adapters from an Embedded Adapter model. + + Args: + source (str): a HuggingFace Hub repo ID. + revision (str): Git revision when loading from HuggingFace Hub. + cache_dir (str | None): Cache directory for HF downloads. + + Returns: + list[str]: Names of the registered intrinsics. + """ + import os + + adapters = EmbeddedIntrinsicAdapter.from_hub( + source, revision=revision, cache_dir=cache_dir + ) + + for adapter in adapters: + self.add_adapter(adapter) + + return [a.intrinsic_name for a in adapters] + @property def _async_client(self) -> openai.AsyncOpenAI: """OpenAI's client usually handles changing event loops but explicitly handle it here for edge cases.""" @@ -363,10 +458,6 @@ async def _generate_from_context( "The Openai backend only supports chat-like contexts." ) - assert not isinstance(action, Intrinsic), ( - "The openai backend does not currently support adapters, intrinsics, loras, or aloras." - ) - # Start span without auto-closing (will be closed in post_processing) span = start_generate_span( backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls @@ -374,6 +465,58 @@ async def _generate_from_context( _model_id_str = str(getattr(self, "model_id", "unknown")) with with_context(request_id=generate_request_id(), model_id=_model_id_str): + await self.do_generate_walk(action) + + model_opts = self._simplify_and_merge( + model_options, is_chat_context=ctx.is_chat_context + ) + + # Requirements can be automatically rerouted to a requirement adapter. + if isinstance(action, Requirement): + reroute_to_alora = self.default_to_constraint_checking_alora + adapter_name = "requirement_check" + + if isinstance(action, ALoraRequirement): + reroute_to_alora = True + adapter_name = action.intrinsic_name + alora_action = action + else: + assert action.description is not None, ( + "must have a description when generating from a requirement" + ) + alora_action = ALoraRequirement(action.description, adapter_name) + + alora_req_adapter = get_adapter_for_intrinsic( + adapter_name, [AdapterType.ALORA], self._added_adapters + ) + if alora_req_adapter is None: + if reroute_to_alora and isinstance(action, ALoraRequirement): + MelleaLogger.get_logger().warning( + f"attempted to use an AloraRequirement but backend {self} " + f"doesn't have the specified adapter added {adapter_name}; " + f"defaulting to regular generation" + ) + reroute_to_alora = False + + if issubclass(type(action), LLMaJRequirement): + reroute_to_alora = False + + if reroute_to_alora: + mot = await self._generate_from_intrinsic( + alora_action, ctx, model_options=model_opts + ) + if span is not None: + mot._meta["_telemetry_span"] = span + return mot, ctx.add(alora_action).add(mot) + + elif isinstance(action, Intrinsic): + mot = await self._generate_from_intrinsic( + action, ctx, model_options=model_opts + ) + if span is not None: + mot._meta["_telemetry_span"] = span + return mot, ctx.add(action).add(mot) + result = await self.generate_from_chat_context( action, ctx, @@ -381,12 +524,215 @@ async def _generate_from_context( model_options=model_options, tool_calls=tool_calls, ) + # Store span in ModelOutputThunk for later use in post_processing mot, new_ctx = result if span is not None: mot._meta["_telemetry_span"] = span return mot, new_ctx + async def _generate_from_intrinsic( + self, action: Intrinsic, ctx: Context, *, model_options: dict[str, Any] + ) -> ModelOutputThunk: + """Generate a completion for an intrinsic action using an embedded adapter. + + Applies the intrinsic's I/O rewriter to transform the conversation, + injects ``intrinsic_name`` into ``chat_template_kwargs`` so that the + Granite Switch chat template activates the correct adapter, and + post-processes the model output through the intrinsic's result + processor. + + Args: + action (Intrinsic): The intrinsic component to execute. + ctx (Context): The current generation context (must be a chat context). + model_options (dict[str, Any]): Merged model options for this call. + + Returns: + ModelOutputThunk: A thunk that lazily resolves to the processed + intrinsic output. + + Raises: + ValueError: If no embedded adapter is registered for the requested + intrinsic. + """ + if not ctx.is_chat_context: + raise NotImplementedError("Intrinsics require a chat context.") + + # Intrinsics don't support streaming because of their post-processing step. + if model_options.get(ModelOption.STREAM, False): + raise NotImplementedError("Intrinsics do not support streaming.") + + if len(model_options.items()) > 0: + MelleaLogger.get_logger().info( + "passing in model options when generating with an intrinsic; only temperature and seed are kept from model options" + ) + + # --- adapter lookup ------------------------------------------------ + adapter = get_adapter_for_intrinsic( + action.intrinsic_name, action.adapter_types, self._added_adapters + ) + if adapter is None: + raise ValueError( + f"backend ({self}) has no adapter for processing intrinsic: " + f"{action.intrinsic_name}" + ) + + # TODO: OpenAIBackend only supports EmbeddedAdapters. + # It should be refactored into a specific adapter.transform() function. + if not isinstance(adapter, EmbeddedIntrinsicAdapter): + raise TypeError( + f"OpenAIBackend only supports EmbeddedIntrinsicAdapter, got: {type(adapter).__name__}" + ) + + intrinsic_config = adapter.config + assert intrinsic_config is not None + + rewriter = granite_formatters.IntrinsicsRewriter( + config_dict=intrinsic_config, model_name=adapter.name + ) + result_processor = granite_formatters.IntrinsicsResultProcessor( + config_dict=intrinsic_config + ) + + # --- linearize context and build conversation ---------------------- + linearized_context = ctx.view_for_generation() + assert linearized_context is not None, ( + "If ctx.is_chat_context, then the context should be linearizable." + ) + + # NOTE: Explicitly do not add the action to the context here. + # Intrinsics modify the context through their rewriters. + messages: list[Message] = self.formatter.to_chat_messages(linearized_context) + + conversation: list[dict] = [] + conversation.extend([message_to_openai_message(m) for m in messages]) + + docs = messages_to_docs(messages) + + # Seed and temperature are used in the api call. The rewriter doesn't pass them + # along so we set them explicitly below. + seed = model_options.get(ModelOption.SEED, None) + temperature = model_options.get(ModelOption.TEMPERATURE, None) + + # Convert our conversation into a proper chat completions dict. + request_json: dict = { + "messages": conversation, + "extra_body": {"documents": docs}, + } + + rewritten = rewriter.transform(request_json, **action.intrinsic_kwargs) + + # --- prepare extra_body and api_params -------------------------------- + extra_body = {} + if rewritten.extra_body is not None: + extra_body = rewritten.extra_body.model_dump(exclude_unset=True) + + api_params: dict[str, Any] = {} + if rewriter.parameters: + api_params.update(rewriter.parameters) + + # Embedded adapters activate via control tokens in the chat template. + if isinstance(adapter, EmbeddedIntrinsicAdapter): + chat_template_kwargs = extra_body.pop("chat_template_kwargs", {}) or {} + chat_template_kwargs["adapter_name"] = action.intrinsic_name + extra_body["chat_template_kwargs"] = chat_template_kwargs + # The rewriter config may set `model` to the adapter name, but + # for embedded adapters the actual model is self._model_id. + api_params.pop("model", None) + + if seed is not None: + api_params["seed"] = seed + + if temperature is not None: + api_params["temperature"] = temperature + + # --- call the OpenAI-compatible API -------------------------------- + # The rewriter may add instruction messages where 'role' is a default + # (e.g. UserMessage with role="user"). exclude_unset would drop it, + # so we always force 'role' into the serialized dict. + messages_dicts = [] + for m in rewritten.messages: + d = m.model_dump(exclude_unset=True) + if "role" not in d: + d["role"] = m.role + messages_dicts.append(d) + + chat_response = self._async_client.chat.completions.create( + model=self._model_id, + messages=messages_dicts, # type: ignore + extra_body=extra_body, + **api_params, + ) + + # --- wire up ModelOutputThunk with intrinsic post-processing ------ + output = ModelOutputThunk(None) + output._start = datetime.datetime.now() + output._context = linearized_context + output._action = action + output._model_options = model_options + + async def granite_formatters_processing( + mot: ModelOutputThunk, + chunk: ChatCompletion, + rewritten: granite_formatters.ChatCompletion, + result_processor: granite_formatters.IntrinsicsResultProcessor, + ): + """Accumulate content and apply intrinsic result processing.""" + import json as _json + + # Delegate standard metadata storage to the shared processing method. + await self.processing(mot, chunk) + + # Apply intrinsic-specific result transformation on top. + response_dict = chunk.model_dump() + try: + res = result_processor.transform(response_dict, rewritten) + except _json.JSONDecodeError as e: + raise Exception( + f"Intrinsic did not return a JSON: " + f"{chunk.choices[0].message.content}" + ) from e + + # Overwrite the value accumulated by processing() with the + # post-processed intrinsic output. + mot._underlying_value = res.choices[0].message.content + + # Processing functions only pass the ModelOutputThunk (and current chunk + # of response). Bind the other vars necessary for each processing step. + output._process = functools.partial( + granite_formatters_processing, + rewritten=rewritten, + result_processor=result_processor, + ) + + output._post_process = functools.partial( + self.post_processing, + tools={}, + conversation=conversation, + thinking=None, + seed=seed, + _format=None, + ) + + try: + # To support lazy computation, will need to remove this create_task + # and store just the unexecuted coroutine. + # We can also support synchronous calls by adding a flag and changing + # this ._generate function. + + # This function should always be called from a running event loop so + # we don't have to worry about scheduling the task to a specific + # event loop here. + output._generate = asyncio.create_task( + send_to_queue(chat_response, output._async_queue) + ) + output._generate_type = GenerateType.ASYNC + except RuntimeError as e: + # Most likely cause is running this function without an event loop present. + raise e + + return output + async def generate_from_chat_context( self, action: Component[C] | CBlock, @@ -449,7 +795,7 @@ async def _generate_from_chat_context_standard( match action: case ALoraRequirement(): raise Exception( - "The OpenAI backend does not support currently support activated LoRAs." + "The OpenAI backend does not currently support activated LoRAs." ) case _: messages.extend(self.formatter.to_chat_messages([action])) diff --git a/mellea/stdlib/components/intrinsic/_util.py b/mellea/stdlib/components/intrinsic/_util.py index 2d5740088..4818f32e8 100644 --- a/mellea/stdlib/components/intrinsic/_util.py +++ b/mellea/stdlib/components/intrinsic/_util.py @@ -3,7 +3,12 @@ import json from ....backends import ModelOption -from ....backends.adapters import AdapterMixin, AdapterType, IntrinsicAdapter +from ....backends.adapters import ( + AdapterMixin, + AdapterType, + EmbeddedIntrinsicAdapter, + IntrinsicAdapter, +) from ....stdlib import functional as mfuncs from ...context import ChatContext from .intrinsic import Intrinsic @@ -26,11 +31,33 @@ def call_intrinsic( base_model_name = backend.base_model_name if base_model_name is None: raise ValueError("Backend has no model ID") - adapter = IntrinsicAdapter( - intrinsic_name, adapter_type=AdapterType.LORA, base_model_name=base_model_name + + # Check if the backend already has the adapter. + has_adapter = any( + qualified_name.startswith(f"{intrinsic_name}_") + for qualified_name in backend.list_adapters() ) - if adapter.qualified_name not in backend.list_adapters(): - backend.add_adapter(adapter) + + # TODO: We should improve this logic. For now, we know that there are two cases of + # adapter loading: 1. regular adapters, and 2. embedded adapters. + if not has_adapter: + # EmbeddedAdapters get grabbed directly from the hf repo. + if getattr(backend, "_uses_embedded_adapters", False): + repo_id = getattr(backend, "_model_id", backend.base_model_name) + adapters = EmbeddedIntrinsicAdapter.from_hub( + repo_id, intrinsic_name=intrinsic_name + ) + # Only one adapter should be returned, but we add any returned here in case. + for adapter in adapters: + backend.add_adapter(adapter) + else: + # Regular IntrinsicAdapters utilize a catalog to download during their instantiation. + intrinsic_adapter = IntrinsicAdapter( + intrinsic_name, + adapter_type=AdapterType.LORA, + base_model_name=base_model_name, + ) + backend.add_adapter(intrinsic_adapter) # Create the AST node for the action we wish to perform. intrinsic = Intrinsic(intrinsic_name, intrinsic_kwargs=kwargs) diff --git a/pyproject.toml b/pyproject.toml index 0b6d3c9ff..bffcf6dcc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -102,6 +102,10 @@ sandbox = [ "llm-sandbox[docker]>=0.3.23", ] +switch = [ + "huggingface-hub>=0.33.4", +] + backends = ["mellea[watsonx,hf,litellm]"] hooks = [ @@ -109,7 +113,7 @@ hooks = [ "grpcio>=1.78.0", ] -all = ["mellea[backends,docling,tools,telemetry,server,sandbox,granite_retriever,hooks,cli]"] +all = ["mellea[backends,docling,tools,telemetry,server,sandbox,switch,granite_retriever,hooks,cli]"] [dependency-groups] # Development groups: uv sync --all-groups @@ -318,7 +322,7 @@ disable_error_code = [ # ----------------------------- [tool.codespell] -ignore-words-list = 'mellea,hashi,noo,Asai,asai,nd,mot,rouge,Rouge,Strat,strat,Wight,Aer,aer,preemptable' +ignore-words-list = 'mellea,hashi,noo,Asai,asai,nd,mot,rouge,Rouge,Strat,strat,Wight,Aer,aer,preemptable,ans' check-filenames = true check-hidden = false regex = "(?", + "token_visible": "", + "id": 100366, + "id_visible": 100367, + }, + }, + { + "adapter_index": 2, + "adapter_name": "citations", + "technology": "lora", + "io_config": "io_configs/citations/io.yaml", + "control_token": { + "token": "", + "token_visible": "", + "id": 100354, + "id_visible": 100355, + }, + }, + ], +} + + +@pytest.fixture +def model_dir(tmp_path): + """Create a mock Granite Switch model directory with adapter_index.json and io configs.""" + (tmp_path / "adapter_index.json").write_text(json.dumps(_SAMPLE_ADAPTER_INDEX)) + + ans_dir = tmp_path / "io_configs" / "answerability" + ans_dir.mkdir(parents=True) + (ans_dir / "io.yaml").write_text(yaml.dump(_ANSWERABILITY_CONFIG)) + + cit_dir = tmp_path / "io_configs" / "citations" + cit_dir.mkdir(parents=True) + (cit_dir / "io.yaml").write_text(yaml.dump(_CITATIONS_CONFIG)) + + return tmp_path + + +# ---- EmbeddedIntrinsicAdapter.__init__ ---- + + +class TestEmbeddedIntrinsicAdapterInit: + def test_alora_technology(self): + adapter = EmbeddedIntrinsicAdapter( + intrinsic_name="answerability", + config=_ANSWERABILITY_CONFIG, + technology="alora", + ) + assert adapter.intrinsic_name == "answerability" + assert adapter.name == "answerability" + assert adapter.technology == "alora" + assert adapter.adapter_type == AdapterType.ALORA + assert adapter.qualified_name == "answerability_alora" + assert adapter.config is _ANSWERABILITY_CONFIG + + def test_lora_technology(self): + adapter = EmbeddedIntrinsicAdapter( + intrinsic_name="citations", config=_CITATIONS_CONFIG, technology="lora" + ) + assert adapter.adapter_type == AdapterType.LORA + assert adapter.qualified_name == "citations_lora" + + def test_default_technology_is_lora(self): + adapter = EmbeddedIntrinsicAdapter( + intrinsic_name="test", config={"model": None} + ) + assert adapter.technology == "lora" + assert adapter.adapter_type == AdapterType.LORA + + def test_invalid_technology_raises(self): + with pytest.raises(ValueError, match="must be 'lora' or 'alora'"): + EmbeddedIntrinsicAdapter( + intrinsic_name="test", config={"model": None}, technology="qlora" + ) + + def test_inherited_adapter_defaults(self): + adapter = EmbeddedIntrinsicAdapter( + intrinsic_name="test", config={"model": None} + ) + assert adapter.backend is None + assert adapter.path is None + + +# ---- EmbeddedIntrinsicAdapter.from_model_directory ---- + + +class TestFromModelDirectory: + def test_loads_all_adapters(self, model_dir): + adapters = EmbeddedIntrinsicAdapter.from_model_directory(model_dir) + + assert len(adapters) == 2 + names = {a.intrinsic_name for a in adapters} + assert names == {"answerability", "citations"} + + ans = next(a for a in adapters if a.intrinsic_name == "answerability") + assert ans.technology == "alora" + assert ans.config["parameters"]["max_completion_tokens"] == 6 + + cit = next(a for a in adapters if a.intrinsic_name == "citations") + assert cit.technology == "lora" + + def test_accepts_string_path(self, model_dir): + adapters = EmbeddedIntrinsicAdapter.from_model_directory(str(model_dir)) + assert len(adapters) == 2 + + def test_missing_adapter_index(self, tmp_path): + with pytest.raises(FileNotFoundError, match=r"adapter_index\.json"): + EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + + def test_missing_io_yaml(self, tmp_path): + (tmp_path / "adapter_index.json").write_text(json.dumps(_SAMPLE_ADAPTER_INDEX)) + with pytest.raises(ValueError, match=r"io\.yaml.*not found"): + EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + + def test_skips_entry_without_io_config(self, tmp_path): + """Entries with io_config=None are silently skipped.""" + index = { + "adapters": [ + {"adapter_name": "no_config", "technology": "lora"}, + { + "adapter_name": "has_config", + "technology": "lora", + "io_config": "io_configs/has_config/io.yaml", + }, + ] + } + (tmp_path / "adapter_index.json").write_text(json.dumps(index)) + cfg_dir = tmp_path / "io_configs" / "has_config" + cfg_dir.mkdir(parents=True) + (cfg_dir / "io.yaml").write_text(yaml.dump({"model": None})) + + adapters = EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + assert len(adapters) == 1 + assert adapters[0].intrinsic_name == "has_config" + + def test_defaults_technology_to_lora(self, tmp_path): + """Entries without a 'technology' key default to lora.""" + index = { + "adapters": [ + { + "adapter_name": "test", + "io_config": "io_configs/test/io.yaml", + # no "technology" key + } + ] + } + (tmp_path / "adapter_index.json").write_text(json.dumps(index)) + cfg_dir = tmp_path / "io_configs" / "test" + cfg_dir.mkdir(parents=True) + (cfg_dir / "io.yaml").write_text(yaml.dump({"model": None})) + + adapters = EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + assert len(adapters) == 1 + assert adapters[0].technology == "lora" + + def test_empty_adapters_list(self, tmp_path): + (tmp_path / "adapter_index.json").write_text(json.dumps({"adapters": []})) + with pytest.raises(ValueError, match="No adapters found"): + EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + + def test_no_adapters_key(self, tmp_path): + """Index with no 'adapters' key raises ValueError.""" + (tmp_path / "adapter_index.json").write_text(json.dumps({})) + with pytest.raises(ValueError, match="No adapters found"): + EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + + def test_filter_single_intrinsic(self, model_dir): + adapters = EmbeddedIntrinsicAdapter.from_model_directory( + model_dir, intrinsic_name="answerability" + ) + assert len(adapters) == 1 + assert adapters[0].intrinsic_name == "answerability" + + def test_filter_nonexistent_intrinsic(self, model_dir): + with pytest.raises( + ValueError, match="No adapter found for intrinsic 'nonexistent'" + ): + EmbeddedIntrinsicAdapter.from_model_directory( + model_dir, intrinsic_name="nonexistent" + ) + + def test_adapter_name_key(self, tmp_path): + """Index with 'adapter_name' key is read correctly.""" + index = { + "adapters": [ + { + "adapter_name": "answerability", + "technology": "alora", + "io_config": "io_configs/answerability/io.yaml", + } + ] + } + (tmp_path / "adapter_index.json").write_text(json.dumps(index)) + cfg_dir = tmp_path / "io_configs" / "answerability" + cfg_dir.mkdir(parents=True) + (cfg_dir / "io.yaml").write_text(yaml.dump(_ANSWERABILITY_CONFIG)) + + adapters = EmbeddedIntrinsicAdapter.from_model_directory(tmp_path) + assert len(adapters) == 1 + assert adapters[0].intrinsic_name == "answerability" + + +# ---- EmbeddedIntrinsicAdapter.from_hub ---- + + +class TestFromHub: + def test_downloads_and_delegates(self, model_dir): + """from_hub calls snapshot_download then delegates to from_model_directory.""" + with patch( + "huggingface_hub.snapshot_download", return_value=str(model_dir) + ) as mock_dl: + adapters = EmbeddedIntrinsicAdapter.from_hub( + "ibm-granite/granite-switch-micro", + revision="test-rev", + cache_dir="/tmp/test-cache", + ) + + mock_dl.assert_called_once_with( + repo_id="ibm-granite/granite-switch-micro", + allow_patterns=["adapter_index.json", "io_configs/**"], + cache_dir="/tmp/test-cache", + revision="test-rev", + ) + assert len(adapters) == 2 + + def test_filter_single_intrinsic(self, model_dir): + with patch( + "huggingface_hub.snapshot_download", return_value=str(model_dir) + ) as mock_dl: + adapters = EmbeddedIntrinsicAdapter.from_hub( + "ibm-granite/granite-switch-micro", intrinsic_name="citations" + ) + + mock_dl.assert_called_once_with( + repo_id="ibm-granite/granite-switch-micro", + allow_patterns=["adapter_index.json", "io_configs/**"], + cache_dir=None, + revision="main", + ) + assert len(adapters) == 1 + assert adapters[0].intrinsic_name == "citations" + + def test_missing_huggingface_hub_raises(self): + with patch.dict("sys.modules", {"huggingface_hub": None}): + with pytest.raises(ImportError, match="huggingface_hub is required"): + EmbeddedIntrinsicAdapter.from_hub("some/repo") + + +# ---- OpenAIBackend adapter integration ---- + + +class TestOpenAIBackendRegistration: + @pytest.fixture + def backend(self): + os.environ.setdefault("OPENAI_API_KEY", "test-key") + from mellea.backends.openai import OpenAIBackend + + return OpenAIBackend( + model_id="granite-switch", base_url="http://localhost:8000/v1" + ) + + def test_add_adapter(self, backend): + adapter = EmbeddedIntrinsicAdapter( + intrinsic_name="answerability", + config=_ANSWERABILITY_CONFIG, + technology="alora", + ) + backend.add_adapter(adapter) + assert "answerability_alora" in backend._added_adapters + assert backend._added_adapters["answerability_alora"] is adapter + assert adapter.backend is backend + + def test_add_non_embedded_adapter_raises(self, backend): + mock_adapter = MagicMock(spec=[]) + with pytest.raises(TypeError, match="only supports EmbeddedIntrinsicAdapter"): + backend.add_adapter(mock_adapter) + + def test_list_adapters(self, backend): + backend.add_adapter( + EmbeddedIntrinsicAdapter( + "answerability", config=_ANSWERABILITY_CONFIG, technology="alora" + ) + ) + backend.add_adapter( + EmbeddedIntrinsicAdapter( + "citations", config=_CITATIONS_CONFIG, technology="lora" + ) + ) + assert set(backend.list_adapters()) == {"answerability_alora", "citations_lora"} + + def test_load_unload_are_noops(self, backend): + """load_adapter and unload_adapter succeed silently for embedded adapters.""" + backend.add_adapter( + EmbeddedIntrinsicAdapter( + "answerability", config=_ANSWERABILITY_CONFIG, technology="alora" + ) + ) + # These should not raise. + backend.load_adapter("answerability_alora") + backend.unload_adapter("answerability_alora") + # Adapter is still registered after load/unload. + assert "answerability_alora" in backend._added_adapters + + def test_base_model_name(self, backend): + assert backend.base_model_name == "granite-switch" + + def test_register_embedded_adapter_model(self, backend, model_dir): + with patch("huggingface_hub.snapshot_download", return_value=str(model_dir)): + names = backend.register_embedded_adapter_model( + "ibm-granite/granite-switch-micro" + ) + + assert set(names) == {"answerability", "citations"} + assert len(backend._added_adapters) == 2 + + def test_register_overwrites_existing(self, backend): + config1 = {"model": None, "parameters": {"max_completion_tokens": 10}} + config2 = {"model": None, "parameters": {"max_completion_tokens": 20}} + + backend.add_adapter(EmbeddedIntrinsicAdapter("test", config=config1)) + backend.add_adapter(EmbeddedIntrinsicAdapter("test", config=config2)) + + assert ( + backend._added_adapters["test_lora"].config["parameters"][ + "max_completion_tokens" + ] + == 20 + ) + + def test_embedded_adapters_flag_loads_from_model_id(self, model_dir): + """embedded_adapters=True auto-registers adapters using model_id as source.""" + from mellea.backends.openai import OpenAIBackend + + os.environ.setdefault("OPENAI_API_KEY", "test-key") + with patch("huggingface_hub.snapshot_download", return_value=str(model_dir)): + backend = OpenAIBackend( + model_id="ibm-granite/granite-switch-micro", + base_url="http://localhost:8000/v1", + load_embedded_adapters=True, + ) + assert len(backend._added_adapters) == 2 + assert set(backend.list_adapters()) == {"answerability_alora", "citations_lora"} + + def test_embedded_adapters_flag_defaults_to_false(self, backend): + """Without the flag, no adapters are loaded.""" + assert len(backend._added_adapters) == 0 + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "-s"]) diff --git a/test/backends/test_openai_intrinsics.py b/test/backends/test_openai_intrinsics.py new file mode 100644 index 000000000..e4e0c7938 --- /dev/null +++ b/test/backends/test_openai_intrinsics.py @@ -0,0 +1,366 @@ +"""E2E tests for intrinsics on the OpenAI backend with a Granite Switch model. + +Starts a vLLM server hosting a Granite Switch model, creates an OpenAIBackend with +``embedded_adapters=True``, and runs each intrinsic that has matching test data +through the full generation path. +""" + +import json +import os +import pathlib +import signal +import subprocess +import time + +import pytest +import requests + +from test.predicates import require_gpu + +# --------------------------------------------------------------------------- +# Module-level markers +# --------------------------------------------------------------------------- +pytestmark = [ + pytest.mark.openai, + pytest.mark.e2e, + pytest.mark.vllm, + require_gpu(min_vram_gb=12), + pytest.mark.skipif( + int(os.environ.get("CICD", 0)) == 1, + reason="Skipping OpenAI intrinsics tests in CI", + ), + pytest.mark.skip( + reason="Requires additional VLLM setup that isn't yet streamlined. Re-enable once nightlies can run this." + ), +] + +# --------------------------------------------------------------------------- +# Imports (after markers so collection-time skips fire first) +# --------------------------------------------------------------------------- +from mellea.backends.model_ids import IBM_GRANITE_SWITCH_4_1_3B +from mellea.backends.openai import OpenAIBackend +from mellea.formatters import TemplateFormatter +from mellea.stdlib import functional as mfuncs +from mellea.stdlib.components import Intrinsic, Message +from mellea.stdlib.components.docs.document import Document +from mellea.stdlib.components.intrinsic import rag +from mellea.stdlib.context import ChatContext +from test.formatters.granite.test_intrinsics_formatters import ( + _YAML_JSON_COMBOS_WITH_MODEL, + YamlJsonCombo, +) + +# --------------------------------------------------------------------------- +# Configuration +# --------------------------------------------------------------------------- +SWITCH_MODEL_ID = os.environ.get( + "GRANITE_SWITCH_MODEL_ID", IBM_GRANITE_SWITCH_4_1_3B.hf_model_name +) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- +@pytest.fixture(scope="module") +def vllm_switch_process(): + """Module-scoped vLLM process serving a Granite Switch model. + + If ``VLLM_SWITCH_TEST_BASE_URL`` is set the server is assumed to be running + externally and no subprocess is started. + """ + if os.environ.get("VLLM_SWITCH_TEST_BASE_URL"): + # Verify the external server is serving the expected model. + base = os.environ["VLLM_SWITCH_TEST_BASE_URL"] + try: + resp = requests.get(f"{base}/v1/models", timeout=5) + resp.raise_for_status() + served = {m["id"] for m in resp.json().get("data", [])} + if SWITCH_MODEL_ID not in served: + pytest.skip( + f"External vLLM server at {base} is not serving " + f"'{SWITCH_MODEL_ID}' (serving: {served})", + allow_module_level=True, + ) + except requests.RequestException as exc: + pytest.skip( + f"Cannot reach external vLLM server at {base}: {exc}", + allow_module_level=True, + ) + yield None + return + + # Require CUDA — vLLM does not support MPS + try: + subprocess.run(["nvidia-smi", "-L"], check=True, capture_output=True) + except (subprocess.CalledProcessError, FileNotFoundError): + pytest.skip( + "No CUDA GPU detected — skipping vLLM OpenAI intrinsics tests", + allow_module_level=True, + ) + + vllm_venv = os.environ.get("VLLM_VENV_PATH", ".vllm-venv") + vllm_python = os.path.join(vllm_venv, "bin", "python") + if not os.path.isfile(vllm_python): + subprocess.run(["uv", "venv", vllm_venv, "--python", "3.11"], check=True) + subprocess.run( + ["uv", "pip", "install", "--python", vllm_python, "vllm"], check=True + ) + + process = None + try: + process = subprocess.Popen( + [ + vllm_python, + "-m", + "vllm.entrypoints.openai.api_server", + "--model", + SWITCH_MODEL_ID, + "--served-model-name", + SWITCH_MODEL_ID, + "--dtype", + "bfloat16", + "--enable-prefix-caching", + "--gpu-memory-utilization", + "0.4", + "--max-num-seqs", + "256", + "--max-model-len", + "4096", + ], + start_new_session=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + ) + url = "http://127.0.0.1:8000/ping" + timeout = 600 + start_time = time.time() + + while True: + if process.poll() is not None: + output = process.stdout.read() if process.stdout else "" + raise RuntimeError( + f"vLLM server exited before startup (code {process.returncode}).\n" + f"--- vLLM output ---\n{output}\n--- end ---" + ) + try: + response = requests.get(url, timeout=2) + if response.status_code == 200: + break + except requests.RequestException: + pass + if time.time() - start_time > timeout: + raise TimeoutError(f"Timed out waiting for vLLM health check at {url}") + + yield process + + except Exception as e: + output = "" + if process is not None and process.stdout: + try: + output = process.stdout.read() + except Exception: + pass + skip_msg = ( + f"vLLM process not available: {e}\n" + f"--- vLLM output ---\n{output}\n--- end ---" + ) + print(skip_msg) + pytest.skip(skip_msg, allow_module_level=True) + + finally: + if process is not None: + try: + os.killpg(process.pid, signal.SIGTERM) + process.wait(timeout=30) + except Exception: + try: + os.killpg(process.pid, signal.SIGKILL) + except Exception: + pass + process.wait() + + +@pytest.fixture(scope="module") +def backend(vllm_switch_process): + """OpenAI backend with embedded adapters auto-loaded from the switch model.""" + base_url = ( + os.environ.get("VLLM_SWITCH_TEST_BASE_URL", "http://127.0.0.1:8000") + "/v1" + ) + return OpenAIBackend( + model_id=SWITCH_MODEL_ID, + formatter=TemplateFormatter(model_id=SWITCH_MODEL_ID), + base_url=base_url, + api_key="EMPTY", + load_embedded_adapters=True, + ) + + +def _registered_intrinsic_names(backend: OpenAIBackend) -> set[str]: + """Return the set of intrinsic names that have registered adapters.""" + names = set() + for adapter in backend._added_adapters.values(): + names.add(adapter.intrinsic_name) + return names + + +def _get_matching_combos(backend: OpenAIBackend) -> dict[str, YamlJsonCombo]: + """Filter test combos to those whose task matches a registered adapter.""" + registered = _registered_intrinsic_names(backend) + return { + k: v for k, v in _YAML_JSON_COMBOS_WITH_MODEL.items() if v.task in registered + } + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- +def _build_chat_context(input_json: dict) -> ChatContext: + """Build a ChatContext from the raw input JSON used by intrinsics tests. + + Parses messages and attaches any documents from ``extra_body.documents`` + to the last user message. + """ + ctx = ChatContext() + messages_data = input_json.get("messages", []) + docs_data = input_json.get("extra_body", {}).get("documents", []) + + documents = [ + Document(text=d["text"], title=d.get("title"), doc_id=d.get("doc_id")) + for d in docs_data + ] + + for i, msg in enumerate(messages_data): + role = msg["role"] + content = msg["content"] + # Attach documents to the last message + is_last = i == len(messages_data) - 1 + if is_last and documents: + ctx = ctx.add(Message(role, content, documents=documents)) + else: + ctx = ctx.add(Message(role, content)) + + return ctx + + +# --------------------------------------------------------------------------- +# Parametrized test +# --------------------------------------------------------------------------- +@pytest.fixture(scope="module", params=list(_YAML_JSON_COMBOS_WITH_MODEL.keys())) +def intrinsic_combo(request, backend) -> YamlJsonCombo: + """Yield each test combo that matches a registered adapter on the backend.""" + combo_name = request.param + combo = _YAML_JSON_COMBOS_WITH_MODEL[combo_name] + + registered = _registered_intrinsic_names(backend) + if combo.task not in registered: + pytest.skip( + f"Intrinsic '{combo.task}' has no registered adapter on this backend " + f"(available: {registered})" + ) + + return combo._resolve_yaml() + + +@pytest.mark.qualitative +def test_intrinsic_generation(intrinsic_combo: YamlJsonCombo, backend: OpenAIBackend): + """Run an intrinsic through the OpenAI backend and validate the result.""" + cfg = intrinsic_combo + + # Load input + with open(cfg.inputs_file, encoding="utf-8") as f: + input_json = json.load(f) + + # Load optional intrinsic kwargs + intrinsic_kwargs = {} + if cfg.arguments_file: + with open(cfg.arguments_file, encoding="utf-8") as f: + intrinsic_kwargs = json.load(f) + + # Build context and intrinsic action + ctx = _build_chat_context(input_json) + assert cfg.task is not None + intrinsic = Intrinsic(cfg.task, intrinsic_kwargs=intrinsic_kwargs) + + # Run the full generation path + result, _new_ctx = mfuncs.act(intrinsic, ctx, backend, strategy=None) + + # Validate that we got a non-empty result + assert result.value is not None, f"Intrinsic '{cfg.task}' returned None" + assert len(result.value) > 0, f"Intrinsic '{cfg.task}' returned empty string" + + # Validate that the result is parseable JSON + try: + json.loads(result.value) + except json.JSONDecodeError: + pytest.fail( + f"Intrinsic '{cfg.task}' did not return valid JSON: {result.value[:200]}" + ) + + +# --------------------------------------------------------------------------- +# call_intrinsic tests — exercise the high-level convenience wrappers +# --------------------------------------------------------------------------- +_RAG_TEST_DATA = ( + pathlib.Path(__file__).parent.parent + / "stdlib" + / "components" + / "intrinsic" + / "testdata" + / "input_json" +) + + +@pytest.fixture(scope="module") +def call_intrinsic_backend(vllm_switch_process): + """OpenAI backend with embedded_adapters=False so call_intrinsic loads them dynamically.""" + base_url = ( + os.environ.get("VLLM_SWITCH_TEST_BASE_URL", "http://127.0.0.1:8000") + "/v1" + ) + return OpenAIBackend( + model_id=SWITCH_MODEL_ID, + formatter=TemplateFormatter(model_id=SWITCH_MODEL_ID), + base_url=base_url, + api_key="EMPTY", + load_embedded_adapters=False, + ) + + +def _read_rag_input(file_name: str) -> tuple[ChatContext, str, list[Document]]: + """Load RAG test data and convert to Mellea types.""" + with open(_RAG_TEST_DATA / file_name, encoding="utf-8") as f: + data = json.load(f) + + context = ChatContext() + for m in data["messages"][:-1]: + context = context.add(Message(m["role"], m["content"])) + + last_turn = data["messages"][-1]["content"] + + documents = [ + Document(text=d["text"], doc_id=d.get("doc_id")) + for d in data.get("extra_body", {}).get("documents", []) + ] + return context, last_turn, documents + + +@pytest.mark.qualitative +def test_call_intrinsic_answerability(call_intrinsic_backend): + """call_intrinsic path: check_answerability returns a score between 0 and 1.""" + context, question, documents = _read_rag_input("answerability.json") + result = rag.check_answerability( + question, documents, context, call_intrinsic_backend + ) + assert isinstance(result, float) + assert 0.0 <= result <= 1.0 + + +@pytest.mark.qualitative +def test_call_intrinsic_context_relevance(call_intrinsic_backend): + """call_intrinsic path: check_context_relevance returns a score between 0 and 1.""" + context, question, documents = _read_rag_input("context_relevance.json") + result = rag.check_context_relevance( + question, documents[0], context, call_intrinsic_backend + ) + assert isinstance(result, float) + assert 0.0 <= result <= 1.0 diff --git a/test/backends/test_openai_intrinsics_unit.py b/test/backends/test_openai_intrinsics_unit.py new file mode 100644 index 000000000..cac90f3a6 --- /dev/null +++ b/test/backends/test_openai_intrinsics_unit.py @@ -0,0 +1,266 @@ +"""Unit tests for the OpenAI backend intrinsic generation path — no server required. + +Mocks the OpenAI async client to verify that ``_generate_from_intrinsic`` correctly: +- injects ``intrinsic_name`` into ``chat_template_kwargs`` +- applies the ``IntrinsicsResultProcessor`` to the raw response +- forwards ``temperature`` and ``seed`` from model options +- raises when no adapter is registered or streaming is requested +""" + +import json +import pathlib +from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch + +import pytest +import yaml +from openai.types.chat import ChatCompletion, ChatCompletionMessage +from openai.types.chat.chat_completion import Choice, ChoiceLogprobs +from openai.types.chat.chat_completion_token_logprob import ( + ChatCompletionTokenLogprob, + TopLogprob, +) +from openai.types.completion_usage import CompletionUsage + +from mellea.backends import ModelOption +from mellea.backends.adapters.adapter import EmbeddedIntrinsicAdapter +from mellea.backends.openai import OpenAIBackend +from mellea.stdlib import functional as mfuncs +from mellea.stdlib.components import Intrinsic, Message +from mellea.stdlib.context import ChatContext + +_TEST_DIR = pathlib.Path(__file__).parent +_INTRINSICS_DATA = _TEST_DIR / "test_adapters" / "intrinsics-data" + +# --------------------------------------------------------------------------- +# Configs +# --------------------------------------------------------------------------- + +# Minimal config: no transformations, no logprobs. Good enough for most tests +# that only inspect the API call (chat_template_kwargs, temp/seed forwarding). +_SIMPLE_CONFIG = { + "model": None, + "response_format": None, + "transformations": None, + "instruction": None, + "parameters": {"max_completion_tokens": 64}, + "sentence_boundaries": None, +} + +# Full answerability config with likelihood + nest transformations. +_ANSWERABILITY_CONFIG = yaml.safe_load( + (_INTRINSICS_DATA / "answerability.yaml").read_text() +) + +# --------------------------------------------------------------------------- +# Canned responses +# --------------------------------------------------------------------------- + + +def _simple_chat_completion(content: str = '{"result": "ok"}') -> ChatCompletion: + """Build a minimal ChatCompletion with no logprobs.""" + return ChatCompletion( + id="test-simple", + created=0, + model="granite-switch", + object="chat.completion", + choices=[ + Choice( + index=0, + finish_reason="stop", + message=ChatCompletionMessage(role="assistant", content=content), + ) + ], + usage=CompletionUsage(prompt_tokens=10, completion_tokens=4, total_tokens=14), + ) + + +def _answerability_chat_completion() -> ChatCompletion: + """Build a ChatCompletion that the answerability result processor can parse. + + The likelihood transformation reads top_logprobs to compute an expected value. + """ + return ChatCompletion( + id="test-ans", + created=0, + model="granite-switch", + object="chat.completion", + choices=[ + Choice( + index=0, + finish_reason="stop", + message=ChatCompletionMessage(role="assistant", content='"answerable"'), + logprobs=ChoiceLogprobs( + content=[ + ChatCompletionTokenLogprob( + token='"', + logprob=-0.01, + top_logprobs=[TopLogprob(token='"', logprob=-0.01)], + bytes=None, + ), + ChatCompletionTokenLogprob( + token="answerable", + logprob=-0.05, + top_logprobs=[ + TopLogprob(token="answerable", logprob=-0.05), + TopLogprob(token="unanswerable", logprob=-5.0), + ], + bytes=None, + ), + ChatCompletionTokenLogprob( + token='"', + logprob=-0.001, + top_logprobs=[TopLogprob(token='"', logprob=-0.001)], + bytes=None, + ), + ] + ), + ) + ], + usage=CompletionUsage(prompt_tokens=10, completion_tokens=3, total_tokens=13), + ) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +def _make_backend_with_adapter( + config: dict, *, model_options: dict | None = None +) -> OpenAIBackend: + """Return an OpenAIBackend with a registered embedded answerability adapter.""" + backend = OpenAIBackend( + model_id="granite-switch", + api_key="fake-key", + base_url="http://localhost:9999/v1", + model_options=model_options, + ) + adapter = EmbeddedIntrinsicAdapter( + intrinsic_name="answerability", config=config, technology="alora" + ) + backend.add_adapter(adapter) + return backend + + +def _make_context() -> ChatContext: + """Return a simple two-turn chat context.""" + return ChatContext().add(Message("user", "What is the square root of 4?")) + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +async def test_chat_template_kwargs_set(): + """_generate_from_intrinsic injects intrinsic_name into chat_template_kwargs.""" + backend = _make_backend_with_adapter(_SIMPLE_CONFIG) + ctx = _make_context() + mock_create = AsyncMock(return_value=_simple_chat_completion()) + + mock_client = MagicMock() + mock_client.chat.completions.create = mock_create + + with patch.object( + OpenAIBackend, + "_async_client", + new_callable=PropertyMock, + return_value=mock_client, + ): + mot, _ = await mfuncs.aact( + Intrinsic("answerability"), ctx, backend, strategy=None + ) + await mot.avalue() + + mock_create.assert_called_once() + call_kwargs = mock_create.call_args + extra_body = call_kwargs.kwargs.get("extra_body", {}) + + assert "chat_template_kwargs" in extra_body + assert extra_body["chat_template_kwargs"]["adapter_name"] == "answerability" + + +async def test_result_processor_applied(): + """Full answerability config: likelihood + nest transforms produce the expected JSON.""" + backend = _make_backend_with_adapter(_ANSWERABILITY_CONFIG) + ctx = _make_context() + mock_create = AsyncMock(return_value=_answerability_chat_completion()) + + mock_client = MagicMock() + mock_client.chat.completions.create = mock_create + + with patch.object( + OpenAIBackend, + "_async_client", + new_callable=PropertyMock, + return_value=mock_client, + ): + mot, _ = await mfuncs.aact( + Intrinsic("answerability"), ctx, backend, strategy=None + ) + await mot.avalue() + + # The result processor should have applied likelihood (logprobs → float) + # and nest (wrap in {"answerability_likelihood": }). + parsed = json.loads(mot.value) + assert "answerability_likelihood" in parsed + score = parsed["answerability_likelihood"] + assert isinstance(score, float) + assert 0.0 <= score <= 1.0 + + +async def test_temperature_and_seed_forwarded(): + """temperature and seed from model_options are forwarded to the API call.""" + backend = _make_backend_with_adapter(_SIMPLE_CONFIG) + ctx = _make_context() + mock_create = AsyncMock(return_value=_simple_chat_completion()) + + mock_client = MagicMock() + mock_client.chat.completions.create = mock_create + + with patch.object( + OpenAIBackend, + "_async_client", + new_callable=PropertyMock, + return_value=mock_client, + ): + mot, _ = await mfuncs.aact( + Intrinsic("answerability"), + ctx, + backend, + strategy=None, + model_options={ModelOption.TEMPERATURE: 0.5, ModelOption.SEED: 42}, + ) + await mot.avalue() + + call_kwargs = mock_create.call_args + assert call_kwargs.kwargs.get("temperature") == 0.5 + assert call_kwargs.kwargs.get("seed") == 42 + + +async def test_no_adapter_raises_valueerror(): + """Calling an intrinsic with no registered adapter raises ValueError.""" + backend = OpenAIBackend( + model_id="granite-switch", + api_key="fake-key", + base_url="http://localhost:9999/v1", + ) + ctx = _make_context() + + with pytest.raises(ValueError, match="has no adapter"): + await mfuncs.aact(Intrinsic("answerability"), ctx, backend, strategy=None) + + +async def test_streaming_raises(): + """Intrinsics do not support streaming — should raise NotImplementedError.""" + backend = _make_backend_with_adapter(_SIMPLE_CONFIG) + ctx = _make_context() + + with pytest.raises(NotImplementedError, match="do not support streaming"): + await mfuncs.aact( + Intrinsic("answerability"), + ctx, + backend, + strategy=None, + model_options={ModelOption.STREAM: True}, + ) diff --git a/test/package/test_dependency_isolation.py b/test/package/test_dependency_isolation.py index b1dc768ef..ede5d0b44 100644 --- a/test/package/test_dependency_isolation.py +++ b/test/package/test_dependency_isolation.py @@ -46,6 +46,7 @@ "from mellea.backends import Backend, ModelIdentifier, ModelOption", "from mellea.backends.ollama import OllamaModelBackend", "from mellea.backends.openai import OpenAIBackend", + "from mellea.backends.adapters.adapter import EmbeddedIntrinsicAdapter", "from mellea.core import Backend", ], "hf": ["from mellea.backends.huggingface import LocalHFBackend"], @@ -64,6 +65,7 @@ "cli": ["from cli.m import cli"], "server": ["from cli.serve.app import run_server"], "sandbox": ["import llm_sandbox"], + "switch": ["import huggingface_hub"], "hooks": [ "import cpex" # We directly import a non-Mellea class/package here since the hooks classes are defined regardless of if cpex is installed. ], @@ -104,6 +106,7 @@ "granite_retriever", "server", "sandbox", + "switch", "hooks", "cli", "backends", @@ -382,6 +385,11 @@ def test_sandbox() -> None: _run_check(extra="sandbox", should_succeed=[*IMPORTS["core"], *IMPORTS["sandbox"]]) +def test_switch() -> None: + """mellea[switch]: huggingface_hub is available for embedded adapter downloads.""" + 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