feat: migrate to instructor.from_provider for universal provider support (#2424)

A fix to support latest instructor, as they removed `from_anthropic` and
`from_gemini` methods for a more standard `from_provider`. Ref: [PR
1898](567-labs/instructor#1898)

Also added support for #2422

Author: anistark

src/ragas/llms/base.py

@@ -590,6 +590,62 @@ def __repr__(self) -> str:
         return f"{self.__class__.__name__}(llm={self.llm.__class__.__name__}(...))"
 
 
+def _patch_client_for_provider(client: t.Any, provider: str) -> t.Any:
+    """
+    Patch a client with Instructor for generic providers.
+
+    Maps provider names to Provider enum and instantiates Instructor/AsyncInstructor.
+    Supports anthropic, google, and any other provider Instructor recognizes.
+    """
+    from instructor import Provider
+
+    provider_map = {
+        "anthropic": Provider.ANTHROPIC,
+        "google": Provider.GENAI,
+        "gemini": Provider.GENAI,
+        "azure": Provider.OPENAI,
+        "groq": Provider.GROQ,
+        "mistral": Provider.MISTRAL,
+        "cohere": Provider.COHERE,
+        "xai": Provider.XAI,
+        "bedrock": Provider.BEDROCK,
+        "deepseek": Provider.DEEPSEEK,
+    }
+
+    provider_enum = provider_map.get(provider, Provider.OPENAI)
+
+    if hasattr(client, "acompletion"):
+        return instructor.AsyncInstructor(
+            client=client,
+            create=client.messages.create,
+            provider=provider_enum,
+        )
+    else:
+        return instructor.Instructor(
+            client=client,
+            create=client.messages.create,
+            provider=provider_enum,
+        )
+
+
+def _get_instructor_client(client: t.Any, provider: str) -> t.Any:
+    """
+    Get an instructor-patched client for the specified provider.
+
+    Uses provider-specific methods when available, falls back to generic patcher.
+    """
+    provider_lower = provider.lower()
+
+    if provider_lower == "openai":
+        return instructor.from_openai(client)
+    elif provider_lower == "litellm":
+        return instructor.from_litellm(client)
+    elif provider_lower == "perplexity":
+        return instructor.from_perplexity(client)
+    else:
+        return _patch_client_for_provider(client, provider_lower)
+
+
 def llm_factory(
     model: str,
     provider: str = "openai",
@@ -605,8 +661,8 @@ def llm_factory(
 
     Args:
         model: Model name (e.g., "gpt-4o", "gpt-4o-mini", "claude-3-sonnet").
-        provider: LLM provider. Default: "openai".
-                 Supported: openai, anthropic, google, litellm.
+        provider: LLM provider (default: "openai").
+                 Can be any provider supported by Instructor: openai, anthropic, google, litellm, etc.
         client: Pre-initialized client instance (required). For OpenAI, can be
                OpenAI(...) or AsyncOpenAI(...).
         **kwargs: Additional model arguments (temperature, max_tokens, top_p, etc).
@@ -621,13 +677,18 @@ def llm_factory(
         from openai import OpenAI
 
         client = OpenAI(api_key="...")
-        llm = llm_factory("gpt-4o", client=client)
+        llm = llm_factory("gpt-4o-mini", client=client)
         response = llm.generate(prompt, ResponseModel)
 
+        # Anthropic
+        from anthropic import Anthropic
+        client = Anthropic(api_key="...")
+        llm = llm_factory("claude-3-sonnet", provider="anthropic", client=client)
+
         # Async
         from openai import AsyncOpenAI
         client = AsyncOpenAI(api_key="...")
-        llm = llm_factory("gpt-4o", client=client)
+        llm = llm_factory("gpt-4o-mini", client=client)
         response = await llm.agenerate(prompt, ResponseModel)
     """
     if client is None:
@@ -646,21 +707,8 @@ def llm_factory(
 
     provider_lower = provider.lower()
 
-    instructor_funcs = {
-        "openai": lambda c: instructor.from_openai(c),
-        "anthropic": lambda c: instructor.from_anthropic(c),
-        "google": lambda c: instructor.from_gemini(c),
-        "litellm": lambda c: instructor.from_litellm(c),
-    }
-
-    if provider_lower not in instructor_funcs:
-        raise ValueError(
-            f"Unsupported provider: '{provider}'. "
-            f"Supported: {', '.join(instructor_funcs.keys())}"
-        )
-
     try:
-        patched_client = instructor_funcs[provider_lower](client)
+        patched_client = _get_instructor_client(client, provider_lower)
     except Exception as e:
         raise ValueError(
             f"Failed to initialize {provider} client with instructor. "
@@ -753,29 +801,33 @@ def _map_provider_params(self) -> t.Dict[str, t.Any]:
 
         Each provider may have different parameter requirements:
         - Google: Wraps parameters in generation_config and renames max_tokens
-        - OpenAI: Maps max_tokens to max_completion_tokens for o-series models
+        - OpenAI/Azure: Maps max_tokens to max_completion_tokens for o-series models
         - Anthropic: No special handling required (pass-through)
         - LiteLLM: No special handling required (routes internally, pass-through)
         """
         provider_lower = self.provider.lower()
 
         if provider_lower == "google":
             return self._map_google_params()
-        elif provider_lower == "openai":
+        elif provider_lower in ("openai", "azure"):
             return self._map_openai_params()
         else:
-            # Anthropic, LiteLLM - pass through unchanged
+            # Anthropic, LiteLLM, and other providers - pass through unchanged
             return self.model_args.copy()
 
     def _map_openai_params(self) -> t.Dict[str, t.Any]:
-        """Map parameters for OpenAI reasoning models with special constraints.
+        """Map parameters for OpenAI/Azure reasoning models with special constraints.
 
         Reasoning models (o-series and gpt-5 series) have unique requirements:
         1. max_tokens must be mapped to max_completion_tokens
         2. temperature must be set to 1.0 (only supported value)
         3. top_p parameter must be removed (not supported)
 
-        Legacy OpenAI models (gpt-4, gpt-4o, etc.) continue to use max_tokens unchanged.
+        Legacy OpenAI/Azure models (gpt-4, gpt-4o, etc.) continue to use max_tokens unchanged.
+
+        Note on Azure deployments: Some Azure deployments restrict temperature to 1.0.
+        If your Azure deployment has this constraint, pass temperature=1.0 explicitly:
+        llm_factory("gpt-4o-mini", provider="azure", client=client, temperature=1.0)
 
         For GPT-5 and o-series models with structured output (Pydantic models):
         - Default max_tokens=1024 may not be sufficient

tests/unit/llms/test_instructor_factory.py

@@ -17,18 +17,22 @@ def __init__(self, is_async=False):
         self.is_async = is_async
         self.chat = Mock()
         self.chat.completions = Mock()
+        self.messages = Mock()
+        self.messages.create = Mock()
         if is_async:
 
             async def async_create(*args, **kwargs):
                 return LLMResponseModel(response="Mock response")
 
             self.chat.completions.create = async_create
+            self.messages.create = async_create
         else:
 
             def sync_create(*args, **kwargs):
                 return LLMResponseModel(response="Mock response")
 
             self.chat.completions.create = sync_create
+            self.messages.create = sync_create
 
 
 class MockInstructor:
@@ -109,11 +113,12 @@ def mock_from_openai(client):
     assert llm.model_args.get("temperature") == 0.7  # type: ignore
 
 
-def test_unsupported_provider():
-    """Test that unsupported providers raise ValueError."""
+def test_unsupported_provider(monkeypatch):
+    """Test that invalid clients are handled gracefully for unknown providers."""
     mock_client = Mock()
+    mock_client.messages = None
 
-    with pytest.raises(ValueError, match="Unsupported provider"):
+    with pytest.raises(ValueError, match="Failed to initialize"):
         llm_factory("test-model", provider="unsupported", client=mock_client)
 
 
@@ -168,28 +173,29 @@ def mock_from_openai(client):
         asyncio.run(llm.agenerate("Test prompt", LLMResponseModel))
 
 
-def test_provider_support():
-    """Test that all expected providers are supported."""
-    supported_providers = {
-        "openai": "from_openai",
-        "anthropic": "from_anthropic",
-        "google": "from_gemini",
-        "litellm": "from_litellm",
-    }
+def test_provider_support(monkeypatch):
+    """Test that major providers are supported."""
 
-    for provider, func_name in supported_providers.items():
-        mock_client = Mock()
-
-        import instructor
+    # OpenAI and LiteLLM use provider-specific methods
+    def mock_from_openai(client):
+        return MockInstructor(client)
 
-        mock_instructor_func = Mock(return_value=MockInstructor(mock_client))
-        setattr(instructor, func_name, mock_instructor_func)
+    def mock_from_litellm(client):
+        return MockInstructor(client)
 
-        try:
-            llm = llm_factory("test-model", provider=provider, client=mock_client)
-            assert llm.model == "test-model"  # type: ignore
-        except Exception as e:
-            pytest.fail(f"Provider {provider} should be supported but got error: {e}")
+    monkeypatch.setattr("instructor.from_openai", mock_from_openai)
+    monkeypatch.setattr("instructor.from_litellm", mock_from_litellm)
+
+    for provider in ["openai", "litellm"]:
+        mock_client = MockClient(is_async=False)
+        llm = llm_factory("test-model", provider=provider, client=mock_client)
+        assert llm.model == "test-model"  # type: ignore
+
+    # Anthropic and Google use generic wrapper
+    for provider in ["anthropic", "google"]:
+        mock_client = MockClient(is_async=False)
+        llm = llm_factory("test-model", provider=provider, client=mock_client)
+        assert llm.model == "test-model"  # type: ignore
 
 
 def test_llm_model_args_storage(mock_sync_client, monkeypatch):