Bring back QA_MODEL and keep compatibility with previous versions

docs/configuration.md

@@ -55,12 +55,13 @@ OPENAI_API_KEY="your-api-key"
 
 ## Question Answering Providers
 
-Configure which LLM provider to use for question answering using the `provider:model` format. Any provider and model supported by [Pydantic AI](https://ai.pydantic.dev/models/) can be used.
+Configure which LLM provider to use for question answering. Any provider and model supported by [Pydantic AI](https://ai.pydantic.dev/models/) can be used.
 
 ### Ollama (Default)
 
 ```bash
-QA_PROVIDER="ollama:qwen3"
+QA_PROVIDER="ollama"
+QA_MODEL="qwen3"
 OLLAMA_BASE_URL="http://localhost:11434"
 ```
 
@@ -69,7 +70,8 @@ OLLAMA_BASE_URL="http://localhost:11434"
 OpenAI QA is included in the default installation. Simply configure:
 
 ```bash
-QA_PROVIDER="openai:gpt-4o-mini"  # or openai:gpt-4, openai:gpt-3.5-turbo, etc.
+QA_PROVIDER="openai"
+QA_MODEL="gpt-4o-mini"  # or gpt-4, gpt-3.5-turbo, etc.
 OPENAI_API_KEY="your-api-key"
 ```
 
@@ -78,7 +80,8 @@ OPENAI_API_KEY="your-api-key"
 Anthropic QA is included in the default installation. Simply configure:
 
 ```bash
-QA_PROVIDER="anthropic:claude-3-5-haiku-20241022"  # or anthropic:claude-3-5-sonnet-20241022, etc.
+QA_PROVIDER="anthropic"
+QA_MODEL="claude-3-5-haiku-20241022"  # or claude-3-5-sonnet-20241022, etc.
 ANTHROPIC_API_KEY="your-api-key"
 ```
 
@@ -88,13 +91,16 @@ Any provider supported by Pydantic AI can be used. Examples include:
 
 ```bash
 # Google Gemini
-QA_PROVIDER="gemini:gemini-1.5-flash"
+QA_PROVIDER="gemini"
+QA_MODEL="gemini-1.5-flash"
 
 # Groq
-QA_PROVIDER="groq:llama-3.3-70b-versatile"
+QA_PROVIDER="groq"
+QA_MODEL="llama-3.3-70b-versatile"
 
 # Mistral
-QA_PROVIDER="mistral:mistral-small-latest"
+QA_PROVIDER="mistral"
+QA_MODEL="mistral-small-latest"
 ```
 
 See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.

src/haiku/rag/config.py

@@ -22,7 +22,8 @@ class AppConfig(BaseModel):
     RERANK_PROVIDER: str = "ollama"
     RERANK_MODEL: str = "qwen3"
 
-    QA_PROVIDER: str = "ollama:qwen3"
+    QA_PROVIDER: str = "ollama"
+    QA_MODEL: str = "qwen3"
 
     CHUNK_SIZE: int = 256
     CONTEXT_CHUNK_RADIUS: int = 0

src/haiku/rag/qa/__init__.py

@@ -4,10 +4,12 @@ from haiku.rag.qa.agent import QuestionAnswerAgent
 
 
 def get_qa_agent(client: HaikuRAG, use_citations: bool = False) -> QuestionAnswerAgent:
-    provider_model = Config.QA_PROVIDER
+    provider = Config.QA_PROVIDER
+    model_name = Config.QA_MODEL
 
     return QuestionAnswerAgent(
         client=client,
-        provider_model=provider_model,
+        provider=provider,
+        model=model_name,
         use_citations=use_citations,
     )

src/haiku/rag/qa/agent.py

@@ -23,14 +23,15 @@ class QuestionAnswerAgent:
     def __init__(
         self,
         client: HaikuRAG,
-        provider_model: str,
+        provider: str,
+        model: str,
         use_citations: bool = False,
         q: float = 0.0,
     ):
         self._client = client
 
         system_prompt = SYSTEM_PROMPT_WITH_CITATIONS if use_citations else SYSTEM_PROMPT
-        model_obj = self._get_model(provider_model)
+        model_obj = self._get_model(provider, model)
 
         self._agent = Agent(
             model=model_obj,
@@ -57,21 +58,16 @@ class QuestionAnswerAgent:
                 for chunk, score in expanded_results
             ]
 
-    def _get_model(self, provider_model: str):
-        """Get the appropriate model object for the provider:model format."""
-        if ":" not in provider_model:
-            raise ValueError(f"Invalid provider:model format: {provider_model}")
-
-        provider, model = provider_model.split(":", 1)
-
+    def _get_model(self, provider: str, model: str):
+        """Get the appropriate model object for the provider."""
         if provider == "ollama":
             return OpenAIModel(
                 model_name=model,
                 provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
             )
         else:
-            # For other providers, use the provider:model string directly
-            return provider_model
+            # For all other providers, use the provider:model format
+            return f"{provider}:{model}"
 
     async def answer(self, question: str) -> str:
         """Answer a question using the RAG system."""

tests/llm_judge.py

@@ -37,24 +37,12 @@ class LLMJudgeResponseSchema(BaseModel):
 class LLMJudge:
     """LLM-as-judge for evaluating answer equivalence using Pydantic AI."""
 
-    def __init__(self, provider_model: str = Config.QA_PROVIDER):
-        self.provider_model = provider_model
-
-        # Parse provider:model format
-        if ":" not in provider_model:
-            raise ValueError(f"Invalid provider:model format: {provider_model}")
-
-        provider, model = provider_model.split(":", 1)
-
-        if provider == "ollama":
-            # Create Ollama model
-            ollama_model = OpenAIModel(
-                model_name=model,
-                provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
-            )
-        else:
-            # For other providers, use the provider:model string directly
-            ollama_model = provider_model
+    def __init__(self, model: str = Config.QA_MODEL):
+        # Create Ollama model
+        ollama_model = OpenAIModel(
+            model_name=model,
+            provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
+        )
 
         # Create Pydantic AI agent
         self._agent = Agent(

tests/test_qa.py

@@ -15,7 +15,7 @@ ANTHROPIC_AVAILABLE = bool(Config.ANTHROPIC_API_KEY)
 async def test_qa_ollama(qa_corpus: Dataset):
     """Test Ollama QA with LLM judge."""
     client = HaikuRAG(":memory:")
-    qa = QuestionAnswerAgent(client, provider_model="ollama:qwen3")
+    qa = QuestionAnswerAgent(client, "ollama", "qwen3")
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]
@@ -39,7 +39,7 @@ async def test_qa_ollama(qa_corpus: Dataset):
 async def test_qa_openai(qa_corpus: Dataset):
     """Test OpenAI QA with LLM judge."""
     client = HaikuRAG(":memory:")
-    qa = QuestionAnswerAgent(client, provider_model="openai:gpt-4o-mini")
+    qa = QuestionAnswerAgent(client, "openai", "gpt-4o-mini")
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]
@@ -63,9 +63,7 @@ async def test_qa_openai(qa_corpus: Dataset):
 async def test_qa_anthropic(qa_corpus: Dataset):
     """Test Anthropic QA with LLM judge."""
     client = HaikuRAG(":memory:")
-    qa = QuestionAnswerAgent(
-        client, provider_model="anthropic:claude-3-5-haiku-20241022"
-    )
+    qa = QuestionAnswerAgent(client, "anthropic", "claude-3-5-haiku-20241022")
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]