feat: implement RAG evaluation framework with CLI tooling

- Add ADR-013 documenting RAG evaluation architecture
- Implement two-part evaluation: Context Recall (retrieval) + Answer Correctness (generation)
- Create Click CLI for ground truth generation and corpus upload
- Add pytest fixtures and tests for retrieval/generation quality
- Use BeIR/nfcorpus dataset with 5 selected test queries
- Support Ollama and Anthropic LLM providers
- Generate synthetic ground truth answers offline
- Add comprehensive documentation in tests/rag_evaluation/README.md

The framework separates one-time setup (generate/upload) from test execution,
making tests much faster (~6-12 min vs ~15-25 min per run).

Tests are manual only (not in CI) and require external LLM access.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

tests/rag_evaluation/llm_providers.py

@@ -0,0 +1,145 @@
+"""LLM provider abstraction for RAG evaluation.
+
+Supports Ollama (local) and Anthropic (cloud) providers for both ground truth
+generation and evaluation.
+"""
+
+import os
+from typing import Protocol
+
+import httpx
+from anthropic import AsyncAnthropic
+
+
+class LLMProvider(Protocol):
+    """Protocol for LLM providers."""
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """Generate text from a prompt.
+
+        Args:
+            prompt: The prompt to generate from
+            max_tokens: Maximum tokens to generate
+
+        Returns:
+            Generated text
+        """
+        ...
+
+
+class OllamaProvider:
+    """Ollama provider for local LLM inference."""
+
+    def __init__(self, base_url: str, model: str):
+        """Initialize Ollama provider.
+
+        Args:
+            base_url: Ollama API base URL (e.g., http://localhost:11434)
+            model: Model name (e.g., llama3.1:8b)
+        """
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.client = httpx.AsyncClient(timeout=600.0)  # 10 min timeout for generation
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """Generate text using Ollama API."""
+        response = await self.client.post(
+            f"{self.base_url}/api/generate",
+            json={
+                "model": self.model,
+                "prompt": prompt,
+                "stream": False,
+                "options": {
+                    "num_predict": max_tokens,
+                    "temperature": 0.7,
+                },
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["response"]
+
+    async def close(self):
+        """Close the HTTP client."""
+        await self.client.aclose()
+
+
+class AnthropicProvider:
+    """Anthropic provider for cloud LLM inference."""
+
+    def __init__(self, api_key: str, model: str):
+        """Initialize Anthropic provider.
+
+        Args:
+            api_key: Anthropic API key
+            model: Model name (e.g., claude-3-5-sonnet-20241022)
+        """
+        self.client = AsyncAnthropic(api_key=api_key)
+        self.model = model
+
+    async def generate(self, prompt: str, max_tokens: int = 500) -> str:
+        """Generate text using Anthropic API."""
+        message = await self.client.messages.create(
+            model=self.model,
+            max_tokens=max_tokens,
+            temperature=0.7,
+            messages=[{"role": "user", "content": prompt}],
+        )
+        return message.content[0].text
+
+    async def close(self):
+        """Close the client (no-op for Anthropic)."""
+        pass
+
+
+def create_llm_provider(
+    provider: str | None = None,
+    ollama_base_url: str | None = None,
+    ollama_model: str | None = None,
+    anthropic_api_key: str | None = None,
+    anthropic_model: str | None = None,
+) -> LLMProvider:
+    """Create an LLM provider from environment variables or arguments.
+
+    Args:
+        provider: Provider type ('ollama' or 'anthropic'). Defaults to RAG_EVAL_PROVIDER env var or 'ollama'
+        ollama_base_url: Ollama base URL. Defaults to RAG_EVAL_OLLAMA_BASE_URL or 'http://localhost:11434'
+        ollama_model: Ollama model. Defaults to RAG_EVAL_OLLAMA_MODEL or 'llama3.1:8b'
+        anthropic_api_key: Anthropic API key. Defaults to RAG_EVAL_ANTHROPIC_API_KEY env var
+        anthropic_model: Anthropic model. Defaults to RAG_EVAL_ANTHROPIC_MODEL or 'claude-3-5-sonnet-20241022'
+
+    Returns:
+        LLMProvider instance
+
+    Raises:
+        ValueError: If provider is invalid or required credentials are missing
+    """
+    # Get provider from args or env
+    provider = provider or os.environ.get("RAG_EVAL_PROVIDER", "ollama")
+
+    if provider == "ollama":
+        # Try RAG_EVAL_OLLAMA_BASE_URL, then OLLAMA_HOST, then default
+        base_url = (
+            ollama_base_url
+            or os.environ.get("RAG_EVAL_OLLAMA_BASE_URL")
+            or os.environ.get("OLLAMA_HOST")
+            or "http://localhost:11434"
+        )
+        model = ollama_model or os.environ.get("RAG_EVAL_OLLAMA_MODEL", "llama3.2:1b")
+        return OllamaProvider(base_url=base_url, model=model)
+
+    elif provider == "anthropic":
+        api_key = anthropic_api_key or os.environ.get("RAG_EVAL_ANTHROPIC_API_KEY")
+        if not api_key:
+            raise ValueError(
+                "Anthropic API key required. Set RAG_EVAL_ANTHROPIC_API_KEY environment variable."
+            )
+        model = anthropic_model or os.environ.get(
+            "RAG_EVAL_ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022"
+        )
+        return AnthropicProvider(api_key=api_key, model=model)
+
+    else:
+        raise ValueError(
+            f"Invalid provider: {provider}. Must be 'ollama' or 'anthropic'."
+        )