feat: add conversation persistence and context management layer

- SQLAlchemy async database layer (SQLite dev, PostgreSQL prod)
- Conversation and Message models with UUID primary keys
- Token counting utilities using litellm
- Context summarization at 80% token threshold
- REST API endpoints for multi-turn conversations
- 19 conversation tests, 6 token tests (176 total passing)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

webber-api/src/shared/tokens.py

@@ -0,0 +1,74 @@
+"""
+Token counting utilities for context management.
+
+Uses litellm for accurate multi-model token counting.
+"""
+from src.shared.logging import get_logger
+
+logger = get_logger(__name__)
+
+# Default model for token counting (Mistral Nemo)
+DEFAULT_MODEL = "mistral/mistral-nemo"
+
+
+def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
+    """
+    Count tokens in a text string.
+
+    Args:
+        text: Text to count tokens for
+        model: Model identifier for tokenizer selection
+
+    Returns:
+        Token count
+    """
+    try:
+        from litellm import token_counter
+        return token_counter(model=model, text=text)
+    except Exception as e:
+        # Fallback to rough estimate if litellm fails
+        logger.warning(f"Token counting failed, using estimate: {e}")
+        return len(text) // 4
+
+
+def count_message_tokens(
+    messages: list[dict[str, str]],
+    model: str = DEFAULT_MODEL
+) -> int:
+    """
+    Count tokens for a list of chat messages.
+
+    Args:
+        messages: List of message dicts with 'role' and 'content' keys
+        model: Model identifier for tokenizer selection
+
+    Returns:
+        Total token count including message overhead
+    """
+    try:
+        from litellm import token_counter
+        return token_counter(model=model, messages=messages)
+    except Exception as e:
+        # Fallback to rough estimate
+        logger.warning(f"Token counting failed, using estimate: {e}")
+        total = 0
+        for msg in messages:
+            total += len(msg.get("content", "")) // 4
+            total += 4  # Overhead per message
+        return total
+
+
+def estimate_tokens(text: str) -> int:
+    """
+    Quick token estimate without external library.
+
+    Uses ~4 characters per token heuristic.
+    Less accurate but faster for rough estimates.
+
+    Args:
+        text: Text to estimate
+
+    Returns:
+        Estimated token count
+    """
+    return len(text) // 4