fix: replace litellm with tiktoken for token counting

- litellm had dependency conflicts with pydantic-ai
- tiktoken is lighter and already required by pydantic-ai
- Updated documentation (README.md, architecture.md)

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

webber-api/src/shared/tokens.py

@@ -1,53 +1,64 @@
 """
 Token counting utilities for context management.
 
-Uses litellm for accurate multi-model token counting.
+Uses tiktoken for token counting. While tiktoken is OpenAI's tokenizer,
+cl100k_base encoding provides reasonable estimates for most LLMs.
 """
+from functools import lru_cache
+
 from src.shared.logging import get_logger
 
 logger = get_logger(__name__)
 
-# Default model for token counting (Mistral Nemo)
-DEFAULT_MODEL = "mistral/mistral-nemo"
+
+@lru_cache(maxsize=1)
+def _get_encoding():
+    """Get tiktoken encoding (cached)."""
+    import tiktoken
+    # cl100k_base is used by GPT-4 and provides reasonable estimates for most models
+    return tiktoken.get_encoding("cl100k_base")
 
 
-def count_tokens(text: str, model: str = DEFAULT_MODEL) -> int:
+def count_tokens(text: str) -> 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)
+        encoding = _get_encoding()
+        return len(encoding.encode(text))
     except Exception as e:
-        # Fallback to rough estimate if litellm fails
+        # Fallback to rough estimate if tiktoken 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:
+def count_message_tokens(messages: list[dict[str, str]]) -> 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)
+        encoding = _get_encoding()
+        total = 0
+        for msg in messages:
+            # Each message has ~4 tokens overhead for role/formatting
+            total += 4
+            total += len(encoding.encode(msg.get("content", "")))
+            total += len(encoding.encode(msg.get("role", "")))
+        # Add 2 tokens for assistant response priming
+        total += 2
+        return total
     except Exception as e:
         # Fallback to rough estimate
         logger.warning(f"Token counting failed, using estimate: {e}")