Version 1 (#160)

New front-end
Launch Chat API
Manage Sources
Enable re-embedding of all contents
Sources can be added without a notebook now
Improved settings
Enable model selector on all chats
Background processing for better experience
Dark mode
Improved Notes

Improved Docs: 
- Remove all Streamlit references from documentation
- Update deployment guides with React frontend setup
- Fix Docker environment variables format (SURREAL_URL, SURREAL_PASSWORD)
- Update docker image tag from :latest to :v1-latest
- Change navigation references (Settings → Models to just Models)
- Update development setup to include frontend npm commands
- Add MIGRATION.md guide for users upgrading from Streamlit
- Update quick-start guide with correct environment variables
- Add port 5055 documentation for API access
- Update project structure to reflect frontend/ directory
- Remove outdated source-chat documentation files

open_notebook/utils/token_utils.py

@@ -0,0 +1,38 @@
+"""
+Token utilities for Open Notebook.
+Handles token counting and cost calculations for language models.
+"""
+
+
+def token_count(input_string: str) -> int:
+    """
+    Count the number of tokens in the input string using the 'o200k_base' encoding.
+
+    Args:
+        input_string (str): The input string to count tokens for.
+
+    Returns:
+        int: The number of tokens in the input string.
+    """
+    try:
+        import tiktoken
+        encoding = tiktoken.get_encoding("o200k_base")
+        tokens = encoding.encode(input_string)
+        return len(tokens)
+    except ImportError:
+        # Fallback: simple word count estimation
+        return int(len(input_string.split()) * 1.3)
+
+
+def token_cost(token_count: int, cost_per_million: float = 0.150) -> float:
+    """
+    Calculate the cost of tokens based on the token count and cost per million tokens.
+
+    Args:
+        token_count (int): The number of tokens.
+        cost_per_million (float): The cost per million tokens. Default is 0.150.
+
+    Returns:
+        float: The calculated cost for the given token count.
+    """
+    return cost_per_million * (token_count / 1_000_000)