fecf6decfb
* base requirements * autofix * Configure ruff for Python linting and formatting - Set up minimal ruff configuration with basic checks (E, W, F, I) - Add temporary ignores for common issues during migration - Configure pre-commit hooks to use ruff with pass_filenames - This enables gradual migration from black to ruff * Delete sdj * autofixed only * migrate lint action * more autofixed * more fixes * change precommit * try changing the hook * try this stuff
128 lines
4.7 KiB
Python
128 lines
4.7 KiB
Python
import hashlib
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import json
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List
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from letta.helpers.decorators import async_redis_cache
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from letta.llm_api.anthropic_client import AnthropicClient
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from letta.otel.tracing import trace_method
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from letta.schemas.openai.chat_completion_request import Tool as OpenAITool
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from letta.utils import count_tokens
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class TokenCounter(ABC):
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"""Abstract base class for token counting strategies"""
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@abstractmethod
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async def count_text_tokens(self, text: str) -> int:
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"""Count tokens in a text string"""
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@abstractmethod
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async def count_message_tokens(self, messages: List[Dict[str, Any]]) -> int:
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"""Count tokens in a list of messages"""
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@abstractmethod
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async def count_tool_tokens(self, tools: List[Any]) -> int:
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"""Count tokens in tool definitions"""
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@abstractmethod
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def convert_messages(self, messages: List[Any]) -> List[Dict[str, Any]]:
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"""Convert messages to the appropriate format for this counter"""
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class AnthropicTokenCounter(TokenCounter):
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"""Token counter using Anthropic's API"""
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def __init__(self, anthropic_client: AnthropicClient, model: str):
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self.client = anthropic_client
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self.model = model
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@trace_method
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@async_redis_cache(
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key_func=lambda self, text: f"anthropic_text_tokens:{self.model}:{hashlib.sha256(text.encode()).hexdigest()[:16]}",
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prefix="token_counter",
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ttl_s=3600, # cache for 1 hour
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)
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async def count_text_tokens(self, text: str) -> int:
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if not text:
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return 0
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return await self.client.count_tokens(model=self.model, messages=[{"role": "user", "content": text}])
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@trace_method
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@async_redis_cache(
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key_func=lambda self,
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messages: f"anthropic_message_tokens:{self.model}:{hashlib.sha256(json.dumps(messages, sort_keys=True).encode()).hexdigest()[:16]}",
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prefix="token_counter",
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ttl_s=3600, # cache for 1 hour
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)
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async def count_message_tokens(self, messages: List[Dict[str, Any]]) -> int:
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if not messages:
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return 0
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return await self.client.count_tokens(model=self.model, messages=messages)
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@trace_method
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@async_redis_cache(
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key_func=lambda self,
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tools: f"anthropic_tool_tokens:{self.model}:{hashlib.sha256(json.dumps([t.model_dump() for t in tools], sort_keys=True).encode()).hexdigest()[:16]}",
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prefix="token_counter",
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ttl_s=3600, # cache for 1 hour
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)
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async def count_tool_tokens(self, tools: List[OpenAITool]) -> int:
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if not tools:
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return 0
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return await self.client.count_tokens(model=self.model, tools=tools)
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def convert_messages(self, messages: List[Any]) -> List[Dict[str, Any]]:
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return [m.to_anthropic_dict() for m in messages]
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class TiktokenCounter(TokenCounter):
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"""Token counter using tiktoken"""
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def __init__(self, model: str):
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self.model = model
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@trace_method
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@async_redis_cache(
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key_func=lambda self, text: f"tiktoken_text_tokens:{self.model}:{hashlib.sha256(text.encode()).hexdigest()[:16]}",
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prefix="token_counter",
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ttl_s=3600, # cache for 1 hour
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)
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async def count_text_tokens(self, text: str) -> int:
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if not text:
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return 0
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return count_tokens(text)
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@trace_method
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@async_redis_cache(
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key_func=lambda self,
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messages: f"tiktoken_message_tokens:{self.model}:{hashlib.sha256(json.dumps(messages, sort_keys=True).encode()).hexdigest()[:16]}",
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prefix="token_counter",
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ttl_s=3600, # cache for 1 hour
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)
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async def count_message_tokens(self, messages: List[Dict[str, Any]]) -> int:
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if not messages:
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return 0
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from letta.local_llm.utils import num_tokens_from_messages
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return num_tokens_from_messages(messages=messages, model=self.model)
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@trace_method
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@async_redis_cache(
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key_func=lambda self,
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tools: f"tiktoken_tool_tokens:{self.model}:{hashlib.sha256(json.dumps([t.model_dump() for t in tools], sort_keys=True).encode()).hexdigest()[:16]}",
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prefix="token_counter",
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ttl_s=3600, # cache for 1 hour
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)
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async def count_tool_tokens(self, tools: List[OpenAITool]) -> int:
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if not tools:
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return 0
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from letta.local_llm.utils import num_tokens_from_functions
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# Extract function definitions from OpenAITool objects
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functions = [t.function.model_dump() for t in tools]
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return num_tokens_from_functions(functions=functions, model=self.model)
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def convert_messages(self, messages: List[Any]) -> List[Dict[str, Any]]:
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return [m.to_openai_dict() for m in messages]
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