db7c179991
Removes the hacky min() workaround that was capping max_tokens to DEFAULT_MAX_TOKENS (8192) in the AnthropicClient. This fix allows the client to respect the max_tokens parameter passed by callers, particularly for edge extraction operations that may require higher token limits (e.g., 16384). The new implementation aligns with how other LLM clients (OpenAI, Gemini) handle max_tokens by using the provided value or falling back to the instance max_tokens without an arbitrary cap. Resolves TODO in anthropic_client.py:207-208.
355 lines
13 KiB
Python
355 lines
13 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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import logging
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import os
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import typing
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from json import JSONDecodeError
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from typing import TYPE_CHECKING, Literal
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from pydantic import BaseModel, ValidationError
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from ..prompts.models import Message
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from .client import LLMClient
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from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize
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from .errors import RateLimitError, RefusalError
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if TYPE_CHECKING:
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import anthropic
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from anthropic import AsyncAnthropic
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from anthropic.types import MessageParam, ToolChoiceParam, ToolUnionParam
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else:
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try:
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import anthropic
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from anthropic import AsyncAnthropic
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from anthropic.types import MessageParam, ToolChoiceParam, ToolUnionParam
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except ImportError:
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raise ImportError(
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'anthropic is required for AnthropicClient. '
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'Install it with: pip install graphiti-core[anthropic]'
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) from None
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logger = logging.getLogger(__name__)
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AnthropicModel = Literal[
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'claude-3-7-sonnet-latest',
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'claude-3-7-sonnet-20250219',
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'claude-3-5-haiku-latest',
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'claude-3-5-haiku-20241022',
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'claude-3-5-sonnet-latest',
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'claude-3-5-sonnet-20241022',
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'claude-3-5-sonnet-20240620',
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'claude-3-opus-latest',
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'claude-3-opus-20240229',
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'claude-3-sonnet-20240229',
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'claude-3-haiku-20240307',
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'claude-2.1',
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'claude-2.0',
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]
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DEFAULT_MODEL: AnthropicModel = 'claude-3-7-sonnet-latest'
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class AnthropicClient(LLMClient):
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"""
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A client for the Anthropic LLM.
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Args:
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config: A configuration object for the LLM.
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cache: Whether to cache the LLM responses.
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client: An optional client instance to use.
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max_tokens: The maximum number of tokens to generate.
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Methods:
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generate_response: Generate a response from the LLM.
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Notes:
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- If a LLMConfig is not provided, api_key will be pulled from the ANTHROPIC_API_KEY environment
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variable, and all default values will be used for the LLMConfig.
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"""
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model: AnthropicModel
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def __init__(
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self,
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config: LLMConfig | None = None,
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cache: bool = False,
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client: AsyncAnthropic | None = None,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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) -> None:
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if config is None:
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config = LLMConfig()
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config.api_key = os.getenv('ANTHROPIC_API_KEY')
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config.max_tokens = max_tokens
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if config.model is None:
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config.model = DEFAULT_MODEL
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super().__init__(config, cache)
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# Explicitly set the instance model to the config model to prevent type checking errors
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self.model = typing.cast(AnthropicModel, config.model)
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if not client:
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self.client = AsyncAnthropic(
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api_key=config.api_key,
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max_retries=1,
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)
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else:
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self.client = client
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def _extract_json_from_text(self, text: str) -> dict[str, typing.Any]:
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"""Extract JSON from text content.
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A helper method to extract JSON from text content, used when tool use fails or
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no response_model is provided.
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Args:
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text: The text to extract JSON from
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Returns:
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Extracted JSON as a dictionary
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Raises:
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ValueError: If JSON cannot be extracted or parsed
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"""
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try:
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json_start = text.find('{')
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json_end = text.rfind('}') + 1
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if json_start >= 0 and json_end > json_start:
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json_str = text[json_start:json_end]
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return json.loads(json_str)
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else:
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raise ValueError(f'Could not extract JSON from model response: {text}')
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except (JSONDecodeError, ValueError) as e:
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raise ValueError(f'Could not extract JSON from model response: {text}') from e
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def _create_tool(
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self, response_model: type[BaseModel] | None = None
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) -> tuple[list[ToolUnionParam], ToolChoiceParam]:
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"""
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Create a tool definition based on the response_model if provided, or a generic JSON tool if not.
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Args:
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response_model: Optional Pydantic model to use for structured output.
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Returns:
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A list containing a single tool definition for use with the Anthropic API.
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"""
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if response_model is not None:
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# Use the response_model to define the tool
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model_schema = response_model.model_json_schema()
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tool_name = response_model.__name__
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description = model_schema.get('description', f'Extract {tool_name} information')
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else:
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# Create a generic JSON output tool
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tool_name = 'generic_json_output'
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description = 'Output data in JSON format'
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model_schema = {
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'type': 'object',
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'additionalProperties': True,
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'description': 'Any JSON object containing the requested information',
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}
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tool = {
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'name': tool_name,
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'description': description,
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'input_schema': model_schema,
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}
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tool_list = [tool]
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tool_list_cast = typing.cast(list[ToolUnionParam], tool_list)
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tool_choice = {'type': 'tool', 'name': tool_name}
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tool_choice_cast = typing.cast(ToolChoiceParam, tool_choice)
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return tool_list_cast, tool_choice_cast
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async def _generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int | None = None,
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model_size: ModelSize = ModelSize.medium,
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) -> dict[str, typing.Any]:
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"""
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Generate a response from the Anthropic LLM using tool-based approach for all requests.
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Args:
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messages: List of message objects to send to the LLM.
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response_model: Optional Pydantic model to use for structured output.
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max_tokens: Maximum number of tokens to generate.
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Returns:
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Dictionary containing the structured response from the LLM.
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Raises:
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RateLimitError: If the rate limit is exceeded.
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RefusalError: If the LLM refuses to respond.
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Exception: If an error occurs during the generation process.
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"""
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system_message = messages[0]
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user_messages = [{'role': m.role, 'content': m.content} for m in messages[1:]]
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user_messages_cast = typing.cast(list[MessageParam], user_messages)
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# Use the provided max_tokens or fall back to the instance max_tokens
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# This aligns with how other LLM clients handle max_tokens
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max_creation_tokens: int = max_tokens or self.max_tokens
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try:
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# Create the appropriate tool based on whether response_model is provided
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tools, tool_choice = self._create_tool(response_model)
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result = await self.client.messages.create(
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system=system_message.content,
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max_tokens=max_creation_tokens,
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temperature=self.temperature,
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messages=user_messages_cast,
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model=self.model,
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tools=tools,
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tool_choice=tool_choice,
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)
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# Extract the tool output from the response
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for content_item in result.content:
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if content_item.type == 'tool_use':
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if isinstance(content_item.input, dict):
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tool_args: dict[str, typing.Any] = content_item.input
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else:
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tool_args = json.loads(str(content_item.input))
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return tool_args
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# If we didn't get a proper tool_use response, try to extract from text
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for content_item in result.content:
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if content_item.type == 'text':
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return self._extract_json_from_text(content_item.text)
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else:
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raise ValueError(
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f'Could not extract structured data from model response: {result.content}'
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)
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# If we get here, we couldn't parse a structured response
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raise ValueError(
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f'Could not extract structured data from model response: {result.content}'
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)
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except anthropic.RateLimitError as e:
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raise RateLimitError(f'Rate limit exceeded. Please try again later. Error: {e}') from e
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except anthropic.APIError as e:
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# Special case for content policy violations. We convert these to RefusalError
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# to bypass the retry mechanism, as retrying policy-violating content will always fail.
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# This avoids wasting API calls and provides more specific error messaging to the user.
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if 'refused to respond' in str(e).lower():
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raise RefusalError(str(e)) from e
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raise e
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except Exception as e:
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raise e
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async def generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int | None = None,
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model_size: ModelSize = ModelSize.medium,
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group_id: str | None = None,
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prompt_name: str | None = None,
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) -> dict[str, typing.Any]:
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"""
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Generate a response from the LLM.
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Args:
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messages: List of message objects to send to the LLM.
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response_model: Optional Pydantic model to use for structured output.
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max_tokens: Maximum number of tokens to generate.
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Returns:
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Dictionary containing the structured response from the LLM.
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Raises:
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RateLimitError: If the rate limit is exceeded.
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RefusalError: If the LLM refuses to respond.
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Exception: If an error occurs during the generation process.
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"""
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if max_tokens is None:
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max_tokens = self.max_tokens
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# Wrap entire operation in tracing span
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with self.tracer.start_span('llm.generate') as span:
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attributes = {
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'llm.provider': 'anthropic',
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'model.size': model_size.value,
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'max_tokens': max_tokens,
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}
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if prompt_name:
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attributes['prompt.name'] = prompt_name
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span.add_attributes(attributes)
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retry_count = 0
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max_retries = 2
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last_error: Exception | None = None
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while retry_count <= max_retries:
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try:
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response = await self._generate_response(
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messages, response_model, max_tokens, model_size
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)
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# If we have a response_model, attempt to validate the response
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if response_model is not None:
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# Validate the response against the response_model
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model_instance = response_model(**response)
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return model_instance.model_dump()
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# If no validation needed, return the response
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return response
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except (RateLimitError, RefusalError):
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# These errors should not trigger retries
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span.set_status('error', str(last_error))
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raise
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except Exception as e:
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last_error = e
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if retry_count >= max_retries:
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if isinstance(e, ValidationError):
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logger.error(
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f'Validation error after {retry_count}/{max_retries} attempts: {e}'
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)
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else:
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logger.error(f'Max retries ({max_retries}) exceeded. Last error: {e}')
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span.set_status('error', str(e))
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span.record_exception(e)
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raise e
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if isinstance(e, ValidationError):
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response_model_cast = typing.cast(type[BaseModel], response_model)
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error_context = f'The previous response was invalid. Please provide a valid {response_model_cast.__name__} object. Error: {e}'
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else:
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error_context = (
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f'The previous response attempt was invalid. '
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f'Error type: {e.__class__.__name__}. '
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f'Error details: {str(e)}. '
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f'Please try again with a valid response.'
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)
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# Common retry logic
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retry_count += 1
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messages.append(Message(role='user', content=error_context))
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logger.warning(
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f'Retrying after error (attempt {retry_count}/{max_retries}): {e}'
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)
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# If we somehow get here, raise the last error
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span.set_status('error', str(last_error))
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raise last_error or Exception('Max retries exceeded with no specific error')
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