14d5ce0b36
* Override default max tokens for Anthropic and Groq clients * Override default max tokens for Anthropic and Groq clients * Override default max tokens for Anthropic and Groq clients
75 lines
2.5 KiB
Python
75 lines
2.5 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 typing
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import anthropic
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from anthropic import AsyncAnthropic
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from openai import AsyncOpenAI
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from ..prompts.models import Message
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from .client import LLMClient
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from .config import LLMConfig
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from .errors import RateLimitError
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logger = logging.getLogger(__name__)
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DEFAULT_MODEL = 'claude-3-5-sonnet-20240620'
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DEFAULT_MAX_TOKENS = 8192
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class AnthropicClient(LLMClient):
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def __init__(self, config: LLMConfig | None = None, cache: bool = False):
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if config is None:
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config = LLMConfig(max_tokens=DEFAULT_MAX_TOKENS)
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elif config.max_tokens is None:
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config.max_tokens = DEFAULT_MAX_TOKENS
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super().__init__(config, cache)
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self.client = AsyncAnthropic(
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api_key=config.api_key,
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# we'll use tenacity to retry
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max_retries=1,
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)
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def get_embedder(self) -> typing.Any:
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openai_client = AsyncOpenAI()
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return openai_client.embeddings
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async def _generate_response(self, messages: list[Message]) -> dict[str, typing.Any]:
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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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{'role': 'assistant', 'content': '{'}
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]
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try:
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result = await self.client.messages.create(
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system='Only include JSON in the response. Do not include any additional text or explanation of the content.\n'
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+ system_message.content,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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messages=user_messages, # type: ignore
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model=self.model or DEFAULT_MODEL,
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)
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return json.loads('{' + result.content[0].text) # type: ignore
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except anthropic.RateLimitError as e:
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raise RateLimitError from e
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except Exception as e:
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logger.error(f'Error in generating LLM response: {e}')
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raise
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