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311 lines
12 KiB
Python
311 lines
12 KiB
Python
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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import copy
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import os
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from typing import Any, Dict, List, Optional, Type, Union
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from openai import AsyncStream, Stream
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from pydantic import BaseModel
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from camel.configs import MoonshotConfig
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from camel.logger import get_logger
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from camel.messages import OpenAIMessage
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from camel.models._interleaved_thinking_mixin import InterleavedThinkingMixin
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from camel.models._utils import try_modify_message_with_format
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from camel.models.openai_compatible_model import OpenAICompatibleModel
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from camel.types import (
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ChatCompletion,
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ChatCompletionChunk,
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ModelType,
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)
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from camel.utils import (
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BaseTokenCounter,
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api_keys_required,
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)
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logger = get_logger(__name__)
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if os.environ.get("LANGFUSE_ENABLED", "False").lower() == "true":
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try:
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from langfuse.decorators import observe
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except ImportError:
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from camel.utils import observe
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elif os.environ.get("TRACEROOT_ENABLED", "False").lower() == "true":
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try:
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from traceroot import trace as observe # type: ignore[import]
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except ImportError:
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from camel.utils import observe
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else:
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from camel.utils import observe
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class MoonshotModel(InterleavedThinkingMixin, OpenAICompatibleModel):
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r"""Moonshot API in a unified OpenAICompatibleModel interface.
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Args:
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model_type (Union[ModelType, str]): Model for which a backend is
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created, one of Moonshot series.
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model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
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that will be fed into :obj:`openai.ChatCompletion.create()`. If
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:obj:`None`, :obj:`MoonshotConfig().as_dict()` will be used.
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(default: :obj:`None`)
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api_key (Optional[str], optional): The API key for authenticating with
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the Moonshot service. (default: :obj:`None`)
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url (Optional[str], optional): The url to the Moonshot service.
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For Chinese users, use :obj:`https://api.moonshot.cn/v1`.
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For overseas users, the default endpoint will be used.
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(default: :obj:`https://api.moonshot.ai/v1`)
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token_counter (Optional[BaseTokenCounter], optional): Token counter to
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use for the model. If not provided, :obj:`OpenAITokenCounter(
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ModelType.GPT_4)` will be used.
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(default: :obj:`None`)
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timeout (Optional[float], optional): The timeout value in seconds for
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API calls. If not provided, will fall back to the MODEL_TIMEOUT
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environment variable or default to 180 seconds.
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(default: :obj:`None`)
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max_retries (int, optional): Maximum number of retries for API calls.
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(default: :obj:`3`)
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**kwargs (Any): Additional arguments to pass to the client
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initialization.
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"""
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@api_keys_required([("api_key", "MOONSHOT_API_KEY")])
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def __init__(
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self,
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model_type: Union[ModelType, str],
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model_config_dict: Optional[Dict[str, Any]] = None,
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api_key: Optional[str] = None,
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url: Optional[str] = None,
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token_counter: Optional[BaseTokenCounter] = None,
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timeout: Optional[float] = None,
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max_retries: int = 3,
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**kwargs: Any,
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) -> None:
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if model_config_dict is None:
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model_config_dict = MoonshotConfig().as_dict()
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api_key = api_key or os.environ.get("MOONSHOT_API_KEY")
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# Preserve default URL if not provided
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if url is None:
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url = (
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os.environ.get("MOONSHOT_API_BASE_URL")
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or "https://api.moonshot.ai/v1"
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)
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timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
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super().__init__(
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model_type=model_type,
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model_config_dict=model_config_dict,
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api_key=api_key,
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url=url,
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token_counter=token_counter,
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timeout=timeout,
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max_retries=max_retries,
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**kwargs,
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)
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# Initialize interleaved thinking state
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self._init_thinking_state()
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def _prepare_request(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Dict[str, Any]:
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r"""Prepare the request configuration for Moonshot API.
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Args:
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messages (List[OpenAIMessage]): Message list with the chat history
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in OpenAI API format.
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response_format (Optional[Type[BaseModel]]): The format of the
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response.
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tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
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use for the request.
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Returns:
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Dict[str, Any]: The prepared request configuration.
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"""
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request_config = copy.deepcopy(self.model_config_dict)
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# Remove internal config params that are not part of the API
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request_config.pop("interleaved_thinking", None)
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if tools:
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# Clean tools to remove null types (Moonshot API incompatibility)
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cleaned_tools = self._clean_tool_schemas(tools)
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request_config["tools"] = cleaned_tools
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elif response_format:
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# Use the same approach as DeepSeek for structured output
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try_modify_message_with_format(messages[-1], response_format)
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request_config["response_format"] = {"type": "json_object"}
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return request_config
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def _clean_tool_schemas(
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self, tools: List[Dict[str, Any]]
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) -> List[Dict[str, Any]]:
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r"""Clean tool schemas to remove null types for Moonshot compatibility.
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Moonshot API doesn't accept {"type": "null"} in anyOf schemas.
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This method removes null type definitions from parameters.
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Args:
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tools (List[Dict[str, Any]]): Original tool schemas.
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Returns:
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List[Dict[str, Any]]: Cleaned tool schemas.
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"""
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def remove_null_from_schema(schema: Any) -> Any:
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"""Recursively remove null types from schema."""
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if isinstance(schema, dict):
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# Create a copy to avoid modifying the original
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result = {}
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for key, value in schema.items():
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if key == 'type' and isinstance(value, list):
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# Handle type arrays like ["string", "null"]
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filtered_types = [t for t in value if t != 'null']
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if len(filtered_types) == 1:
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# Single type remains, convert to string
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result[key] = filtered_types[0]
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elif len(filtered_types) > 1:
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# Multiple types remain, keep as array
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result[key] = filtered_types
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else:
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# All were null, use string as fallback
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logger.warning(
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"All types in tool schema type array "
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"were null, falling back to 'string' "
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"type for Moonshot API compatibility. "
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"Original tool schema may need review."
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)
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result[key] = 'string'
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elif key == 'anyOf':
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# Handle anyOf with null types
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filtered = [
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item
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for item in value
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if not (
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isinstance(item, dict)
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and item.get('type') == 'null'
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)
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]
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if len(filtered) == 1:
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# If only one type remains, flatten it
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return remove_null_from_schema(filtered[0])
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elif len(filtered) > 1:
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result[key] = [
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remove_null_from_schema(item)
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for item in filtered
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]
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else:
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# All were null, return string type as fallback
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logger.warning(
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"All types in tool schema anyOf were null, "
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"falling back to 'string' type for "
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"Moonshot API compatibility. Original "
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"tool schema may need review."
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)
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return {"type": "string"}
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else:
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# Recursively process other values
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result[key] = remove_null_from_schema(value)
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return result
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elif isinstance(schema, list):
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return [remove_null_from_schema(item) for item in schema]
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else:
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return schema
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cleaned_tools = copy.deepcopy(tools)
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for tool in cleaned_tools:
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if 'function' in tool and 'parameters' in tool['function']:
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params = tool['function']['parameters']
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if 'properties' in params:
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params['properties'] = remove_null_from_schema(
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params['properties']
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)
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return cleaned_tools
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@observe()
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def _run(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
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r"""Runs inference of Moonshot chat completion.
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Args:
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messages (List[OpenAIMessage]): Message list with the chat history
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in OpenAI API format.
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response_format (Optional[Type[BaseModel]]): The format of the
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response.
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tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
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use for the request.
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Returns:
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Union[ChatCompletion, Stream[ChatCompletionChunk]]:
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`ChatCompletion` in the non-stream mode, or
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`Stream[ChatCompletionChunk]` in the stream mode.
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"""
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self._log_and_trace()
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request_config = self._prepare_request(
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messages, response_format, tools
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)
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return self._call_client(
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self._client.chat.completions.create,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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@observe()
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async def _arun(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
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r"""Runs inference of Moonshot chat completion asynchronously.
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Args:
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messages (List[OpenAIMessage]): Message list with the chat history
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in OpenAI API format.
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response_format (Optional[Type[BaseModel]]): The format of the
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response.
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tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
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use for the request.
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Returns:
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Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
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`ChatCompletion` in the non-stream mode, or
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`AsyncStream[ChatCompletionChunk]` in the stream mode.
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"""
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self._log_and_trace()
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request_config = self._prepare_request(
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messages, response_format, tools
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)
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return await self._acall_client(
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self._async_client.chat.completions.create,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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