Merge branch 'master' into omegaPRM_openR

camel/configs/__init__.py

@@ -30,6 +30,7 @@
     SambaCloudAPIConfig,
     SambaVerseAPIConfig,
 )
+from .sglang_config import SGLANG_API_PARAMS, SGLangConfig
 from .togetherai_config import TOGETHERAI_API_PARAMS, TogetherAIConfig
 from .vllm_config import VLLM_API_PARAMS, VLLMConfig
 from .yi_config import YI_API_PARAMS, YiConfig
@@ -55,6 +56,8 @@
     'Gemini_API_PARAMS',
     'VLLMConfig',
     'VLLM_API_PARAMS',
+    'SGLangConfig',
+    'SGLANG_API_PARAMS',
     'MistralConfig',
     'MISTRAL_API_PARAMS',
     'RekaConfig',

camel/configs/ollama_config.py

@@ -13,7 +13,9 @@
 # ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
 from __future__ import annotations
 
-from typing import Sequence, Union
+from typing import Sequence, Type, Union
+
+from pydantic import BaseModel
 
 from camel.configs.base_config import BaseConfig
 from camel.types import NOT_GIVEN, NotGiven
@@ -75,7 +77,7 @@ class OllamaConfig(BaseConfig):
     stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
     max_tokens: Union[int, NotGiven] = NOT_GIVEN
     presence_penalty: float = 0.0
-    response_format: Union[dict, NotGiven] = NOT_GIVEN
+    response_format: Union[Type[BaseModel], dict, NotGiven] = NOT_GIVEN
     frequency_penalty: float = 0.0
 
 

camel/configs/sglang_config.py

@@ -0,0 +1,71 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+from camel.configs.base_config import BaseConfig
+from camel.types import NOT_GIVEN, NotGiven
+
+
+class SGLangConfig(BaseConfig):
+    r"""Defines the parameters for generating chat completions using the
+    OpenAI API.
+
+    Reference: https://sgl-project.github.io/references/sampling_params.html
+
+    Args:
+        stop (str or list, optional): Up to :obj:`4` sequences where the API
+            will stop generating further tokens. (default: :obj:`None`)
+        temperature (float, optional): Sampling temperature to use, between
+            :obj:`0` and :obj:`2`. Higher values make the output more random,
+            while lower values make it more focused and deterministic.
+            (default: :obj:`1.0`)
+        top_p (float, optional): An alternative to sampling with temperature,
+            called nucleus sampling, where the model considers the results of
+            the tokens with top_p probability mass. So :obj:`0.1` means only
+            the tokens comprising the top 10% probability mass are considered.
+            (default: :obj:`1.0`)
+        n (int, optional): How many chat completion choices to generate for
+            each input message. (default: :obj:`1`)
+        frequency_penalty (float, optional): Number between :obj:`-2.0` and
+            :obj:`2.0`. Positive values penalize new tokens based on their
+            existing frequency in the text so far, decreasing the model's
+            likelihood to repeat the same line verbatim. See more information
+            about frequency and presence penalties. (default: :obj:`0.0`)
+        presence_penalty (float, optional): Number between :obj:`-2.0` and
+            :obj:`2.0`. Positive values penalize new tokens based on whether
+            they appear in the text so far, increasing the model's likelihood
+            to talk about new topics. See more information about frequency and
+            presence penalties. (default: :obj:`0.0`)
+        stream (bool, optional): Whether to stream the generated output in
+            chunks. If set to `True`, the response will be streamed as it is
+            generated. (default: :obj:`False`)
+        max_tokens (int, optional): The maximum number of tokens to generate
+            in the chat completion. The total length of input tokens and
+            generated tokens is limited by the model's context length.
+            (default: :obj:`None`)
+    """
+
+    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
+    temperature: float = 1.0
+    top_p: float = 1.0
+    n: int = 1
+    frequency_penalty: float = 0.0
+    presence_penalty: float = 0.0
+    stream: bool = False
+    max_tokens: Union[int, NotGiven] = NOT_GIVEN
+
+
+SGLANG_API_PARAMS = {param for param in SGLangConfig.model_fields.keys()}

camel/data_collector/__init__.py

@@ -0,0 +1,19 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+from .alpaca_collector import AlpacaDataCollector
+from .base import BaseDataCollector
+from .sharegpt_collector import ShareGPTDataCollector
+
+__all__ = ["BaseDataCollector", "AlpacaDataCollector", "ShareGPTDataCollector"]

camel/data_collector/alpaca_collector.py

@@ -0,0 +1,127 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+from typing import Any, Dict, List, Optional, Union
+
+from typing_extensions import Self
+
+from camel.agents import ChatAgent
+from camel.data_collector.base import BaseDataCollector
+from camel.messages import AlpacaItem, BaseMessage
+from camel.schemas import OpenAISchemaConverter
+
+# ruff: noqa: E501
+DEFAULT_CONVERTER_PROMPTS = """
+    Extract key entities and attributes from the conversations
+    and convert them into a structured JSON format.
+    For example:
+    Instruction: You are a helpful assistant. 
+    User: When is the release date of the video game Portal?
+    Assistant: The release date of the video game Portal is October 9.
+    Your output should be:
+    {
+        "instruction": "You are a helpful assistant. When is the release date of the video game Portal?",
+        "input": "",
+        "output": "The release date of the video game Portal is October 9."
+    }
+"""
+
+
+class AlpacaDataCollector(BaseDataCollector):
+    def __init__(self) -> None:
+        super().__init__()
+        self.system_message: Optional[BaseMessage] = None
+        self.agent_name: Optional[str] = None
+
+    def record(
+        self,
+        agent: Union[List[ChatAgent], ChatAgent],
+    ) -> Self:
+        r"""Inject an agent into the data collector.
+
+        Args:
+            agent (Union[List[ChatAgent], ChatAgent]):
+                The agent to inject.
+        """
+        if not self.agent_name:
+            _agent = agent if isinstance(agent, ChatAgent) else agent[0]
+            self.agent_name = _agent.role_name
+            self.system_message = _agent._system_message
+        super().record(agent)
+        return self
+
+    def convert(self) -> Dict[str, Any]:
+        r"""Convert the collected data into a dictionary."""
+        if self.agent_name is None:
+            raise ValueError("No agent injected")
+
+        history = self.get_agent_history(self.agent_name)
+        if not history:
+            raise ValueError("No data collected.")
+
+        # Validate and process history
+        if len(history) == 3 and history[0].role == "system":
+            history = history[1:]  # Ignore the system message.
+        elif len(history) != 2:
+            raise ValueError(
+                f"AlpacaDataCollector only supports one message pair, but "
+                f"got {len(history)}"
+            )
+
+        input_message, output_message = history
+        instruction = (
+            self.system_message.content if self.system_message else ""
+        ) + str(input_message.message)
+
+        data = {
+            "instruction": instruction,
+            "input": "",
+            "output": output_message.message,
+        }
+        self.data.append(data)
+        return data
+
+    def llm_convert(
+        self,
+        converter: Optional[OpenAISchemaConverter] = None,
+        prompt: Optional[str] = None,
+    ) -> Dict[str, str]:
+        r"""Convert collected data using an LLM schema converter.
+
+        Args:
+            converter (Optional[OpenAISchemaConverter], optional):
+                The converter to use. (default: :obj:`OpenAISchemaConverter`)
+            prompt (Optional[str], optional): Prompt to guide the conversion.
+                (default: :obj:`DEFAULT_CONVERTER_PROMPTS`)
+
+        Returns:
+            Dict[str, str]: The converted data.
+
+        Raises:
+            ValueError: If no agent is injected or data cannot be collected.
+        """
+        prompt = prompt or DEFAULT_CONVERTER_PROMPTS
+        converter = converter or OpenAISchemaConverter()
+
+        system = self.system_message.content if self.system_message else ""
+        context = [f"Instruction: {system}\n"]
+
+        for message in self.get_agent_history(str(self.agent_name)):
+            if message.role == "user":
+                context.append(f"User: {message.message}\n")
+            else:
+                context.append(f"{message.name}: {message.message}\n")
+        return converter.convert(
+            "\n".join(context), AlpacaItem, prompt=prompt
+        ).model_dump()