feat: Integrate cohere models

camel/configs/__init__.py

@@ -13,6 +13,7 @@
 # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
 from .anthropic_config import ANTHROPIC_API_PARAMS, AnthropicConfig
 from .base_config import BaseConfig
+from .cohere_config import COHERE_API_PARAMS, CohereConfig
 from .gemini_config import Gemini_API_PARAMS, GeminiConfig
 from .groq_config import GROQ_API_PARAMS, GroqConfig
 from .litellm_config import LITELLM_API_PARAMS, LiteLLMConfig
@@ -58,4 +59,6 @@
     'SAMBA_CLOUD_API_PARAMS',
     'TogetherAIConfig',
     'TOGETHERAI_API_PARAMS',
+    'CohereConfig',
+    'COHERE_API_PARAMS',
 ]

camel/configs/cohere_config.py

@@ -0,0 +1,76 @@
+# =========== Copyright 2023 @ 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 @ CAMEL-AI.org. All Rights Reserved. ===========
+from __future__ import annotations
+
+from typing import List, Optional
+
+from camel.configs.base_config import BaseConfig
+
+
+class CohereConfig(BaseConfig):
+    r"""Defines the parameters for generating chat completions using the
+    Cohere API.
+
+    Args:
+        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:`0.3`)
+        documents (dict, optional): A list of relevant documents that the
+            model can cite to generate a more accurate reply. Each document is
+            either a string or document object with content and metadata.
+            (default: :obj:`None`)
+        max_tokens (int, optional): The maximum number of tokens the model
+            will generate as part of the response. (default: :obj:`None`)
+        stop_sequences (List(str), optional): A list of up to 5 strings that
+            the model will use to stop generation. If the model generates a
+            string that matches any of the strings in the list, it will stop
+            generating tokens and return the generated text up to that point
+            not including the stop sequence. (default: :obj:`None`)
+        seed (int, optional): If specified, the backend will make a best
+            effort to sample tokens deterministically, such that repeated
+            requests with the same seed and parameters should return the same
+            result. However, determinism cannot be totally guaranteed.
+            (default: :obj:`None`)
+        frequency_penalty (float, optional): Min value of `0.0`, max value of
+            `1.0`. Used to reduce repetitiveness of generated tokens. The
+            higher the value, the stronger a penalty is applied to previously
+            present tokens, proportional to how many times they have already
+            appeared in the prompt or prior generation. (default: :obj:`0.0`)
+        presence_penalty (float, optional): Min value of `0.0`, max value of
+            `1.0`. Used to reduce repetitiveness of generated tokens. Similar
+            to `frequency_penalty`, except that this penalty is applied
+            equally to all tokens that have already appeared, regardless of
+            their exact frequencies. (default: :obj:`0.0`)
+        k (int, optional): Ensures only the top k most likely tokens are
+            considered for generation at each step. Min value of `0`, max
+            value of `500`. (default: :obj:`0`)
+        p (float, optional): Ensures that only the most likely tokens, with
+            total probability mass of `p`, are considered for generation at
+            each step. If both k and p are enabled, `p` acts after `k`. Min
+            value of `0.01`, max value of `0.99`. (default: :obj:`0.75`)
+    """
+
+    temperature: Optional[float] = 0.2
+    documents: Optional[dict] = None
+    max_tokens: Optional[int] = None
+    stop_sequences: Optional[List[str]] = None
+    seed: Optional[int] = None
+    frequency_penalty: Optional[float] = 0.0
+    presence_penalty: Optional[float] = 0.0
+    k: Optional[int] = 0
+    p: Optional[float] = 0.75
+
+
+COHERE_API_PARAMS = {param for param in CohereConfig().model_fields.keys()}

camel/models/__init__.py

@@ -14,6 +14,7 @@
 from .anthropic_model import AnthropicModel
 from .azure_openai_model import AzureOpenAIModel
 from .base_model import BaseModelBackend
+from .cohere_model import CohereModel
 from .gemini_model import GeminiModel
 from .groq_model import GroqModel
 from .litellm_model import LiteLLMModel
@@ -40,6 +41,7 @@
     'GroqModel',
     'StubModel',
     'ZhipuAIModel',
+    'CohereModel',
     'ModelFactory',
     'LiteLLMModel',
     'OpenAIAudioModels',

camel/models/cohere_model.py

@@ -0,0 +1,261 @@
+# =========== Copyright 2023 @ 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 @ CAMEL-AI.org. All Rights Reserved. ===========
+import logging
+import os
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+if TYPE_CHECKING:
+    from cohere.types import ChatMessageV2, ChatResponse
+
+from camel.configs import COHERE_API_PARAMS
+from camel.messages import OpenAIMessage
+from camel.models import BaseModelBackend
+from camel.types import ChatCompletion, ModelType
+from camel.utils import (
+    BaseTokenCounter,
+    OpenAITokenCounter,
+    api_keys_required,
+)
+
+try:
+    import os
+
+    if os.getenv("AGENTOPS_API_KEY") is not None:
+        from agentops import LLMEvent, record
+    else:
+        raise ImportError
+except (ImportError, AttributeError):
+    LLMEvent = None
+
+
+class CohereModel(BaseModelBackend):
+    r"""Cohere API in a unified BaseModelBackend interface."""
+
+    def __init__(
+        self,
+        model_type: ModelType,
+        model_config_dict: Dict[str, Any],
+        api_key: Optional[str] = None,
+        url: Optional[str] = None,
+        token_counter: Optional[BaseTokenCounter] = None,
+    ):
+        import cohere
+
+        api_key = api_key or os.environ.get("COHERE_API_KEY")
+        url = url or os.environ.get("COHERE_SERVER_URL")
+        super().__init__(
+            model_type, model_config_dict, api_key, url, token_counter
+        )
+        self._client = cohere.ClientV2(api_key=self._api_key)
+
+    def _to_openai_response(self, response: 'ChatResponse') -> ChatCompletion:
+        usage = {
+            "prompt_tokens": response.usage.tokens.input_tokens or 0,  # type: ignore[union-attr]
+            "completion_tokens": response.usage.tokens.output_tokens or 0,  # type: ignore[union-attr]
+            "total_tokens": (response.usage.tokens.input_tokens or 0)  # type: ignore[union-attr]
+            + (response.usage.tokens.output_tokens or 0),  # type: ignore[union-attr]
+        }
+
+        tool_calls = response.message.tool_calls  # type: ignore[union-attr]
+        choices = []
+        if tool_calls:
+            for tool_call in tool_calls:
+                openai_tool_calls = [
+                    dict(
+                        id=tool_call.id,  # type: ignore[union-attr]
+                        function={
+                            "name": tool_call.function.name,  # type: ignore[union-attr]
+                            "arguments": tool_call.function.arguments,  # type: ignore[union-attr]
+                        },
+                        type=tool_call.type,
+                    )
+                ]
+
+                choice = dict(
+                    index=None,
+                    message={
+                        "role": "assistant",
+                        "content": response.message.tool_plan,  # type: ignore[union-attr]
+                        "tool_calls": openai_tool_calls,
+                    },
+                    finish_reason=response.finish_reason
+                    if response.finish_reason
+                    else None,
+                )
+                choices.append(choice)
+
+        else:
+            openai_tool_calls = None
+
+            choice = dict(
+                index=None,
+                message={
+                    "role": "assistant",
+                    "content": response.message.content[0].text,  # type: ignore[union-attr,index]
+                    "tool_calls": openai_tool_calls,
+                },
+                finish_reason=response.finish_reason
+                if response.finish_reason
+                else None,
+            )
+            choices.append(choice)
+
+        obj = ChatCompletion.construct(
+            id=response.id,
+            choices=choices,
+            created=None,
+            model=self.model_type,
+            object="chat.completion",
+            usage=usage,
+        )
+        return obj
+
+    def _to_cohere_chatmessage(
+        self, messages: List[OpenAIMessage]
+    ) -> List["ChatMessageV2"]:
+        from cohere.types import ToolCallV2Function
+        from cohere.types.chat_message_v2 import (
+            AssistantChatMessageV2,
+            SystemChatMessageV2,
+            ToolCallV2,
+            ToolChatMessageV2,
+            UserChatMessageV2,
+        )
+
+        new_messages = []
+        for msg in messages:
+            role = msg.get("role")
+            content = msg.get("content")
+            function_call = msg.get("function_call")
+
+            if role == "user":
+                new_message = UserChatMessageV2(role="user", content=content)  # type: ignore[arg-type]
+            elif role == "function":
+                new_message = ToolChatMessageV2(
+                    role="tool",
+                    tool_call_id="0",
+                    content=content,  # type: ignore[assignment,arg-type]
+                )
+            elif role == "assistant":
+                new_message = AssistantChatMessageV2(  # type: ignore[assignment]
+                    role="assistant",
+                    tool_calls=[
+                        ToolCallV2(
+                            id="0",
+                            type="function",
+                            function=ToolCallV2Function(
+                                name=function_call.get("name"),  # type: ignore[attr-defined]
+                                arguments=function_call.get("arguments"),  # type: ignore[attr-defined]
+                            ),
+                        )
+                    ]
+                    if function_call
+                    else None,
+                    content=content,  # type: ignore[arg-type]
+                )
+            elif role == "system":
+                new_message = SystemChatMessageV2(  # type: ignore[assignment]
+                    role="system",
+                    content=content,  # type: ignore[arg-type]
+                )
+            else:
+                raise ValueError(f"Unsupported message role: {role}")
+
+            new_messages.append(new_message)
+
+        return new_messages  # type: ignore[return-value]
+
+    @property
+    def token_counter(self) -> BaseTokenCounter:
+        r"""Initialize the token counter for the model backend.
+
+        Returns:
+            BaseTokenCounter: The token counter following the model's
+                tokenization style.
+        """
+        if not self._token_counter:
+            self._token_counter = OpenAITokenCounter(
+                model=ModelType.GPT_4O_MINI
+            )
+        return self._token_counter
+
+    @api_keys_required("COHERE_API_KEY")
+    def run(self, messages: List[OpenAIMessage]) -> ChatCompletion:
+        r"""Runs inference of Cohere chat completion.
+
+        Args:
+            messages (List[OpenAIMessage]): Message list with the chat history
+                in OpenAI API format.
+        Returns:
+            ChatCompletion.
+        """
+        from cohere.core.api_error import ApiError
+
+        cohere_messages = self._to_cohere_chatmessage(messages)
+
+        try:
+            response = self._client.chat(
+                messages=cohere_messages,
+                model=self.model_type,
+                **self.model_config_dict,
+            )
+        except ApiError as e:
+            logging.error(f"Cohere API Error: {e.status_code}")
+            logging.error(f"Error body: {e.body}")
+            raise
+        except Exception as e:
+            logging.error(f"Unexpected error when calling Cohere API: {e!s}")
+            raise
+
+        openai_response = self._to_openai_response(response)
+
+        # Add AgentOps LLM Event tracking
+        if LLMEvent:
+            llm_event = LLMEvent(
+                thread_id=openai_response.id,
+                prompt=" ".join(
+                    [message.get("content") for message in messages]  # type: ignore[misc]
+                ),
+                prompt_tokens=openai_response.usage.prompt_tokens,  # type: ignore[union-attr]
+                completion=openai_response.choices[0].message.content,
+                completion_tokens=openai_response.usage.completion_tokens,  # type: ignore[union-attr]
+                model=self.model_type,
+            )
+            record(llm_event)
+
+        return openai_response
+
+    def check_model_config(self):
+        r"""Check whether the model configuration contains any
+        unexpected arguments to Cohere API.
+        Raises:
+            ValueError: If the model configuration dictionary contains any
+                unexpected arguments to Cohere API.
+        """
+        for param in self.model_config_dict:
+            if param not in COHERE_API_PARAMS:
+                raise ValueError(
+                    f"Unexpected argument `{param}` is "
+                    "input into Cohere model backend."
+                )
+
+    @property
+    def stream(self) -> bool:
+        r"""Returns whether the model is in stream mode, which sends partial
+        results each time. Current it's not supported.
+
+        Returns:
+            bool: Whether the model is in stream mode.
+        """
+        return False

camel/models/model_factory.py

@@ -16,6 +16,7 @@
 from camel.models.anthropic_model import AnthropicModel
 from camel.models.azure_openai_model import AzureOpenAIModel
 from camel.models.base_model import BaseModelBackend
+from camel.models.cohere_model import CohereModel
 from camel.models.gemini_model import GeminiModel
 from camel.models.groq_model import GroqModel
 from camel.models.litellm_model import LiteLLMModel
@@ -106,6 +107,8 @@ def create(
             model_class = MistralModel
         elif model_platform.is_reka and model_type.is_reka:
             model_class = RekaModel
+        elif model_platform.is_cohere and model_type.is_cohere:
+            model_class = CohereModel
         elif model_type == ModelType.STUB:
             model_class = StubModel