feat: Support nexa model with nexa-sdk

camel/configs/__init__.py

@@ -17,6 +17,7 @@
 from .groq_config import GROQ_API_PARAMS, GroqConfig
 from .litellm_config import LITELLM_API_PARAMS, LiteLLMConfig
 from .mistral_config import MISTRAL_API_PARAMS, MistralConfig
+from .nexa_config import NEXA_API_PARAMS, NexaConfig
 from .ollama_config import OLLAMA_API_PARAMS, OllamaConfig
 from .openai_config import OPENAI_API_PARAMS, ChatGPTConfig
 from .reka_config import REKA_API_PARAMS, RekaConfig
@@ -62,4 +63,6 @@
     'SAMBA_CLOUD_API_PARAMS',
     'TogetherAIConfig',
     'TOGETHERAI_API_PARAMS',
+    'NEXA_API_PARAMS',
+    'NexaConfig',
 ]

camel/configs/nexa_config.py

@@ -0,0 +1,55 @@
+# =========== 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 Sequence, Union
+
+from openai._types import NOT_GIVEN, NotGiven
+
+from camel.configs.base_config import BaseConfig
+
+
+class NexaConfig(BaseConfig):
+    r"""Defines the parameters for generating chat completions using Nexa 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:`0.7`)
+        max_tokens (int, optional): The maximum number of new 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:`1024`)
+        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`)
+        stream (bool, optional): If True, partial message deltas will be sent
+            as data-only server-sent events as they become available.
+            (default: :obj:`False`)
+        stop (str or list, optional): List of stop words for early stopping
+            generating further tokens. (default: :obj:`None`)
+
+    """
+
+    temperature: float = 0.7
+    max_tokens: int = 1024
+    top_p: float = 1.0
+    stream: bool = False
+    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
+
+
+NEXA_API_PARAMS = {param for param in NexaConfig.model_fields.keys()}

camel/models/__init__.py

@@ -20,6 +20,7 @@
 from .mistral_model import MistralModel
 from .model_factory import ModelFactory
 from .nemotron_model import NemotronModel
+from .nexa_model import NexaModel
 from .ollama_model import OllamaModel
 from .openai_audio_models import OpenAIAudioModels
 from .openai_compatible_model import OpenAICompatibleModel
@@ -51,4 +52,5 @@
     'RekaModel',
     'SambaModel',
     'TogetherAIModel',
+    'NexaModel',
 ]

camel/models/model_factory.py

@@ -20,6 +20,7 @@
 from camel.models.groq_model import GroqModel
 from camel.models.litellm_model import LiteLLMModel
 from camel.models.mistral_model import MistralModel
+from camel.models.nexa_model import NexaModel  # Add this import
 from camel.models.ollama_model import OllamaModel
 from camel.models.openai_compatible_model import OpenAICompatibleModel
 from camel.models.openai_model import OpenAIModel
@@ -81,6 +82,8 @@ def create(
             model_class = OllamaModel
         elif model_platform.is_vllm:
             model_class = VLLMModel
+        elif model_platform.is_nexa:
+            model_class = NexaModel
         elif model_platform.is_openai_compatible_model:
             model_class = OpenAICompatibleModel
         elif model_platform.is_samba:

camel/models/nexa_model.py

@@ -0,0 +1,171 @@
+# =========== 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 os
+import subprocess
+from typing import Any, Dict, List, Optional, Union
+
+from openai import OpenAI, Stream
+
+from camel.configs import NEXA_API_PARAMS, NexaConfig
+from camel.messages import OpenAIMessage
+from camel.models import BaseModelBackend
+from camel.types import ChatCompletion, ChatCompletionChunk, ModelType
+from camel.utils import BaseTokenCounter, OpenAITokenCounter
+
+
+class NexaModel(BaseModelBackend):
+    """Nexa service interface."""
+
+    def __init__(
+        self,
+        model_type: Union[ModelType, str],
+        model_config_dict: Optional[Dict[str, Any]] = None,
+        api_key: Optional[str] = None,
+        url: Optional[str] = None,
+        token_counter: Optional[BaseTokenCounter] = None,
+    ) -> None:
+        """Constructor for Nexa backend.
+
+        Args:
+            model_type (Union[ModelType, str]): Model for which a backend is
+                created.
+            model_config_dict (Optional[Dict[str, Any]], optional): A
+            dictionary that will be fed into the API call. If None, default
+                configuration will be used. (default: None)
+            api_key (Optional[str], optional): The API key for the model
+                service. (default: None)
+            url (Optional[str], optional): The url to the model service. If not
+                provided, environment variable or default URL will be used.
+                (default: None)
+            token_counter (Optional[BaseTokenCounter], optional): Token counter
+                to use for the model. If not provided,
+                OpenAITokenCounter(ModelType.GPT_4O_MINI) will be used.
+                (default: None)
+        """
+        if model_config_dict is None:
+            model_config_dict = NexaConfig().as_dict()
+        url = url or os.environ.get("NEXA_BASE_URL")
+        super().__init__(
+            model_type, model_config_dict, api_key, url, token_counter
+        )
+
+        if not self._url:
+            self._start_server()
+
+        self._client = OpenAI(
+            timeout=60,
+            max_retries=3,
+            base_url=self._url,
+            api_key=self._api_key,
+        )
+
+    def _start_server(self) -> None:
+        """Starts the Nexa server in a subprocess."""
+        try:
+            subprocess.Popen(
+                [
+                    "nexa",
+                    "server",
+                    self.model_type,
+                    "--port",
+                    "8000",
+                ],
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+            print(
+                f"Nexa server started on http://localhost:8000 "
+                f"for {self.model_type} model."
+            )
+        except Exception as e:
+            print(f"Failed to start Nexa server: {e}.")
+
+    @property
+    def token_counter(self) -> BaseTokenCounter:
+        """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(ModelType.GPT_4O_MINI)
+        return self._token_counter
+
+    def check_model_config(self):
+        """Check whether the model configuration contains any
+        unexpected arguments to Nexa API.
+
+        Raises:
+            ValueError: If the model configuration dictionary contains any
+                unexpected arguments to Nexa API.
+        """
+        for param in self.model_config_dict:
+            if param not in NEXA_API_PARAMS:
+                raise ValueError(
+                    f"Unexpected argument `{param}` is "
+                    "input into Nexa model backend."
+                )
+
+    def run(
+        self,
+        messages: List[OpenAIMessage],
+    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
+        """Runs inference of Nexa chat completion.
+
+        Args:
+            messages (List[OpenAIMessage]): Message list with the chat history
+                in OpenAI API format.
+
+        Returns:
+            Union[ChatCompletion, Stream[ChatCompletionChunk]]:
+                `ChatCompletion` in the non-stream mode, or
+                `Stream[ChatCompletionChunk]` in the stream mode.
+        """
+        try:
+            response = self._client.chat.completions.create(
+                messages=messages,
+                model=self.model_type,
+                **self.model_config_dict,
+            )
+            return response
+        except Exception as e:
+            raise Exception(f"Error calling Nexa API: {e!s}")
+
+    @property
+    def token_limit(self) -> int:
+        """Returns the maximum token limit for the given model.
+
+        Returns:
+            int: The maximum token limit for the given model.
+        """
+        max_tokens = self.model_config_dict.get("max_tokens")
+        if isinstance(max_tokens, int):
+            return max_tokens
+        print(
+            "Must set `max_tokens` as an integer in `model_config_dict`"
+            " when setting up the model. Using 4096 as default value."
+        )
+        return 4096
+
+    @property
+    def stream(self) -> bool:
+        """Returns whether the model is in stream mode, which sends partial
+        results each time.
+
+        Returns:
+            bool: Whether the model is in stream mode.
+        """
+        return self.model_config_dict.get('stream', False)

camel/types/enums.py

@@ -439,6 +439,7 @@ class ModelPlatformType(Enum):
     TOGETHER = "together"
     OPENAI_COMPATIBLE_MODEL = "openai-compatible-model"
     SAMBA = "samba-nova"
+    NEXA = "nexa"
 
     @property
     def is_openai(self) -> bool:
@@ -511,6 +512,11 @@ def is_samba(self) -> bool:
         r"""Returns whether this platform is Samba Nova."""
         return self is ModelPlatformType.SAMBA
 
+    @property
+    def is_nexa(self) -> bool:
+        """Returns whether this platform is Nexa."""
+        return self is ModelPlatformType.NEXA
+
 
 class AudioModelType(Enum):
     TTS_1 = "tts-1"

test/models/test_nexa_model.py

@@ -0,0 +1,50 @@
+# =========== 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 re
+
+import pytest
+
+from camel.configs import NexaConfig
+from camel.models import NexaModel
+from camel.utils import OpenAITokenCounter
+
+
+@pytest.mark.model_backend
+@pytest.mark.parametrize(
+    "model_type",
+    ["llama3.2"],
+)
+def test_nexa_model(model_type: str):
+    model = NexaModel(model_type, url="http://localhost:8000/v1")
+    assert model.model_type == model_type
+    assert model.model_config_dict == NexaConfig().as_dict()
+    assert isinstance(model.token_counter, OpenAITokenCounter)
+    assert isinstance(model.token_limit, int)
+
+
+@pytest.mark.model_backend
+def test_nexa_model_unexpected_argument():
+    model_type = "llama3.2"
+    model_config_dict = {"unexpected_arg": "value"}
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            (
+                "Unexpected argument `unexpected_arg` is "
+                "input into Nexa model backend."
+            )
+        ),
+    ):
+        _ = NexaModel(model_type, model_config_dict, api_key="nexa_api_key")