Add support for DeepSeek LLM

src/pipecat/services/deepseek.py

@@ -0,0 +1,88 @@
+#
+# Copyright (c) 2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+from typing import List
+
+from loguru import logger
+
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai import OpenAILLMService
+
+try:
+    from openai import (
+        AsyncStream,
+    )
+    from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error(
+        "In order to use Fireworks, you need to `pip install pipecat-ai[deepseek]`. Also, set `DEEPSEEK_API_KEY` environment variable."
+    )
+    raise Exception(f"Missing module: {e}")
+
+
+class DeepSeekLLMService(OpenAILLMService):
+    """A service for interacting with DeepSeek's API using the OpenAI-compatible interface.
+
+    This service extends OpenAILLMService to connect to DeepSeek's API endpoint while
+    maintaining full compatibility with OpenAI's interface and functionality.
+
+    Args:
+        api_key (str): The API key for accessing DeepSeek's API
+        base_url (str, optional): The base URL for DeepSeek API. Defaults to "https://api.deepseek.com/v1"
+        model (str, optional): The model identifier to use. Defaults to "deepseek-chat"
+        **kwargs: Additional keyword arguments passed to OpenAILLMService
+    """
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        base_url: str = "https://api.deepseek.com/v1",
+        model: str = "deepseek-chat",
+        **kwargs,
+    ):
+        super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
+
+    def create_client(self, api_key=None, base_url=None, **kwargs):
+        """Create OpenAI-compatible client for DeepSeek API endpoint."""
+        logger.debug(f"Creating DeepSeek client with api {base_url}")
+        return super().create_client(api_key, base_url, **kwargs)
+
+    async def get_chat_completions(
+        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
+    ) -> AsyncStream[ChatCompletionChunk]:
+        """Create a streaming chat completion using Cerebras's API.
+
+        Args:
+        context (OpenAILLMContext): The context object containing tools configuration
+            and other settings for the chat completion.
+        messages (List[ChatCompletionMessageParam]): The list of messages comprising
+            the conversation history and current request.
+
+        Returns:
+        AsyncStream[ChatCompletionChunk]: A streaming response of chat completion
+            chunks that can be processed asynchronously.
+        """
+        params = {
+            "model": self.model_name,
+            "stream": True,
+            "messages": messages,
+            "tools": context.tools,
+            "tool_choice": context.tool_choice,
+            "stream_options": {"include_usage": True},
+            "frequency_penalty": self._settings["frequency_penalty"],
+            "presence_penalty": self._settings["presence_penalty"],
+            "temperature": self._settings["temperature"],
+            "top_p": self._settings["top_p"],
+            "max_tokens": self._settings["max_tokens"],
+        }
+
+        params.update(self._settings["extra"])
+
+        chunks = await self._client.chat.completions.create(**params)
+        return chunks