Base OpenAI LLM service

src/dailyai/services/ollama_ai_services.py

@@ -1,42 +1,7 @@
-from openai import AsyncOpenAI
-
-import json
-from collections.abc import AsyncGenerator
-
-from dailyai.services.ai_services import LLMService
+from dailyai.services.openai_api_llm_service import BaseOpenAILLMService
 
 
-class OLLamaLLMService(LLMService):
-    def __init__(self, model="llama2", base_url='http://localhost:11434/v1'):
-        super().__init__()
-        self._model = model
-        self._client = AsyncOpenAI(api_key="ollama", base_url=base_url)
+class OLLamaLLMService(BaseOpenAILLMService):
 
-    async def get_response(self, messages, stream):
-        return await self._client.chat.completions.create(
-            stream=stream,
-            messages=messages,
-            model=self._model
-        )
-
-    async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
-        messages_for_log = json.dumps(messages)
-        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
-
-        chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages)
-        async for chunk in chunks:
-            if len(chunk.choices) == 0:
-                continue
-
-            if chunk.choices[0].delta.content:
-                yield chunk.choices[0].delta.content
-
-    async def run_llm(self, messages) -> str | None:
-        messages_for_log = json.dumps(messages)
-        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
-
-        response = await self._client.chat.completions.create(model=self._model, stream=False, messages=messages)
-        if response and len(response.choices) > 0:
-            return response.choices[0].message.content
-        else:
-            return None
+    def __init__(self, model="llama2", base_url="http://localhost:11434/v1"):
+        super().__init__(model=model, base_url=base_url, api_key="ollama")