refactor and deps (#2184)

application/llm/docsgpt_provider.py

@@ -1,75 +1,19 @@
-import json
-
-from openai import OpenAI
-
 from application.core.settings import settings
-from application.llm.base import BaseLLM
+from application.llm.openai import OpenAILLM
 
+DOCSGPT_API_KEY = "sk-docsgpt-public"
+DOCSGPT_BASE_URL = "https://oai.arc53.com"
+DOCSGPT_MODEL = "docsgpt"
 
-class DocsGPTAPILLM(BaseLLM):
-
-    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
-
-        super().__init__(*args, **kwargs)
-        self.api_key = "sk-docsgpt-public"
-        self.client = OpenAI(api_key=self.api_key, base_url="https://oai.arc53.com")
-        self.user_api_key = user_api_key
-
-    def _clean_messages_openai(self, messages):
-        cleaned_messages = []
-        for message in messages:
-            role = message.get("role")
-            content = message.get("content")
-
-            if role == "model":
-                role = "assistant"
-            if role and content is not None:
-                if isinstance(content, str):
-                    cleaned_messages.append({"role": role, "content": content})
-                elif isinstance(content, list):
-                    for item in content:
-                        if "text" in item:
-                            cleaned_messages.append(
-                                {"role": role, "content": item["text"]}
-                            )
-                        elif "function_call" in item:
-                            cleaned_args = self._remove_null_values(
-                                item["function_call"]["args"]
-                            )
-                            tool_call = {
-                                "id": item["function_call"]["call_id"],
-                                "type": "function",
-                                "function": {
-                                    "name": item["function_call"]["name"],
-                                    "arguments": json.dumps(cleaned_args),
-                                },
-                            }
-                            cleaned_messages.append(
-                                {
-                                    "role": "assistant",
-                                    "content": None,
-                                    "tool_calls": [tool_call],
-                                }
-                            )
-                        elif "function_response" in item:
-                            cleaned_messages.append(
-                                {
-                                    "role": "tool",
-                                    "tool_call_id": item["function_response"][
-                                        "call_id"
-                                    ],
-                                    "content": json.dumps(
-                                        item["function_response"]["response"]["result"]
-                                    ),
-                                }
-                            )
-                        else:
-                            raise ValueError(
-                                f"Unexpected content dictionary format: {item}"
-                            )
-                else:
-                    raise ValueError(f"Unexpected content type: {type(content)}")
-        return cleaned_messages
+class DocsGPTAPILLM(OpenAILLM):
+    def __init__(self, api_key=None, user_api_key=None, base_url=None, *args, **kwargs):
+        super().__init__(
+            api_key=DOCSGPT_API_KEY,
+            user_api_key=user_api_key,
+            base_url=DOCSGPT_BASE_URL,
+            *args,
+            **kwargs,
+        )
 
     def _raw_gen(
         self,
@@ -79,23 +23,19 @@ class DocsGPTAPILLM(BaseLLM):
         stream=False,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
+        response_format=None,
         **kwargs,
     ):
-        messages = self._clean_messages_openai(messages)
-        if tools:
-            response = self.client.chat.completions.create(
-                model="docsgpt",
-                messages=messages,
-                stream=stream,
-                tools=tools,
-                **kwargs,
-            )
-            return response.choices[0]
-        else:
-            response = self.client.chat.completions.create(
-                model="docsgpt", messages=messages, stream=stream, **kwargs
-            )
-            return response.choices[0].message.content
+        return super()._raw_gen(
+            baseself,
+            DOCSGPT_MODEL,
+            messages,
+            stream=stream,
+            tools=tools,
+            engine=engine,
+            response_format=response_format,
+            **kwargs,
+        )
 
     def _raw_gen_stream(
         self,
@@ -105,34 +45,16 @@ class DocsGPTAPILLM(BaseLLM):
         stream=True,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
+        response_format=None,
         **kwargs,
     ):
-        messages = self._clean_messages_openai(messages)
-        if tools:
-            response = self.client.chat.completions.create(
-                model="docsgpt",
-                messages=messages,
-                stream=stream,
-                tools=tools,
-                **kwargs,
-            )
-        else:
-            response = self.client.chat.completions.create(
-                model="docsgpt", messages=messages, stream=stream, **kwargs
-            )
-        try:
-            for line in response:
-                if (
-                    len(line.choices) > 0
-                    and line.choices[0].delta.content is not None
-                    and len(line.choices[0].delta.content) > 0
-                ):
-                    yield line.choices[0].delta.content
-                elif len(line.choices) > 0:
-                    yield line.choices[0]
-        finally:
-            if hasattr(response, "close"):
-                response.close()
-
-    def _supports_tools(self):
-        return True
+        return super()._raw_gen_stream(
+            baseself,
+            DOCSGPT_MODEL,
+            messages,
+            stream=stream,
+            tools=tools,
+            engine=engine,
+            response_format=response_format,
+            **kwargs,
+        )