Sweep: Feature req: Please integrate apipie.ai

gpt_all_star/core/llm.py

@@ -13,6 +13,7 @@ class LLM_TYPE(str, Enum):
     AZURE = "AZURE"
     ANTHROPIC = "ANTHROPIC"
     ANTHROPIC_TOOLS = "ANTHROPIC_TOOLS"
+    APIPIE = "APIPIE"
 
 
 def create_llm(llm_name: LLM_TYPE) -> BaseChatModel:
@@ -45,6 +46,11 @@ def create_llm(llm_name: LLM_TYPE) -> BaseChatModel:
             model_name=os.getenv("ANTHROPIC_API_MODEL", "claude-3-opus-20240229"),
             temperature=0.1,
         )
+    elif llm_name == LLM_TYPE.APIPIE:
+        return _create_chat_apipie(
+            model_name=os.getenv("APIPIE_API_MODEL", "default-model"),
+            temperature=0.1,
+        )
     else:
         raise ValueError(f"Unsupported LLM type: {llm_name}")
 
@@ -76,6 +82,14 @@ def _create_azure_chat_openai(
         streaming=True,
     )
 
+def _create_chat_apipie(model_name: str, temperature: float) -> ChatAPIpie:
+    return ChatAPIpie(
+        model_name=model_name,
+        temperature=temperature,
+        streaming=True,
+        client=openai.chat.completions,  # Assuming similar client setup as OpenAI
+    )
+
 
 def _create_chat_anthropic(
     anthropic_api_key: str, model_name: str, temperature: float

gpt_all_star/core/tools/chat_apipie.py

@@ -0,0 +1,29 @@
+import requests
+from langchain_core.language_models.chat_models import BaseChatModel
+
+
+class ChatAPIpie(BaseChatModel):
+    def __init__(self, model_name: str, temperature: float, streaming: bool, api_key: str):
+        self.model_name = model_name
+        self.temperature = temperature
+        self.streaming = streaming
+        self.api_key = api_key
+        self.api_url = "https://api.apipie.ai/v1/chat/completions"
+
+    def send_request(self, prompt: str):
+        headers = {"Authorization": f"Bearer {self.api_key}"}
+        data = {
+            "model": self.model_name,
+            "prompt": prompt,
+            "temperature": self.temperature,
+            "stream": self.streaming
+        }
+        response = requests.post(self.api_url, json=data, headers=headers)
+        response.raise_for_status()
+        return self.process_response(response.json())
+
+    def process_response(self, response):
+        if "choices" in response and len(response["choices"]) > 0:
+            return response["choices"][0]["text"]
+        else:
+            raise ValueError("No response from APIpie API")