add Azure OpenAI manifold pipeline example

examples/pipelines/providers/azure_openai_manifold_pipeline.py

@@ -0,0 +1,99 @@
+from typing import List, Union, Generator, Iterator
+from pydantic import BaseModel
+import requests
+import os
+
+
+class Pipeline:
+    class Valves(BaseModel):
+        # You can add your custom valves here.
+        AZURE_OPENAI_API_KEY: str
+        AZURE_OPENAI_ENDPOINT: str
+        AZURE_OPENAI_API_VERSION: str
+        AZURE_OPENAI_MODELS: str
+        AZURE_OPENAI_MODEL_NAMES: str
+
+    def __init__(self):
+        self.type = "manifold"
+        self.name = "Azure OpenAI: "
+        self.valves = self.Valves(
+            **{
+                "AZURE_OPENAI_API_KEY": os.getenv("AZURE_OPENAI_API_KEY", "your-azure-openai-api-key-here"),
+                "AZURE_OPENAI_ENDPOINT": os.getenv("AZURE_OPENAI_ENDPOINT", "your-azure-openai-endpoint-here"),
+                "AZURE_OPENAI_API_VERSION": os.getenv("AZURE_OPENAI_API_VERSION", "2024-02-01"),
+                "AZURE_OPENAI_MODELS": os.getenv("AZURE_OPENAI_MODELS", "gpt-35-turbo;gpt-4o"),
+                "AZURE_OPENAI_MODEL_NAMES": os.getenv("AZURE_OPENAI_MODEL_NAMES", "GPT-35 Turbo;GPT-4o"),
+            }
+        )
+        self.set_pipelines()
+        pass
+
+    def set_pipelines(self):
+        models = self.valves.AZURE_OPENAI_MODELS.split(";")
+        model_names = self.valves.AZURE_OPENAI_MODEL_NAMES.split(";")
+        self.pipelines = [
+            {"id": model, "name": name} for model, name in zip(models, model_names)
+        ]
+        print(f"azure_openai_manifold_pipeline - models: {self.pipelines}")
+        pass
+
+    async def on_valves_updated(self):
+        self.set_pipelines()        
+
+    async def on_startup(self):
+        # This function is called when the server is started.
+        print(f"on_startup:{__name__}")
+        pass
+
+    async def on_shutdown(self):
+        # This function is called when the server is stopped.
+        print(f"on_shutdown:{__name__}")
+        pass
+
+    def pipe(
+            self, user_message: str, model_id: str, messages: List[dict], body: dict
+    ) -> Union[str, Generator, Iterator]:
+        # This is where you can add your custom pipelines like RAG.
+        print(f"pipe:{__name__}")
+
+        print(messages)
+        print(user_message)
+
+        headers = {
+            "api-key": self.valves.AZURE_OPENAI_API_KEY,
+            "Content-Type": "application/json",
+        }
+
+        url = f"{self.valves.AZURE_OPENAI_ENDPOINT}/openai/deployments/{model_id}/chat/completions?api-version={self.valves.AZURE_OPENAI_API_VERSION}"
+
+        allowed_params = {'messages', 'temperature', 'role', 'content', 'contentPart', 'contentPartImage',
+                          'enhancements', 'dataSources', 'n', 'stream', 'stop', 'max_tokens', 'presence_penalty',
+                          'frequency_penalty', 'logit_bias', 'user', 'function_call', 'funcions', 'tools',
+                          'tool_choice', 'top_p', 'log_probs', 'top_logprobs', 'response_format', 'seed'}
+        # remap user field
+        if "user" in body and not isinstance(body["user"], str):
+            body["user"] = body["user"]["id"] if "id" in body["user"] else str(body["user"])
+        filtered_body = {k: v for k, v in body.items() if k in allowed_params}
+        # log fields that were filtered out as a single line
+        if len(body) != len(filtered_body):
+            print(f"Dropped params: {', '.join(set(body.keys()) - set(filtered_body.keys()))}")
+
+        try:
+            r = requests.post(
+                url=url,
+                json=filtered_body,
+                headers=headers,
+                stream=True,
+            )
+
+            r.raise_for_status()
+            if body["stream"]:
+                return r.iter_lines()
+            else:
+                return r.json()
+        except Exception as e:
+            if r:
+                text = r.text
+                return f"Error: {e} ({text})"
+            else:
+                return f"Error: {e}"