feat: body param added

main.py

@@ -129,7 +129,11 @@ async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
         if form_data.stream:
 
             def stream_content():
-                res = get_response(user_message, messages=form_data.messages)
+                res = get_response(
+                    user_message,
+                    messages=form_data.messages,
+                    body=form_data.model_dump_json(),
+                )
 
                 print(f"stream:true:{res}")
 
@@ -164,7 +168,11 @@ async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
 
             return StreamingResponse(stream_content(), media_type="text/event-stream")
         else:
-            res = get_response(user_message, messages=form_data.messages)
+            res = get_response(
+                user_message,
+                messages=form_data.messages,
+                body=form_data.model_dump_json(),
+            )
             print(f"stream:false:{res}")
 
             message = ""

pipelines/examples/haystack_pipeline.py

@@ -79,7 +79,7 @@ class Pipeline:
         pass
 
     def get_response(
-        self, user_message: str, messages: List[OpenAIChatMessage]
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
     ) -> Union[str, Generator]:
         # This is where you can add your custom RAG pipeline.
         # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.

pipelines/examples/llamaindex_ollama_github_pipeline.py

@@ -70,7 +70,7 @@ class Pipeline:
         pass
 
     def get_response(
-        self, user_message: str, messages: List[OpenAIChatMessage]
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
     ) -> Union[str, Generator]:
         # This is where you can add your custom RAG pipeline.
         # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.

pipelines/examples/llamaindex_ollama_pipeline.py

@@ -30,7 +30,7 @@ class Pipeline:
         pass
 
     def get_response(
-        self, user_message: str, messages: List[OpenAIChatMessage]
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
     ) -> Union[str, Generator]:
         # This is where you can add your custom RAG pipeline.
         # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.

pipelines/examples/llamaindex_pipeline.py

@@ -25,7 +25,7 @@ class Pipeline:
         pass
 
     def get_response(
-        self, user_message: str, messages: List[OpenAIChatMessage]
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
     ) -> Union[str, Generator]:
         # This is where you can add your custom RAG pipeline.
         # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.

pipelines/examples/openai_pipeline.py

@@ -0,0 +1,49 @@
+from typing import List, Union, Generator
+from schemas import OpenAIChatMessage
+import requests
+
+
+class Pipeline:
+    def __init__(self):
+        pass
+
+    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 get_response(
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
+    ) -> Union[str, Generator]:
+        # This is where you can add your custom pipelines like RAG.'
+        print(f"get_response:{__name__}")
+
+        print(messages)
+        print(user_message)
+        OPENAI_API_KEY = "your-api-key-here"
+
+        headers = {}
+        headers["Authorization"] = f"Bearer {OPENAI_API_KEY}"
+        headers["Content-Type"] = "application/json"
+
+        r = requests.request(
+            method="POST",
+            url="https://api.openai.com/v1",
+            data=body,
+            headers=headers,
+            stream=True,
+        )
+
+        r.raise_for_status()
+
+        # Check if response is SSE
+        if "text/event-stream" in r.headers.get("Content-Type", ""):
+            return r.iter_content(chunk_size=8192)
+        else:
+            response_data = r.json()
+            return f"{response_data['choices'][0]['text']}"

pipelines/examples/pipeline_example.py

@@ -17,12 +17,13 @@ class Pipeline:
         pass
 
     def get_response(
-        self, user_message: str, messages: List[OpenAIChatMessage]
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
     ) -> Union[str, Generator]:
         # This is where you can add your custom pipelines like RAG.'
         print(f"get_response:{__name__}")
 
         print(messages)
         print(user_message)
+        print(body)
 
         return f"{__name__} response to: {user_message}"

pipelines/pipeline.py

@@ -17,7 +17,7 @@ class Pipeline:
         pass
 
     def get_response(
-        self, user_message: str, messages: List[OpenAIChatMessage]
+        self, user_message: str, messages: List[OpenAIChatMessage], body: dict
     ) -> Union[str, Generator]:
         # This is where you can add your custom pipelines like RAG.'
         print(f"get_response:{__name__}")