update .gitignore to include pipeline files

.gitignore

@@ -5,8 +5,10 @@ __pycache__
 
 
 pipelines/*
+!pipelines/*.*
 !pipelines/.gitignore
 .DS_Store
 
 .venv
-venv/
+venv/
+__pycache__

pipelines/first_pipeline.py

@@ -0,0 +1,52 @@
+"""
+title: Haystack Pipeline
+author: open-webui
+date: 2024-05-30
+version: 1.0
+license: MIT
+description: A pipeline for retrieving relevant information from a knowledge base using the Haystack library.
+requirements: haystack-ai, datasets>=2.6.1, sentence-transformers>=2.2.0
+"""
+
+from typing import List, Union, Generator, Iterator, Optional
+from schemas import OpenAIChatMessage
+
+
+class Pipeline:
+    def __init__(self):
+        self.chat_id = None
+        pass
+
+    async def on_startup(self):
+        pass
+
+    async def on_shutdown(self):
+        # This function is called when the server is stopped.
+        pass
+
+    async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
+      print(f"inlet:{__name__}")
+      print(f"user: {user}")
+      print(f"body: {body}")
+      # Store the chat_id from body
+      self.chat_id = body.get("chat_id")
+      print(f"Stored chat_id: {self.chat_id}")
+
+      return body
+
+    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 RAG pipeline.
+        # Typically, you would retrieve relevant information from your knowledge base and synthesize it to generate a response.
+
+        print("Body is: ")
+        print(body)
+        print("Chat ID is: ")
+        print(self.chat_id)
+        # print(messages)
+        # print(user_message)
+
+        # question = user_message
+
+        return user_message

pipelines/first_pipeline/valves.json

@@ -0,0 +1 @@
+{}