Merge pull request #170 from ther3zz/patch-1

Update langfuse_filter_pipeline.py

examples/filters/langfuse_filter_pipeline.py

@@ -11,6 +11,7 @@ requirements: langfuse
 from typing import List, Optional
 from schemas import OpenAIChatMessage
 import os
+import uuid
 
 from utils.pipelines.main import get_last_user_message, get_last_assistant_message
 from pydantic import BaseModel
@@ -20,64 +21,36 @@ from langfuse.api.resources.commons.errors.unauthorized_error import Unauthorize
 
 class Pipeline:
     class Valves(BaseModel):
-        # List target pipeline ids (models) that this filter will be connected to.
-        # If you want to connect this filter to all pipelines, you can set pipelines to ["*"]
-        # e.g. ["llama3:latest", "gpt-3.5-turbo"]
         pipelines: List[str] = []
-
-        # Assign a priority level to the filter pipeline.
-        # The priority level determines the order in which the filter pipelines are executed.
-        # The lower the number, the higher the priority.
         priority: int = 0
-
-        # Valves
         secret_key: str
         public_key: str
         host: str
 
     def __init__(self):
-        # Pipeline filters are only compatible with Open WebUI
-        # You can think of filter pipeline as a middleware that can be used to edit the form data before it is sent to the OpenAI API.
         self.type = "filter"
-
-        # Optionally, you can set the id and name of the pipeline.
-        # Best practice is to not specify the id so that it can be automatically inferred from the filename, so that users can install multiple versions of the same pipeline.
-        # The identifier must be unique across all pipelines.
-        # The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
-        # self.id = "langfuse_filter_pipeline"
         self.name = "Langfuse Filter"
-
-        # Initialize
         self.valves = self.Valves(
             **{
-                "pipelines": ["*"],  # Connect to all pipelines
+                "pipelines": ["*"],
                 "secret_key": os.getenv("LANGFUSE_SECRET_KEY", "your-secret-key-here"),
                 "public_key": os.getenv("LANGFUSE_PUBLIC_KEY", "your-public-key-here"),
                 "host": os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com"),
             }
         )
-
         self.langfuse = None
         self.chat_generations = {}
-        pass
 
     async def on_startup(self):
-        # This function is called when the server is started.
         print(f"on_startup:{__name__}")
         self.set_langfuse()
-        pass
 
     async def on_shutdown(self):
-        # This function is called when the server is stopped.
         print(f"on_shutdown:{__name__}")
         self.langfuse.flush()
-        pass
 
     async def on_valves_updated(self):
-        # This function is called when the valves are updated.
-
         self.set_langfuse()
-        pass
 
     def set_langfuse(self):
         try:
@@ -97,6 +70,22 @@ class Pipeline:
 
     async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
         print(f"inlet:{__name__}")
+        print(f"Received body: {body}")
+        print(f"User: {user}")
+
+        # Check for presence of required keys and generate chat_id if missing
+        if "chat_id" not in body:
+            unique_id = f"SYSTEM MESSAGE {uuid.uuid4()}"
+            body["chat_id"] = unique_id
+            print(f"chat_id was missing, set to: {unique_id}")
+
+        required_keys = ["model", "messages"]
+        missing_keys = [key for key in required_keys if key not in body]
+        
+        if missing_keys:
+            error_message = f"Error: Missing keys in the request body: {', '.join(missing_keys)}"
+            print(error_message)
+            raise ValueError(error_message)
 
         trace = self.langfuse.trace(
             name=f"filter:{__name__}",
@@ -128,9 +117,6 @@ class Pipeline:
         user_message = get_last_user_message(body["messages"])
         generated_message = get_last_assistant_message(body["messages"])
 
-        # Update usage cost based on the length of the input and output messages
-        # Below does not reflect the actual cost of the API
-        # You can adjust the cost based on your requirements
         generation.end(
             output=generated_message,
             usage={