feat: llm translation pipeline

examples/filters/llm_translate_filter_pipeline.py

@@ -0,0 +1,157 @@
+from typing import List, Optional
+from schemas import OpenAIChatMessage
+from pydantic import BaseModel
+import requests
+import os
+
+from utils.pipelines.main import get_last_user_message, get_last_assistant_message
+
+
+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
+
+        OPENAI_API_BASE_URL: str = "https://api.openai.com/v1"
+        OPENAI_API_KEY: str = ""
+        TASK_MODEL: str = "gpt-3.5-turbo"
+
+        # Source and target languages
+        # User message will be translated from source_user to target_user
+        source_user: Optional[str] = "auto"
+        target_user: Optional[str] = "en"
+
+        # Assistant languages
+        # Assistant message will be translated from source_assistant to target_assistant
+        source_assistant: Optional[str] = "en"
+        target_assistant: Optional[str] = "es"
+
+    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 = "libretranslate_filter_pipeline"
+        self.name = "LLM Translate Filter"
+
+        # Initialize
+        self.valves = self.Valves(
+            **{
+                "pipelines": ["*"],  # Connect to all pipelines
+                "OPENAI_API_KEY": os.getenv(
+                    "OPENAI_API_KEY", "your-openai-api-key-here"
+                ),
+            }
+        )
+
+        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
+
+    async def on_valves_updated(self):
+        # This function is called when the valves are updated.
+        pass
+
+    def translate(self, text: str, source: str, target: str) -> str:
+        headers = {}
+        headers["Authorization"] = f"Bearer {self.valves.OPENAI_API_KEY}"
+        headers["Content-Type"] = "application/json"
+
+        payload = {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": f"Translate the following text to {target}. Provide only the translated text and nothing else.",
+                },
+                {"role": "user", "content": text},
+            ],
+            "model": self.valves.TASK_MODEL,
+        }
+        print(payload)
+
+        try:
+            r = requests.post(
+                url=f"{self.valves.OPENAI_API_BASE_URL}/chat/completions",
+                json=payload,
+                headers=headers,
+                stream=False,
+            )
+
+            r.raise_for_status()
+            response = r.json()
+            print(response)
+            return response["choices"][0]["message"]["content"]
+        except Exception as e:
+            return f"Error: {e}"
+
+    async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
+        print(f"inlet:{__name__}")
+
+        messages = body["messages"]
+        user_message = get_last_user_message(messages)
+
+        print(f"User message: {user_message}")
+
+        # Translate user message
+        translated_user_message = self.translate(
+            user_message,
+            self.valves.source_user,
+            self.valves.target_user,
+        )
+
+        print(f"Translated user message: {translated_user_message}")
+
+        for message in reversed(messages):
+            if message["role"] == "user":
+                message["content"] = translated_user_message
+                break
+
+        body = {**body, "messages": messages}
+        return body
+
+    async def outlet(self, body: dict, user: Optional[dict] = None) -> dict:
+        if "title" in body:
+            return body
+
+        print(f"outlet:{__name__}")
+
+        messages = body["messages"]
+        assistant_message = get_last_assistant_message(messages)
+
+        print(f"Assistant message: {assistant_message}")
+
+        # Translate assistant message
+        translated_assistant_message = self.translate(
+            assistant_message,
+            self.valves.source_assistant,
+            self.valves.target_assistant,
+        )
+
+        print(f"Translated assistant message: {translated_assistant_message}")
+
+        for message in reversed(messages):
+            if message["role"] == "assistant":
+                message["content"] = translated_assistant_message
+                break
+
+        body = {**body, "messages": messages}
+        return body