refac: valves

examples/anthropic_manifold_pipeline.py

@@ -19,15 +19,17 @@
 
 
 class Pipeline:
+    class Valves(BaseModel):
+        ANTHROPIC_API_KEY: str = ""
+
     def __init__(self):
         self.type = "manifold"
         self.id = "anthropic"
         self.name = "anthropic/"
 
-        class Valves(BaseModel):
-            ANTHROPIC_API_KEY: str
-
-        self.valves = Valves(**{"ANTHROPIC_API_KEY": os.getenv("ANTHROPIC_API_KEY")})
+        self.valves = self.Valves(
+            **{"ANTHROPIC_API_KEY": os.getenv("ANTHROPIC_API_KEY")}
+        )
         self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
 
     def get_anthropic_models(self):

examples/cohere_manifold_pipeline.py

@@ -18,16 +18,16 @@
 
 
 class Pipeline:
+    class Valves(BaseModel):
+        COHERE_API_BASE_URL: str = "https://api.cohere.com/v1"
+        COHERE_API_KEY: str = ""
+
     def __init__(self):
         self.type = "manifold"
         self.id = "cohere"
         self.name = "cohere/"
 
-        class Valves(BaseModel):
-            COHERE_API_BASE_URL: str = "https://api.cohere.com/v1"
-            COHERE_API_KEY: str
-
-        self.valves = Valves(**{"COHERE_API_KEY": os.getenv("COHERE_API_KEY")})
+        self.valves = self.Valves(**{"COHERE_API_KEY": os.getenv("COHERE_API_KEY")})
 
         self.pipelines = self.get_cohere_models()
 

examples/conversation_turn_limit_filter.py

@@ -6,6 +6,20 @@
 
 
 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 ["*"]
+        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 for conversation turn limiting
+        target_user_roles: List[str] = ["user"]
+        max_turns: Optional[int] = None
+
     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.
@@ -17,21 +31,7 @@ def __init__(self):
         self.id = "conversation_turn_limit_filter_pipeline"
         self.name = "Conversation Turn Limit Filter"
 
-        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 ["*"]
-            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 for conversation turn limiting
-            target_user_roles: List[str] = ["user"]
-            max_turns: Optional[int] = None
-
-        self.valves = Valves(
+        self.valves = self.Valves(
             **{
                 "pipelines": os.getenv("CONVERSATION_TURN_PIPELINES", "*").split(","),
                 "max_turns": 10,

examples/detoxify_filter_pipeline.py

@@ -16,6 +16,17 @@
 
 
 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
+
     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.
@@ -28,19 +39,8 @@ def __init__(self):
         self.id = "detoxify_filter_pipeline"
         self.name = "Detoxify Filter"
 
-        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
-
         # Initialize
-        self.valves = Valves(
+        self.valves = self.Valves(
             **{
                 "pipelines": ["*"],  # Connect to all pipelines
             }

examples/example_pipeline.py

@@ -1,8 +1,12 @@
 from typing import List, Union, Generator, Iterator
 from schemas import OpenAIChatMessage
+from pydantic import BaseModel
 
 
 class Pipeline:
+    class Valves(BaseModel):
+        pass
+
     def __init__(self):
         # Optionally, you can set the id and name of the pipeline.
         # Assign a unique identifier to the pipeline.

examples/filter_pipeline.py

@@ -14,6 +14,19 @@
 
 
 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 ["*"]
+        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
+
+        # Add your custom parameters here
+        pass
+
     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.
@@ -26,20 +39,7 @@ def __init__(self):
         self.id = "filter_pipeline"
         self.name = "Filter"
 
-        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 ["*"]
-            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
-
-            # Add your custom parameters here
-            pass
-
-        self.valves = Valves(**{"pipelines": ["llama3:latest"]})
+        self.valves = self.Valves(**{"pipelines": ["llama3:latest"]})
 
         pass
 

examples/langfuse_filter_pipeline.py

@@ -18,6 +18,23 @@
 
 
 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.
@@ -30,24 +47,8 @@ def __init__(self):
         self.id = "langfuse_filter_pipeline"
         self.name = "Langfuse Filter"
 
-        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
-
         # Initialize
-        self.valves = Valves(
+        self.valves = self.Valves(
             **{
                 "pipelines": ["*"],  # Connect to all pipelines
                 "secret_key": os.getenv("LANGFUSE_SECRET_KEY"),
@@ -94,7 +95,7 @@ async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
             input=body,
             user_id=user["id"],
             metadata={"name": user["name"]},
-            session_id=body["chat_id"]
+            session_id=body["chat_id"],
         )
 
         print(trace.get_trace_url())

examples/libretranslate_filter_pipeline.py

@@ -8,6 +8,31 @@
 
 
 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
+        libretranslate_url: str
+
+        # 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.
@@ -20,32 +45,8 @@ def __init__(self):
         self.id = "libretranslate_filter_pipeline"
         self.name = "LibreTranslate Filter"
 
-        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
-            libretranslate_url: str
-
-            # 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"
-
         # Initialize
-        self.valves = Valves(
+        self.valves = self.Valves(
             **{
                 "pipelines": ["*"],  # Connect to all pipelines
                 "libretranslate_url": os.getenv(

examples/litellm_manifold_pipeline.py

@@ -14,6 +14,10 @@
 
 
 class Pipeline:
+
+    class Valves(BaseModel):
+        LITELLM_BASE_URL: str
+
     def __init__(self):
         # You can also set the pipelines that are available in this pipeline.
         # Set manifold to True if you want to use this pipeline as a manifold.
@@ -29,11 +33,8 @@ def __init__(self):
         # Optionally, you can set the name of the manifold pipeline.
         self.name = "LiteLLM: "
 
-        class Valves(BaseModel):
-            LITELLM_BASE_URL: str
-
         # Initialize rate limits
-        self.valves = Valves(**{"LITELLM_BASE_URL": "http://localhost:4001"})
+        self.valves = self.Valves(**{"LITELLM_BASE_URL": "http://localhost:4001"})
         self.pipelines = []
         pass
 

examples/litellm_subprocess_manifold_pipeline.py

@@ -21,6 +21,12 @@
 
 
 class Pipeline:
+    class Valves(BaseModel):
+        LITELLM_CONFIG_DIR: str = "./litellm/config.yaml"
+        LITELLM_PROXY_PORT: int = 4001
+        LITELLM_PROXY_HOST: str = "127.0.0.1"
+        litellm_config: dict = {}
+
     def __init__(self):
         # You can also set the pipelines that are available in this pipeline.
         # Set manifold to True if you want to use this pipeline as a manifold.
@@ -36,14 +42,8 @@ def __init__(self):
         # Optionally, you can set the name of the manifold pipeline.
         self.name = "LiteLLM: "
 
-        class Valves(BaseModel):
-            LITELLM_CONFIG_DIR: str = "./litellm/config.yaml"
-            LITELLM_PROXY_PORT: int = 4001
-            LITELLM_PROXY_HOST: str = "127.0.0.1"
-            litellm_config: dict = {}
-
         # Initialize Valves
-        self.valves = Valves(**{"LITELLM_CONFIG_DIR": f"./litellm/config.yaml"})
+        self.valves = self.Valves(**{"LITELLM_CONFIG_DIR": f"./litellm/config.yaml"})
         self.background_process = None
         pass
 

examples/ollama_manifold_pipeline.py

@@ -5,6 +5,10 @@
 
 
 class Pipeline:
+
+    class Valves(BaseModel):
+        OLLAMA_BASE_URL: str
+
     def __init__(self):
         # You can also set the pipelines that are available in this pipeline.
         # Set manifold to True if you want to use this pipeline as a manifold.
@@ -20,10 +24,7 @@ def __init__(self):
         # Optionally, you can set the name of the manifold pipeline.
         self.name = "Ollama: "
 
-        class Valves(BaseModel):
-            OLLAMA_BASE_URL: str
-
-        self.valves = Valves(**{"OLLAMA_BASE_URL": "http://localhost:11435"})
+        self.valves = self.Valves(**{"OLLAMA_BASE_URL": "http://localhost:11435"})
         self.pipelines = []
         pass