Renamed BaseFilter into BaseHistoryFilter, added API reference

chatsky/llm/__init__.py

@@ -1,3 +1,3 @@
-from chatsky.llm.filters import BaseFilter, FromModel, IsImportant
+from chatsky.llm.filters import BaseHistoryFilter, FromModel, IsImportant
 from chatsky.llm.methods import BaseMethod, LogProb, Contains
 from chatsky.llm.llm_api import LLM_API

chatsky/llm/filters.py

@@ -16,7 +16,7 @@
 logger = Logger(name=__name__)
 
 
-class BaseFilter(BaseModel, abc.ABC):
+class BaseHistoryFilter(BaseModel, abc.ABC):
     """
     Base class for all message history filters.
     """
@@ -50,7 +50,7 @@ def __call__(self, ctx: Context, request: Message, response: Message, model_name
         return []
 
 
-class MessageFilter(BaseFilter):
+class MessageFilter(BaseHistoryFilter):
     @abc.abstractmethod
     def call(self, ctx, message, model_name):
         raise NotImplemented
@@ -59,7 +59,7 @@ def __call__(self, ctx, request, response, model_name):
         return self.call(ctx, request, model_name) + self.call(ctx, response, model_name)
 
 
-class IsImportant(BaseFilter):
+class IsImportant(BaseHistoryFilter):
     """
     Filter that checks if the "important" field in a Message.misc is True.
     """
@@ -72,7 +72,7 @@ def call(self, ctx: Context, request: Message, response: Message, model_name: st
         return False
 
 
-class FromModel(BaseFilter):
+class FromModel(BaseHistoryFilter):
     """
     Filter that checks if the message was sent by the model.
     """

chatsky/llm/utils.py

@@ -10,7 +10,7 @@
 from chatsky.core.context import Context
 from chatsky.core.message import Image, Message
 from chatsky.llm._langchain_imports import HumanMessage, SystemMessage, AIMessage, check_langchain_available
-from chatsky.llm.filters import BaseFilter
+from chatsky.llm.filters import BaseHistoryFilter
 
 
 async def message_to_langchain(
@@ -45,7 +45,7 @@ async def message_to_langchain(
 
 
 async def context_to_history(
-    ctx: Context, length: int, filter_func: BaseFilter, model_name: str, max_size: int
+    ctx: Context, length: int, filter_func: BaseHistoryFilter, model_name: str, max_size: int
 ) -> list[HumanMessage | AIMessage | SystemMessage]:
     """
     Convert context to list of langchain messages.

chatsky/responses/llm.py

@@ -13,7 +13,7 @@
 from chatsky.core.context import Context
 from chatsky.llm.utils import message_to_langchain, context_to_history
 from chatsky.llm._langchain_imports import SystemMessage, check_langchain_available
-from chatsky.llm.filters import BaseFilter
+from chatsky.llm.filters import BaseHistoryFilter
 from chatsky.core.script_function import BaseResponse, AnyResponse
 
 
@@ -32,7 +32,7 @@ class LLMResponse(BaseResponse):
     model_name: str
     prompt: AnyResponse = Field(default="", validate_default=True)
     history: int = 5
-    filter_func: BaseFilter = BaseFilter()
+    filter_func: BaseHistoryFilter = BaseHistoryFilter()
     message_schema: Union[None, Type[Message], Type[BaseModel]] = None
     max_size: int = 1000
 

docs/source/user_guides/llm_integration.rst

@@ -145,5 +145,5 @@ Another way of dealing with unwanted messages is by using filtering functions.
     from chatsky.llm import IsImportant
     RESPONSE: LLMResponse(model_name="model_name_1", history=15, filter_func=IsImportant)
 
-These functions should be classes inheriting from ``BaseFilter``, having a ``__call__`` function with the following signature:
+These functions should be classes inheriting from ``BaseHistoryFilter``, having a ``__call__`` function with the following signature:
 ``def __call__(self, ctx: Context, request: Message, response: Message, model_name: str) -> bool``

tutorials/llm/3_filtering_history.py

@@ -25,11 +25,10 @@
 from chatsky.utils.testing import is_interactive_mode
 from chatsky.llm import LLM_API
 from chatsky.responses.llm import LLMResponse
-from chatsky.llm.filters import BaseFilter
+from chatsky.llm.filters import BaseHistoryFilter
 from chatsky.core.context import Context
 
 
-
 # %%
 model = LLM_API(
     ChatOllama(model="phi3:instruct", temperature=0),
@@ -41,11 +40,13 @@
 """
 In this example we will use very simple filtering function to
 retrieve only the important messages.
+If you want to learn more about filters see
+[API ref](%doclink(api,llm.filters,BaseHistoryFilter)).
 """
 
 
 # %%
-class FilterImportant(BaseFilter):
+class FilterImportant(BaseHistoryFilter):
     def __call__(
         self,
         ctx: Context = None,

tutorials/llm/4_structured_output.py

@@ -29,14 +29,15 @@
 from langchain_core.pydantic_v1 import BaseModel, Field
 
 
-
 # %% [markdown]
 """
 In this tutorial we will define two models.
 """
 # %%
 assistant_model = LLM_API(ChatOllama(model="llama3.2:1b", temperature=0))
-movie_model = LLM_API(ChatOllama(model="kuqoi/qwen2-tools:latest", temperature=0))
+movie_model = LLM_API(
+    ChatOllama(model="kuqoi/qwen2-tools:latest", temperature=0)
+)
 
 # %% [markdown]
 """

tutorials/llm/5_llm_slots.py

@@ -33,7 +33,6 @@
 from chatsky.slots.llm import LLMSlot, LLMGroupSlot
 
 
-
 # %% [markdown]
 """
 In this example we define LLM Group Slot with two LLM Slots in it.