lint

chatsky/llm/conditions.py

@@ -1,5 +1,6 @@
 from chatsky.llm.methods import BaseMethod
 
+
 def llm_condition(model_name: str, prompt: str, method: BaseMethod):
     """
     Basic function for using LLM in condition cases.

chatsky/llm/filters.py

@@ -14,6 +14,7 @@ class BaseFilter(BaseModel, abc.ABC):
     """
     Base class for all message history filters.
     """
+
     @abc.abstractmethod
     def __call__(self, ctx: Context, request: Message, response: Message, model_name: str) -> bool:
         """
@@ -29,6 +30,7 @@ class IsImportant(BaseFilter):
     """
     Filter that checks if the "important" field in a Message.misc is True.
     """
+
     def __call__(
         self, ctx: Context = None, request: Message = None, response: Message = None, model_name: str = None
     ) -> bool:
@@ -43,6 +45,7 @@ class FromTheModel(BaseFilter):
     """
     Filter that checks if the message was sent by the model.
     """
+
     def __call__(
         self, ctx: Context = None, request: Message = None, response: Message = None, model_name: str = None
     ) -> bool:

chatsky/llm/llm_api.py

@@ -12,6 +12,7 @@
     from langchain_mistralai import ChatMistralAI
     from langchain_core.output_parsers import StrOutputParser
     from langchain_core.language_models.chat_models import BaseChatModel
+
     langchain_available = True
 except ImportError:
     langchain_available = False
@@ -78,7 +79,10 @@ async def respond(
 
     async def condition(self, prompt: str, method: BaseMethod, return_schema=None):
         async def process_input(ctx: Context, _: Pipeline) -> bool:
-            condition_history = [await message_to_langchain(Message(prompt), pipeline=_, source="system"), await message_to_langchain(ctx.last_request, pipeline=_, source="human")]
+            condition_history = [
+                await message_to_langchain(Message(prompt), pipeline=_, source="system"),
+                await message_to_langchain(ctx.last_request, pipeline=_, source="human"),
+            ]
             result = method(ctx, await self.model.agenerate([condition_history], logprobs=True, top_logprobs=10))
             return result
 

chatsky/llm/methods.py

@@ -15,6 +15,7 @@ class BaseMethod(BaseModel, abc.ABC):
     """
     Base class to evaluate models response as condition.
     """
+
     @abc.abstractmethod
     async def __call__(self, ctx: Context, model_result: LLMResult) -> bool:
         raise NotImplementedError
@@ -35,6 +36,7 @@ class Contains(BaseMethod):
     :return: True if pattern is contained in model result
     :rtype: bool
     """
+
     pattern: str
 
     async def __call__(self, ctx: Context, model_result: LLMResult) -> bool:
@@ -52,11 +54,13 @@ class LogProb(BaseMethod):
     :return: True if logprob is higher then threshold
     :rtype: bool
     """
+
     target_token: str
     threshold: float = -0.5
+
     async def __call__(self, ctx: Context, model_result: LLMResult) -> bool:
         try:
-            result = model_result.generations[0][0].generation_info['logprobs']['content'][0]['top_logprobs']
+            result = model_result.generations[0][0].generation_info["logprobs"]["content"][0]["top_logprobs"]
         except ValueError:
             raise ValueError("LogProb method can only be applied to OpenAI models.")
         for tok in result:

chatsky/llm/responses.py

@@ -7,7 +7,14 @@
 from pydantic import BaseModel
 
 
-def llm_response(model_name: str, prompt: str = "", history: int = 5, filter_func: Callable = lambda *args: True, message_schema: Union[None, Type[Message], Type[BaseModel]] = None, max_size: int=1000):
+def llm_response(
+    model_name: str,
+    prompt: str = "",
+    history: int = 5,
+    filter_func: Callable = lambda *args: True,
+    message_schema: Union[None, Type[Message], Type[BaseModel]] = None,
+    max_size: int = 1000,
+):
     """
     Basic function for receiving LLM responses.
     :param ctx: Context object. (Assigned automatically)
@@ -33,19 +40,25 @@ async def wrapped(ctx: Context, pipeline: Pipeline) -> Message:
             # populate history with global and local prompts
             if "global_prompt" in current_misc:
                 global_prompt = current_misc["global_prompt"]
-                history_messages.append(await message_to_langchain(Message(global_prompt), pipeline=pipeline, source="system"))
+                history_messages.append(
+                    await message_to_langchain(Message(global_prompt), pipeline=pipeline, source="system")
+                )
             if "local_prompt" in current_misc:
                 local_prompt = current_misc["local_prompt"]
-                history_messages.append(await message_to_langchain(Message(local_prompt), pipeline=pipeline, source="system"))
+                history_messages.append(
+                    await message_to_langchain(Message(local_prompt), pipeline=pipeline, source="system")
+                )
             if "prompt" in current_misc:
                 node_prompt = current_misc["prompt"]
-                history_messages.append(await message_to_langchain(Message(node_prompt), pipeline=pipeline, source="system"))
+                history_messages.append(
+                    await message_to_langchain(Message(node_prompt), pipeline=pipeline, source="system")
+                )
 
         # iterate over context to retrieve history messages
         if not (history == 0 or len(ctx.responses) == 0 or len(ctx.requests) == 0):
             pairs = zip(
-                [ctx.requests[x] for x in range(1, len(ctx.requests)+1)],
-                [ctx.responses[x] for x in range(1, len(ctx.responses)+1)],
+                [ctx.requests[x] for x in range(1, len(ctx.requests) + 1)],
+                [ctx.responses[x] for x in range(1, len(ctx.responses) + 1)],
             )
             # print(f"Pairs: {[p for p in pairs]}")
             if history != -1:
@@ -64,4 +77,3 @@ async def wrapped(ctx: Context, pipeline: Pipeline) -> Message:
         return await model.respond(history_messages, message_schema=message_schema)
 
     return wrapped
-

chatsky/llm/utils.py

@@ -4,7 +4,7 @@
 from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
 
 
-async def message_to_langchain(message: Message, pipeline: Pipeline, source: str = "human", max_size: int=1000):
+async def message_to_langchain(message: Message, pipeline: Pipeline, source: str = "human", max_size: int = 1000):
     """
     Creates a langchain message from a ~chatsky.script.core.message.Message object.
 
@@ -19,13 +19,19 @@ async def message_to_langchain(message: Message, pipeline: Pipeline, source: str
     if len(message.text) > max_size:
         raise ValueError("Message is too long.")
 
-    if message.text is None: message.text = ""
+    if message.text is None:
+        message.text = ""
     content = [{"type": "text", "text": message.text}]
 
     if message.attachments:
         for image in message.attachments:
             if isinstance(image, Image):
-                content.append({"type": "image_url", "image_url": {"url": await __attachment_to_content(image, pipeline.messenger_interface)}})
+                content.append(
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": await __attachment_to_content(image, pipeline.messenger_interface)},
+                    }
+                )
 
     if source == "human":
         return HumanMessage(content=content)