Global reformat - default line lengths

.devcontainer/devcontainer.json

@@ -1,13 +1,26 @@
 {
   "name": "dimos-dev",
-  "image": "ghcr.io/dimensionalos/dimos-dev:latest",
-  "postCreateCommand": "pre-commit install",
+  "image": "ghcr.io/dimensionalos/dev:dev",
   "customizations": {
     "vscode": {
       "extensions": [
         "charliermarsh.ruff",
         "ms-python.vscode-pylance"
       ]
     }
+  },
+
+  "settings": {
+    "notebook.formatOnSave.enabled": true,
+    "notebook.codeActionsOnSave": {
+      "notebook.source.fixAll": "explicit",
+      "notebook.source.organizeImports": "explicit"
+    },
+    "editor.codeActionsOnSave": {
+      "source.fixAll": "explicit",
+      "source.organizeImports": "explicit"
+    },
+    "editor.defaultFormatter": "charliermarsh.ruff",
+    "editor.formatOnSave": true
   }
 }

dimos/agents/agent_config.py

@@ -15,6 +15,7 @@
 from typing import List
 from dimos.agents.agent import Agent
 
+
 class AgentConfig:
     def __init__(self, agents: List[Agent] = None):
         """

dimos/agents/agent_ctransformers_gguf.py

@@ -15,7 +15,6 @@
 from __future__ import annotations
 
 # Standard library imports
-import json
 import logging
 import os
 from typing import Any, Optional
@@ -26,14 +25,11 @@
 from reactivex.scheduler import ThreadPoolScheduler
 from reactivex.subject import Subject
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Local imports
 from dimos.agents.agent import LLMAgent
 from dimos.agents.memory.base import AbstractAgentSemanticMemory
 from dimos.agents.prompt_builder.impl import PromptBuilder
-from dimos.agents.tokenizer.base import AbstractTokenizer
-from dimos.agents.tokenizer.huggingface_tokenizer import HuggingFaceTokenizer
 from dimos.utils.logging_config import setup_logger
 
 # Initialize environment variables
@@ -44,6 +40,7 @@
 
 from ctransformers import AutoModelForCausalLM as CTransformersModel
 
+
 class CTransformersTokenizerAdapter:
     def __init__(self, model):
         self.model = model
@@ -66,7 +63,9 @@ def detokenize_text(self, tokenized_text):
         except Exception as e:
             raise ValueError(f"Failed to detokenize text. Error: {str(e)}")
 
-    def apply_chat_template(self, conversation, tokenize=False, add_generation_prompt=True):
+    def apply_chat_template(
+        self, conversation, tokenize=False, add_generation_prompt=True
+    ):
         prompt = ""
         for message in conversation:
             role = message["role"]
@@ -84,26 +83,27 @@ def apply_chat_template(self, conversation, tokenize=False, add_generation_promp
 
 # CTransformers Agent Class
 class CTransformersGGUFAgent(LLMAgent):
-    def __init__(self,
-                 dev_name: str,
-                 agent_type: str = "HF-LLM",
-                 model_name: str = "TheBloke/Llama-2-7B-GGUF",
-                 model_file: str = "llama-2-7b.Q4_K_M.gguf",
-                 model_type: str = "llama",
-                 gpu_layers: int = 50,
-                 device: str = "auto",
-                 query: str = "How many r's are in the word 'strawberry'?",
-                 input_query_stream: Optional[Observable] = None,
-                 input_video_stream: Optional[Observable] = None,
-                 output_dir: str = os.path.join(os.getcwd(), "assets", "agent"),
-                 agent_memory: Optional[AbstractAgentSemanticMemory] = None,
-                 system_query: Optional[str] = "You are a helpful assistant.",
-                 max_output_tokens_per_request: int = 10,
-                 max_input_tokens_per_request: int = 250,
-                 prompt_builder: Optional[PromptBuilder] = None,
-                 pool_scheduler: Optional[ThreadPoolScheduler] = None,
-                 process_all_inputs: Optional[bool] = None,):
-
+    def __init__(
+        self,
+        dev_name: str,
+        agent_type: str = "HF-LLM",
+        model_name: str = "TheBloke/Llama-2-7B-GGUF",
+        model_file: str = "llama-2-7b.Q4_K_M.gguf",
+        model_type: str = "llama",
+        gpu_layers: int = 50,
+        device: str = "auto",
+        query: str = "How many r's are in the word 'strawberry'?",
+        input_query_stream: Optional[Observable] = None,
+        input_video_stream: Optional[Observable] = None,
+        output_dir: str = os.path.join(os.getcwd(), "assets", "agent"),
+        agent_memory: Optional[AbstractAgentSemanticMemory] = None,
+        system_query: Optional[str] = "You are a helpful assistant.",
+        max_output_tokens_per_request: int = 10,
+        max_input_tokens_per_request: int = 250,
+        prompt_builder: Optional[PromptBuilder] = None,
+        pool_scheduler: Optional[ThreadPoolScheduler] = None,
+        process_all_inputs: Optional[bool] = None,
+    ):
         # Determine appropriate default for process_all_inputs if not provided
         if process_all_inputs is None:
             # Default to True for text queries, False for video streams
@@ -120,7 +120,7 @@ def __init__(self,
             process_all_inputs=process_all_inputs,
             system_query=system_query,
             max_output_tokens_per_request=max_output_tokens_per_request,
-            max_input_tokens_per_request=max_input_tokens_per_request
+            max_input_tokens_per_request=max_input_tokens_per_request,
         )
 
         self.query = query
@@ -141,14 +141,13 @@ def __init__(self,
             model_name,
             model_file=model_file,
             model_type=model_type,
-            gpu_layers=gpu_layers
+            gpu_layers=gpu_layers,
         )
 
         self.tokenizer = CTransformersTokenizerAdapter(self.model)
 
         self.prompt_builder = prompt_builder or PromptBuilder(
-            self.model_name,
-            tokenizer=self.tokenizer
+            self.model_name, tokenizer=self.tokenizer
         )
 
         self.max_output_tokens_per_request = max_output_tokens_per_request
@@ -167,12 +166,13 @@ def __init__(self,
         if self.input_video_stream is not None:
             logger.info("Subscribing to input video stream...")
             self.disposables.add(
-                self.subscribe_to_image_processing(self.input_video_stream))
+                self.subscribe_to_image_processing(self.input_video_stream)
+            )
         if self.input_query_stream is not None:
             logger.info("Subscribing to input query stream...")
             self.disposables.add(
-                self.subscribe_to_query_processing(self.input_query_stream))
-
+                self.subscribe_to_query_processing(self.input_query_stream)
+            )
 
     def _send_query(self, messages: list) -> Any:
         try:
@@ -186,22 +186,26 @@ def _send_query(self, messages: list) -> Any:
                 content = msg["content"]
                 if isinstance(content, list):
                     # Assume it's a list of {'type': 'text', 'text': ...}
-                    text_parts = [c["text"] for c in content if isinstance(c, dict) and "text" in c]
+                    text_parts = [
+                        c["text"]
+                        for c in content
+                        if isinstance(c, dict) and "text" in c
+                    ]
                     content = " ".join(text_parts)
                 flat_messages.append({"role": role, "content": content})
 
             print(f"{_BLUE_PRINT_COLOR}Messages: {flat_messages}{_RESET_COLOR}")
 
             print("Applying chat template...")
             prompt_text = self.tokenizer.apply_chat_template(
-                conversation=flat_messages,
-                tokenize=False,
-                add_generation_prompt=True
+                conversation=flat_messages, tokenize=False, add_generation_prompt=True
             )
             print("Chat template applied.")
             print(f"Prompt text:\n{prompt_text}")
 
-            response = self.model(prompt_text, max_new_tokens=self.max_output_tokens_per_request)
+            response = self.model(
+                prompt_text, max_new_tokens=self.max_output_tokens_per_request
+            )
             print("Model response received.")
             return response
 
@@ -213,7 +217,11 @@ def stream_query(self, query_text: str) -> Subject:
         """
         Creates an observable that processes a text query and emits the response.
         """
-        return create(lambda observer, _: self._observable_query(
-            observer, incoming_query=query_text))
+        return create(
+            lambda observer, _: self._observable_query(
+                observer, incoming_query=query_text
+            )
+        )
+
 
 # endregion HuggingFaceLLMAgent Subclass (HuggingFace-Specific Implementation)