Testing and bug fixes

AgentLLM.py

@@ -49,7 +49,7 @@ def trim_context(self, context: List[str], max_tokens: int) -> List[str]:
                 break
         return trimmed_context
 
-    def run(self, task: str, max_context_tokens: int = 500, long_term_access: bool = False):
+    def run(self, task: str, max_context_tokens: int = 4096, long_term_access: bool = False):
         if not self.CFG.NO_MEMORY:
             self.yaml_memory.log_interaction("USER", task)
             context = self.context_agent(query=task, top_results_num=5, long_term_access=long_term_access)
@@ -59,9 +59,15 @@ def run(self, task: str, max_context_tokens: int = 500, long_term_access: bool =
             commands_prompt = self.commands.get_prompt()
             self.response = self.instruct(f"{commands_prompt}\n{prompt}")
         else:
-            self.response = self.instruct(prompt)
+            # Chunk the prompt if it's too long
+            prompt_chunks = self.chunk_content(prompt)
+            responses = []
+            for chunk in prompt_chunks:
+                response = self.instruct(chunk)
+                responses.append(response)
+            self.response = " ".join(responses)
 
-        if not self.CFG.NO_MEMORY:
+        if self.CFG.NO_MEMORY:
             self.store_result(task, self.response)
             self.yaml_memory.log_interaction(self.CFG.AGENT_NAME, self.response)
 
@@ -79,6 +85,7 @@ def context_agent(self, query: str, top_results_num: int, long_term_access: bool
         if long_term_access:
             interactions = self.yaml_memory.memory["interactions"]
             context = [interaction["message"] for interaction in interactions[-top_results_num:]]
+            context = self.chunk_content("\n\n".join(context))[:top_results_num]
         else:
             count = self.collection.count()
             if count == 0:
@@ -103,7 +110,7 @@ def chunk_content(self, content: str, max_length: int = 500) -> List[str]:
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument("--prompt", type=str, default="What is the weather like today?")
-    parser.add_argument("--max_context_tokens", type=int, default=500)
+    parser.add_argument("--max_context_tokens", type=int, default=4096)
     parser.add_argument("--long_term_access", type=bool, default=False)
     args = parser.parse_args()
     prompt = args.prompt

Commands.py

@@ -14,7 +14,7 @@ def load_commands(self):
         for command_file in command_files:
             module_name = os.path.splitext(os.path.basename(command_file))[0]
             module = importlib.import_module(f"commands.{module_name}")
-            if issubclass(module.__dict__.get(module_name), Commands):
+            if issubclass(getattr(module, module_name), Commands):
                 command_class = getattr(module, module_name)()
                 for command_name, command_function in command_class.commands.items():
                     params = self.get_command_params(command_function)
@@ -37,23 +37,11 @@ def get_prompt(self):
         for i, (command_name, command_function_name, params) in enumerate(self.commands, 1):
             formatted_params = {f"{k}": repr(v) for k, v in params.items()}
             commands_str += f'{i}. "{command_name}" - {command_function_name} {formatted_params}\n'
-
-        system_prompt = f"""
-You are an AI language model. Your name is {self.CFG.AGENT_NAME}. Your role is to do anything asked of you with precision. You have the following constraints:
-1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
-2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
-3. No user assistance.
-4. Exclusively use the commands listed in double quotes e.g. "command name".
-
-You have the following resources:
-1. Internet access for searches and information gathering.
-2. Long Term memory management.
-3. GPT-3.5 powered Agents for delegation of simple tasks.
-4. File output.
-
-You have the following commands available:
-{commands_str}
-        """
+        # Get prompt from model-prompts/{CFG.AI_MODEL}/system.txt
+        with open(f"model-prompts/{self.CFG.AI_MODEL}/system.txt", "r") as f:
+            system_prompt = f.read()
+        system_prompt = system_prompt.replace("{{COMMANDS}}", commands_str)
+        system_prompt = system_prompt.replace("{{AGENT_NAME}}", self.CFG.AGENT_NAME)
         return system_prompt
 
     def find_command(self, command_name: str):

PROMPTS.md

@@ -8,6 +8,7 @@ The Plugin System uses prompts to provide instructions to different AI agents. E
 2. model-prompts/{model}/priority.txt
 3. model-prompts/{model}/system.txt
 4. model-prompts/{model}/task.txt
+5. model-prompts/{model}/script.txt
 ### Prompt Formats
 
 Each prompt has a specific format for providing instructions to the AI agents. The following section explains the format for each prompt type.
@@ -81,6 +82,9 @@ Return the tasks as an array.
 
 
 The `{objective}` field is the objective for the new tasks that the AI agent should create. The `{result}` field is the result of the previous task. The `{task_description}` field is the description of the previous task. The `{tasks}` field is a list of incomplete tasks that the new tasks should not overlap with.
+
+#### script.txt
+
 ### Conclusion
 
 This README file provided an overview of the Plugin System and the four types of prompts used to instruct AI agents. It also provided the format for each prompt type.

commands/execute_code.py

@@ -7,7 +7,7 @@
 
 CFG = Config()
 
-class ContainerActions(Commands):
+class execute_code(Commands):
     def __init__(self):
         super().__init__()
         self.commands = {

commands/web_playwright.py

@@ -7,7 +7,7 @@
 
 CFG = Config()
 
-class WebScraping(Commands):
+class web_playwright(Commands):
     def __init__(self):
         super().__init__()
         self.commands = {

requirements.txt

@@ -19,4 +19,5 @@ gitpython
 flask-restful
 flask_swagger
 flask_swagger_ui
-flasgger
+flasgger
+pynacl