added dscriptions to yaml files and little refactoring.

examples/yaml_initialization_example.yaml

@@ -1,8 +1,5 @@
-class: "LLMSession"
-just_streaming_method: "openai"
-system_prompt: "You are helpfull assistant that could count letters in word. To acomplish this use tool letter_count()"
-completion_remove_key_on_error: True
-completion_max_tries: 2
+system_prompt: "You are helpful assistant that could count letters in word. 
+  To acomplish this use tool letter_count()"
 options:
   model: "groq/llama-3.1-70b-versatile"
   api_base: "https://api.groq.com/openai/v1"

just_agents/config/cot_agent_prompt.yaml

@@ -1,4 +1,5 @@
-class: "ChainOfThoughtAgent"
+class: "ChainOfThoughtAgent" # class name to create could be LLMSession, ChainOfThoughtAgent. Default is LLMSession
+system_prompt_path: # path to system prompt exclude use of system_prompt
 system_prompt: "You are an expert AI assistant that explains your reasoning step by step. 
   For each step, provide a title that describes what you're doing in that step, along with the content. 
   Decide if you need another step or if you're ready to give the final answer. 
@@ -19,23 +20,22 @@ system_prompt: "You are an expert AI assistant that explains your reasoning step
                   'content': 'To begin solving this problem, we need to carefully examine the given information and identify the crucial elements that will guide our solution process. This involves...',
                   'next_action': 'continue'
               }```
-  "
-system_prompt_path:
-final_prompt: "Please provide the final answer based solely on your reasoning above."
-title: "title"
-content: "content"
-next_action: "next_action"
-action_continue: "continue"
-action_final: "final_answer"
-thought_max_tokes: 300
-max_steps: 25
-final_max_tokens: 1200
-tools:
+  " # system prompt exclude use of system_prompt_path
+final_prompt: "Please provide the final answer based solely on your reasoning above." # prompt that will be passed before final answer
+title: "title" # title parameter in system prompt
+content: "content" # content parameter in system prompt
+next_action: "next_action" # next_action parameter in system prompt
+action_continue: "continue" # state of next_action parameter determine that next answer will be not final
+action_final: "final_answer" # state of next_action parameter determine that next answer will be not final
+thought_max_tokes: 300 # maximum length of thought
+final_max_tokens: 1200 # maximum length of final answer
+max_steps: 25 # maximum number of thought steps
+tools: # list of functions that will be used as tools, each record should contain package and function name
 #  - package:
 #    function:
-llm_session:
-  just_streaming_method: "openai"
-  completion_remove_key_on_error: True
-  completion_max_tries: 2
-  backup_options:
-  key_list_path:
+llm_session: # initialization parameters for inner object LLMSession
+  just_streaming_method: "openai" # protocol to handle llm format for function calling
+  completion_remove_key_on_error: True # in case of using list of keys removing key from the list after error call with this key
+  completion_max_tries: 2 # maximum number of completion retries before giving up
+  backup_options: # options that will be used after we give up with main options, one more completion call will be done with backup options
+  key_list_path: # path to text file with list of api keys, one key per line

just_agents/config/llm_session_schema.yaml

@@ -1,11 +1,11 @@
-class: "LLMSession"
-just_streaming_method: "openai"
-system_prompt_path:
-system_prompt:
-completion_remove_key_on_error: True
-completion_max_tries: 2
-backup_options:
-key_list_path:
-tools:
+class: "LLMSession" # class name to create could be LLMSession, ChainOfThoughtAgent. Default is LLMSession
+just_streaming_method: "openai" # protocol to handle llm format for function calling
+system_prompt_path: # path to system prompt exclude use of system_prompt
+system_prompt: # system prompt exclude use of system_prompt_path
+completion_remove_key_on_error: True # in case of using list of keys removing key from the list after error call with this key
+completion_max_tries: 2 # maximum number of completion retries before giving up
+backup_options: # options that will be used after we give up with main options, one more completion call will be done with backup options
+key_list_path: # path to text file with list of api keys, one key per line
+tools: # list of functions that will be used as tools, each record should contain package and function name
 #  - package:
 #    function:

just_agents/interfaces/IAgent.py

@@ -7,41 +7,14 @@ def build_agent(agent_schema: str | Path | dict):
     from just_agents.llm_session import LLMSession
     agent_schema = resolve_agent_schema(agent_schema)
     class_name = agent_schema.get("class", None)
-    if class_name is None:
-        raise ValueError("Error class_name field should not be empty in agent_schema param during IAgent.build() call.")
-    elif class_name == "LLMSession":
+    if class_name is None or class_name == "LLMSession":
         return LLMSession(agent_schema=agent_schema)
     elif class_name == "ChainOfThoughtAgent":
         return ChainOfThoughtAgent(agent_schema=agent_schema)
 
 class IAgent:
-
-    # @staticmethod
-    # def build(agent_schema: dict):
-    #     import importlib
-    #     try:
-    #         package_name = agent_schema.get("package", None)
-    #         class_name = agent_schema.get("class", None)
-    #
-    #         if package_name is None:
-    #             raise ValueError("Error package_name field should not be empty in agent_schema param during IAgent.build() call.")
-    #         if class_name is None:
-    #             raise ValueError("Error class_name field should not be empty in agent_schema param during IAgent.build() call.")
-    #         # Dynamically import the package
-    #         package = importlib.import_module(package_name)
-    #         # Get the class from the package
-    #         class_ = getattr(package, class_name)
-    #         # Create an instance of the class
-    #         instance = class_(agent_schema=agent_schema)
-    #
-    #         return instance
-    #     except (ImportError, AttributeError) as e:
-    #         print(f"Error creating instance of {class_name} from {package_name}: {e}")
-    #         return None
-
-
     def stream(self, input: str | dict | list[dict]) -> AsyncGenerator[Any, None]:
-        raise NotImplementedError("You need to impelement stream_add_all() first!")
+        raise NotImplementedError("You need to impelement stream() first!")
 
     def query(self, input: str | dict | list[dict]) -> str:
-        raise NotImplementedError("You need to impelement query_add_all() first!")
+        raise NotImplementedError("You need to impelement query() first!")