added experiment chat mode for experimenting in the same session

src/openagi/actions/obs_rag.py

@@ -21,5 +21,5 @@ class MemoryRagAction(BaseAction):
 
     def execute(self):
         resp = self.memory.search(query=self.query, n_results=self.max_results or 10)
-        logging.debug(f"Retreived MEMORY DATA  -  {resp}")
+        logging.info(f"Retreived MEMORY DATA  -  {resp}")
         return resp

src/openagi/actions/tools/ddg_search.py

@@ -23,7 +23,7 @@ class DuckDuckGoSearch(BaseAction):
     )
 
     max_results: int = Field(
-        default=10,
+        default=5,
         description="Total results, in int, to be executed from the search. Defaults to 10.",
     )
 

src/openagi/agent.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 from textwrap import dedent
-from typing import Any, Dict, List, Optional, Union, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 from pydantic import BaseModel, Field, field_validator
 
 from openagi.actions.base import BaseAction
@@ -16,9 +16,7 @@
 from openagi.prompts.worker_task_execution import WorkerAgentTaskExecution
 from openagi.tasks.lists import TaskLists
 from openagi.utils.extraction import (
-    find_last_r_failure_content,
-    get_act_classes_from_json,
-    get_last_json,
+    get_act_classes_from_json, get_last_json,
 )
 from openagi.utils.helper import get_default_llm
 from openagi.utils.tool_list import get_tool_list
@@ -141,7 +139,7 @@ def run_planner(self, query: str, description: str, long_term_context: str):
     def _generate_tasks_list(self, planned_tasks):
         task_lists = TaskLists()
         task_lists.add_tasks(tasks=planned_tasks)
-        logging.debug(f"Created {task_lists.get_tasks_queue().qsize()} Tasks.")
+        logging.info(f"Created {task_lists.get_tasks_queue().qsize()} Tasks.")
         return task_lists
 
     def get_previous_task_contexts(self, task_lists: TaskLists):
@@ -261,7 +259,7 @@ def auto_workers_assignment(self, query: str, description: str, task_lists: Task
         main_task_list = TaskLists()
         while not task_lists.all_tasks_completed:
             cur_task = task_lists.get_next_unprocessed_task()
-            print(cur_task)
+            # print(cur_task)
             logging.info(f"**** Executing Task - {cur_task.name} [{cur_task.id}] ****")
 
             worker_config = cur_task.worker_config
@@ -430,6 +428,19 @@ def single_agent_execution(self, query: str, description: str, task_lists: TaskL
         logging.debug(f"Execution Completed for Session ID - {self.memory.session_id}")
         return output
 
+    def experiment(self, description: str):
+        logging.info("Starting Experiment mode...")
+        logging.info(f"SessionID - {self.memory.session_id}")
+
+        while True:
+            query = self.input_action.execute(prompt="Enter the query to be processed:")
+            logging.info(f"Query: {query}")
+            result = self.run(query=query, description=description)
+            logging.info(f"Query: {query}\n\nResult: {result}\n\n")
+            cont = self.input_action.execute(prompt="Do you want to continue experimenting (y/n):").strip()
+            if cont.lower() == "n":
+                break
+        logging.info("Exiting experiment mode.")
 
     def run(self, query: str, description: str,planned_tasks: Optional[List[Dict]] = None):
         logging.info("Running Admin Agent...")
@@ -472,7 +483,7 @@ def run(self, query: str, description: str,planned_tasks: Optional[List[Dict]] =
 
 
         logging.info("Tasks Planned...")
-        logging.debug(f"{planned_tasks=}")
+        logging.info(f"{planned_tasks=}")
 
         task_lists: TaskLists = self._generate_tasks_list(planned_tasks=planned_tasks)
 
@@ -510,12 +521,6 @@ def run(self, query: str, description: str,planned_tasks: Optional[List[Dict]] =
                 self.save_ltm("add", session)
         return result
 
-    def _can_task_execute(self, llm_resp: str) -> Union[bool, Optional[str]]:
-        content: str = find_last_r_failure_content(text=llm_resp)
-        if content:
-            return False, content
-        return True, content
-
     def get_supported_actions_for_worker(self, actions_list: List[str],tool_list: List[str]):
         """
         This function takes a list of action names (strings) and returns a list of class objects

src/openagi/memory/base.py

@@ -50,7 +50,7 @@ def __init__(self, **data: Any):
                 collection_name="long_term_memory",
                 persist_path=self.long_term_dir
             )
-            assert 1 >= self.ltm_threshold >= 0.6, "Semantic similarity threshold should be between 0.6 and 1"
+            assert 1 >= self.ltm_threshold >= 0.7, "Semantic similarity threshold should be between 0.7 and 1"
 
         logging.info(f"Session ID initialized: {self.session_id}")
         if self.long_term:
@@ -114,7 +114,7 @@ def save_task(self, task: Task) -> None:
         )
         logging.info(f"Task saved: {task.id}")
 
-    def save_planned_tasks(self, tasks: TaskLists) -> None:
+    def save_planned_tasks(self, tasks: List[Task]) -> None:
         """
         Save a list of planned tasks into Memory.
 

src/openagi/planner/task_decomposer.py

@@ -202,7 +202,7 @@ def plan(
         if not tasks:
             raise LLMResponseError("Note: This not a error => No tasks was planned in the Planner response. Tweak the prompt and actions, then try again")
 
-        print(f"\n\nTasks: {tasks}\n\n")
+        # print(f"\n\nTasks: {tasks}\n\n")
         return tasks
 
     def _extract_task_with_retry(self, llm_response: str, prompt: str) -> Dict: