feat : planner agent improvement

sources/agents/planner_agent.py

@@ -1,9 +1,11 @@
 import json
+from typing import List, Tuple, Type, Dict
 from sources.utility import pretty_print, animate_thinking
 from sources.agents.agent import Agent
 from sources.agents.code_agent import CoderAgent
 from sources.agents.file_agent import FileAgent
 from sources.agents.browser_agent import BrowserAgent
+from sources.text_to_speech import Speech
 from sources.tools.tools import Tools
 
 class PlannerAgent(Agent):
@@ -61,18 +63,22 @@ class PlannerAgent(Agent):
             return zip(names, tasks)
         return zip(tasks_names, tasks)
     
-    def make_prompt(self, task, needed_infos):
-        if needed_infos is None:
-            needed_infos = "No needed informations."
+    def make_prompt(self, task: dict, agent_infos_dict: dict):
+        infos = ""
+        if agent_infos_dict is None or len(agent_infos_dict) == 0:
+            infos = "No needed informations."
+        else:
+            for agent_id, info in agent_infos_dict:
+                infos += f"\t- According to agent {agent_id}:\n{info}\n\n"
         prompt = f"""
-        You are given the following informations:
-        {needed_infos}
+        You are given informations from your AI friends work:
+        {infos}
         Your task is:
         {task}
         """
         return prompt
     
-    def show_plan(self, json_plan):
+    def show_plan(self, json_plan: dict) -> None:
         agents_tasks = self.parse_agent_tasks(json_plan)
         if agents_tasks == (None, None):
             return
@@ -81,11 +87,10 @@ class PlannerAgent(Agent):
             pretty_print(f"{task['agent']} -> {task['task']}", color="info")
         pretty_print("▔▗ E N D ▖▔", color="status")
     
-    def process(self, prompt, speech_module) -> str:
+    def make_plan(self, prompt: str) -> str:
         ok = False
-        agents_tasks = (None, None)
+        answer = None
         while not ok:
-            self.wait_message(speech_module)
             animate_thinking("Thinking...", color="status")
             self.memory.push('user', prompt)
             answer, _ = self.llm_request()
@@ -96,25 +101,42 @@ class PlannerAgent(Agent):
                 ok = True
             else:
                 prompt = input("Please reformulate: ")
+        return answer
+    
+    def start_agent_process(self, task: str, required_infos: dict | None) -> str:
+        agent_prompt = self.make_prompt(task['task'], required_infos)
+        pretty_print(f"Agent {task['agent']} started working...", color="status")
+        agent_answer, _ = self.agents[task['agent'].lower()].process(agent_prompt, None)
+        self.agents[task['agent'].lower()].show_answer()
+        pretty_print(f"Agent {task['agent']} completed task.", color="status")
+        return agent_answer
+    
+    def get_work_result_agent(self, task_needs, agents_work_result):
+        return {k: agents_work_result[k] for k in task_needs if k in agents_work_result}
 
+    def process(self, prompt: str, speech_module: Speech) -> str:
+        agents_tasks = (None, None)
+        required_infos = None
+        agents_work_result = dict()
+
+        answer = self.make_plan(prompt)
         agents_tasks = self.parse_agent_tasks(answer)
+
         if agents_tasks == (None, None):
             return "Failed to parse the tasks", reasoning
-        prev_agent_answer = None
         for task_name, task in agents_tasks:
             pretty_print(f"I will {task_name}.", color="info")
-            agent_prompt = self.make_prompt(task['task'], prev_agent_answer)
             pretty_print(f"Assigned agent {task['agent']} to {task_name}", color="info")
             if speech_module: speech_module.speak(f"I will {task_name}. I assigned the {task['agent']} agent to the task.")
+
+            if agents_work_result is not None:
+                required_infos = self.get_work_result_agent(task['need'], agents_work_result)
             try:
-                prev_agent_answer, _ = self.agents[task['agent'].lower()].process(agent_prompt, speech_module)
-                pretty_print(f"-- Agent answer ---\n\n", color="output")
-                self.agents[task['agent'].lower()].show_answer()
-                pretty_print(f"\n\n", color="output")
+                self.last_answer = self.start_agent_process(task, required_infos)
             except Exception as e:
                 raise e
-        self.last_answer = prev_agent_answer
-        return prev_agent_answer, ""
+            agents_work_result[task['id']] = self.last_answer
+        return self.last_answer, ""
 
 if __name__ == "__main__":
     pass

sources/browser.py

@@ -6,7 +6,7 @@ from selenium.webdriver.support.ui import WebDriverWait
 from selenium.webdriver.support import expected_conditions as EC
 from selenium.common.exceptions import TimeoutException, WebDriverException
 from selenium.webdriver.common.action_chains import ActionChains
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 from bs4 import BeautifulSoup
 from urllib.parse import urlparse
 from fake_useragent import UserAgent

sources/interaction.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 
 from sources.text_to_speech import Speech
 from sources.utility import pretty_print, animate_thinking

sources/language.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 import re
 import langid
 import nltk

sources/llm_provider.py

@@ -294,22 +294,7 @@ class Provider:
         This function is used to conduct tests.
         """
         thought = """
-hello!
-```python
-print("Hello world from python")
-```
-
-This is ls -la from bash.
-```bash
-ls -la
-```
-
-This is pwd from bash. 
-```bash
-pwd
-```
-
-goodbye!
+\n\n```json\n{\n  \"plan\": [\n    {\n      \"agent\": \"Web\",\n      \"id\": \"1\",\n      \"need\": null,\n      \"task\": \"Conduct a comprehensive web search to identify at least five AI startups located in Osaka. Use reliable sources and websites such as Crunchbase, TechCrunch, or local Japanese business directories. Capture the company names, their websites, areas of expertise, and any other relevant details.\"\n    },\n    {\n      \"agent\": \"Web\",\n      \"id\": \"2\",\n      \"need\": null,\n      \"task\": \"Perform a similar search to find at least five AI startups in Tokyo. Again, use trusted sources like Crunchbase, TechCrunch, or Japanese business news websites. Gather the same details as for Osaka: company names, websites, areas of focus, and additional information.\"\n    },\n    {\n      \"agent\": \"File\",\n      \"id\": \"3\",\n      \"need\": [\"1\", \"2\"],\n      \"task\": \"Create a new text file named research_japan.txt in the user's home directory. Organize the data collected from both searches into this file, ensuring it is well-structured and formatted for readability. Include headers for Osaka and Tokyo sections, followed by the details of each startup found.\"\n    }\n  ]\n}\n```
         """
         return thought
 

sources/logger.py

@@ -1,5 +1,5 @@
 import os, sys
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 import datetime
 import logging
 

sources/memory.py

@@ -4,7 +4,7 @@ import uuid
 import os
 import sys
 import json
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 

sources/router.py

@@ -1,7 +1,7 @@
 import os
 import sys
 import torch
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 
 from transformers import pipeline
 from adaptive_classifier import AdaptiveClassifier

sources/text_to_speech.py

@@ -3,7 +3,7 @@ import re
 import platform
 import subprocess
 from sys import modules
-from typing import List, Tuple, Type, Dict, Tuple
+from typing import List, Tuple, Type, Dict
 
 from kokoro import KPipeline
 from IPython.display import display, Audio