mirror of
https://github.com/tcsenpai/agenticSeek.git
synced 2025-06-06 11:05:26 +00:00
feat : html to actual markdown for browser + better logging system
This commit is contained in:
martin legrand
parent
a09b6bf8aa
commit
688e94d97c
@ -192,10 +192,10 @@ class BrowserAgent(Agent):
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return f"""
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Following a human request:
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{user_query}
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A web AI made the following finding across different pages:
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A web browsing AI made the following finding across different pages:
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{search_note}
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Summarize the finding or step that lead to success, and provide a conclusion that answer the request.
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Expand on the finding or step that lead to success, and provide a conclusion that answer the request. Include link when possible.
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"""
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def search_prompt(self, user_prompt: str) -> str:
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@ -306,7 +306,7 @@ class BrowserAgent(Agent):
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prompt = self.make_navigation_prompt(user_prompt, page_text)
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prompt = self.conclude_prompt(user_prompt)
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mem_last_idx = self.memory.push('assistant', prompt)
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mem_last_idx = self.memory.push('user', prompt)
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answer, reasoning = self.llm_request()
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pretty_print(answer, color="output")
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self.memory.clear_section(mem_begin_idx, mem_last_idx)
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@ -21,12 +21,8 @@ import markdownify
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import sys
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import re
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if __name__ == "__main__":
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from utility import pretty_print, animate_thinking
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from logger import Logger
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else:
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from sources.utility import pretty_print, animate_thinking
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from sources.logger import Logger
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from sources.utility import pretty_print, animate_thinking
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from sources.logger import Logger
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def get_chrome_path() -> str:
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"""Get the path to the Chrome executable."""
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@ -163,19 +159,29 @@ class Browser:
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return (word_count >= 5 and (has_punctuation or is_long_enough))
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def get_text(self) -> str | None:
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"""Get page text."""
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"""Get page text as formatted Markdown"""
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try:
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soup = BeautifulSoup(self.driver.page_source, 'html.parser')
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for element in soup(['script', 'style']):
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for element in soup(['script', 'style', 'noscript', 'meta', 'link']):
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element.decompose()
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text = soup.get_text()
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lines = (f"{line.strip()}\n" for line in text.splitlines())
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text = "\n".join(chunk for chunk in lines if chunk and self.is_sentence(chunk))
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text = text[:4096]
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#markdown_text = markdownify.markdownify(text, heading_style="ATX")
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return "[Start of page]\n" + text + "\n[End of page]"
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markdown_converter = markdownify.MarkdownConverter(
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heading_style="ATX",
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strip=['a'],
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autolinks=False,
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bullets='•',
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strong_em_symbol='*',
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default_title=False,
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)
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markdown_text = markdown_converter.convert(str(soup.body))
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lines = []
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for line in markdown_text.splitlines():
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stripped = line.strip()
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if stripped and self.is_sentence(stripped):
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cleaned = ' '.join(stripped.split())
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lines.append(cleaned)
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result = "[Start of page]\n\n" + "\n\n".join(lines) + "\n\n[End of page]"
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result = re.sub(r'!\[(.*?)\]\(.*?\)', r'[IMAGE: \1]', result)
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return result[:8192]
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except Exception as e:
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self.logger.error(f"Error getting text: {str(e)}")
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return None
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@ -243,20 +249,25 @@ class Browser:
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if not element.is_enabled():
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return False
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try:
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self.logger.error(f"Scrolling to element for click_element.")
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self.driver.execute_script("arguments[0].scrollIntoView({block: 'center', behavior: 'smooth'});", element)
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time.sleep(0.1)
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element.click()
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return True
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except ElementClickInterceptedException as e:
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self.logger.error(f"Error click_element: {str(e)}")
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return False
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except TimeoutException:
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self.logger.warning(f"Timeout clicking element.")
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return False
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except Exception as e:
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self.logger.error(f"Unexpected error clicking element at {xpath}: {str(e)}")
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return False
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def load_js(self, file_name: str) -> str:
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"""Load javascript from script folder to inject to page."""
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path = os.path.join(self.js_scripts_folder, file_name)
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self.logger.info(f"Loading js at {path}")
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try:
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with open(path, 'r') as f:
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return f.read()
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@ -266,6 +277,7 @@ class Browser:
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raise e
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def find_all_inputs(self, timeout=3):
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"""Find all inputs elements on the page."""
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try:
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WebDriverWait(self.driver, timeout).until(
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EC.presence_of_element_located((By.TAG_NAME, "body"))
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@ -283,6 +295,7 @@ class Browser:
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try:
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input_elements = self.find_all_inputs()
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if not input_elements:
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self.logger.info("No input element on page.")
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return ["No input forms found on the page."]
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form_strings = []
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@ -331,14 +344,7 @@ class Browser:
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return False
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def find_and_click_btn(self, btn_type: str = 'login', timeout: int = 10) -> bool:
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"""
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Find and click a submit button matching the specified type.
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Args:
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btn_type: The type of button to find.
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timeout: time to wait for button to appear.
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Returns:
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bool: True if the button was found and clicked, False otherwise.
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"""
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"""Find and click a submit button matching the specified type."""
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buttons = self.get_buttons_xpath()
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if not buttons:
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self.logger.warning("No visible buttons found")
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@ -446,19 +452,19 @@ class Browser:
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input_elements = self.driver.execute_script(script)
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if __name__ == "__main__":
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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driver = create_driver()
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browser = Browser(driver, anticaptcha_manual_install=True)
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time.sleep(10)
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#browser.go_to("https://coinmarketcap.com/")
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#browser.go_to("https://github.com/Fosowl/agenticSeek")
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#txt = browser.get_text()
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#print(txt)
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#time.sleep(10)
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#browser.go_to("https://practicetestautomation.com/practice-test-login/")
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print("AntiCaptcha / Form Test")
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browser.go_to("https://www.google.com/recaptcha/api2/demo")
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#browser.go_to("https://practicetestautomation.com/practice-test-login/")
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time.sleep(10)
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inputs = browser.get_form_inputs()
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inputs = ['[input1](Martin)', f'[input2](Test)', '[input3](test@gmail.com)']
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#inputs = ['[input1](Martin)', f'[input2](Test)', '[input3](test@gmail.com)']
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browser.fill_form_inputs(inputs)
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browser.find_and_click_submission()
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time.sleep(10)
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@ -112,17 +112,20 @@ class Interaction:
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def think(self) -> bool:
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"""Request AI agents to process the user input."""
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push_last_agent_memory = False
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if self.last_query is None or len(self.last_query) == 0:
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return False
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agent = self.router.select_agent(self.last_query)
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if agent is None:
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return False
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if self.current_agent != agent and self.last_answer is not None:
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push_last_agent_memory = True
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tmp = self.last_answer
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self.current_agent = agent
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self.last_answer, _ = agent.process(self.last_query, self.speech)
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if push_last_agent_memory:
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self.current_agent.memory.push('user', self.last_query)
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self.current_agent.memory.push('assistant', self.last_answer)
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self.current_agent = agent
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tmp = self.last_answer
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self.last_answer, _ = agent.process(self.last_query, self.speech)
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if self.last_answer == tmp:
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self.last_answer = None
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return True
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@ -6,6 +6,7 @@ from nltk.sentiment.vader import SentimentIntensityAnalyzer
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from transformers import MarianMTModel, MarianTokenizer
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from sources.utility import pretty_print, animate_thinking
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from sources.logger import Logger
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class LanguageUtility:
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"""LanguageUtility for language, or emotion identification"""
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@ -14,6 +15,7 @@ class LanguageUtility:
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self.translators_tokenizer = None
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self.translators_model = None
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self.load_model()
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self.logger = Logger("language.log")
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def load_model(self) -> None:
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animate_thinking("Loading language utility...", color="status")
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@ -40,6 +42,7 @@ class LanguageUtility:
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"""
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langid.set_languages(['fr', 'en', 'zh'])
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lang, score = langid.classify(text)
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self.logger.info(f"Identified: {text} as {lang} with conf {score}")
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return lang
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def translate(self, text: str, origin_lang: str) -> str:
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@ -86,6 +89,7 @@ class LanguageUtility:
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dominant_emotion = max(emotions, key=emotions.get)
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if emotions[dominant_emotion] == 0:
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return 'Neutral'
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self.logger.info(f"Emotion: {dominant_emotion} for text: {text}")
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return dominant_emotion
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except Exception as e:
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raise e
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@ -13,6 +13,7 @@ from openai import OpenAI
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from huggingface_hub import InferenceClient
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from typing import List, Tuple, Type, Dict
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from sources.utility import pretty_print, animate_thinking
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from sources.logger import Logger
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class Provider:
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def __init__(self, provider_name, model, server_address = "127.0.0.1:5000", is_local=False):
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@ -42,6 +43,7 @@ class Provider:
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self.check_address_format(self.server_ip)
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if not self.is_ip_online(self.server_ip.split(':')[0]):
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raise Exception(f"Server at {self.server_ip} is offline.")
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self.logger = Logger("provider.log")
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def get_api_key(self, provider):
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load_dotenv()
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@ -50,6 +52,7 @@ class Provider:
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if not api_key:
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api_key = input(f"Please enter your {provider} API key: ")
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set_key(".env", api_key_var, api_key)
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self.logger.info("Set API key in env.")
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load_dotenv()
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return api_key
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@ -73,6 +76,7 @@ class Provider:
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Use the choosen provider to generate text.
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"""
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llm = self.available_providers[self.provider_name]
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self.logger.info(f"Using provider: {self.provider_name} at {self.server_ip}")
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try:
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thought = llm(history, verbose)
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except ConnectionError as e:
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@ -98,11 +102,14 @@ class Provider:
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if output.returncode == 0:
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return True
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else:
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self.logger.error(f"Ping command returned code: {output.returncode}")
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return False
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except subprocess.TimeoutExpired:
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self.logger.error("Ping subprocess timeout.")
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return False
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except Exception as e:
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pretty_print(f"Error with ping request {str(e)}", color="failure")
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self.logger.error(f"Ping error: {str(e)}")
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return False
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def server_fn(self, history, verbose = False):
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@ -8,9 +8,8 @@ from typing import List, Tuple, Type, Dict, Tuple
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import torch
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from sources.utility import timer_decorator, pretty_print
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from sources.logger import Logger
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class Memory():
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"""
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@ -36,6 +35,7 @@ class Memory():
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self.memory_compression = memory_compression
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self.tokenizer = AutoTokenizer.from_pretrained(self.model)
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self.model = AutoModelForSeq2SeqLM.from_pretrained(self.model)
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self.logger = Logger("memory.log")
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def get_filename(self) -> str:
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"""Get the filename for the save file."""
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@ -44,6 +44,7 @@ class Memory():
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def save_memory(self, agent_type: str = "casual_agent") -> None:
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"""Save the session memory to a file."""
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if not os.path.exists(self.conversation_folder):
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self.logger.info(f"Created folder {self.conversation_folder}.")
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os.makedirs(self.conversation_folder)
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save_path = os.path.join(self.conversation_folder, agent_type)
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if not os.path.exists(save_path):
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@ -52,6 +53,7 @@ class Memory():
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path = os.path.join(save_path, filename)
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json_memory = json.dumps(self.memory)
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with open(path, 'w') as f:
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self.logger.info(f"Saved memory json at {path}")
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f.write(json_memory)
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def find_last_session_path(self, path) -> str:
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@ -63,6 +65,7 @@ class Memory():
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saved_sessions.append((filename, date))
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saved_sessions.sort(key=lambda x: x[1], reverse=True)
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if len(saved_sessions) > 0:
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self.logger.info(f"Last session found at {saved_sessions[0][0]}")
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return saved_sessions[0][0]
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return None
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@ -87,12 +90,14 @@ class Memory():
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self.compress()
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pretty_print("Session recovered successfully", color="success")
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def reset(self, memory: list) -> None:
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def reset(self, memory: list = []) -> None:
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self.logger.info("Memory reset performed.")
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self.memory = memory
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def push(self, role: str, content: str) -> int:
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"""Push a message to the memory."""
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if self.memory_compression and role == 'assistant':
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self.logger.info("Compressing memories on message push.")
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self.compress()
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curr_idx = len(self.memory)
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if self.memory[curr_idx-1]['content'] == content:
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@ -101,10 +106,12 @@ class Memory():
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return curr_idx-1
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def clear(self) -> None:
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self.logger.info("Memory clear performed.")
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self.memory = []
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def clear_section(self, start: int, end: int) -> None:
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"""Clear a section of the memory."""
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self.logger.info(f"Memory section {start} to {end} cleared.")
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self.memory = self.memory[:start] + self.memory[end:]
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def get(self) -> list:
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@ -128,6 +135,7 @@ class Memory():
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str: The summarized text
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"""
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if self.tokenizer is None or self.model is None:
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self.logger.warning("No tokenizer or model to perform summarization.")
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return text
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if len(text) < min_length*1.5:
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return text
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@ -144,6 +152,7 @@ class Memory():
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)
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summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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summary.replace('summary:', '')
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self.logger.info(f"Memory summarization success from len {len(text)} to {len(summary)}.")
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return summary
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#@timer_decorator
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@ -160,6 +169,7 @@ class Memory():
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self.memory[i]['content'] = self.summarize(self.memory[i]['content'])
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if __name__ == "__main__":
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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memory = Memory("You are a helpful assistant.",
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recover_last_session=False, memory_compression=True)
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@ -6,8 +6,6 @@ from typing import List, Tuple, Type, Dict, Tuple
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from transformers import pipeline
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from adaptive_classifier import AdaptiveClassifier
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from sources.agents.agent import Agent
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from sources.agents.code_agent import CoderAgent
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from sources.agents.casual_agent import CasualAgent
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@ -15,6 +13,7 @@ from sources.agents.planner_agent import FileAgent
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from sources.agents.browser_agent import BrowserAgent
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from sources.language import LanguageUtility
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from sources.utility import pretty_print, animate_thinking, timer_decorator
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from sources.logger import Logger
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class AgentRouter:
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"""
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@ -28,6 +27,7 @@ class AgentRouter:
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self.complexity_classifier = self.load_llm_router()
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self.learn_few_shots_tasks()
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self.learn_few_shots_complexity()
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self.logger = Logger("router.log")
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def load_pipelines(self) -> Dict[str, Type[pipeline]]:
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"""
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@ -307,6 +307,7 @@ class AgentRouter:
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llm_router, confidence_llm_router = result_llm_router[0], result_llm_router[1]
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final_score_bart = confidence_bart / (confidence_bart + confidence_llm_router)
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final_score_llm = confidence_llm_router / (confidence_bart + confidence_llm_router)
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self.logger.info(f"Routing Vote: BART: {bart} ({final_score_bart}) LLM-router: {llm_router} ({final_score_llm})")
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if log_confidence:
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pretty_print(f"Agent choice -> BART: {bart} ({final_score_bart}) LLM-router: {llm_router} ({final_score_llm})")
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return bart if final_score_bart > final_score_llm else llm_router
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@ -334,6 +335,7 @@ class AgentRouter:
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return "LOW"
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complexity, confidence = predictions[0][0], predictions[0][1]
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if confidence < 0.4:
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self.logger.info(f"Low confidence in complexity estimation: {confidence}")
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return "LOW"
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if complexity == "HIGH" and len(text) < 64:
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return None # ask for more info
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@ -354,6 +356,7 @@ class AgentRouter:
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if agent.type == "planner_agent":
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return agent
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pretty_print(f"Error finding planner agent. Please add a planner agent to the list of agents.", color="failure")
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self.logger.error("Planner agent not found.")
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return None
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def select_agent(self, text: str) -> Agent:
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@ -380,15 +383,18 @@ class AgentRouter:
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try:
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best_agent = self.router_vote(text, labels, log_confidence=False)
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except Exception as e:
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self.logger.error(f"Router failure: {str(e)}")
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raise e
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for agent in self.agents:
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if best_agent == agent.role["en"]:
|
||||
pretty_print(f"Selected agent: {agent.agent_name} (roles: {agent.role[lang]})", color="warning")
|
||||
return agent
|
||||
pretty_print(f"Error choosing agent.", color="failure")
|
||||
self.logger.error("No agent selected.")
|
||||
return None
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
agents = [
|
||||
CasualAgent("jarvis", "../prompts/base/casual_agent.txt", None),
|
||||
BrowserAgent("browser", "../prompts/base/planner_agent.txt", None),
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
import os
|
||||
import os, sys
|
||||
import re
|
||||
import platform
|
||||
import subprocess
|
||||
@ -9,10 +9,7 @@ from kokoro import KPipeline
|
||||
from IPython.display import display, Audio
|
||||
import soundfile as sf
|
||||
|
||||
if __name__ == "__main__":
|
||||
from utility import pretty_print, animate_thinking
|
||||
else:
|
||||
from sources.utility import pretty_print, animate_thinking
|
||||
from sources.utility import pretty_print, animate_thinking
|
||||
|
||||
class Speech():
|
||||
"""
|
||||
@ -47,22 +44,22 @@ class Speech():
|
||||
if not os.path.exists(path):
|
||||
os.makedirs(path)
|
||||
|
||||
def speak(self, sentence: str, voice_number: int = 1):
|
||||
def speak(self, sentence: str, voice_idx: int = 1):
|
||||
"""
|
||||
Convert text to speech using an AI model and play the audio.
|
||||
|
||||
Args:
|
||||
sentence (str): The text to convert to speech. Will be pre-processed.
|
||||
voice_number (int, optional): Index of the voice to use from the voice map.
|
||||
voice_idx (int, optional): Index of the voice to use from the voice map.
|
||||
"""
|
||||
if not self.pipeline:
|
||||
return
|
||||
if voice_number >= len(self.voice_map[self.language]) or voice_number < 0:
|
||||
if voice_idx >= len(self.voice_map[self.language]):
|
||||
pretty_print("Invalid voice number, using default voice", color="error")
|
||||
voice_number = 0
|
||||
voice_idx = 0
|
||||
sentence = self.clean_sentence(sentence)
|
||||
audio_file = f"{self.voice_folder}/sample_{self.voice_map[self.language][voice_number]}.wav"
|
||||
self.voice = self.voice_map[self.language][voice_number]
|
||||
audio_file = f"{self.voice_folder}/sample_{self.voice_map[self.language][voice_idx]}.wav"
|
||||
self.voice = self.voice_map[self.language][voice_idx]
|
||||
generator = self.pipeline(
|
||||
sentence, voice=self.voice,
|
||||
speed=self.speed, split_pattern=r'\n+'
|
||||
@ -143,6 +140,7 @@ class Speech():
|
||||
return sentence
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
speech = Speech()
|
||||
tosay_en = """
|
||||
I looked up recent news using the website https://www.theguardian.com/world
|
||||
@ -154,8 +152,8 @@ if __name__ == "__main__":
|
||||
J'ai consulté les dernières nouvelles sur le site https://www.theguardian.com/world
|
||||
"""
|
||||
spk = Speech(enable=True, language="en", voice_idx=0)
|
||||
spk.speak(tosay_en)
|
||||
spk.speak(tosay_en, voice_idx=0)
|
||||
spk = Speech(enable=True, language="fr", voice_idx=0)
|
||||
spk.speak(tosay_fr)
|
||||
spk = Speech(enable=True, language="zh", voice_idx=0)
|
||||
spk.speak(tosay_zh)
|
||||
#spk = Speech(enable=True, language="zh", voice_idx=0)
|
||||
#spk.speak(tosay_zh)
|
||||
Loading…
x
Reference in New Issue
Block a user