update extract_content action for BrowserUseTool
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liangxinbing
@@ -392,115 +392,71 @@ class BrowserUseTool(BaseTool, Generic[Context]):
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return ToolResult(
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error="Goal is required for 'extract_content' action"
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)
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page = await context.get_current_page()
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try:
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# Get page content and convert to markdown for better processing
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html_content = await page.content()
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import markdownify
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# Import markdownify here to avoid global import
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try:
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import markdownify
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content = markdownify.markdownify(await page.content())
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content = markdownify.markdownify(html_content)
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except ImportError:
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# Fallback if markdownify is not available
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content = html_content
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# Create prompt for LLM
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prompt_text = """
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prompt = f"""\
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Your task is to extract the content of the page. You will be given a page and a goal, and you should extract all relevant information around this goal from the page. If the goal is vague, summarize the page. Respond in json format.
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Extraction goal: {goal}
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Page content:
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{page}
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{content[:max_content_length]}
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"""
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# Format the prompt with the goal and content
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max_content_length = min(50000, len(content))
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formatted_prompt = prompt_text.format(
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goal=goal, page=content[:max_content_length]
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)
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messages = [{"role": "system", "content": prompt}]
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# Create a proper message list for the LLM
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from app.schema import Message
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messages = [Message.user_message(formatted_prompt)]
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# Define extraction function for the tool
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extraction_function = {
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"type": "function",
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"function": {
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"name": "extract_content",
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"description": "Extract specific information from a webpage based on a goal",
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"parameters": {
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"type": "object",
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"properties": {
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"extracted_content": {
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"type": "object",
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"description": "The content extracted from the page according to the goal",
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"properties": {
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"text": {
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"type": "string",
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"description": "Text content extracted from the page",
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},
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"metadata": {
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"type": "object",
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"description": "Additional metadata about the extracted content",
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"properties": {
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"source": {
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"type": "string",
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"description": "Source of the extracted content",
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}
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},
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# Define extraction function schema
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extraction_function = {
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"type": "function",
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"function": {
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"name": "extract_content",
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"description": "Extract specific information from a webpage based on a goal",
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"parameters": {
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"type": "object",
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"properties": {
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"extracted_content": {
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"type": "object",
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"description": "The content extracted from the page according to the goal",
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"properties": {
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"text": {
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"type": "string",
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"description": "Text content extracted from the page",
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},
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"metadata": {
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"type": "object",
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"description": "Additional metadata about the extracted content",
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"properties": {
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"source": {
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"type": "string",
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"description": "Source of the extracted content",
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}
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},
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},
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}
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},
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"required": ["extracted_content"],
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},
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}
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},
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"required": ["extracted_content"],
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},
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}
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},
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}
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# Use LLM to extract content with required function calling
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response = await self.llm.ask_tool(
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messages,
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tools=[extraction_function],
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tool_choice="required",
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# Use LLM to extract content with required function calling
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response = await self.llm.ask_tool(
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messages,
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tools=[extraction_function],
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tool_choice="required",
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)
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if response and response.tool_calls:
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args = json.loads(response.tool_calls[0].function.arguments)
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extracted_content = args.get("extracted_content", {})
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return ToolResult(
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output=f"Extracted from page:\n{extracted_content}\n"
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)
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# Extract content from function call response
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if (
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response
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and response.tool_calls
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and len(response.tool_calls) > 0
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):
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# Get the first tool call arguments
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tool_call = response.tool_calls[0]
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# Parse the JSON arguments
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try:
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args = json.loads(tool_call.function.arguments)
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extracted_content = args.get("extracted_content", {})
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# Format extracted content as JSON string
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content_json = json.dumps(
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extracted_content, indent=2, ensure_ascii=False
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)
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msg = f"Extracted from page:\n{content_json}\n"
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except Exception as e:
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msg = f"Error parsing extraction result: {str(e)}\nRaw response: {tool_call.function.arguments}"
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else:
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msg = "No content was extracted from the page."
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return ToolResult(output=msg)
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except Exception as e:
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# Provide a more helpful error message
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error_msg = f"Failed to extract content: {str(e)}"
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try:
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# Try to return a portion of the page content as fallback
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return ToolResult(
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output=f"{error_msg}\nHere's a portion of the page content:\n{content[:2000]}..."
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)
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except:
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# If all else fails, just return the error
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return ToolResult(error=error_msg)
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return ToolResult(output="No content was extracted from the page.")
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# Tab management actions
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elif action == "switch_tab":
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