mirror of https://github.com/kortix-ai/suna.git
instant stream
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marko-kraemer
parent
b5e79d264f
commit
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Modern Landing Page</title>
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<link rel="stylesheet" href="reset.css">
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<link rel="stylesheet" href="styles.css">
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</head>
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<body>
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<header class="header container">
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<div class="header-logo">Brand</div>
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<nav>
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<ul class="nav-links">
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<li><a href="#home">Home</a></li>
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<li><a href="#features">Features</a></li>
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<li><a href="#contact">Contact</a></li>
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</ul>
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</nav>
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</header>
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<main>
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<section class="hero">
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<div class="container hero-content">
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<h1 class="hero-title">Transform Your Digital Experience</h1>
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<p class="hero-subtitle">Discover innovative solutions that drive your business forward</p>
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<a href="#features" class="cta-button">Get Started</a>
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</div>
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</section>
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<section id="features" class="features">
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<div class="container">
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<div class="feature-grid">
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<div class="feature">
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<div class="feature-icon">🚀</div>
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<h3>Fast Performance</h3>
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<p>Optimized solutions for maximum efficiency</p>
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</div>
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<div class="feature">
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<div class="feature-icon">🔒</div>
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<h3>Secure Platform</h3>
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<p>Advanced security measures to protect your data</p>
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</div>
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<div class="feature">
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<div class="feature-icon">📊</div>
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<h3>Smart Analytics</h3>
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<p>Comprehensive insights for strategic decisions</p>
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</div>
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</div>
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</div>
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</section>
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</main>
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<footer class="footer">
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<div class="container">
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<div class="footer-content">
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<div class="footer-logo">Brand</div>
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<div class="footer-links">
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<a href="#home">Home</a>
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<a href="#features">Features</a>
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<a href="#contact">Contact</a>
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</div>
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<div class="footer-social">
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<a href="#">Twitter</a>
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<a href="#">LinkedIn</a>
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<a href="#">GitHub</a>
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</div>
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</div>
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<div class="footer-copyright">
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© 2023 Brand. All rights reserved.
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</div>
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</div>
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</footer>
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</body>
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</html>
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*,*::before,*::after{box-sizing:border-box;margin:0;padding:0}html{scroll-behavior:smooth}body{line-height:1.6;font-family:'-apple-system','BlinkMacSystemFont','Segoe UI','Roboto','Oxygen','Ubuntu','Cantarell','Open Sans','Helvetica Neue',sans-serif}img{max-width:100%;height:auto}a{text-decoration:none;color:inherit}
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:root{--primary-color:#3498db;--secondary-color:#2ecc71;--text-color:#333;--background-color:#f4f4f4;--accent-color:#e74c3c}body{scroll-behavior:smooth;font-smooth:always;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}body{background-color:var(--background-color);color:var(--text-color)}.container{max-width:1200px;margin:0 auto;padding:0 15px}.header{display:flex;justify-content:space-between;align-items:center;padding:20px 0;background-color:white;box-shadow:0 2px 4px rgba(0,0,0,0.1)}.header-logo{font-size:24px;font-weight:bold;color:var(--primary-color)}.nav-links{display:flex;list-style:none;gap:20px}.nav-links a{color:var(--text-color);transition:color 0.3s ease}.nav-links a:hover{color:var(--primary-color)}.hero{display:flex;align-items:center;min-height:70vh;background:linear-gradient(135deg,var(--primary-color),var(--secondary-color));color:white;text-align:center}.hero-content{max-width:800px;margin:0 auto}.hero-title{font-size:48px;margin-bottom:20px;font-weight:bold}.hero-subtitle{font-size:24px;margin-bottom:30px}.cta-button{display:inline-block;background-color:white;color:var(--primary-color);padding:12px 30px;border-radius:5px;font-weight:bold;transition:transform 0.3s ease}.cta-button:hover{transform:scale(1.05)}.footer{background-color:#333;color:white;text-align:center;padding:20px 0}.features{padding:80px 0;background-color:white}.feature-grid{display:grid;grid-template-columns:repeat(3,1fr);gap:30px}.feature{text-align:center;padding:30px;border-radius:10px;transition:all 0.3s ease;box-shadow:0 4px 6px rgba(0,0,0,0.1);background-color:white}.feature:hover{transform:translateY(-10px);box-shadow:0 8px 15px rgba(0,0,0,0.15);background-color:var(--background-color)}.feature-icon{font-size:48px;margin-bottom:20px}.feature h3{margin-bottom:15px;font-size:20px}.feature p{color:var(--text-color)}.footer{background-color:#1a1a1a;color:white;padding:40px 0}.footer-content{display:flex;justify-content:space-between;align-items:center;margin-bottom:20px}.footer-logo{font-size:24px;font-weight:bold}.footer-links{display:flex;gap:20px}.footer-links a{color:white;transition:color 0.3s ease}.footer-links a:hover{color:var(--primary-color)}.footer-social{display:flex;gap:15px}.footer-social a{color:white;transition:color 0.3s ease}.footer-social a:hover{color:var(--secondary-color)}.footer-copyright{text-align:center;padding-top:20px;border-top:1px solid rgba(255,255,255,0.1)}@media(max-width:768px){.header{flex-direction:column;text-align:center}.header-logo{margin-bottom:15px}.nav-links{flex-direction:column;align-items:center}.hero{text-align:center}.hero-title{font-size:36px}.hero-subtitle{font-size:18px}.feature-grid{grid-template-columns:1fr}.features{padding:40px 0}.footer-content{flex-direction:column;text-align:center}.footer-links,.footer-social{margin-top:20px;flex-direction:column;align-items:center}}
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@ -1,7 +1,8 @@
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import logging
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import logging
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from typing import Dict, Any, AsyncGenerator, Callable
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from typing import Dict, Any, AsyncGenerator, Callable
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from agentpress.tool_executor import ToolExecutor
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from agentpress.tool_parser import ToolParser
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from agentpress.tool_parser import ToolParser
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from agentpress.tool_executor import ToolExecutor
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import asyncio
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class LLMResponseProcessor:
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class LLMResponseProcessor:
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"""
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"""
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@ -23,6 +24,9 @@ class LLMResponseProcessor:
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tool_calls_buffer (Dict): Buffer for storing incomplete tool calls during streaming
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tool_calls_buffer (Dict): Buffer for storing incomplete tool calls during streaming
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processed_tool_calls (set): Set of already processed tool call IDs
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processed_tool_calls (set): Set of already processed tool call IDs
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current_message (Dict): Current message being processed in streaming mode
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current_message (Dict): Current message being processed in streaming mode
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content_buffer (str): Buffer for accumulating content during streaming
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tool_calls_accumulated (list): List of tool calls accumulated during streaming
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message_added (bool): Flag to indicate if a message has been added to the thread
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"""
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"""
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def __init__(
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def __init__(
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@ -44,7 +48,9 @@ class LLMResponseProcessor:
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# State tracking for streaming responses
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# State tracking for streaming responses
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self.tool_calls_buffer = {}
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self.tool_calls_buffer = {}
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self.processed_tool_calls = set()
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self.processed_tool_calls = set()
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self.current_message = None
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self.content_buffer = ""
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self.tool_calls_accumulated = []
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self.message_added = False
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async def process_stream(
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async def process_stream(
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self,
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self,
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@ -55,40 +61,43 @@ class LLMResponseProcessor:
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"""
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"""
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Process streaming LLM response and handle tool execution.
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Process streaming LLM response and handle tool execution.
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Handles streaming responses chunk by chunk, managing tool execution timing
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Yields chunks immediately as they arrive, while handling tool execution
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based on configuration. Tools can be executed immediately as they are
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and message management in the background.
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identified in the stream, or collected and executed after the complete
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response is received.
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Args:
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response_stream: Stream of response chunks from the LLM
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execute_tools: Whether to execute tool calls at all
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immediate_execution: Whether to execute tools as they appear in the stream
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or wait for complete response
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Yields:
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Processed response chunks
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"""
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"""
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pending_tool_calls = []
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pending_tool_calls = []
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async def process_chunk(chunk):
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async def handle_message_management(chunk):
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# Accumulate content
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if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content:
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self.content_buffer += chunk.choices[0].delta.content
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# Parse and accumulate tool calls
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parsed_message, is_complete = await self.tool_parser.parse_stream(
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parsed_message, is_complete = await self.tool_parser.parse_stream(
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chunk,
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chunk,
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self.tool_calls_buffer
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self.tool_calls_buffer
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)
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)
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if parsed_message and 'tool_calls' in parsed_message:
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if parsed_message and 'tool_calls' in parsed_message:
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# Update or create message
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self.tool_calls_accumulated = parsed_message['tool_calls']
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if not self.current_message:
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self.current_message = parsed_message
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await self.add_message(self.thread_id, self.current_message)
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else:
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self.current_message['tool_calls'] = parsed_message['tool_calls']
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await self.update_message(self.thread_id, self.current_message)
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if execute_tools:
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# Handle message management and tool execution
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if chunk.choices[0].finish_reason or (self.content_buffer and self.tool_calls_accumulated):
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message = {
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"role": "assistant",
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"content": self.content_buffer
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}
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if self.tool_calls_accumulated:
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message["tool_calls"] = self.tool_calls_accumulated
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if not self.message_added:
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await self.add_message(self.thread_id, message)
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self.message_added = True
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else:
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await self.update_message(self.thread_id, message)
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# Handle tool execution
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if execute_tools and self.tool_calls_accumulated:
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new_tool_calls = [
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new_tool_calls = [
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tool_call for tool_call in parsed_message['tool_calls']
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tool_call for tool_call in self.tool_calls_accumulated
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if tool_call['id'] not in self.processed_tool_calls
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if tool_call['id'] not in self.processed_tool_calls
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]
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]
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else:
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else:
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pending_tool_calls.extend(new_tool_calls)
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pending_tool_calls.extend(new_tool_calls)
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# Process any pending tools at end of response
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# Handle end of stream
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if chunk.choices[0].finish_reason and not immediate_execution and pending_tool_calls:
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if chunk.choices[0].finish_reason:
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if not immediate_execution and pending_tool_calls:
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results = await self.tool_executor.execute_tool_calls(
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results = await self.tool_executor.execute_tool_calls(
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tool_calls=pending_tool_calls,
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tool_calls=pending_tool_calls,
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available_functions=self.available_functions,
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available_functions=self.available_functions,
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self.processed_tool_calls.add(result['tool_call_id'])
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self.processed_tool_calls.add(result['tool_call_id'])
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pending_tool_calls.clear()
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pending_tool_calls.clear()
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return chunk
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async for chunk in response_stream:
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async for chunk in response_stream:
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processed_chunk = await process_chunk(chunk)
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# Start background task for message management and tool execution
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yield processed_chunk
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asyncio.create_task(handle_message_management(chunk))
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# Immediately yield the chunk
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yield chunk
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async def process_response(
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async def process_response(
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self,
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self,
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