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added comments for reference
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tcsenpai
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4dc438f536
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311a292c57
@ -4,16 +4,19 @@ import groq
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import time
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from abc import ABC, abstractmethod
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# Abstract base class for API handlers
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class BaseHandler(ABC):
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def __init__(self):
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self.max_attempts = 3
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self.retry_delay = 1
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self.max_attempts = 3 # Maximum number of retry attempts
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self.retry_delay = 1 # Delay between retry attempts in seconds
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@abstractmethod
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def _make_request(self, messages, max_tokens):
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# Abstract method to be implemented by subclasses
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pass
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def make_api_call(self, messages, max_tokens, is_final_answer=False):
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# Attempt to make an API call with retry logic
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for attempt in range(self.max_attempts):
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try:
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response = self._make_request(messages, max_tokens)
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@ -24,15 +27,18 @@ class BaseHandler(ABC):
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time.sleep(self.retry_delay)
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def _process_response(self, response, is_final_answer):
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# Default response processing (can be overridden by subclasses)
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return json.loads(response)
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def _error_response(self, error_msg, is_final_answer):
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# Generate an error response
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return {
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"title": "Error",
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"content": f"Failed to generate {'final answer' if is_final_answer else 'step'} after {self.max_attempts} attempts. Error: {error_msg}",
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"next_action": "final_answer" if is_final_answer else "continue"
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}
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# Handler for Ollama API
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class OllamaHandler(BaseHandler):
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def __init__(self, url, model):
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super().__init__()
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@ -40,6 +46,7 @@ class OllamaHandler(BaseHandler):
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self.model = model
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def _make_request(self, messages, max_tokens):
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# Make a request to the Ollama API
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response = requests.post(
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f"{self.url}/api/chat",
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json={
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@ -57,6 +64,30 @@ class OllamaHandler(BaseHandler):
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print(response.json())
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return response.json()["message"]["content"]
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def _process_response(self, response, is_final_answer):
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# Process the Ollama API response
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if isinstance(response, dict) and 'message' in response:
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content = response['message']['content']
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else:
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content = response
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try:
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parsed_content = json.loads(content)
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if 'final_answer' in parsed_content:
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return {
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"title": "Final Answer",
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"content": parsed_content['final_answer'],
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"next_action": "final_answer"
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}
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return parsed_content
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except json.JSONDecodeError:
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return {
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"title": "Raw Response",
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"content": content,
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"next_action": "final_answer" if is_final_answer else "continue"
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}
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# Handler for Perplexity API
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class PerplexityHandler(BaseHandler):
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def __init__(self, api_key, model):
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super().__init__()
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@ -64,6 +95,7 @@ class PerplexityHandler(BaseHandler):
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self.model = model
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def _clean_messages(self, messages):
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# Clean and consolidate messages for the Perplexity API
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cleaned_messages = []
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last_role = None
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for message in messages:
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@ -74,12 +106,13 @@ class PerplexityHandler(BaseHandler):
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last_role = message["role"]
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elif message["role"] == "user":
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cleaned_messages[-1]["content"] += "\n" + message["content"]
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# If the last message is an assistant message, delete it
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# Remove the last assistant message if present
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if cleaned_messages and cleaned_messages[-1]["role"] == "assistant":
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cleaned_messages.pop()
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return cleaned_messages
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def _make_request(self, messages, max_tokens):
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# Make a request to the Perplexity API
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cleaned_messages = self._clean_messages(messages)
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url = "https://api.perplexity.ai/chat/completions"
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@ -99,6 +132,7 @@ class PerplexityHandler(BaseHandler):
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raise # Re-raise the exception if it's not a 400 error
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def _process_response(self, response, is_final_answer):
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# Process the Perplexity API response
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try:
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return super()._process_response(response, is_final_answer)
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except json.JSONDecodeError:
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@ -110,12 +144,14 @@ class PerplexityHandler(BaseHandler):
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"next_action": "final_answer" if (is_final_answer or forced_final_answer) else "continue"
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}
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# Handler for Groq API
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class GroqHandler(BaseHandler):
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def __init__(self):
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super().__init__()
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self.client = groq.Groq()
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def _make_request(self, messages, max_tokens):
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# Make a request to the Groq API
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response = self.client.chat.completions.create(
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model="llama-3.1-70b-versatile",
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messages=messages,
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17
app/main.py
17
app/main.py
@ -6,21 +6,27 @@ from config_menu import config_menu, display_config
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from logger import logger
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import os
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# Load environment variables
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# Load environment variables from .env file
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load_dotenv()
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def load_css():
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# Load custom CSS styles
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with open(os.path.join(os.path.dirname(__file__), "..", "static", "styles.css")) as f:
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st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
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def setup_page():
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# Configure the Streamlit page
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st.set_page_config(page_title="multi1 - Unified AI Reasoning Chains", page_icon="🧠", layout="wide")
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load_css()
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# Display the main title
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st.markdown("""
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<h1 class="main-title">
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🧠 multi1 - Unified AI Reasoning Chains
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</h1>
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""", unsafe_allow_html=True)
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# Display the app description
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st.markdown("""
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<p class="main-description">
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This app demonstrates AI reasoning chains using different backends: Ollama, Perplexity AI, and Groq.
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@ -29,6 +35,7 @@ def setup_page():
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""", unsafe_allow_html=True)
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def get_api_handler(backend, config):
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# Create and return the appropriate API handler based on the selected backend
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if backend == "Ollama":
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return OllamaHandler(config['OLLAMA_URL'], config['OLLAMA_MODEL'])
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elif backend == "Perplexity AI":
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@ -40,14 +47,17 @@ def main():
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logger.info("Starting the application")
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setup_page()
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# Set up the sidebar for configuration
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st.sidebar.markdown('<h3 class="sidebar-title">⚙️ Settings</h3>', unsafe_allow_html=True)
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config = config_menu()
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# Allow user to select the AI backend
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backend = st.sidebar.selectbox("Choose AI Backend", ["Ollama", "Perplexity AI", "Groq"])
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display_config(backend, config)
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api_handler = get_api_handler(backend, config)
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logger.info(f"Selected backend: {backend}")
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# User input field
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user_query = st.text_input("💬 Enter your query:", placeholder="e.g., How many 'R's are in the word strawberry?")
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if user_query:
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@ -57,22 +67,27 @@ def main():
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time_container = st.empty()
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try:
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# Generate and display the response
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for steps, total_thinking_time in generate_response(user_query, api_handler):
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with response_container.container():
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for title, content, _ in steps:
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if title.startswith("Final Answer"):
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# Display the final answer
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st.markdown(f'<h3 class="expander-title">🎯 {title}</h3>', unsafe_allow_html=True)
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st.markdown(f'<div>{content}</div>', unsafe_allow_html=True)
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logger.info(f"Final answer generated: {content}")
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else:
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# Display intermediate steps
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with st.expander(f"📝 {title}", expanded=True):
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st.markdown(f'<div>{content}</div>', unsafe_allow_html=True)
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logger.debug(f"Step completed: {title}")
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# Display total thinking time
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if total_thinking_time is not None:
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time_container.markdown(f'<p class="thinking-time">⏱️ Total thinking time: {total_thinking_time:.2f} seconds</p>', unsafe_allow_html=True)
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logger.info(f"Total thinking time: {total_thinking_time:.2f} seconds")
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except Exception as e:
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# Handle and display any errors
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logger.error(f"Error generating response: {str(e)}", exc_info=True)
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st.error("An error occurred while generating the response. Please try again.")
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