vndee

#!/bin/bash

# Colors for better output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color

# Function to show help

vndee

col1, col2, col3, col4, col5 = st.columns(5)

with col1:
    st.markdown(f"""
        <div class='metric-container'>
            <div class='metric-value'>{max_width}</div>
            <div class='metric-label'>Search Width</div>
        </div>
    """, unsafe_allow_html=True)

vndee

# Create tabs for results and reasoning process
result_tab, process_tab, viz_tab = st.tabs(["Answer", "Reasoning Process", "Graph Visualization"])
    
with result_tab:
    st.markdown("### Answer")
    # Display the final answer...

with process_tab:
    st.markdown("### Reasoning Steps")
    # Show detailed reasoning steps...

vndee

def rank_relations(self, query: str, entity_name: str, relations: List[Dict]) -> List[Dict]:
    """Rank relations by their relevance to the query"""
    relation_info = '\n'.join([
        f"- {rel['relation_name']} (connects {entity_name} to {rel['target_name']})" 
        for rel in relations
    ])
    
    # Craft a prompt that asks the LLM to evaluate relations
    system_prompt = """
    You are an expert knowledge graph reasoner. Your task is to:

vndee

def iterative_reasoning(self, query: str) -> Tuple[List[Dict], str]:
    # Initialize tracking variables
    reasoning_steps = []
    visited_entities = set()
    
    # Step 1: Initial Topic Entities
    topic_entities = self.identify_topic_entities(query)
    visited_entities.update(topic_entities)
    
    # Initial document retrieval

vndee

class KnowledgeGraph:
    def __init__(self, model=None):
        self.entities = {}
        self.relations = {}
        self.triples = []
        self.documents = {}
        self.model = model
        self.llm = None
    
    def get_relation_candidates(self, entity_id: str) -> List[Dict]:

vndee

# Simplified pseudocode for ToG reasoning loop
for depth in range(max_depth):
    # Explore the knowledge graph
    related_entities = explore_connections(current_entities, query)
    
    # Retrieve and evaluate contextual information
    context_information = retrieve_contexts(related_entities)
    
    # Evaluate whether we have sufficient information
    if has_sufficient_information(context_information, query):

vndee

from redis_data_structures import PriorityQueue
from enum import IntEnum

class TaskPriority(IntEnum):
    CRITICAL = 1
    HIGH = 2
    MEDIUM = 3
    LOW = 4

class TaskScheduler:

vndee

from redis_data_structures import RingBuffer
from datetime import datetime, timedelta

class RateLimiter:
    def __init__(self, window_size: int, max_requests: int):
        self.buffer = RingBuffer("rate-limits", capacity=max_requests)
        self.window_size = window_size
    
    def is_allowed(self, client_id: str) -> bool:
        key = f"rate_limit:{client_id}"

vndee

from redis_data_structures import ConnectionManager
from datetime import timedelta

conn = ConnectionManager(
    host='redis.example.com',
    max_connections=20,
    retry_max_attempts=5,
    circuit_breaker_threshold=10,
    circuit_breaker_timeout=timedelta(minutes=5),
    ssl=True