401 lines
16 KiB
Python
401 lines
16 KiB
Python
"""AI research setup component for the API key manager."""
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import streamlit as st
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from loguru import logger
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from typing import Dict, Any
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from ..manager import APIKeyManager
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from .base import render_navigation_buttons
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import os
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from dotenv import load_dotenv
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import sys
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# Configure logger
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logger.remove() # Remove default handler
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logger.add(
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"logs/ai_research_setup.log",
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rotation="500 MB",
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retention="10 days",
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level="DEBUG",
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format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
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)
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logger.add(
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sys.stdout,
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level="INFO",
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format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
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)
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def get_existing_api_key(key_name: str) -> str:
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"""Get existing API key from environment or .env file.
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Args:
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key_name (str): Name of the API key to retrieve
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Returns:
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str: The API key value if found, empty string otherwise
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"""
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# First try to get from environment
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api_key = os.getenv(key_name)
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# If not in environment, try to get from .env file
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if not api_key and os.path.exists('.env'):
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try:
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with open('.env', 'r') as f:
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for line in f:
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if line.strip().startswith(f"{key_name}="):
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api_key = line.strip().split('=')[1]
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break
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except Exception as e:
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logger.error(f"[get_existing_api_key] Failed to read {key_name} from .env: {str(e)}")
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return api_key if api_key else ""
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def update_env_file(api_keys: Dict[str, str]) -> None:
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"""Update the .env file with new API keys, avoiding duplicates.
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Args:
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api_keys (Dict[str, str]): Dictionary of API keys to update
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"""
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try:
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# Read existing .env file content
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env_content = []
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if os.path.exists('.env'):
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with open('.env', 'r') as f:
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env_content = f.readlines()
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# Remove trailing newlines and empty lines
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env_content = [line.strip() for line in env_content if line.strip()]
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# Create a dictionary of existing variables
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env_dict = {}
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for line in env_content:
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if '=' in line:
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key, value = line.split('=', 1)
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env_dict[key.strip()] = value.strip()
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# Update with new values
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env_dict.update(api_keys)
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# Write back to .env file
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with open('.env', 'w') as f:
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for key, value in env_dict.items():
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f.write(f"{key}={value}\n")
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logger.info("[update_env_file] Successfully updated .env file with API keys")
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except Exception as e:
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logger.error(f"[update_env_file] Error updating .env file: {str(e)}")
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raise
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def render_ai_research_setup(api_key_manager: APIKeyManager) -> Dict[str, Any]:
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"""Render the AI research setup step."""
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logger.info("[render_ai_research_setup] Rendering AI research setup component")
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st.markdown("""
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<div class='setup-header'><h2>🔍 AI Web Research API Setup</h2></div>
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""", unsafe_allow_html=True)
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# Create two columns for different search types
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("### The Usual")
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# Get existing API keys
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existing_serpapi_key = get_existing_api_key("SERPAPI_KEY")
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existing_firecrawl_key = get_existing_api_key("FIRECRAWL_API_KEY")
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serpapi_key = st.text_input(
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"## Enter 🔎 SerpAPI",
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value=existing_serpapi_key,
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type="password",
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key="serpapi_key",
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help="Enter your SerpAPI key",
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placeholder="Access search engine results for research"
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)
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if serpapi_key or existing_serpapi_key:
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st.markdown("""
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<div class="ai-provider-status status-valid">
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✓ API key configured
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</div>
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""", unsafe_allow_html=True)
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st.markdown("""
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<div class="api-info-section">
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<details>
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<summary>📋 How to get your SerpAPI key</summary>
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<div class="api-info-content">
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<p><strong>Step-by-step guide:</strong></p>
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<ol>
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<li>Visit <a href="https://serpapi.com" target="_blank">SerpAPI</a></li>
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<li>Create an account</li>
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<li>Go to your dashboard</li>
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<li>Copy your API key</li>
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<li>Paste it here</li>
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</ol>
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<p><strong>Note:</strong> SerpAPI provides real-time search results from multiple engines.</p>
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</div>
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</details>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("</div></div>", unsafe_allow_html=True)
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firecrawl_key = st.text_input(
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"Enter 🕷️ Firecrawl API Key",
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value=existing_firecrawl_key,
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type="password",
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key="firecrawl_key",
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help="Enter your Firecrawl API key",
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placeholder="Web content extraction and analysis"
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)
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if firecrawl_key or existing_firecrawl_key:
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st.markdown("""
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<div class="ai-provider-status status-valid">
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✓ Firecrawl API key configured
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</div>
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""", unsafe_allow_html=True)
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st.markdown("""
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<div class="api-info-section">
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<details>
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<summary>📋 How to get your Firecrawl API key</summary>
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<div class="api-info-content">
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<p><strong>Step-by-step guide:</strong></p>
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<ol>
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<li>Visit <a href="https://www.firecrawl.dev/account" target="_blank">Firecrawl</a></li>
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<li>Create an account</li>
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<li>Go to your dashboard</li>
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<li>Generate your API key</li>
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<li>Copy and paste it here</li>
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</ol>
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<p><strong>Note:</strong> Firecrawl provides powerful web content extraction and analysis capabilities.</p>
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</div>
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</details>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("</div></div>", unsafe_allow_html=True)
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with col2:
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st.markdown("### AI Deep Research")
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# Get existing API keys
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existing_tavily_key = get_existing_api_key("TAVILY_API_KEY")
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existing_metaphor_key = get_existing_api_key("METAPHOR_API_KEY")
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tavily_key = st.text_input(
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"Enter 🤖 Tavily API Key",
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value=existing_tavily_key,
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type="password",
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key="tavily_key",
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help="Enter your Tavily API key",
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placeholder="AI-powered search with semantic understanding"
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)
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if tavily_key or existing_tavily_key:
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st.markdown("""
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<div class="ai-provider-status status-valid">
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✓ Tavily API key configured
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</div>
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""", unsafe_allow_html=True)
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st.markdown("""
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<div class="api-info-section">
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<details>
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<summary>📋 How to get your Tavily API key</summary>
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<div class="api-info-content">
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<p><strong>Step-by-step guide:</strong></p>
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<ol>
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<li>Visit <a href="https://tavily.com" target="_blank">Tavily</a></li>
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<li>Create an account</li>
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<li>Go to API settings</li>
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<li>Generate a new API key</li>
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<li>Copy and paste it here</li>
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</ol>
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<p><strong>Note:</strong> Tavily provides AI-powered semantic search capabilities.</p>
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</div>
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</details>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("</div></div>", unsafe_allow_html=True)
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metaphor_key = st.text_input(
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"Enter 🧠 Metaphor/Exa API Key",
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value=existing_metaphor_key,
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type="password",
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key="metaphor_key",
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help="Enter your Metaphor/Exa API key",
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placeholder="Neural search engine for deep research"
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)
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if metaphor_key or existing_metaphor_key:
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st.markdown("""
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<div class="ai-provider-status status-valid">
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✓ API key configured
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</div>
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""", unsafe_allow_html=True)
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st.markdown("""
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<div class="api-info-section">
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<details>
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<summary>📋 How to get your Metaphor/Exa API key</summary>
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<div class="api-info-content">
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<p><strong>Step-by-step guide:</strong></p>
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<ol>
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<li>Visit <a href="https://metaphor.systems" target="_blank">Metaphor/Exa</a></li>
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<li>Create an account</li>
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<li>Navigate to API settings</li>
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<li>Generate your API key</li>
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<li>Copy and paste it here</li>
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</ol>
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<p><strong>Note:</strong> Metaphor/Exa provides neural search capabilities for deep research.</p>
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</div>
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</details>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("</div></div>", unsafe_allow_html=True)
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# Track changes
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changes_made = bool(serpapi_key or tavily_key or metaphor_key or firecrawl_key)
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# Navigation buttons with correct arguments
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if render_navigation_buttons(3, 5, changes_made):
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if changes_made:
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try:
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# Prepare API keys dictionary with only non-empty values
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api_keys = {}
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if serpapi_key:
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api_keys['SERPAPI_KEY'] = serpapi_key
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if tavily_key:
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api_keys['TAVILY_API_KEY'] = tavily_key
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if metaphor_key:
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api_keys['METAPHOR_API_KEY'] = metaphor_key
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if firecrawl_key:
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api_keys['FIRECRAWL_API_KEY'] = firecrawl_key
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# Update .env file with new API keys
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update_env_file(api_keys)
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# Update environment variables
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for key, value in api_keys.items():
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os.environ[key] = value
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# Store the API keys in session state
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st.session_state['api_keys'] = {
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'serpapi': serpapi_key,
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'tavily': tavily_key,
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'metaphor': metaphor_key,
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'firecrawl': firecrawl_key
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}
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# Update progress and move to next step
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st.session_state['current_step'] = 4
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st.rerun()
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except Exception as e:
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error_msg = f"Error saving API keys: {str(e)}"
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logger.error(f"[render_ai_research_setup] {error_msg}")
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st.error(error_msg)
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else:
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st.error("Please configure at least one research provider to continue")
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# Detailed Information Section
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st.markdown("---")
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st.markdown("### Understanding Your Research Options")
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# Create four columns for the information popovers
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info_col1, info_col2, info_col3, info_col4 = st.columns(4)
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# The Usual: Traditional Search Popover
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with info_col1:
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with st.popover("#### The Usual: Traditional Search"):
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st.markdown("""
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**SerpAPI**
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- Real-time search results from multiple search engines
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- Access to structured data from search results
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- Great for gathering general information and market research
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- Includes features like:
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- Web search results
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- News articles
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- Knowledge graphs
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- Related questions
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""")
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# AI Deep Research Popover
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with info_col2:
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with st.popover("#### AI Deep Research: Advanced Search Capabilities"):
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st.markdown("""
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**Tavily AI**
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- AI-powered search with semantic understanding
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- Automatically summarizes and analyzes search results
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- Perfect for:
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- Deep research tasks
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- Academic research
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- Fact-checking
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- Real-time information gathering
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**Metaphor/Exa**
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- Neural search engine that understands context and meaning
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- Specialized in finding highly relevant content
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- Ideal for:
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- Technical research
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- Finding similar content
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- Discovering patterns in research
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- Understanding topic landscapes
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""")
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# Choosing the Right Tool Popover
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with info_col3:
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with st.popover("#### Choosing the Right Tool"):
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st.markdown("""
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1. **For General Research:**
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- Start with SerpAPI for broad coverage and structured data
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2. **For Deep Analysis:**
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- Use Tavily AI when you need AI-powered insights
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- Choose Metaphor/Exa for neural search and pattern discovery
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3. **For Comprehensive Research:**
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- Combine multiple tools to get the most complete picture
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- Use SerpAPI for initial research
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- Follow up with AI tools for deeper insights
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> **Pro Tip:** Configure multiple providers to ensure you have backup options and can cross-reference results for better accuracy.
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""")
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# Coming Soon Popover
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with info_col4:
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with st.popover("#### 🔜 Coming Soon - More Search Options"):
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st.markdown("""
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**Bing Search API**
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- Microsoft's powerful search API with comprehensive capabilities
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- Features include:
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- Web search with advanced filtering
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- News articles with sentiment analysis
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- Image search with visual recognition
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- Video search with content understanding
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- Custom search parameters for targeted results
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**Google Search API**
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- Google's programmable search engine with extensive features
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- Capabilities include:
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- Custom search engine creation
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- Site-specific search
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- Image and video search
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- News search with time-based filtering
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- Knowledge graph integration
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**Additional Planned Integrations:**
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- **DuckDuckGo API**: Privacy-focused search with no tracking
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- **Brave Search API**: Independent search engine with unique features
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- **Perplexity API**: AI-powered research assistant with real-time data
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> **Note:** These integrations are under active development and will be available in future updates.
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""")
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return {"current_step": 3, "changes_made": changes_made}
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