🐦 Twitter Post Details

Viewing enriched Twitter post

@llama_index

Implement super-fast RAG using LlamaIndex Workflows and Groq 🚀 Learn how to build a powerful Retrieval-Augmented Generation system with our Workflows feature, including a comparison to alternatives like LangGraph: ➡️ Create an event-driven architecture for complex AI applications ➡️ Integrate Groq's high-performance LLMs for reranking and response synthesis ➡️ Visualize your workflow for better understanding and debugging Step-by-step guide covers: • Data indexing and retrieval • LLM-based reranking • Response synthesis using CompactAndRefine Read the full tutorial: https://t.co/XFSmYMxJld

View on Twitter

🔧 Raw API Response

{
  "user": {
    "created_at": "2022-12-18T00:52:44.000Z",
    "default_profile_image": false,
    "description": "Build LLM agents over your data\n\nGithub: https://t.co/HC19j7vMwc\nDocs: https://t.co/QInqg2zksh\nDiscord: https://t.co/3ktq3zzYII",
    "fast_followers_count": 0,
    "favourites_count": 1261,
    "followers_count": 82614,
    "friends_count": 26,
    "has_custom_timelines": false,
    "is_translator": false,
    "listed_count": 1366,
    "location": "",
    "media_count": 1375,
    "name": "LlamaIndex 🦙",
    "normal_followers_count": 82614,
    "possibly_sensitive": false,
    "profile_banner_url": "https://pbs.twimg.com/profile_banners/1604278358296055808/1696908553",
    "profile_image_url_https": "https://pbs.twimg.com/profile_images/1623505166996742144/n-PNQGgd_normal.jpg",
    "screen_name": "llama_index",
    "statuses_count": 2997,
    "translator_type": "none",
    "url": "https://t.co/epzefqQqZx",
    "verified": true,
    "withheld_in_countries": [],
    "id_str": "1604278358296055808"
  },
  "id": "1864377849295327365",
  "conversation_id": "1864377849295327365",
  "full_text": "Implement super-fast RAG using LlamaIndex Workflows and Groq 🚀\n\nLearn how to build a powerful Retrieval-Augmented Generation system with our Workflows feature, including a comparison to alternatives like LangGraph:\n\n➡️ Create an event-driven architecture for complex AI applications\n➡️ Integrate Groq's high-performance LLMs for reranking and response synthesis\n➡️ Visualize your workflow for better understanding and debugging\n\nStep-by-step guide covers:\n• Data indexing and retrieval\n• LLM-based reranking\n• Response synthesis using CompactAndRefine\n\nRead the full tutorial: https://t.co/XFSmYMxJld",
  "reply_count": 0,
  "retweet_count": 8,
  "favorite_count": 34,
  "hashtags": [],
  "symbols": [],
  "user_mentions": [],
  "urls": [],
  "media": [
    {
      "media_url": "https://pbs.twimg.com/media/Gd-ZwL5a0AYsp3j.jpg",
      "type": "photo"
    }
  ],
  "url": "https://twitter.com/llama_index/status/1864377849295327365",
  "created_at": "2024-12-04T18:34:52.000Z",
  "#sort_index": "1864377849295327365",
  "view_count": 3941,
  "quote_count": 0,
  "is_quote_tweet": false,
  "is_retweet": false,
  "is_pinned": false,
  "is_truncated": true,
  "startUrl": "https://x.com/llama_index/status/1864377849295327365"
}