🐦 Twitter Post Details

Viewing enriched Twitter post

@llama_index

The code behind this case study is now available! Check out the team's in-depth blog post about the project: https://t.co/8tPnIY8VQa Or head straight to the repo: https://t.co/EfUToAARR3 https://t.co/wTpbMhnfUn

Media 1

πŸ“Š Media Metadata

{
  "data": [
    {
      "id": "",
      "type": "photo",
      "url": null,
      "media_url": "https://pbs.twimg.com/media/GbAly2HagAAA3UU.jpg",
      "media_url_https": null,
      "display_url": null,
      "expanded_url": null
    }
  ],
  "score": 0.984,
  "scored_at": "2025-08-09T13:46:07.550469",
  "import_source": "network_archive_import",
  "media": [
    {
      "type": "photo",
      "url": "https://crmoxkoizveukayfjuyo.supabase.co/storage/v1/object/public/media/posts/1851021061594497501/media_0.jpg?",
      "filename": "media_0.jpg",
      "original_url": "https://pbs.twimg.com/media/GbAly2HagAAA3UU.jpg"
    }
  ],
  "storage_migrated": true
}

πŸ”§ 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": 82611,
    "friends_count": 26,
    "has_custom_timelines": false,
    "is_translator": false,
    "listed_count": 1366,
    "location": "",
    "media_count": 1375,
    "name": "LlamaIndex πŸ¦™",
    "normal_followers_count": 82611,
    "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": "1851021061594497501",
  "conversation_id": "1851021061594497501",
  "full_text": "The code behind this case study is now available! Check out the team's in-depth blog post about the project:\nhttps://t.co/8tPnIY8VQa\n\nOr head straight to the repo: https://t.co/EfUToAARR3 https://t.co/wTpbMhnfUn",
  "reply_count": 0,
  "retweet_count": 20,
  "favorite_count": 77,
  "hashtags": [],
  "symbols": [],
  "user_mentions": [],
  "urls": [
    {
      "url": "https://t.co/8tPnIY8VQa",
      "expanded_url": "https://developer.nvidia.com/blog/creating-rag-based-question-and-answer-llm-workflows-at-nvidia/",
      "display_url": "developer.nvidia.com/blog/creating-…"
    },
    {
      "url": "https://t.co/EfUToAARR3",
      "expanded_url": "https://github.com/NVIDIA/GenerativeAIExamples/tree/main/community/routing-multisource-rag",
      "display_url": "github.com/NVIDIA/Generat…"
    }
  ],
  "media": [
    {
      "media_url": "https://pbs.twimg.com/media/GbAly2HagAAA3UU.jpg",
      "type": "photo"
    }
  ],
  "url": "https://twitter.com/llama_index/status/1851021061594497501",
  "created_at": "2024-10-28T21:59:46.000Z",
  "#sort_index": "1851021061594497501",
  "view_count": 11331,
  "quote_count": 0,
  "is_quote_tweet": true,
  "is_retweet": false,
  "is_pinned": false,
  "is_truncated": false,
  "quoted_tweet": {
    "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": 82611,
      "friends_count": 26,
      "has_custom_timelines": false,
      "is_translator": false,
      "listed_count": 1366,
      "location": "",
      "media_count": 1375,
      "name": "LlamaIndex πŸ¦™",
      "normal_followers_count": 82611,
      "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": "1849847301680005583",
    "conversation_id": "1849847301680005583",
    "full_text": "We are thrilled to announce a case study of a successful internal deployment of LlamaIndex at @nvidia, an internal AI assistant for sales πŸ§‘β€πŸ’ΌπŸ€–\n\n* Uses Llama 3.1 405b for simple queries, 70b model for document searches\n* Retrieves from multiple sources: internal docs, NVIDIA site, and web\n* LlamaIndex Workflows handles routing and core functionality\n* @chainlit_io provides the chat interface for sales reps\n* Parallel retrieval system searches multiple sources simultaneously\n* Built-in context augmentation helps handle company acronyms/terms\n* Real-time inference achieved through NVIDIA NIM optimization\n\nSales automation is a top use case for agents. Check out our case study here: https://t.co/AApFNVjp0v",
    "reply_count": 5,
    "retweet_count": 74,
    "favorite_count": 275,
    "hashtags": [],
    "symbols": [],
    "user_mentions": [
      {
        "id_str": "61559439",
        "name": "NVIDIA",
        "screen_name": "nvidia",
        "profile": "https://twitter.com/nvidia"
      }
    ],
    "urls": [],
    "media": [
      {
        "media_url": "https://pbs.twimg.com/media/Gav6TKdaQAADt6P.jpg",
        "type": "photo"
      }
    ],
    "url": "https://twitter.com/llama_index/status/1849847301680005583",
    "created_at": "2024-10-25T16:15:39.000Z",
    "#sort_index": "1851021061594497500",
    "view_count": 51185,
    "quote_count": 4,
    "is_quote_tweet": false,
    "is_retweet": false,
    "is_pinned": false,
    "is_truncated": true
  },
  "startUrl": "https://x.com/llama_index/status/1851021061594497501"
}