🐦 Twitter Post Details

Viewing enriched Twitter post

@llama_index

We are thrilled to announce a case study of a successful internal deployment of LlamaIndex at @nvidia, an internal AI assistant for sales 🧑‍💼🤖 * Uses Llama 3.1 405b for simple queries, 70b model for document searches * Retrieves from multiple sources: internal docs, NVIDIA site, and web * LlamaIndex Workflows handles routing and core functionality * @chainlit_io provides the chat interface for sales reps * Parallel retrieval system searches multiple sources simultaneously * Built-in context augmentation helps handle company acronyms/terms * Real-time inference achieved through NVIDIA NIM optimization Sales automation is a top use case for agents. Check out our case study here: https://t.co/AApFNVjp0v

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": 82611,
    "friends_count": 26,
    "has_custom_timelines": false,
    "is_translator": false,
    "listed_count": 1366,
    "location": "",
    "media_count": 1375,
    "name": "LlamaIndex 🦙",
    "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 🧑‍💼🤖\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"
}