🐦 Twitter Post Details

Viewing enriched Twitter post

@llama_index

Multi-agent system for RFP Response Generation 🔥✍️ We’re excited to release a brand-new guide showing you how to build an agentic workflow that can take in an input Request for Proposal (RFP) template and generate a full response to the RFP, grounded in your knowledge base and adhering to the relevant guidelines. This is much more than a standard RAG or ReAct agent architecture, and requires the careful orchestration of a set of steps + components. 1. Parse the input RFP template using LlamaParse, extract out a set of questions that you would need answered. 2. For each question, use a Research agent (ReAct loop) with access to a set of tools in the knowledge base to retrieve relevant information and generate an answer 3. Aggregate question/answer pairs into a single file 4. Generate the final report with the RFP template and QA pairs as input. Bonuses 💫: it’s fully async, and you get back event and final response streaming! Notebook: https://t.co/5XCkFeUQen LlamaParse signup: https://t.co/yQGTiRSNvj

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": "1845853830485082474",
  "conversation_id": "1845853830485082474",
  "full_text": "Multi-agent system for RFP Response Generation 🔥✍️\n\nWe’re excited to release a brand-new guide showing you how to build an agentic workflow that can take in an input Request for Proposal (RFP) template and generate a full response to the RFP, grounded in your knowledge base and adhering to the relevant guidelines.\n\nThis is much more than a standard RAG or ReAct agent architecture, and requires the careful orchestration of a set of steps + components.\n\n1. Parse the input RFP template using LlamaParse, extract out a set of questions that you would need answered.\n2. For each question, use a Research agent (ReAct loop) with access to a set of tools in the knowledge base to retrieve relevant information and generate an answer\n3. Aggregate question/answer pairs into a single file\n4. Generate the final report with the RFP template and QA pairs as input.\n\nBonuses 💫: it’s fully async, and you get back event and final response streaming!\n\nNotebook: https://t.co/5XCkFeUQen\n\nLlamaParse signup: https://t.co/yQGTiRSNvj",
  "reply_count": 3,
  "retweet_count": 44,
  "favorite_count": 229,
  "hashtags": [],
  "symbols": [],
  "user_mentions": [],
  "urls": [],
  "media": [
    {
      "media_url": "https://pbs.twimg.com/media/GZ3KJdraUAARLLC.jpg",
      "type": "photo"
    }
  ],
  "url": "https://twitter.com/llama_index/status/1845853830485082474",
  "created_at": "2024-10-14T15:47:02.000Z",
  "#sort_index": "1845853830485082474",
  "view_count": 84971,
  "quote_count": 2,
  "is_quote_tweet": false,
  "is_retweet": false,
  "is_pinned": false,
  "is_truncated": true,
  "startUrl": "https://x.com/llama_index/status/1845853830485082474"
}