๐Ÿš€ No-Code Chatbot with Elasticsearch + AWS Bedrock (Talk Summary)

Speaker: Ashish (Senior Developer Advocate, Elastic)
Event: AWS Community Day Mumbai 2024


๐Ÿ”‘ Why Search Still Matters with LLMs

  • LLMs (like ChatGPT) are powerful but face:
    • โŒ Hallucinations
    • ๐Ÿ’ฐ High cost per query
    • ๐Ÿ”’ No access to private / real-time data
  • โœ… Search grounds LLMs in reliable, domain-specific info.

โšก Elasticsearch Capabilities

  • Traditional keyword search + modern vector search.
  • Real-world use cases:
    • ๐Ÿ“ Geospatial queries (ride-sharing, food delivery)
    • โค๏ธ Matchmaking
    • ๐Ÿ“Š Observability dashboards
    • ๐Ÿ“ Centralized logging (Elastic Stack: Elasticsearch, Kibana, Beats, Logstash)

๐Ÿค– Retrieval-Augmented Generation (RAG)

Workflow:

  1. User query โ†’
  2. Elasticsearch retrieves relevant documents โ†’
  3. Context passed to AWS Bedrock LLM โ†’
  4. LLM generates grounded answers

๐Ÿ‘‰ Reduces hallucination, enables chatbots on private data.


๐Ÿ› ๏ธ Demo Stack

  • Flowise AI โ†’ No-code, drag & drop RAG builder
  • Steps:
    1. Ingest data (JSON, PDFs, web crawl)
    2. Chunk text
    3. Generate embeddings (Amazon Titan)
    4. Store embeddings + text in Elasticsearch
    5. Connect Bedrock LLM for Q&A

๐ŸŒ Extensions

  • Text search โœ…
  • Image search ๐Ÿ–ผ๏ธ
  • Audio search (e.g., humming a tune) ๐ŸŽต
  • Multimodal experiences ๐Ÿค

๐ŸŽฏ Key Takeaway

With Elasticsearch + AWS Bedrock + Flowise AI, developers can build domain-specific, no-code RAG chatbots that combine:

  • The precision of search ๐Ÿ”
  • The fluency of LLMs ๐Ÿ’ฌ

Talk video