๐ No-Code Chatbot with Elasticsearch + AWS Bedrock (Talk Summary)
Speaker: Ashish (Senior Developer Advocate, Elastic)
Event: AWS Community Day Mumbai 2024
๐ Why Search Still Matters with LLMs
- LLMs (like ChatGPT) are powerful but face:
- โ Hallucinations
- ๐ฐ High cost per query
- ๐ No access to private / real-time data
- โ Search grounds LLMs in reliable, domain-specific info.
โก Elasticsearch Capabilities
- Traditional keyword search + modern vector search.
- Real-world use cases:
- ๐ Geospatial queries (ride-sharing, food delivery)
- โค๏ธ Matchmaking
- ๐ Observability dashboards
- ๐ Centralized logging (Elastic Stack: Elasticsearch, Kibana, Beats, Logstash)
๐ค Retrieval-Augmented Generation (RAG)
Workflow:
- User query โ
- Elasticsearch retrieves relevant documents โ
- Context passed to AWS Bedrock LLM โ
- LLM generates grounded answers
๐ Reduces hallucination, enables chatbots on private data.
๐ ๏ธ Demo Stack
- Flowise AI โ No-code, drag & drop RAG builder
- Steps:
- Ingest data (JSON, PDFs, web crawl)
- Chunk text
- Generate embeddings (Amazon Titan)
- Store embeddings + text in Elasticsearch
- Connect Bedrock LLM for Q&A
๐ Extensions
- Text search โ
- Image search ๐ผ๏ธ
- Audio search (e.g., humming a tune) ๐ต
- Multimodal experiences ๐ค
๐ฏ Key Takeaway
With Elasticsearch + AWS Bedrock + Flowise AI, developers can build domain-specific, no-code RAG chatbots that combine:
- The precision of search ๐
- The fluency of LLMs ๐ฌ