Content Deep Dive
How to Implement RAG With Amazon Bedrock and LangChain
Blog post from Tiger Data
Post Details
Company
Date Published
Author
Haziqa Sajid
Word Count
2,416
Company Posts That Month
Language
English
Hacker News Points
-
Post removed?
No
Summary
This article explores the implementation of RAG (retrieval-augmented generation) applications using Amazon Bedrock and LangChain. It covers setting up Amazon Bedrock, integrating with LangChain, and utilizing the potent Amazon Titan model for large language model (LLM) applications. The text also discusses how pgvector on Timescale's PostgreSQL cloud platform makes it easier to set up a vector database optimized for efficient storage and powering LLM applications with RAG.
Trends Found in this Post
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 17 | 1,326 | 202 | 88 | -53% |
| RAG | 13 | 893 | 155 | 66 | -52% |
| LLM | 12 | 3,051 | 363 | 148 | -19% |
| Real-time | 2 | 2,475 | 678 | 222 | -5% |
| Serverless | 1 | 603 | 131 | 77 | -41% |
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