What Is Retrieval-Augmented Generation a.k.a. RAG

• pgvector

  78 9,473 9.9 C

  Open-source vector similarity search for Postgres

  

> That is 90% of the work with RAG. RAG requires a surprising amount of QA to ensure "the best" is sufficient for your use case.

Thanks that's insightful (and also adds to my current impostor syndrome), and makes a lot of sense... so it sounds like it's easy to write the query, but knowing the metric value to use is the testing part.

I haven't heard of HNSW but I'll give it a try. It seems PGVector has added HNSW: https://github.com/pgvector/pgvector#hnsw

• private-gpt

  131 52,027 9.2 Python

  Interact with your documents using the power of GPT, 100% privately, no data leaks

  

I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].

The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.

[0] https://gpt4all.io/index.html

[1] https://www.danswer.ai/

[2] https://github.com/imartinez/privateGPT

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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