embedchain VS gpt-researcher

You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.

The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.

[1]: https://github.com/embedchain/embedchain

Langchain is a big wrapper in itself and people can't be bothered to even use that to write 10 lines of code. Look at the traction this project is getting https://github.com/embedchain/embedchain, at it's heart it's just using few modules from langchain. The whole thing, chunking+embedding+retrieval+promoting can be done in 100 lines without langchain and embedchain.

Embedchain: a framework to easily create LLM powered bots over any dataset [Link].

Hey, GPT Researcher shows exactly how to do that with RAG. See here https://github.com/assafelovic/gpt-researcher

🤖🔎 Introducing 'GPT-Researcher’ Wish I had it during college... 😅 The idea is simple - Specify what you want to research, and the AI will autonomously research it for you in minutes! ▸ One prompt generates an unbiased, factual and in depth research report ▸ Generate research, outlines, resource and lessons reports ▸ Aggregates over 20 web sources per research ▸ Includes an easy to use web interface ▸ Open source: https://github.com/assafelovic/gpt-researcher ▸ Scrapes web sources with javascript support ▸ Keeps track and context of visited and used web sources

Link to GitHub page: https://github.com/assafelovic/gpt-researcher