private-gpt VS langchain

PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.

https://github.com/zylon-ai/private-gpt

Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion

Run https://github.com/imartinez/privateGPT

Then

make ingest /path/to/folder/with/files

Then chat to the LLM.

Done.

Docs: https://docs.privategpt.dev/overview/welcome/quickstart

PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.

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

https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...

https://github.com/imartinez/privateGPT

I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.

This resulted in Python package I call OnPrem.LLM.

In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/

Enjoy!

Limited by the current packaging method of Pluto, it does not yet support LangChain's Template Ecosystem. Coming soon

Big fan of Aider.

We are interesting in integrating Aider as a tool for Dosu https://dosu.dev/ to help it navigate and modify a codebase on issues like this https://github.com/langchain-ai/langchain/issues/8263#issuec...

Developed using Langchain and Streamlit technologies for enhanced performance.

Tip: Kenton Blacutt, an AWS Associate Cloud App Developer, collaborated with Langchain, creating the Amazon Dynamodb based memory class that allows us to store the history of a langchain agent in an Amazon DynamoDB.

LangChain was first released in October 2022 as an open-source side project, a framework that makes developing AI applications more flexible. It got so popular that it was promptly turned into a startup.

> Furthermore, the prompt has a spelling error (Let'w) and also overly focuses on the negative about identifying errors - which makes me skeptical that this prompt has been optimized or tested.

Fixed in https://github.com/langchain-ai/langchain/commit/7c6009b76f0...

Being able to plug third party frameworks (Langchain, LlamaIndex) so you can build complex projects