u/docsoc1

https://r2r-docs.sciphi.ai/introduction - r2r is a great option if you can secure llm inference as a separate service

https://r2r-docs.sciphi.ai/cookbooks/local-rag

https://huggingface.co/SciPhi/Triplex - this model is good if you want to extract triples. Numinds model is good if you have a specific schema - https://huggingface.co/numind/NuExtract

https://r2r-docs.sciphi.ai/introduction

If you want to implement RAG (which you probably will at some point) then I'd recommend checking out R2R as one potential system provider - https://r2r-docs.sciphi.ai/introduction

'bad request' when taking link above.

This project is focused on pgvector + local LLMs if you are looking for a quick way to get up and started with RAG - https://r2r-docs.sciphi.ai/cookbooks/local-rag.

Also, nice repo OP. There is still a ton of room in the space to improve on the way we deploy / optimize RAG applications.

llama async is a pretty good speed boost vs. llama index, R2R is also pretty fast - https://www.sciphi.ai/blog/benchmarking

R2R is working on this - https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph

If you run into any issues with R2R I'd be more than happy to dive in and get my hands dirty making it work for your use case - https://r2r-docs.sciphi.ai/cookbooks/local-rag

https://r2r-docs.sciphi.ai/cookbooks/local-rag - you should check out this cookbook to get up and going.

this is amazing!

R2R agents are using ollama tooling support to make locally available agents - https://r2r-docs.sciphi.ai/cookbooks/agent.

postgres+pgvector is goated imo

more graphrag implementations

Sure,

It depends on your use case, but I think LlamaIndex/Langchain introduce more complexity than is warranted for the features they provide.

If you are working on RAG, then I believe R2R provides a similar level of customizability but also provides a ton of useful features out of the box, like document management / user permissions / REST API / ...

Again, it depends on your use case, and I am obvious biased as an author of R2R

This cookbook is easy to get started with and can be ran locally https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph

Discord is very active if you need any help getting it up and going

Would be interested on collaborating to make a massive knowledge graph over AI materials (basically run this with dedupe over a selected corpus https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph)

If you are looking for an alternative, I suggest R2R - https://r2r-docs.sciphi.ai/introduction

Would love to compare the result that you get by plugging into R2R and creating a knowledge graph as you ingest (https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph), lmk if you are interested in chatting about how to go about doing this.

I like the way you structured your API around chats, that's a nice touch.

For RAG specific features you might want to look at R2R. It provides a similarly easy to use API for just RAG https://r2r-docs.sciphi.ai/api-reference/introduction

You might want to take a look at knowledge graphs - https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph

They are great for use cases like this

graph rag for sure, this might be helpful - https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph

I'm biased as I am the one developing it, but I think R2R is the most developer friendly system for building local RAG and has all the advanced techniques you are likely to want - https://r2r-docs.sciphi.ai/introduction

Take a look and let me know if you have any questions, we recently released a model that will let you build a knowledge graph from your laptop: https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph