[D] Hardest thing about building with LLMs?

• embedchain

  6 8,479 9.8 Python

  Personalizing LLM Responses

  

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.

• ripgrep

  348 44,901 9.3 Rust

  ripgrep recursively searches directories for a regex pattern while respecting your gitignore

This is kinda an oversimplifying statement. The details matter a lot. For example the langchain project I linked consider only markdown files. It doesnt even look at the code while cody includes files like pr, commit messages but it also knows it should ignore binary files or other generated code. It supports a lot more complicated search syntax, it can limit the search context based on organization, repo, language, etc. https://docs.sourcegraph.com/getting-started/github-vs-sourcegraph Its retrieval uses more than just embedding+ann, it also uses keyword based search, ripgrip and some pagerank based algorithm(mentioned in the article I linked. You have to parse the files to find the symbols. They develop a new format to index source code). They also use some custom heuristic to rank the results(I think it is partly based on sourcerank).

• 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.

  

• trulens

  14 1,612 9.8 Jupyter Notebook

  Evaluation and Tracking for LLM Experiments

  

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