trulens
ripgrep
trulens | ripgrep | |
---|---|---|
14 | 348 | |
1,646 | 45,156 | |
8.9% | - | |
9.8 | 9.3 | |
2 days ago | 9 days ago | |
Jupyter Notebook | Rust | |
MIT License | The Unlicense |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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trulens
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Why Vector Compression Matters
Retrieval using a single vector is called dense passage retrieval (DPR), because an entire passage (dozens to hundreds of tokens) is encoded as a single vector. ColBERT instead encodes a vector-per-token, where each vector is influenced by surrounding context. This leads to meaningfully better results; for example, here’s ColBERT running on Astra DB compared to DPR using openai-v3-small vectors, compared with TruLens for the Braintrust Coda Help Desk data set. ColBERT easily beats DPR at correctness, context relevance, and groundedness.
- FLaNK AI Weekly 18 March 2024
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First 15 Open Source Advent projects
12. TruLens by TruEra | Github | tutorial
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trulens VS agenta - a user suggested alternative
2 projects | 22 Nov 2023
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How are generative AI companies monitoring their systems in production?
3) Hallucination is probably the biggest problem we solve for. To do evals for hallucination, we typically see our users use a combination of groundedness (does the context support the LLM response) and context relevance (is the retrieved context relevant to the query). There's also a bunch more for the evaluations you mentioned (moderation models, sentiment, usefulness, etc.) and it's pretty easy to add custom evals.
Also - my hot take is that gpt-3.5 is good enough for evals (sometimes better) than gpt-4 if you give the LLM enough instructions on how to do the eval.
website: https://www.trulens.org/
- FLaNK Stack Weekly 28 August 2023
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[P] TruLens-Eval is an open source project for eval & tracking LLM experiments.
The team at TruEra recently released an open source project for evaluation & tracking of LLM applications called TruLens-Eval. We’ve specifically targeted retrieval-augmented QA as a core use case and so far we’ve seen it used for comparing different models and parameters, prompts, vector-db configurations and query planning strategies. I’d love to get your feedback on it.
- [D] Hardest thing about building with LLMs?
- Stop Evaluating LLMs on Vibes
- OSS library for attribution and interpretation methods for deep nets
ripgrep
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Ask HN: What software sparks joy when using?
ripgrep - https://github.com/BurntSushi/ripgrep
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Code Search Is Hard
Basic code searching skills seems like something new developers are never explicitly taught, but which is an absolutely crucial skill to build early on.
I guess the knowledge progression I would recommend would look something kind this:
- Learning about Ctrl+F, which works basically everywhere.
- Transitioning to ripgrep https://github.com/BurntSushi/ripgrep - I wouldn't even call this optional, it's truly an incredible and very discoverable tool. Requires keeping a terminal open, but that's a good thing for a newbie!
- Optional, but highly recommended: Learning one of the powerhouse command line editors. Teenage me recommended Emacs; current me recommends vanilla vim, purely because some flavor of it is installed almost everywhere. This is so that you can grep around and edit in the same window.
- In the same vein, moving back from ripgrep and learning about good old fashioned grep, with a few flags rg uses by default: `grep -r` for recursive search, `grep -ri` for case insensitive recursive search, and `grep -ril` for case insensitive recursive "just show me which files this string is found in" search. Some others too, season to taste.
- Finally hitting the wall with what ripgrep can do for you and switching to an actual indexed, dedicated code search tool.
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Level Up Your Dev Workflow: Conquer Web Development with a Blazing Fast Neovim Setup (Part 1)
live grep: ripgrep
- Ripgrep
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Modern Java/JVM Build Practices
The world has moved on though to opinionated tools, and Rust isn't even the furthest in that direction (That would be Go). The equivalent of those two lines in Cargo.toml would be this example of a basic configuration from the jacoco-maven-plugin: https://www.jacoco.org/jacoco/trunk/doc/examples/build/pom.x... - That's 40 lines in the section to do the "defaults".
Yes, you could add a load of config for files to include/exclude from coverage and so on, but the idea that that's a norm is way more common in Java projects than other languages. Like here's some example Cargo.toml files from complicated Rust projects:
Servo: https://github.com/servo/servo/blob/main/Cargo.toml
rust-gdext: https://github.com/godot-rust/gdext/blob/master/godot-core/C...
ripgrep: https://github.com/BurntSushi/ripgrep/blob/master/Cargo.toml
socketio: https://github.com/1c3t3a/rust-socketio/blob/main/socketio/C...
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Ugrep – a more powerful, ultra fast, user-friendly, compatible grep
I'm not clear on why you're seeing the results you are. It could be because your haystack is so small that you're mostly just measuring noise. ripgrep 14 did introduce some optimizations in workloads like this by reducing match overhead, but I don't think it's anything huge in this case. (And I just tried ripgrep 13 on the same commands above and the timings are similar if a tiny bit slower.)
[1]: https://github.com/radare/ired
[2]: https://github.com/BurntSushi/ripgrep/discussions/2597
- Tell HN: My Favorite Tools
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Potencializando Sua Experiência no Linux: Conheça as Ferramentas em Rust para um Desenvolvimento Eficiente
Explore o Ripgrep no repositório oficial: https://github.com/BurntSushi/ripgrep
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Scrybble is the ReMarkable highlights to Obsidian exporter I have been looking for
🔎🗃️ ripgrep or ugrep (search fast, use regex patterns or fuzzy search, pipe output to bash/zsh shell for further processing V coloring)
- RFC: Add ngram indexing support to ripgrep (2020)
What are some alternatives?
langfuse - 🪢 Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
telescope-live-grep-args.nvim - Live grep with args
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
fd - A simple, fast and user-friendly alternative to 'find'
probability - Probabilistic reasoning and statistical analysis in TensorFlow
ugrep - ugrep 5.1: A more powerful, ultra fast, user-friendly, compatible grep. Includes a TUI, Google-like Boolean search with AND/OR/NOT, fuzzy search, hexdumps, searches (nested) archives (zip, 7z, tar, pax, cpio), compressed files (gz, Z, bz2, lzma, xz, lz4, zstd, brotli), pdfs, docs, and more
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
the_silver_searcher - A code-searching tool similar to ack, but faster.
embedchain - Personalizing LLM Responses
fzf - :cherry_blossom: A command-line fuzzy finder
machine_learning_basics - Plain python implementations of basic machine learning algorithms
alacritty - A cross-platform, OpenGL terminal emulator.