vectara-answer
model.nvim
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13 | 3 | |
217 | 269 | |
1.8% | - | |
8.9 | 9.6 | |
8 days ago | 7 days ago | |
TypeScript | Lua | |
Apache License 2.0 | MIT License |
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vectara-answer
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Show HN: Quepid now works with vetor search
Hi HN!
I lead product for Vectara (https://vectara.com) and we recently worked with OpenSource connections to both evaluate our new home-grown embedding model (Boomerang) as well as to help users start more quantitatively evaluating these systems on their own data/with their own queries.
OSC maintains a fantastic open source tool, Quepid, and we worked with them to integrate Vectara (and to use it to quantitatively evaluate Boomerang). We're hoping this allows more vector/hybrid players to be more transparent about the quality of their systems and any models they use instead of everyone relying on and gaming a benchmark like BIER.
More details on OSC's eval can be found at https://opensourceconnections.com/blog/2023/10/11/learning-t...
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A Comprehensive Guide for Building Rag-Based LLM Applications
RAG is a very useful flow but I agree the complexity is often overwhelming, esp as you move from a toy example to a real production deployment. It's not just choosing a vector DB (last time I checked there were about 50), managing it, deciding on how to chunk data, etc. You also need to ensure your retrieval pipeline is accurate and fast, ensuring data is secure and private, and manage the whole thing as it scales. That's one of the main benefits of using Vectara (https://vectara.com; FD: I work there) - it's a GenAI platform that abstracts all this complexity away, and you can focus on building your application.
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Do we think about vector dbs wrong?
I agree. my experience is that hybrid search does provide better results in many cases, and is honestly not as easy to implement as may seem at first. In general, getting search right can be complicated today and the common thinking of "hey I'm going to put up a vector DB and use that" is simplistic.
Disclaimer: I'm with Vectara (https://vectara.com), we provide an end-to-end platform for building GenAI products.
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What is a GenAI Platform?
In this article I discuss my long-held belief that it's time we shifted the discussion from "which vector database to use" for GenAI and instead think about "how do we make this whole architecture simpler to use", a focus of GenAI platforms like https://vectara.com
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Comparison of Vector Databases
With Vectara (full disclosure: I work there; https://vectara.com) we provide a simple API to implement applications with Grounded Generation (aka retrieval augmented generation). The embeddings model, the vector store, the retrieval engine and all the other functionality - implemented by the Vectara platform, so you don't have to choose which vector DB to use, which embeddings model to use, and so on. Makes life easy and simple, and you can focus on developing your application.
- Vectara, une bonne alternative à l'ingestion de données par les LLMs
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Train a model based on text from pdfs
You can also use vectara to implement this. Just upload the docs via the indexing API and then run queries via the search API. It tends to be less complicated with Vectara since we take care of many things internally (vectorDB, embeddings, etc). Let me know if I can help further with that.
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ChatGPT-like interface for product search
I found vectara.com but all examples seem to be about feeding text. I'm not super technical so I may be missing something. Please let me know if I need to elaborate further.
- Vectara-Answer
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ChatGPT made everyone realize that we don't want to search, we want answers.
yes agreed that if ChatGPT becomes monetized the same way as Google, then it the fun will be over. We'll have to wait and see. I think though that this innovation is not just applicable to web search or consumer search, and with products like vectara.com providing this type of user experience in the enterprise there is a significant net gain here overall.
model.nvim
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A Comprehensive Guide for Building Rag-Based LLM Applications
For local stuff with a handful of documents, you can even just throw it into a json and call it a day. The similarity search is as simple as an np.dot: https://github.com/gsuuon/llm.nvim/blob/main/python3/store.p...
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Show HN: Script to Auto-Generate Commit Messages with AI
My plugin is here: https://github.com/gsuuon/llm.nvim -- one of the "starter prompts" is commit message, so with vim-fugitive I open up the git status window, stage my changes, press 'cc', then ':Llm commit\ message' (or just ':Llm mess' tab complete). Then I make changes as needed. I notice that normally it fails to capture my intent for larger changes (things that should be refactor for example get labeled as feat), and readme only changes are sometimes not labeled as 'docs' correctly.
Here's where the commit message prompt is: https://github.com/gsuuon/llm.nvim/blob/2d771cc882ad9edd8011...
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Burnout Because of ChatGPT?
I plug it directly into my editor (via https://github.com/gsuuon/llm.nvim) and have it fill out code for me. I write what I want with comments and ask it to fill the rest - if it's straightforward enough it basically always works. I also get it to write commit messages (based on git diff) - though I need to improve my prompt a bit as it gets verbose and I end up rewriting it most of the time. I was working on trying to feed it things like hover and tree-sitter information before I got distracted, but that'd be another power boost as well whenever I get around to it.
What are some alternatives?
llama-hub - A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain
llm-applications - A comprehensive guide to building RAG-based LLM applications for production.
go.nvim - A feature-rich Go development plugin, leveraging gopls, treesitter AST, Dap, and various Go tools to enhance the dev experience.
VectorDBBench - A Benchmark Tool for VectorDB
neoai.nvim - Neovim plugin for intracting with GPT models from OpenAI
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
llmflows - LLMFlows - Simple, Explicit and Transparent LLM Apps
motorhead - 🧠 Motorhead is a memory and information retrieval server for LLMs.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)