vectara-answer
private-gpt
vectara-answer | private-gpt | |
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13 | 131 | |
217 | 52,027 | |
1.8% | 2.9% | |
8.9 | 9.2 | |
8 days ago | 7 days ago | |
TypeScript | Python | |
Apache License 2.0 | Apache License 2.0 |
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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.
private-gpt
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
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
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PrivateGPT exploring the Documentation
Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion
- Show HN: I made an app to use local AI as daily driver
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privateGPT VS quivr - a user suggested alternative
2 projects | 12 Jan 2024
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
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
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Mozilla "MemoryCache" Local AI
PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.
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What Is Retrieval-Augmented Generation a.k.a. RAG
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
- LM Studio – Discover, download, and run local LLMs
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Ask HN: Local LLM Recommendation?
https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...
https://github.com/imartinez/privateGPT
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Run ChatGPT-like LLMs on your laptop in 3 lines of code
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!
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
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
llm-applications - A comprehensive guide to building RAG-based LLM applications for production.
gpt4all - gpt4all: run open-source LLMs anywhere
VectorDBBench - A Benchmark Tool for VectorDB
h2ogpt - Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
motorhead - 🧠 Motorhead is a memory and information retrieval server for LLMs.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
llama.cpp - LLM inference in C/C++