vectara-answer
llama-hub
vectara-answer | llama-hub | |
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13 | 5 | |
217 | 3,359 | |
1.8% | - | |
8.9 | 9.6 | |
8 days ago | 3 months ago | |
TypeScript | Jupyter Notebook | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
llama-hub
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LlamaCloud and LlamaParse
mean_faithfulness_score 0.667
Notably, the faithfulness score I measured for the baseline solution was actually higher than that reported for your proprietary LlamaParse based solution.
[1] https://github.com/run-llama/llama-hub/tree/main/llama_hub/l...
- Llama Hub
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A Comprehensive Guide for Building Rag-Based LLM Applications
My favorite example is the asana loader[0] for llama-index. It's literally just the most basic wrapper around the Asana SDK to concatenate some strings.
[0] - https://github.com/emptycrown/llama-hub/blob/main/llama_hub/...
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Outlook local calendar loader for LlamaIndex now in LLamaHub
My loader to get events from the local version of an Outlook calendar into documents suitable for LLamaIndex indexing is now available on github.. Like other loaders (there are a lot of them), it's available at https://github.com/emptycrown/llama-hub To make it easy for developers, this loader has a superset of the functions the Google calandar loader has and the same defaults. Since it works off the local calendar, however, no apikeys are needed. This is Windows only.
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Hello, is there a "BEST OF" prompts list here somewhere?
LLAMA GitHub repository
What are some alternatives?
llm-applications - A comprehensive guide to building RAG-based LLM applications for production.
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
VectorDBBench - A Benchmark Tool for VectorDB
LLMStack - No-code platform to build LLM Agents, workflows and applications with your data
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
model.nvim - Neovim plugin for interacting with LLM's and building editor integrated prompts.
motorhead - 🧠 Motorhead is a memory and information retrieval server for LLMs.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
vectara-ingest - An open source framework to crawl data sources and ingest into Vectara
awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better.