vectara-answer
chroma
vectara-answer | chroma | |
---|---|---|
13 | 32 | |
217 | 12,530 | |
1.8% | 7.1% | |
8.9 | 9.8 | |
7 days ago | 1 day ago | |
TypeScript | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
chroma
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Let’s build AI-tools with the help of AI and Typescript!
Package installer for Python (pip), we use this for installing the Python-based packages, such as Jupyter Lab, and we're going to use this for installing other Python-based tools like the Chroma DB vector database
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Mixtral 8x22B
Optional: You can use SillyTavern[1] for a more "rich" chat experience
The above lets me chat, at least superficially, with my friend. It's nice for simple interactions and banter; I've found it to be a positive and reflective experience.
0. https://www.trychroma.com/
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7 Vector Databases Every Developer Should Know!
Chroma DB is a newer entrant in the vector database arena, designed specifically for handling high-dimensional color vectors. It's particularly useful for applications in digital media, e-commerce, and content discovery, where color similarity plays a crucial role in search and recommendation algorithms.
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AI Grant Traction in OSS Startups
View on GitHub
- Qdrant, the Vector Search Database, raised $28M in a Series A round
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Vector Databases: A Technical Primer [pdf]
For Python I believe Chroma [1] can be used embedded.
For Go I recently started building chromem-go, inspired by the Chroma interface: https://github.com/philippgille/chromem-go
It's neither advanced nor for scale yet, but the RAG demo works.
[1] https://github.com/chroma-core/chroma
- Chroma – the open-source embedding database
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Show HN: Embeddings Solution for Personal Journal
The formatting is a bit off.
The web app is here: https://jumblejournal.org
The DB used is here: https://www.trychroma.com/
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SQLite vs. Chroma: A Comparative Analysis for Managing Vector Embeddings
Whether you’re navigating through well-known options like SQLite, enriched with the sqlite-vss extension, or exploring other avenues like Chroma, an open-source vector database, selecting the right tool is paramount. This article compares these two choices, guiding you through the pros and cons of each, helping you choose the right tool for storing and querying vector embeddings for your project.
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How to use Chroma to store and query vector embeddings
Create a new project directory for our example project. Next, we need to clone the Chroma repository to get started. At the root of your project directory let's clone Chroma into 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
SillyTavern - LLM Frontend for Power Users.
llm-applications - A comprehensive guide to building RAG-based LLM applications for production.
faiss - A library for efficient similarity search and clustering of dense vectors.
VectorDBBench - A Benchmark Tool for VectorDB
golang-ical - A ICS / ICal parser and serialiser for Golang.
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
motorhead - 🧠 Motorhead is a memory and information retrieval server for LLMs.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]