hnsqlite
chroma
hnsqlite | chroma | |
---|---|---|
6 | 32 | |
143 | 12,324 | |
1.4% | 5.5% | |
5.5 | 9.8 | |
10 months ago | 4 days ago | |
Python | 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.
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hnsqlite
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LangChain: The Missing Manual
For anyone thinking about applications of langchain and pinecone but who are looking for something more turn-key check out https://jiggy.ai
The core is actually open source as well, allowing you to take your data back out via sqlite and hnswlib (https://github.com/jiggy-ai/hnsqlite)
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I built an open source website that lets you upload large files, such as in-depth novels or academic papers, and ask ChatGPT questions based on your specific knowledge base. So far, I've tested it with long books like the Odyssey and random research papers that I like, and it works shockingly well.
We are built on open core https://github.com/jiggy-ai. Our open source hnsqlite is light weight, easy to use. And best of all, we make it easy for you to get your data out of JiggyBase. You can download a sqlite file that contains your document text data, metadata, embedding vectors, and embedding index. This can be used directly in the open source hnsqlite package.
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What Is a Vector Database
After working through several projects that utilized local hnswlib and different databases for text and vector persistence, I integrated open source hnswlib with sqlite to create an embedded vector search engine that can easily scale up to millions of embeddings. For self-hosted situations of under 10M embeddings and less than insane throughput I think this combo is hard to beat.
https://github.com/jiggy-ai/hnsqlite
- Show HN: Hnsqlite: hnswlib and SQLite integrated for text embedding search
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Faiss: A library for efficient similarity search
Thanks Leobg!
For anyone else: you pass it directly in metadata see https://github.com/jiggy-ai/hnsqlite/blob/main/test/test_col...
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?
langchainrb - Build LLM-powered applications in Ruby
SillyTavern - LLM Frontend for Power Users.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
faiss - A library for efficient similarity search and clustering of dense vectors.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
golang-ical - A ICS / ICal parser and serialiser for Golang.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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.
GPT4Memory
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
raft - RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]