chroma
marqo
chroma | marqo | |
---|---|---|
32 | 114 | |
12,324 | 4,124 | |
5.5% | 1.6% | |
9.8 | 9.3 | |
6 days ago | 7 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.
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.
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:
marqo
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Are we at peak vector database?
We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo
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Qdrant, the Vector Search Database, raised $28M in a Series A round
Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)
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Ask HN: Is there any good semantic search GUI for images or documents?
Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).
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90x Faster Than Pgvector – Lantern's HNSW Index Creation Time
That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.
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Embeddings: What they are and why they matter
Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.
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Choosing vector database: a side-by-side comparison
As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.
- Show HN: Marqo – Vectorless Vector Search
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AI for AWS Documentation
Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo
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[N] Open-source search engine Meilisearch launches vector search
Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo
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Ask HN: Which Vector Database do you recommend for LLM applications?
Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo
What are some alternatives?
SillyTavern - LLM Frontend for Power Users.
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
faiss - A library for efficient similarity search and clustering of dense vectors.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
golang-ical - A ICS / ICal parser and serialiser for Golang.
Milvus - A cloud-native vector database, storage for next generation AI applications
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.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]