autofaiss
marqo
autofaiss | marqo | |
---|---|---|
3 | 114 | |
748 | 4,124 | |
1.7% | 1.6% | |
5.6 | 9.3 | |
7 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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autofaiss
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You Don't Need LangChain;
I might be wrong here. I just know some product quantization techniques, but you can reduce the index by a lot! However, from my research, the more size you reduce, the more retrieval quality is also reduced.
Quoting from https://github.com/criteo/autofaiss
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Cheapest Vector Database
Autofaiss - https://github.com/criteo/autofaiss can be configured to make extremely tiny and efficient indexes.
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Vector database built for scalable similarity search
Don't start with mullivus if you're learning. Too much yak shaving. Try https://github.com/criteo/autofaiss.
Also, TBH, it is a lot cheaper to run a simple faiss index.
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?
vespa - AI + Data, online. https://vespa.ai
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.
sqlite-vss - A SQLite extension for efficient vector search, based on Faiss!
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
milvus-lite - A lightweight version of Milvus wrapped with Python.
Milvus - A cloud-native vector database, storage for next generation AI applications
typesense-instantsearch-semantic-search-demo - A demo that shows how to build a semantic search experience with Typesense's vector search feature and Instantsearch.js
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
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.
pgvector - Open-source vector similarity search for Postgres
marqo - Tensor search for humans. [Moved to: https://github.com/marqo-ai/marqo]