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Faiss Alternatives
Similar projects and alternatives to faiss
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txtai
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
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polars
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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qdrant
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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Milvus
A cloud-native vector database, storage for next generation AI applications
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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.
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ann-benchmarks
Benchmarks of approximate nearest neighbor libraries in Python
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annoy
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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hnsqlite
hnsqlite integrates hnswlib and sqlite for simple text embedding search
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TorchPQ
Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better faiss alternative or higher similarity.
faiss reviews and mentions
Posts with mentions or reviews of faiss.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-05.
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Show HN: Chromem-go – Embeddable vector database for Go
Or just use FAISS https://github.com/facebookresearch/faiss
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You Shouldn't Invest in Vector Databases?
You can try txtai (https://github.com/neuml/txtai) with a Faiss backend.
This Faiss wiki article might help (https://github.com/facebookresearch/faiss/wiki/Indexing-1G-v...).
For example, a partial Faiss configuration with 4-bit PQ quantization and only using 5% of the data to train an IVF index is shown below.
faiss={"components": "IVF,PQ384x4fs", "sample": 0.05}
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Approximate Nearest Neighbors Oh Yeah
If you want to experiment with vector stores, you can do that locally with something like faiss which has good platform support: https://github.com/facebookresearch/faiss
Doing full retrieval-augmented generation (RAG) and getting LLMs to interpret the results has more steps but you get a lot of flexibility, and there's no standard best-practice. When you use a vector DB you get the most similar texts back (or an index integer in the case of faiss), you then feed those to an LLM like a normal prompt.
The codifer for the RAG workflow is LangChain, but their demo is substantially more complex and harder-to-use than even a homegrown implementation: https://news.ycombinator.com/item?id=36725982
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Can someone please help me with this problem?
According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.
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Code Search with Vector Embeddings: A Transformer's Approach
As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.
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Unum: Vector Search engine in a single file
But FAISS has their own version ("FastScan") https://github.com/facebookresearch/faiss/wiki/Fast-accumula...
- Introduction to Vector Similarity Search
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Vector Databases 101
If you want to go larger you could still use some simple setup in conjunction with faiss, annoy or hnsw.
- I'm an undergraduate data science intern and trying to run kmodes clustering. Did this elbow method to figure out how many clusters to use, but I don't really see an "elbow". Tips on number of clusters?
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Disrupting the AI Scene with Open Source and Open Innovation
Facebook research for having open source licensed an amazing vector based indexing library
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A note from our sponsor - WorkOS
workos.com | 17 Apr 2024
Stats
Basic faiss repo stats
70
27,924
9.4
5 days ago
facebookresearch/faiss is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of faiss is C++.
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