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Faiss Alternatives
Similar projects and alternatives to faiss
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txtai
💡 All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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Milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
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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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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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haystack
AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
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annoy
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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usearch
Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
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bootcamp
Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc. (by milvus-io)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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 discussion
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 2025-04-25.
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6 retrieval augmented generation (RAG) techniques you should know
This allows for efficient and scalable handling of large datasets. There are two vector database possibilities for RAG: Pinecone is a vector database delivered as a PaaS, while FAISS (from Meta) is a library that can be used on-premises.
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AWS Graviton 3 > Graviton 4 for Vector Similarity Search
For these single-threaded benchmarks, we used FAISS (compiled with SVE) and USearch.
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Langchain — RAG — Retrieval Augmented Generation
FAISS Documentation
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IndexIVFFlat y IndexIVFPQ
Citations: [1] https://www.pinecone.io/learn/series/faiss/faiss-tutorial/ [2] https://www.pinecone.io/learn/series/faiss/product-quantization/ [3] https://www.pinecone.io/learn/series/faiss/composite-indexes/ [4] https://github.com/facebookresearch/faiss/wiki/Faiss-indexes/9df19586b3a75e4cb1c2fb915f2c695755a599b8 [5] https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexIVFFlat.html [6] https://pub.towardsai.net/unlocking-the-power-of-efficient-vector-search-in-rag-applications-c2e3a0c551d5?gi=71a82e3ea10e [7] https://www.pingcap.com/article/mastering-faiss-vector-database-a-beginners-handbook/ [8] https://wangzwhu.github.io/home/file/acmmm-t-part3-ann.pdf [9] https://github.com/alonsoir/ubiquitous-carnival/blob/main/contextual-data-faiss-IndexIVFPQ.py [10] https://github.com/alonsoir/ubiquitous-carnival/blob/main/contextual-data-faiss-indexivfflat.py
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Embeddings index format for open data access
Each file can be read without txtai. JSON, MessagePack and Faiss all have libraries in multiple programming languages.
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Is My Approach to Vectorizing and Storing 1.5T Tokens Reasonable?
Here’s the text formatted for Stack Overflow:
---
I'm planning to index and store 1.5 trillion tokens using Faiss and would love some feedback on my approach:
1. *Partitioning:* I'm thinking of using distributed k-means and inverted multi-index quantizers for efficient data partitioning.
2. *On-Disk Storage:* Due to the scale, I'm storing everything on disk using a Compressed Sparse Row format.
3. *Distributed Search:* I plan to implement a client-server model with multiple servers to handle search operations.
Does this approach sound feasible, or am I overlooking something crucial? Any advice or suggestions?
I'm mostly working off of this article: [Indexing 1T Vectors](https://github.com/facebookresearch/faiss/wiki/Indexing-1T-vectors). I think the data is too big for AutoFaiss, but I can use that for experiments.
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Introducing vectorlite: A Fast and Tunable Vector Search Extension for SQLite
Sqlite-vss uses faiss to do vector seaching. It is a great library opensourced by Meta(facebook) and provides a wide range of algorithms for vector search. However, it is optimized for batch operations over a large dataset, making it slow for a single vector query and incremental indexing on CPU. However, SQLite's extensibility model (called virtual table) doesn't provide APIs for batch operations and only exposes API to insert/update/delete a single row at a time. Besides, sqlite-vss only support single-vector search, which faiss is not good at. As a result, sqlite-vss can't fully exploit faiss's performance.
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OpenAI api RAG system with Qdrant
You can swap out any of the components in this project with something else. You could use Faiss instead of qdrant, you could use OpenAI models for everything(embeddings/chat completion) or you could use open models.
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Haystack DB – 10x faster than FAISS with binary embeddings by default
There are also FAISS binary indexes[0], so it'd be great to compare binary index vs binary index. Otherwise it seems a little misleading to say it is a FAISS vs not FAISS comparison, since really it would be a binary index vs not binary index comparison. I'm not too familiar with binary indexes, so if there's a significant difference between the types of binary index then it'd be great to explain what that is too.
[0] https://github.com/facebookresearch/faiss/wiki/Binary-indexe...
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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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A note from our sponsor - InfluxDB
www.influxdata.com | 13 May 2025
Stats
Basic faiss repo stats
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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++.
Sponsored
InfluxDB – Built for High-Performance Time Series Workloads
InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
www.influxdata.com