codequestion
faiss
codequestion | faiss | |
---|---|---|
15 | 71 | |
509 | 28,308 | |
0.6% | 2.3% | |
5.5 | 9.4 | |
8 months ago | 2 days ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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.
codequestion
-
Introducing the Overflow Offline project
GitHub | Article
-
The Overflow Offline Project โ Stack Overflow Blog
There was a recent HN Post for codequestion which builds an offline semantic index on the Stack Overflow dumps on archive.org - https://news.ycombinator.com/item?id=33110219
GitHub: https://github.com/neuml/codequestion
Article: https://medium.com/neuml/find-answers-with-codequestion-2-0-...
-
[P] Stack Overflow Semantic Search
Release Announcement - https://medium.com/neuml/find-answers-with-codequestion-2-0-50b2cfd8c8fe Release Notes - https://github.com/neuml/codequestion/releases/tag/v2.0.0 GitHub - https://github.com/neuml/codequestion
-
Semantic search of Stack Overflow with codequestion
Release Announcement - https://medium.com/neuml/find-answers-with-codequestion-2-0-50b2cfd8c8fe Release Notes - https://github.com/neuml/codequestion/releases/tag/v2.0.0
- Show HN: Semantic search of Stack Overflow with codequestion
faiss
-
Haystack DB โ 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...
-
Show HN: Chromem-go โ Embeddable vector database for Go
Or just use FAISS https://github.com/facebookresearch/faiss
- OpenAI: New embedding models and API updates
-
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}
-
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
-
Can someone please help me with this problem?
According to this documentation page, faiss-gpu is only supported on Linux, not on Windows.
-
Ask HN: Are there any unsolved problems with vector databases
Indexes for vector databases in high dimensions are nowhere near are effective as the 2-d indexes used in GIS or the 1-d B-tree indexes that are commonly used in databases.
Back around 2005 I was interested in similarity search and read a lot of conference proceedings on the top and was basically depressed at the state of vector database indexes and felt that at least for the systems I was prototyping I was OK with a full scan and later in 2013 I had the assignment of getting a search engine for patents using vector embeddings in front of customers and we got performance we found acceptable with full scan.
My impression today is that the scene is not too different than it was in 2005 but I can't say I haven't missed anything. That is, you have tradeoffs between faster algorithms that miss some results and slower algorithms that are more correct.
I think it's already a competitive business. You have Pinecone which had the good fortune of starting before the gold rush. Many established databases are adding vector extension. I know so many engineering managers who love postgresql and they're just going to load a vector extension and go. My RSS reader YOShInOn uses SBERT embeddings to cluster and classify text and certainly More Like This and semantic search are on the agenda, I'd expect it to take about an hour to get
https://github.com/facebookresearch/faiss
up and working, I could spend more time stuck on some "little" front end problem like getting something to look right in Bootstrap than it would take to get working.
I can totally believe somebody could make a better vector db than what's out there but will it be better enough? A startup going through YC now could spend 2-3 to get a really good product and find customers and that is forever in a world where everybody wants to build AI applications right now.
-
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.
-
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
https://github.com/facebookresearch/faiss
What are some alternatives?
txtai - ๐ก All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
Milvus - A cloud-native vector database, storage for next generation AI applications
tldrstory - ๐ Semantic search for headlines and story text
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
tika-python - Tika-Python is a Python binding to the Apache Tikaโข REST services allowing Tika to be called natively in the Python community.
pgvector - Open-source vector similarity search for Postgres
paperai - ๐ ๐ค Semantic search and workflows for medical/scientific papers
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 databaseโ.
freeCodeCamp - freeCodeCamp.org's open-source codebase and curriculum. Learn to code for free.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/