faiss
txtai.go
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faiss | txtai.go | |
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70 | 10 | |
28,054 | 63 | |
3.8% | - | |
9.4 | 4.3 | |
8 days ago | 8 days ago | |
C++ | Go | |
MIT License | Apache License 2.0 |
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faiss
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Show HN: Chromem-go – Embeddable vector database for Go
Or just use FAISS https://github.com/facebookresearch/faiss
- OpenAI: New embedding models and API updates
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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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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.
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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...
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Introduction to Vector Similarity Search
https://github.com/facebookresearch/faiss
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Any Suggestions on good open source model for Document QA which we can run on prod ? 13b + models?
Not a model, but I would use this Dense Passage Retrieval for Open Domain QA simply fine-tuning two BERT models, one for questions and one for queries, and then fine-tuning using contrastive loss between positive key/value pairs of document embeddings (the [CLS]) token. You can then use a vector database (Like Faiss, Elasticsearch, Vespa or similar) for querying the question.
txtai.go
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# Run txtai in native code
txtai currently has two main methods of execution: Python or via a HTTP API. There are API bindings for JavaScript, Java, Rust and Go.
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Build semantic search applications with txtai
In addition to running in Python, txtai can run as an API service and has bindings for JavaScript, Java, Go and Rust.
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Transform tabular data with composable workflows
Next we'll execute the workflow. txtai has API bindings for JavaScript, Java, Rust and Golang. But to keep things simple, we'll just run the commands via cURL.
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txtai 3.4 released - Build AI-powered semantic search applications in Go
txtai has a full API service that supports Go - https://github.com/neuml/txtai.go
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txtai - Semantic search backed by machine-learning powered workflows
🔗 API bindings for JavaScript, Java, Rust and Go
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txtai 3.0 released - Machine-learning workflows, similarity search and Go support via API
Example in Go - https://github.com/neuml/txtai.go/tree/master/examples
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txtai: AI-powered search engine for Go
Labeling with zero-shot classification
What are some alternatives?
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Milvus - A cloud-native vector database, storage for next generation AI applications
blast - Blast is a full text search and indexing server, written in Go, built on top of Bleve.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
pgvector - Open-source vector similarity search for Postgres
elasticsql - convert sql to elasticsearch DSL in golang(go)
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.
txtai.rs - Rust client for txtai
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/