lucene
pgvector
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lucene | pgvector | |
---|---|---|
11 | 78 | |
2,358 | 9,211 | |
4.0% | 10.4% | |
9.8 | 9.9 | |
about 11 hours ago | 1 day ago | |
Java | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
lucene
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Building an efficient sparse keyword index in Python
First, a review of the landscape. As said in the introduction, there aren't a ton of good options. Apache Lucene is by far the best traditional search index from a speed, performance and functionality standpoint. It's the base for Elasticsearch/OpenSearch and many other projects. But it requires Java.
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Java Panama Vector API Integrated with Apache Lucene
https://github.com/apache/lucene/issues/10047
2. The Panama Vector API allows CPU's that support it to accelerate vector operations: https://openjdk.org/jeps/438
So this allows fast ANN on Lucene for semantic search!
How did people do this before Lucene supported it? Only through entirely different tools?
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What Is a Vector Database
Are they forking Lucene or somehow getting the Lucene devs to increase that limit? Because this PR has been open for over a year now: https://github.com/apache/lucene/issues/11507
- An alternative to Elasticsearch that runs on a few MBs of RAM
- Lucene 9.4 (optionally) uses Panama's mapped MemorySegments when JDK 19 is detected
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A primer on Roaring bitmaps: what they are and how they work
Lucene's adaptation of Roaring uses the complement idea on a block-wise basis:
https://github.com/apache/lucene/blob/84cae4f27cfd3feb3bb42d...
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How are documents stored in Elasticsearch?
Like someone said, it's in locations as specified in the path.data. Depending on sharing and replication, it could be on more than one host. Elastic uses Apache Lucene to store documents, since it's open source, that rabbit hole will welcome research :-)
- panama/foreign status update
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Amazon Elasticsearch Service Is Now Amazon OpenSearch Service
It is pretty clear to me that Elastic is planning to build their ANN features differently than OpenDistro's k-NN implementation, or other plugins modules that extend Easticsearch in similar ways. They now will build on the Apache Lucene capabilities that were collaboratively built "upstream" by a number of individuals, some that work for Amazon and some that work for Elastic.
From the linked issue, it seemed that they were originally planning to develop this as a proprietary feature of Elasticsearch, without contributing the functionality to Apache Lucene, but then changed direction when the Apache Lucene developers (some of which are currently employed to do such work by Amazon) started to build its approximate nearest neighbor (ANN) vector search capabilities. [1]
It's great to see folks that work for Elastic collaborating and building on what is in Apache Lucene to extend the utility of ANN with Hierarchical Navigable Small World Graphs (HNSW) [2]! From this, I think it should be possible to implement an Open Source version of the functionality with a compatible API, if that is something that OpenSearch users seek.
[1] https://issues.apache.org/jira/browse/LUCENE-9004
[2] https://github.com/apache/lucene/pull/250
pgvector
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Integrate txtai with Postgres
# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"
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Vector Database solutions on AWS
When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.
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Using pgvector To Locate Similarities In Enterprise Data
For this example, I wanted to focus on how pgvector – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.
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pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
pgvector supports dense vector search well, but it does not have plan to support sparse vector.
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Pg_vectorize: The simplest way to do vector search and RAG on Postgres
There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455
I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily
- FLaNK 04 March 2024
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Vector Database and Spring IA
The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)
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Use pgvector for searching images on Azure Cosmos DB for PostgreSQL
Official GitHub repository of the pgvector extension
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pgvector 0.6.0: 30x faster with parallel index builds
pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.
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Store embeddings in Azure Cosmos DB for PostgreSQL with pgvector
The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt:
What are some alternatives?
pisa - PISA: Performant Indexes and Search for Academia
Milvus - A cloud-native vector database, storage for next generation AI applications
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
faiss - A library for efficient similarity search and clustering of dense vectors.
RoaringBitmap - A better compressed bitset in Java: used by Apache Spark, Netflix Atlas, Apache Pinot, Tablesaw, and many others
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.
OpenSearch - 🔎 Open source distributed and RESTful search engine.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
Apache Solr - Apache Lucene and Solr open-source search software
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
resin - Vector space search engine. Available as a HTTP service or as an embedded library.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python