txtai
pgvector
txtai | pgvector | |
---|---|---|
356 | 78 | |
7,033 | 9,349 | |
2.6% | 7.0% | |
9.3 | 9.9 | |
3 days ago | 2 days ago | |
Python | 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.
txtai
- Show HN: FileKitty – Combine and label text files for LLM prompt contexts
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What contributing to Open-source is, and what it isn't
I tend to agree with this sentiment. Many junior devs and/or those in college want to contribute. Then they feel entitled to merge a PR that they worked hard on often without guidance. I'm all for working with people but projects have standards and not all ideas make sense. In many cases, especially with commercial open source, the project is the base of a companies identity. So it's not just for drive-by ideas to pad a resume or finish a school project.
For those who do want to do this, I'd recommend writing an issue and/or reaching out to the developers to engage in a dialogue. This takes work but it will increase the likelihood of a PR being merged.
Disclaimer: I'm the primary developer of txtai (https://github.com/neuml/txtai), an open-source vector database + RAG framework
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Build knowledge graphs with LLM-driven entity extraction
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Bootstrap or VC?
Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.
With NeuML (https://neuml.com), I've went the bootstrapping route. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. It's a "live within your means" strategy.
VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.
I would say both have their pros and cons. Not all ideas have the luxury of time.
- txtai: An embeddings database for semantic search, graph networks and RAG
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Ask HN: What happened to startups, why is everything so polished?
I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.
With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.
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Are we at peak vector database?
I'll add txtai (https://github.com/neuml/txtai) to the list.
There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.
- Txtai: An all-in-one embeddings database for semantic search and LLM workflows
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Generate knowledge with Semantic Graphs and RAG
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Show HN: Open-source Rule-based PDF parser for RAG
Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.
Here's a couple examples:
- https://neuml.hashnode.dev/build-rag-pipelines-with-txtai
- https://neuml.hashnode.dev/extract-text-from-documents
Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).
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?
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
Milvus - A cloud-native vector database, storage for next generation AI applications
tika-python - Tika-Python is a Python binding to the Apache Tikaâ„¢ REST services allowing Tika to be called natively in the Python community.
faiss - A library for efficient similarity search and clustering of dense vectors.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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​.
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python