typesense-instantsearch-semantic-search-demo
towhee
typesense-instantsearch-semantic-search-demo | towhee | |
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1 | 26 | |
22 | 3,010 | |
- | 2.3% | |
3.0 | 8.6 | |
9 months ago | 4 months ago | |
JavaScript | Python | |
Apache License 2.0 | Apache License 2.0 |
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typesense-instantsearch-semantic-search-demo
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Vector database built for scalable similarity search
We added HNSW-based vector search to Typesense as well recently: https://typesense.org/docs/0.24.0/api/vector-search.html
So you can combine attribute-based filters along with nearest-neighbor search.
Put together this semantic search + filtering demo just last week: https://github.com/typesense/typesense-instantsearch-semanti...
towhee
- FLaNK Stack Weekly for 14 Aug 2023
- Welcome to generate your embeddings with Towhee
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Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
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What Is DocArray?
The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).
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Deep Dive into Real-World Image Search Engine with Python
Benchmarking the models with towhee is as simple as:
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A quick tip on DataFrame.apply
The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.
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Build an Image Search Engine in Minutes
I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:
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Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
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A python framework for unstructured data processing
You can check the result from the tutorial.
What are some alternatives?
sqlite-vss - A SQLite extension for efficient vector search, based on Faiss!
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
autofaiss - Automatically create Faiss knn indices with the most optimal similarity search parameters.
Milvus - A cloud-native vector database, storage for next generation AI applications
milvus-lite - A lightweight version of Milvus
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
awesome-vector-search - Collections of vector search related libraries, service and research papers
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
pgvector - Open-source vector similarity search for Postgres
AI - Artificial Intelligence Projects
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.