typesense-instantsearch-semanti
autofaiss
| typesense-instantsearch-semanti | autofaiss | |
|---|---|---|
| 1 | 3 | |
| - | 752 | |
| - | 2.3% | |
| - | 5.6 | |
| - | 17 days ago | |
| Python | ||
| - | Apache License 2.0 |
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typesense-instantsearch-semanti
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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...
autofaiss
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You Don't Need LangChain;
I might be wrong here. I just know some product quantization techniques, but you can reduce the index by a lot! However, from my research, the more size you reduce, the more retrieval quality is also reduced.
Quoting from https://github.com/criteo/autofaiss
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Cheapest Vector Database
Autofaiss - https://github.com/criteo/autofaiss can be configured to make extremely tiny and efficient indexes.
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Vector database built for scalable similarity search
Don't start with mullivus if you're learning. Too much yak shaving. Try https://github.com/criteo/autofaiss.
Also, TBH, it is a lot cheaper to run a simple faiss index.
What are some alternatives?
typesense-instantsearch-semantic-search-demo - A demo that shows how to build a semantic search experience with Typesense's vector search feature and Instantsearch.js
vespa - AI + Data, online. https://vespa.ai
pgvector - Open-source vector similarity search for Postgres
sqlite-vss - A SQLite extension for efficient vector search, based on Faiss!
milvus-lite - A lightweight version of Milvus
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Milvus - A cloud-native vector database, storage for next generation AI applications
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.
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.