🚀😊 Introducing sqlite-vec v0.1.0: A SQLite extension for Vector Database 😊🚀

Publicado em: 05 Setembro 2024
no canal de: Dilip Dubey

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Welcome

The first “stable” v0.1.0 release of sqlite-vec is out!!! I have tried it on Google Colab and tried to cover important points in Question-Answer format.

Q. What is sqlite-vec?
Ans: sqlite-vec is a vector search extension for the SQLite database that adds vector search functionality. This SQLite extension is written entirely in C with no dependencies, extremely portable, works in most operating systems and environments, and is MIT/Apache-2.0 dual licensed.

Q: Who and when sqlite-vec could be used?
Ans: Most of the real-world AI applications does not have billions of vectors. Instead, these deals with thousands or maybe hundreds of vectors. ANN (approximate nearest neighbors) are used for indexing which in turn slower down the search operation. sqlite-vec uses fast brute-force search. So, for small applications (or databases), sqlite-vec could be used.

Q: What are the advantages of sqlite-vec
Ans: Followings are a few advantages of sqlite-vec:
1. No other dependencies
2. Uses virtual tables
3. Stores chunks of vector in shadow tables,
4. Avoids loading everything into memory at once.

Q: What is Vector Quantization? Why is it important? How does vector quantization mak
Ans: “Vector quantization” is a vector compression technique. Let us understand by following example.
Every vector in float vector takes up to 4 bytes of space. Suppose we have to store 1 million vectors and length of each vector is 1500-dimensions. In such way, 1500 x 4 x 1e6 = 6 GB space size is required. Sqlite-vec supports bit-vector alongside float vector. In other words, in sqlite-vec, one vector is represented by 1 bit. So, size is reduced by a factor of 32.

Q: What are the consequences of vector quantization?
Ans: Vector quantization or any other vector compression technique causes quality loss. Depending upon the embedding model, accuracy will be reduced by ~5-10% but query will be faster by ~10x times.

Q: What are the other advantages of using sqlite-vec?
Ans: It runs everywhere. sqlite-vec works for MacOS, Linux, and Windows. It runs in the web browser with WebAssembly, in command line tools, and inside web applications on the server. It compiles successfully on Android and theoretically on iOS.

Q: What indexing technique is used in sqlite-vec?
Ans: Currently, sqlite-vec supports brute-force search only.

Thank you for watching my video. Please feel free to contact at todilipdubey@gmail.com for such interesting projects.

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