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Query");console.log(results);Example of a binary hamming distance query over BigUint64Array (64 bits processed at a time using pure JS):(Insanely Fast) Query Binary Embeddings Using Hamming Distance Over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles)The WebAssembly SIMD implementation processes 128 bits per iteration (via v128.xor) compared to 64 bits per iteration in the JavaScript implementation using BigUint64Array. This alone gives a theoretical 2x speedup.However SIMD instructions execute XOR, popcount, and similar operations on multiple data lanes in parallel. This reduces the number of CPU cycles required for the same amount of work compared to sequential bitwise operations in JavaScript. SIMD in WebAssembly is likely 2x to 4x faster or more over big vectors.Check haming_distance_simd.wat for the WASM source code. Compiled using wat2wasm.const results = await db.queryBinarySIMD( "Find similar texts based on this query");console.log(results);Example of a binary hamming distance query over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles):The logs show offsets (0, 16, 32), which means the code processes 128 bits (16 bytes) at a time. Since the total vector is 384 bits, it takes 3 steps (384 ÷ 128 = 3), confirming 128-bit SIMD processing.For NextjsIf you're using Next.js, you may need to configure Webpack to work with Transformers.js. Add the following to your next.config.js file: { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },"> webpack: (config) => { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },ContributingFeel free to fork the repository, create issues, and submit pull requests. We welcome

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Called by the source) "label": "f", "file": "...\\input-scripts\\simple-scripts\\functioncall-arithmetic.js", "start": { # The start point of the target node with row-column based position.. "row": 3, "column": 0 }, "end": { # The end point of the target node with row-column based position. "row": 5, "column": 1 }, "range": { # The position of the target node in index-based representation. "start": 14, "end": 51 } } }]Filter file formatAny valid regular expression can be specified in the filter file. The order of the including and excluding lines are important since they are processed sequentially.The first character of each line represents the type of the filtering:# Comment line- Exclude+ IncludeAn example for filtering:# Filter out all source files starting with "test":-test[^\.]*.js# But test576.js is needed:+test576.js# Furthermore, files beginning with "test1742" are also needed:+test1742[^\.]*.js# Finally, test1742_b.js is not needed:-test1742_b.jsList of arguments-h : List of command line arguments--fg : print flow graph--cg : print call graph--time : print timings--strategy : interprocedural propagation strategy; one of NONE, ONESHOT (default), DEMAND, and FULL (not yet implemented)--countCB : Counts the number of callbacks.--reqJs : Make a RequireJS dependency graph.--output : The output file name into which the result JSON should be saved. (extension: .json)--filter : Path to the filter file.ContributingLooking to contribute something? Here's how you can help.LicenseThis code is licensed under the Eclipse Public License (v2.0), a copy of which is included in this repository in file LICENSE.

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DatabaseIf you need to update an existing vector in the database:await db.update("1234", { vector: [0.4, 0.5, 0.6], // Updated vector data metadata: { name: "Updated Item" }, // Additional updated data});Deleting DataYou can delete a vector by its key.Experimental: Binary VectorsWhile querying vectors by cosine similarity is already extremely fast, sometimes you want to go faster than light. Binary vectors are simplified versions of dense vectors where each value is turned into either a 0 or a 1 by comparing it to the middle value (median). This makes them smaller to store and faster to compare, which helps when working with a lot of data.Note that this simplification can reduce the quality of the results because some detailed information in the original dense vector is lost. Use it for very long searches. For example, the set of vectors produced by all-MiniLM-L6-v2:[ -0.020319879055023193, 0.07605013996362686, 0.020568927749991417, ...]after being binarized becomes:[ 0, 1, 1, ...]For better JS processing, the binary vectors are packed into 64-bit integers (e.g., using BigUint64Array).Each 64-bit integer represents 64 binary values, and we use the XOR operation on 64-bit integers to find mismatched bits and then count the 1s.Inserting Data and Generate Binarized EmbeddingsTo insert data to be vectorized and then binarized, use insertBinary.Note: to query over binarized vectors, use queryBinary or queryBinarySIMD.await db.insertBinary({ text: "This is a sample text to embed and binarize",});(Very Fast) Query Binary Embeddings Using Hamming Distance Over Native JS (64 bits at a time max)To query over binarized vectors use queryBinary.While cosine similarity measures the angle between two vectors in a multi-dimensional space, Hamming distance counts the number of positions where two binary vectors differ. It measures dissimilarity as a simple count of mismatches. For binarized vectors Hamming really is the tool for the job.const results = await db.queryBinary("Find similar texts based on this