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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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Every device that uses an IPv6 address will also compute and join a solicited node multicast group address. This address is required for IPv6 Neighbor Discovery which we use for layer two address discovery.All solicited node multicast group addresses start with FF02::1:FF /104:FF /8 is the IPv6 multicast range.FF02 /16 is the multicast link local scope.Let’s take a look on a Cisco IOS router to see what these solicited node multicast group addresses look like:R1(config)#interface FastEthernet 0/0R1(config-if)#ipv6 enableI just enabled IPv6 on an interface, this causes the router to create a link-local IPv6 address. It will also compute and join the solicited node multicast group address:R1#show ipv6 interface FastEthernet 0/0FastEthernet0/0 is up, line protocol ibs up IPv6 is enabled, link-local address is FE80::21D:A1FF:FE8B:36D0 No Virtual link-local address(es): No global unicast address is configured Joined group address(es): FF02::1 FF02::1:FF8B:36D0Above you can see that the router joined FF02::1:FF8B:36D0. The last 6 hexadecimal characters were copied from the link local address. Here’s a picture:Above you can see the complete uncompressed solicited node multicast address.I can configure multiple IPv6 addresses on the interface, if the last 6 hexadecimal characters are similar then there is no need to join another multicast address. For example, let’s configure an IPv6 unicast address:R1(config)#interface FastEthernet 0/0R1(config-if)#ipv6 address 2001:DB8:1212:1212::/64 eui-64I’ll use EUI-64 to generate the last 64 bits. Take a look at the joined group addresses:R1#show ipv6 interface FastEthernet 0/0FastEthernet0/0 is up, line protocol is up IPv6 is enabled, link-local address is FE80::21D:A1FF:FE8B:36D0 No Virtual link-local address(es): Global unicast address(es): 2001:DB8:1212:1212:21D:A1FF:FE8B:36D0, subnet is 2001:DB8:1212:1212::/64 [EUI] Joined group address(es): FF02::1 FF02::1:FF8B:36D0The last 64 bits of the link local and unicast address are the same so the solicited node multicast group address remains the same. If we configure an IPv6 address where the last 6 hexadecimal characters are different then the router will join another multicast group. Let’s try that:. Compiling node.js on 32-bit system. 4. Azure Website Node.JS doesn't run 64 bit. 16. How do I build 32-bit binaries on a 64-bit system using npm? 1. Node.js and 64-bit varints. 1. Create Node Js Executable App for Mac. 2. Is there way to indicate architecture (32 or 64 bit) when configuring node js in azure? 1.

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IPV6 has been developed to replace IPV4 which is running out of addresses.Although it has been around almost 10 years it is still not widely deployed and supported.However adoption rates are increasing rapidly and IPv6 traffic crossed the 10% threshold in February 2016 (wiki) .For small business/home and home office networks it is likely to be many years before IPV6 becomes an issue.All modern computers and mobile phones support both IPv4 and IPv6, and if you look at your device IP addresses you will probably see both.In this tutorial I want to take a quick look at IPv6 addresses, and how they relate to IPv4 addresses.Therefore it will help if you are already familiar with IPv4 -See IPv4 Addressing and classes for BeginnersIPv6 addressesAn Ipv6 address uses 128 bits as opposed to 32 bits in IPv4.IPv6 addresses are written using hexadecimal, as opposed to dotted decimal in IPv4. See Binary numbers explainedBecause an hexadecimal number uses 4 bits this means that an IPv6 address consists of 32 hexadecimal numbers.These numbers are grouped in 4’s giving 8 groups or blocks. The groups are written with a : (colon) as a separator.group1:group2: ……etc…. :group8Here is an IPv6 address example:Note: Because of the length of IPv6 addresses various shortening techniques are employed.The main technique being to omit repetitive 0’s as shown in the example above.Network And Node AddressesIn IPv4 an address is split into two components a network component and a node component.This was done initially using Address classes and later using subnet masking.In IPv6 we do the same. The first step is to split the address into two parts.The address is split into 2 64 bit segments the top 64 bits is the network part and the lower 64 bits the node part:The upper 64 bits are used for routing.The lower 64 bits identify the address of the interface or node, and is derived from the actual physical or MAC address using IEEE’s Extended Unique Identifier (EUI-64) format. See this Wiki description for exact details.If we look at the upper 64 bits in more detail we can see that it is split into 2 blocks of 48 and 16 bits respectively the lower 16 bits are used for subnets on an internal networks, and are controlled by a network administrator.The upper 48 bits are used for the global network addresses and are for routing over the internet.Address Types and ScopeIPv6 addresses have three types:Global Unicast Address –Scope Internet- routed on InternetUnique Local — Scope Internal Network or VPN internally routable, but Not routed on InternetLink Local – Scope network link- Not Routed internally or externally.Global and Public AddressesGlobal addresses are routable on the internet and start with 2001:These addresses are known as

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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