{"id":134940,"date":"2026-09-07T13:22:26","date_gmt":"2026-09-07T07:52:26","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=134940"},"modified":"2026-09-07T13:22:29","modified_gmt":"2026-09-07T07:52:29","slug":"faiss-tutorial-similarity-search-library","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/faiss-tutorial-similarity-search-library\/","title":{"rendered":"FAISS Tutorial: Facebook&#8217;s Similarity Search Library"},"content":{"rendered":"\n<p>AI applications often need to quickly find information that is similar to a given query. <strong>FAISS Similarity Search<\/strong> is a library designed for efficient similarity search and clustering of dense vectors. It is widely useful in applications involving embeddings, recommendation systems, semantic search, and retrieval-augmented generation (RAG). This guide explains <strong>FAISS<\/strong>, how vector similarity search works, its key components, and practical applications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h2>\n\n\n\n<ul>\n<li>FAISS enables fast similarity search over vector embeddings.<\/li>\n\n\n\n<li>It is designed to handle large collections of vectors.<\/li>\n\n\n\n<li>Different indexes support different search requirements.<\/li>\n\n\n\n<li>FAISS is useful for semantic search and RAG.<\/li>\n\n\n\n<li>It can also support recommendation and clustering workflows.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Direct Answer<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table has-medium-font-size\"><table><tbody><tr><td><strong>FAISS Similarity Search<\/strong> is a library for efficiently searching and clustering dense vectors. It allows AI applications to find vectors that are most similar to a query vector, making it useful for semantic search, recommendation systems, image retrieval, and RAG applications. FAISS provides different indexing methods to balance search speed, memory usage, and retrieval accuracy.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why FAISS Matters for AI<\/strong><\/h2>\n\n\n\n<p>Modern AI models frequently convert text, images, and other data into embeddings. Searching millions of embeddings directly can become computationally expensive.<\/p>\n\n\n\n<p>FAISS helps make this process more efficient by providing optimized methods for:<\/p>\n\n\n\n<ul>\n<li>Vector similarity search<\/li>\n\n\n\n<li>Large-scale retrieval<\/li>\n\n\n\n<li>Embedding indexing<\/li>\n\n\n\n<li>Clustering<\/li>\n\n\n\n<li>Recommendation systems<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is FAISS?<\/strong><\/h2>\n\n\n\n<p><strong>FAISS (Facebook AI Similarity Search)<\/strong> is a library developed by Meta for efficient similarity search and clustering of dense vectors.<\/p>\n\n\n\n<p>Instead of comparing a query against every vector individually, FAISS provides indexing techniques that can make large-scale searches much faster.<\/p>\n\n\n\n<p>For example, an AI application could convert thousands of documents into embeddings and use FAISS to quickly retrieve the documents whose vectors are most similar to a user&#8217;s query.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How FAISS Similarity Search Works<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"672\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11-1200x672.webp\" alt=\"How FAISS Similarity Search Works\" class=\"wp-image-134942\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11-1200x672.webp 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11-300x168.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11-768x430.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11-1536x861.webp 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11-150x84.webp 150w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/How-11.webp 1676w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>A basic workflow involves several steps.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Generate Embeddings<\/strong><\/h3>\n\n\n\n<p>Convert documents, images, or other data into numerical vectors using an embedding model.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Create an Index<\/strong><\/h3>\n\n\n\n<p>Choose an appropriate FAISS index for the application&#8217;s search requirements.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Add Vectors<\/strong><\/h3>\n\n\n\n<p>Store the embedding vectors in the FAISS index.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 4: Convert the Query<\/strong><\/h3>\n\n\n\n<p>Transform the user&#8217;s query into an embedding using the same embedding approach.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 5: Search the Index<\/strong><\/h3>\n\n\n\n<p>FAISS compares the query vector with indexed vectors using a selected similarity or distance measure.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 6: Retrieve Results<\/strong><\/h3>\n\n\n\n<p>The closest vectors are returned, allowing the application to retrieve their associated documents or other data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key FAISS Concepts<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"645\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1-1200x645.webp\" alt=\"Key FAISS Concepts\" class=\"wp-image-134945\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1-1200x645.webp 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1-300x161.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1-768x413.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1-1536x825.webp 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1-150x81.webp 150w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/Key-1-1.webp 1711w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Vectors<\/strong><\/h3>\n\n\n\n<p>Numerical representations of data such as text, images, or other objects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Indexes<\/strong><\/h3>\n\n\n\n<p>Data structures that organize vectors for efficient similarity search.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Distance Metrics<\/strong><\/h3>\n\n\n\n<p>Methods used to determine how similar or different vectors are.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Nearest Neighbors<\/strong><\/h3>\n\n\n\n<p>Vectors that are closest to a given query according to the selected distance or similarity measure.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common FAISS Index Types<\/strong><\/h2>\n\n\n\n<p>FAISS provides multiple indexing approaches for different workloads.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Flat Index<\/strong><\/h3>\n\n\n\n<p>Performs an exact search across the stored vectors. It is straightforward and useful when accuracy is prioritized and the dataset is manageable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Inverted File Index<\/strong><\/h3>\n\n\n\n<p>Partitions vectors into groups and searches relevant groups rather than examining every vector.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Product Quantization<\/strong><\/h3>\n\n\n\n<p>Compresses vectors to reduce memory requirements and support efficient large-scale search.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>HNSW Index<\/strong><\/h3>\n\n\n\n<p>Uses a graph-based structure to perform efficient approximate nearest-neighbor searches.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAISS in a RAG Pipeline<\/strong><\/h2>\n\n\n\n<p>FAISS can serve as the vector-search component of a RAG system:<\/p>\n\n\n\n<p><strong>Documents \u2192 Chunking \u2192 Embeddings \u2192 FAISS Index \u2192 Similarity Search \u2192 Relevant Context \u2192 LLM<\/strong><\/p>\n\n\n\n<p>When a user submits a question, the system converts the query into an embedding, searches the FAISS index, retrieves relevant content, and provides that content to the LLM as context.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Applications<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Semantic Search<\/strong><\/h3>\n\n\n\n<p>Retrieve documents based on meaning rather than exact keyword matches.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Recommendation Systems<\/strong><\/h3>\n\n\n\n<p>Find items with embeddings similar to a user&#8217;s preferences or previous interactions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Image Retrieval<\/strong><\/h3>\n\n\n\n<p>Search for visually or semantically similar images using image embeddings.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>RAG Applications<\/strong><\/h3>\n\n\n\n<p>Retrieve relevant document chunks before generating an LLM response.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\"> \n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \nChoosing a FAISS index involves a trade-off between search speed, memory usage, and accuracy. Start with a simple index for smaller datasets and evaluate more advanced indexing methods when the scale or latency requirements demand them.\n\n\n\n\n\n\n\n\n\n\n \n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Benefits of FAISS Similarity Search<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Fast Retrieval<\/strong><\/h3>\n\n\n\n<p>FAISS is optimized for efficient vector similarity search.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Scalability<\/strong><\/h3>\n\n\n\n<p>It provides indexing methods designed for large collections of vectors.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Flexible Indexing<\/strong><\/h3>\n\n\n\n<p>Different index types allow developers to adapt search behavior to their workload.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Broad AI Applications<\/strong><\/h3>\n\n\n\n<p>FAISS can support semantic search, recommendations, image retrieval, and <a href=\"https:\/\/www.guvi.in\/blog\/guide-for-retrieval-augmented-generation\/\" target=\"_blank\" rel=\"noreferrer noopener\">RAG<\/a> systems.<\/p>\n\n\n\n<p>Professionals interested in vector databases, embeddings, RAG, and AI application development can strengthen their expertise through <strong>HCL GUVI&#8217;s <a href=\"https:\/\/www.guvi.in\/courses\/bundles\/artificial-intelligence-machine-learning\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=faiss-tutorial\" target=\"_blank\" rel=\"noreferrer noopener\">Artificial Intelligence and Machine Learning Course<\/a><\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>When Should You Use FAISS?<\/strong><\/h2>\n\n\n\n<p><strong>FAISS Similarity Search<\/strong> is useful when an AI application needs fast retrieval from a collection of dense vector embeddings.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Semantic Search<\/strong><\/h3>\n\n\n\n<p>Use FAISS to find documents or content that are semantically similar to a user&#8217;s query.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>RAG Applications<\/strong><\/h3>\n\n\n\n<p>Retrieve relevant document chunks before sending context to an <a href=\"https:\/\/www.guvi.in\/blog\/guide-to-large-language-models\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=FAISS+Tutorial%3A+Facebook%27s+Similarity+Search+Library\" target=\"_blank\" rel=\"noreferrer noopener\">LLM<\/a> for answer generation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Recommendation Systems<\/strong><\/h3>\n\n\n\n<p>Find products, articles, images, or other items with embeddings similar to a user&#8217;s preferences.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Image Retrieval<\/strong><\/h3>\n\n\n\n<p>Search large image collections using vector representations generated by vision models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Large-Scale Vector Search<\/strong><\/h3>\n\n\n\n<p>Use FAISS when searching large embedding collections requires efficient indexing and retrieval.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Concepts to Remember<\/strong><\/h2>\n\n\n\n<ul>\n<li><strong>Vectors<\/strong> represent data as numerical embeddings.<\/li>\n\n\n\n<li><strong>Indexes<\/strong> organize vectors for efficient search.<\/li>\n\n\n\n<li><strong>Distance metrics<\/strong> determine vector similarity.<\/li>\n\n\n\n<li><strong>Nearest neighbors<\/strong> are the vectors closest to a query.<\/li>\n\n\n\n<li>Different indexes provide different speed, memory, and accuracy trade-offs.<\/li>\n\n\n\n<li>FAISS can perform both exact and approximate similarity search.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A Practical FAISS Workflow<\/strong><\/h2>\n\n\n\n<p>A basic similarity-search system can follow these steps:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Prepare the Data<\/strong><\/h3>\n\n\n\n<p>Collect documents, images, products, or other content that needs to be searchable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Generate Embeddings<\/strong><\/h3>\n\n\n\n<p>Convert the data into numerical vectors using an appropriate embedding model.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Select an Index<\/strong><\/h3>\n\n\n\n<p>Choose a FAISS index based on dataset size, latency requirements, memory constraints, and desired search accuracy.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Add Vectors<\/strong><\/h3>\n\n\n\n<p>Store the generated embeddings in the FAISS index.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Embed the Query<\/strong><\/h3>\n\n\n\n<p>Convert the user&#8217;s search query into a vector using the same embedding approach.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6. Search the Index<\/strong><\/h3>\n\n\n\n<p>Run a nearest-neighbor search to identify the most similar vectors.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>7. Retrieve the Original Content<\/strong><\/h3>\n\n\n\n<p>Use the returned vector identifiers to retrieve the associated documents, images, or other information.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Real-World Applications<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Enterprise Search<\/strong><\/h3>\n\n\n\n<p>Search internal documents and knowledge bases using semantic similarity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>E-Commerce<\/strong><\/h3>\n\n\n\n<p>Find products that are semantically or visually similar to a user&#8217;s search or preferences.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Content Recommendation<\/strong><\/h3>\n\n\n\n<p>Retrieve articles, videos, or other content based on embedding similarity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>RAG Systems<\/strong><\/h3>\n\n\n\n<p>Retrieve relevant context from a document collection before generating an LLM response.<\/p>\n\n\n\n<p>The <strong>HCL GUVI&#8217;s Artificial Intelligence <\/strong><a href=\"https:\/\/www.guvi.in\/mlp\/genai-ebook\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=FAISS+Tutorial%3A+Facebook%27s+Similarity+Search+Library\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>eBook<\/strong><\/a> introduces artificial intelligence, machine learning, generative AI, and intelligent automation concepts, helping learners understand the technologies and workflows used to build modern AI applications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Best Practices<\/strong><\/h2>\n\n\n\n<ul>\n<li>Choose an embedding model suited to your data.<\/li>\n\n\n\n<li>Normalize vectors when required by the selected similarity metric.<\/li>\n\n\n\n<li>Select an index based on your scale and latency requirements.<\/li>\n\n\n\n<li>Benchmark retrieval quality before production deployment.<\/li>\n\n\n\n<li>Keep track of vector-to-document mappings.<\/li>\n\n\n\n<li>Test search results using representative queries.<\/li>\n\n\n\n<li>Rebuild or update indexes when the underlying embedding data changes.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p><strong>FAISS Similarity Search<\/strong> provides an efficient way to search large collections of vector embeddings. By combining embedding models with appropriate FAISS indexes, developers can build semantic search, recommendation, image retrieval, and RAG systems. The right index depends on the application&#8217;s scale, memory limits, search speed, and accuracy requirements, making benchmarking an important part of implementation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1787476973275\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. What is FAISS?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p><strong>FAISS (Facebook AI Similarity Search)<\/strong> is a library designed for efficient similarity search and clustering of dense vectors.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787476983805\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2. What is FAISS used for?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>FAISS is commonly used for semantic search, recommendation systems, image retrieval, embedding search, and RAG applications.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787476993757\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3. What is a FAISS index?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A FAISS index is a data structure used to organize vectors so that similarity searches can be performed efficiently.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787477003095\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4. What is nearest-neighbor search?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Nearest-neighbor search identifies vectors that are closest to a query vector according to a selected similarity or distance measure.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787477013792\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5. Can FAISS be used for RAG?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. FAISS can retrieve relevant document embeddings in a RAG pipeline, allowing an LLM to receive relevant context before generating an answer.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787477029111\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6. Which FAISS index should beginners use?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A simple exact-search index can be a useful starting point for smaller datasets. More advanced indexes can be evaluated as dataset size and performance requirements increase.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787477039461\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7. What affects FAISS search performance?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p><strong>Index type, dataset size, embedding dimensions, distance metric, hardware, and search configuration<\/strong> can all affect retrieval speed, memory usage, and search quality.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>AI applications often need to quickly find information that is similar to a given query. FAISS Similarity Search is a library designed for efficient similarity search and clustering of dense vectors. It is widely useful in applications involving embeddings, recommendation systems, semantic search, and retrieval-augmented generation (RAG). This guide explains FAISS, how vector similarity search [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":136108,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933],"tags":[],"views":"21","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/08\/FAISS-Tutorial-Facebooks-Similarity-Search-Library-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134940"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=134940"}],"version-history":[{"count":2,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134940\/revisions"}],"predecessor-version":[{"id":137490,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134940\/revisions\/137490"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/136108"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=134940"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=134940"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=134940"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}