Query a knowledge base for text chunks that are most semantically relevant to the query.
Details
Given a query expressed as an embedding, this API runs a similarity search amongst the embeddings indexed in the knowledge base to find the most relevant chunk embeddings. To use this API, specify the knowledge_base_id of the knowledge base you want to query, pass in the natural language query that you want to search for, specify the value top_k, which is the number of similar chunks that will be returned, and specify whether you want the returned chunks to include_embeddings.
Similarity search is used to efficiently find, retrieve, and rank chunks based on their similarity to a given query, which is also expressed as an embedding. Similarity scores ( using the cosine similarity metric) are calculated between each chunk embedding and the embedded query, and the chunks are ranked based on similarity score. The top-ranked chunks are returned as the query results.
We are using the Hierarchical Navigable Small World (HNSW) algorithm to perform a k nearest neighbors search in the vector space. This algorithm returns an estimate of the best k nearest neighbors and is optimized for datasets with hundreds of thousands of vectors. You can read more about the specifics of this algorithm here.
Backwards Compatibility
V2 and V1 Knowledge Bases are entirely separate and not backwards compatible. Users who have existing V1 knowledge bases will need to migrate their data to V2 knowledge bases.
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AI","navMaps":{"firstHrefInVersion":{"V5":{"href":"/docs/v5/overview","matchLevel":4},"V4 (legacy)":{"href":"/reference/v4/knowledge-bases/query-relevant-chunks","matchLevel":0}},"firstHrefInLanguage":{}},"lastModified":"2026-05-29T12:48:38.054Z","originalFileLocation":"reference/v4/knowledge-bases/query-relevant-chunks.mdx"},"headHtml":"\u003cscript data-mint-head>(function(){var d=document.documentElement,p=null;if(!p){try{p=localStorage.getItem(\"isDarkMode\")}catch(e){}if(p==='true')p='dark';else if(p==='false')p='light';if(p!=='light'\u0026\u0026p!=='dark'\u0026\u0026p!=='system')p=\"system\"}var r=p==='system'?(matchMedia(\"(prefers-color-scheme: dark)\").matches?'dark':'light'):p;d.classList.remove(r==='dark'?'light':'dark');d.classList.add(r);d.setAttribute(\"data-theme-preference\",p)})();\u003c/script>\u003cmeta data-mint-head name=\"description\" content=\"### Description Query a knowledge base for text chunks that are most semantically relevant to the query. ### Details Given a query expressed as an embedding, this API runs a similarity search amongst the embeddings indexed in the knowledge base to find the most relevant chunk embeddings. To use this API, specify the knowledge_base_id of the knowledge base you want to query, pass in the natural language query that you want to search for, specify the value top_k, which is the number of similar chunks that will be returned, and specify whether you want the returned chunks to include_embeddings. Similarity search is used to efficiently find, retrieve, and rank chunks based on their similarity to a given query, which is also expressed as an embedding. Similarity scores ( using the cosine similarity metric) are calculated between each chunk embedding and the embedded query, and the chunks are ranked based on similarity score. The top-ranked chunks are returned as the query results. We are using the Hierarchical Navigable Small World (HNSW) algorithm to perform a k nearest neighbors search in the vector space. This algorithm returns an estimate of the best k nearest neighbors and is optimized for datasets with hundreds of thousands of vectors. You can read more about the specifics of this algorithm here. #### Backwards Compatibility V2 and V1 Knowledge Bases are entirely separate and not backwards compatible. Users who have existing V1 knowledge bases will need to migrate their data to V2 knowledge bases.\">\u003cmeta data-mint-head name=\"application-name\" content=\"Scale AI\">\u003cmeta data-mint-head name=\"generator\" content=\"Mintlify\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"192x192\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon/android-chrome-192x192.png\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"apple-touch-icon\" type=\"image/png\" sizes=\"180x180\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon/apple-touch-icon.png\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"16x16\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon/favicon-16x16.png\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"32x32\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon/favicon-32x32.png\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"shortcut icon\" type=\"image/x-icon\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon/favicon.ico\" media=\"(prefers-color-scheme: light)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"192x192\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon-dark/android-chrome-192x192.png\" media=\"(prefers-color-scheme: dark)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"16x16\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon-dark/favicon-16x16.png\" media=\"(prefers-color-scheme: dark)\">\u003clink data-mint-head rel=\"icon\" type=\"image/png\" sizes=\"32x32\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon-dark/favicon-32x32.png\" media=\"(prefers-color-scheme: dark)\">\u003clink data-mint-head rel=\"shortcut icon\" type=\"image/x-icon\" href=\"/mintlify-assets/_mintlify/favicons/scalegp/UksUEh3sxckFW0X-/_generated/favicon-dark/favicon.ico\" media=\"(prefers-color-scheme: dark)\">\u003cmeta data-mint-head property=\"og:title\" content=\"Query Relevant Chunks - Scale AI\">\u003cmeta data-mint-head property=\"og:description\" content=\"### Description Query a knowledge base for text chunks that are most semantically relevant to the query. ### Details Given a query expressed as an embedding, this API runs a similarity search amongst the embeddings indexed in the knowledge base to find the most relevant chunk embeddings. To use this API, specify the knowledge_base_id of the knowledge base you want to query, pass in the natural language query that you want to search for, specify the value top_k, which is the number of similar chunks that will be returned, and specify whether you want the returned chunks to include_embeddings. Similarity search is used to efficiently find, retrieve, and rank chunks based on their similarity to a given query, which is also expressed as an embedding. Similarity scores ( using the cosine similarity metric) are calculated between each chunk embedding and the embedded query, and the chunks are ranked based on similarity score. The top-ranked chunks are returned as the query results. We are using the Hierarchical Navigable Small World (HNSW) algorithm to perform a k nearest neighbors search in the vector space. This algorithm returns an estimate of the best k nearest neighbors and is optimized for datasets with hundreds of thousands of vectors. You can read more about the specifics of this algorithm here. #### Backwards Compatibility V2 and V1 Knowledge Bases are entirely separate and not backwards compatible. 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To use this API, specify the knowledge_base_id of the knowledge base you want to query, pass in the natural language query that you want to search for, specify the value top_k, which is the number of similar chunks that will be returned, and specify whether you want the returned chunks to include_embeddings. Similarity search is used to efficiently find, retrieve, and rank chunks based on their similarity to a given query, which is also expressed as an embedding. Similarity scores ( using the cosine similarity metric) are calculated between each chunk embedding and the embedded query, and the chunks are ranked based on similarity score. The top-ranked chunks are returned as the query results. We are using the Hierarchical Navigable Small World (HNSW) algorithm to perform a k nearest neighbors search in the vector space. This algorithm returns an estimate of the best k nearest neighbors and is optimized for datasets with hundreds of thousands of vectors. You can read more about the specifics of this algorithm here. #### Backwards Compatibility V2 and V1 Knowledge Bases are entirely separate and not backwards compatible. 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This API will query from the Vector Database using\nthe passed in filters and optionally can return the embeddings.\n\n ### Details\n This API can be used to get a list of chunks from a knowledge base. Given a chunk id, a metadata field and value, or both, matching chunks are searched for in the knowledge base given by knowledge base id.","deprecated":null,"version":null,"openapi":"https://api.sgp.scale.com/openapi-versions/v4/openapi.json get /v4/knowledge-bases/{knowledge_base_id}/chunks","href":"/reference/v4/knowledge-bases/fetch-chunks-by-id-or-metadata"},{"title":"Query Relevant Chunks","deprecated":null,"version":null,"openapi":"https://api.sgp.scale.com/openapi-versions/v4/openapi.json post /v4/knowledge-bases/{knowledge_base_id}/query","href":"/reference/v4/knowledge-bases/query-relevant-chunks"},{"title":"Delete Data Source Connection","description":"Removes a data source connection from a knowledge base, asynchronously deleting all artifacts and chunks that were ingested from that data source. Pass dry_run=true to preview the operation without deleting anything: the response's artifacts_to_delete map reports how many artifacts would be removed, and no workflow is started. 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get /v4/knowledge-bases/{knowledge_base_id}/artifacts","href":"/reference/v4/knowledge-bases/list-tracked-artifacts"},{"title":"Get Tracked Artifact Details","deprecated":null,"version":null,"openapi":"https://api.sgp.scale.com/openapi-versions/v4/openapi.json get /v4/knowledge-bases/{knowledge_base_id}/artifacts/{artifact_id}","href":"/reference/v4/knowledge-bases/get-tracked-artifact-details"},{"title":"Delete Locally Stored Artifact","deprecated":null,"version":null,"openapi":"https://api.sgp.scale.com/openapi-versions/v4/openapi.json delete /v4/knowledge-bases/{knowledge_base_id}/artifacts/{artifact_id}","href":"/reference/v4/knowledge-bases/delete-locally-stored-artifact"},{"title":"Patch Artifact Information","deprecated":null,"version":null,"openapi":"https://api.sgp.scale.com/openapi-versions/v4/openapi.json patch /v4/knowledge-bases/{knowledge_base_id}/artifacts/{artifact_id}","href":"/reference/v4/knowledge-bases/patch-artifact-information"},{"title":"List Chunks of Artifacts 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