237 lines
9.2 KiB
V
237 lines
9.2 KiB
V
module meilisearch
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import freeflowuniverse.herolib.core.httpconnection
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import x.json2
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import json
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// add_documents adds documents to an index
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pub fn (mut client MeilisearchClient) add_documents[T](uid string, documents []T) !AddDocumentResponse {
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/documents'
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method: .post
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data: json2.encode(documents)
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}
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mut http := client.httpclient()!
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response := http.post_json_str(req)!
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return json2.decode[AddDocumentResponse](response)!
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}
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@[params]
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struct GetDocumentArgs {
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pub mut:
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uid string @[required]
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document_id int @[required]
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fields []string
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retrieve_vectors bool @[json: 'retrieveVectors']
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}
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// get_document retrieves one document by its id
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pub fn (mut client MeilisearchClient) get_document[T](args GetDocumentArgs) !T {
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mut params := map[string]string{}
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if args.fields.len > 0 {
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params['fields'] = args.fields.join(',')
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}
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params['retrieveVectors'] = args.retrieve_vectors.str()
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req := httpconnection.Request{
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prefix: 'indexes/${args.uid}/documents/${args.document_id}'
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params: params
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}
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mut http := client.httpclient()!
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response := http.get_json(req)!
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return json.decode(T, response)
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}
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// get_documents retrieves documents with optional parameters
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pub fn (mut client MeilisearchClient) get_documents[T](uid string, query DocumentsQuery) ![]T {
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mut params := map[string]string{}
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params['limit'] = query.limit.str()
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params['offset'] = query.offset.str()
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if query.fields.len > 0 {
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params['fields'] = query.fields.join(',')
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}
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if query.filter.len > 0 {
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params['filter'] = query.filter
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}
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if query.sort.len > 0 {
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params['sort'] = query.sort.join(',')
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}
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/documents'
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params: params
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}
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mut http := client.httpclient()!
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response := http.get_json(req)!
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decoded := json.decode(ListResponse[T], response)!
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return decoded.results
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}
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@[params]
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struct DeleteDocumentArgs {
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pub mut:
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uid string @[required]
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document_id int @[required]
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}
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// delete_document deletes one document by its id
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pub fn (mut client MeilisearchClient) delete_document(args DeleteDocumentArgs) !DeleteDocumentResponse {
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req := httpconnection.Request{
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prefix: 'indexes/${args.uid}/documents/${args.document_id}'
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method: .delete
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}
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mut http := client.httpclient()!
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response := http.delete(req)!
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return json2.decode[DeleteDocumentResponse](response)!
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}
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// delete_all_documents deletes all documents in an index
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pub fn (mut client MeilisearchClient) delete_all_documents(uid string) !DeleteDocumentResponse {
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/documents'
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method: .delete
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}
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mut http := client.httpclient()!
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response := http.delete(req)!
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return json2.decode[DeleteDocumentResponse](response)!
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}
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// update_documents updates documents in an index
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pub fn (mut client MeilisearchClient) update_documents(uid string, documents string) !TaskInfo {
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/documents'
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method: .put
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data: documents
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}
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mut http := client.httpclient()!
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response := http.post_json_str(req)!
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return json2.decode[TaskInfo](response)!
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}
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@[params]
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struct SearchArgs {
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pub mut:
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q string @[json: 'q'; required]
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offset int @[json: 'offset']
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limit int = 20 @[json: 'limit']
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hits_per_page int = 1 @[json: 'hitsPerPage']
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page int = 1 @[json: 'page']
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filter ?string
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facets ?[]string
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attributes_to_retrieve []string = ['*'] @[json: 'attributesToRetrieve']
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attributes_to_crop ?[]string @[json: 'attributesToCrop']
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crop_length int = 10 @[json: 'cropLength']
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crop_marker string = '...' @[json: 'cropMarker']
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attributes_to_highlight ?[]string @[json: 'attributesToHighlight']
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highlight_pre_tag string = '<em>' @[json: 'highlightPreTag']
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highlight_post_tag string = '</em>' @[json: 'highlightPostTag']
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show_matches_position bool @[json: 'showMatchesPosition']
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sort ?[]string
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matching_strategy string = 'last' @[json: 'matchingStrategy']
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show_ranking_score bool @[json: 'showRankingScore']
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show_ranking_score_details bool @[json: 'showRankingScoreDetails']
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ranking_score_threshold ?f64 @[json: 'rankingScoreThreshold']
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attributes_to_search_on []string = ['*'] @[json: 'attributesToSearchOn']
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hybrid ?map[string]string
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vector ?[]f64
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retrieve_vectors bool @[json: 'retrieveVectors']
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locales ?[]string
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}
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// search performs a search query on an index
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pub fn (mut client MeilisearchClient) search[T](uid string, args SearchArgs) !SearchResponse[T] {
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/search'
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method: .post
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data: json.encode(args)
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}
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mut http := client.httpclient()!
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rsponse := http.post_json_str(req)!
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return json.decode(SearchResponse[T], rsponse)
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}
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@[params]
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struct FacetSearchArgs {
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facet_name ?string @[json: 'facetName'] // Facet name to search values on
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facet_query ?string @[json: 'facetQuery'] // Search query for a given facet value. Defaults to placeholder search if not specified.
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q string // Query string
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filter ?string // Filter queries by an attribute's value
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matching_strategy string = 'last' @[json: 'matchingStrategy'] // Strategy used to match query terms within documents
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attributes_to_search_on ?[]string @[json: 'attributesToSearchOn'] // Restrict search to the specified attributes
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}
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@[params]
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struct FacetSearchHitsResponse {
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value string @[json: 'value'] // Facet value matching the facetQuery
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count int @[json: 'count'] // Number of documents with a facet value matching value
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}
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@[params]
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struct FacetSearchResponse {
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facet_hits []FacetSearchHitsResponse @[json: 'facetHits'] // Facet value matching the facetQuery
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facet_query string @[json: 'facetQuery'] // The original facetQuery
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processing_time_ms int @[json: 'processingTimeMs'] // Processing time of the query
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}
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pub fn (mut client MeilisearchClient) facet_search(uid string, args FacetSearchArgs) !FacetSearchResponse {
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/facet-search'
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method: .post
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data: json.encode(args)
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}
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mut http := client.httpclient()!
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rsponse := http.post_json_str(req)!
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return json.decode(FacetSearchResponse, rsponse)
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}
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@[params]
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struct SimilarDocumentsArgs {
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id SimilarDocumentsID @[json: 'id'] // Identifier of the target document (mandatory)
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embedder string = 'default' @[json: 'embedder'] // Embedder to use when computing recommendations
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attributes_to_retrieve []string = ['*'] @[json: 'attributesToRetrieve'] // Attributes to display in the returned documents
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offset int @[json: 'offset'] // Number of documents to skip
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limit int = 20 @[json: 'limit'] // Maximum number of documents returned
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filter ?string @[json: 'filter'] // Filter queries by an attribute's value
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show_ranking_score bool @[json: 'showRankingScore'] // Display the global ranking score of a document
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show_ranking_score_details bool @[json: 'showRankingScoreDetails'] // Display detailed ranking score information
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ranking_score_threshold ?f64 @[json: 'rankingScoreThreshold'] // Exclude results with low ranking scores
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retrieve_vectors bool @[json: 'retrieveVectors'] // Return document vector data
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}
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type SimilarDocumentsID = string | int
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@[params]
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struct SimilarDocumentsResponse {
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hits []SimilarDocumentsHit @[json: 'hits'] // List of hit items
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id string @[json: 'id'] // Identifier of the response
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processing_time_ms int @[json: 'processingTimeMs'] // Processing time in milliseconds
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limit int = 20 @[json: 'limit'] // Maximum number of documents returned
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offset int @[json: 'offset'] // Number of documents to skip
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estimated_total_hits int @[json: 'estimatedTotalHits'] // Estimated total number of hits
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}
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struct SimilarDocumentsHit {
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id SimilarDocumentsID @[json: 'id'] // Identifier of the hit item
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title string @[json: 'title'] // Title of the hit item
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}
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pub fn (mut client MeilisearchClient) similar_documents(uid string, args SimilarDocumentsArgs) !SimilarDocumentsResponse {
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req := httpconnection.Request{
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prefix: 'indexes/${uid}/similar'
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method: .post
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data: json.encode(args)
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}
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res := client.enable_eperimental_feature(vector_store: true)! // Enable the feature first.
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mut http := client.httpclient()!
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rsponse := http.post_json_str(req)!
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println('rsponse: ${rsponse}')
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return json.decode(SimilarDocumentsResponse, rsponse)
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}
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