> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://api.qdrant.tech/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://api.qdrant.tech/_mcp/server. # Query points POST http://localhost:6333/collections/{collection_name}/points/query Content-Type: application/json Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries. Reference: https://api.qdrant.tech/api-reference/search/query-points ## OpenAPI Specification ```yaml openapi: 3.1.0 info: title: API version: 1.0.0 paths: /collections/{collection_name}/points/query: post: operationId: query-points summary: Query points description: >- Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries. tags: - subpackage_search parameters: - name: collection_name in: path description: Name of the collection to query required: true schema: type: string - name: consistency in: query description: Define read consistency guarantees for the operation required: false schema: $ref: '#/components/schemas/ReadConsistency' - name: timeout in: query description: If set, overrides global timeout for this request. Unit is seconds. required: false schema: type: integer - name: api-key in: header required: true schema: type: string responses: '200': description: successful operation content: application/json: schema: $ref: '#/components/schemas/Search_query_points_Response_200' requestBody: description: Describes the query to make to the collection content: application/json: schema: $ref: '#/components/schemas/QueryRequest' servers: - url: http://localhost:6333 description: http - url: https://localhost:6333 description: https components: schemas: ReadConsistencyType: type: string enum: - majority - quorum - all description: >- * `majority` - send N/2+1 random request and return points, which present on all of them * `quorum` - send requests to all nodes and return points which present on majority of nodes * `all` - send requests to all nodes and return points which present on all nodes title: ReadConsistencyType ReadConsistency: oneOf: - type: integer - $ref: '#/components/schemas/ReadConsistencyType' description: >- Read consistency parameter Defines how many replicas should be queried to get the result * `N` - send N random request and return points, which present on all of them * `majority` - send N/2+1 random request and return points, which present on all of them * `quorum` - send requests to all nodes and return points which present on majority of them * `all` - send requests to all nodes and return points which present on all of them Default value is `Factor(1)` title: ReadConsistency ShardKey: oneOf: - type: string - type: integer format: uint64 title: ShardKey ShardKeySelector1: type: array items: $ref: '#/components/schemas/ShardKey' title: ShardKeySelector1 ShardKeyWithFallback: type: object properties: target: $ref: '#/components/schemas/ShardKey' fallback: $ref: '#/components/schemas/ShardKey' required: - target - fallback title: ShardKeyWithFallback ShardKeySelector: oneOf: - $ref: '#/components/schemas/ShardKey' - $ref: '#/components/schemas/ShardKeySelector1' - $ref: '#/components/schemas/ShardKeyWithFallback' title: ShardKeySelector QueryRequestShardKey: oneOf: - $ref: '#/components/schemas/ShardKeySelector' - description: Any type title: QueryRequestShardKey PrefetchPrefetch1: type: array items: $ref: '#/components/schemas/Prefetch' title: PrefetchPrefetch1 PrefetchPrefetch: oneOf: - $ref: '#/components/schemas/Prefetch' - $ref: '#/components/schemas/PrefetchPrefetch1' - description: Any type description: >- Sub-requests to perform first. If present, the query will be performed on the results of the prefetches. title: PrefetchPrefetch SparseVector: type: object properties: indices: type: array items: type: integer format: uint description: Indices must be unique values: type: array items: type: number format: double description: Values and indices must be the same length required: - indices - values description: Sparse vector structure title: SparseVector ExtendedPointId: oneOf: - type: integer format: uint64 - type: string format: uuid description: Type, used for specifying point ID in user interface title: ExtendedPointId TokenizerType: type: string enum: - prefix - whitespace - word - multilingual title: TokenizerType Language: type: string enum: - arabic - azerbaijani - basque - bengali - catalan - chinese - danish - dutch - english - finnish - french - german - greek - hebrew - hinglish - hungarian - indonesian - italian - japanese - kazakh - nepali - norwegian - portuguese - romanian - russian - slovene - spanish - swedish - tajik - turkish title: Language StopwordsSet: type: object properties: languages: type: - array - 'null' items: $ref: '#/components/schemas/Language' description: >- Set of languages to use for stopwords. Multiple pre-defined lists of stopwords can be combined. custom: type: - array - 'null' items: type: string description: Custom stopwords set. Will be merged with the languages set. title: StopwordsSet StopwordsInterface: oneOf: - $ref: '#/components/schemas/Language' - $ref: '#/components/schemas/StopwordsSet' title: StopwordsInterface Bm25ConfigStopwords: oneOf: - $ref: '#/components/schemas/StopwordsInterface' - description: Any type description: >- Configuration of the stopwords filter. Supports list of pre-defined languages and custom stopwords. Default: initialized for specified `language` or English if not specified. title: Bm25ConfigStopwords Snowball: type: string enum: - snowball title: Snowball SnowballLanguage: type: string enum: - arabic - armenian - danish - dutch - english - finnish - french - german - greek - hungarian - italian - norwegian - portuguese - romanian - russian - spanish - swedish - tamil - turkish description: Languages supported by snowball stemmer. title: SnowballLanguage SnowballParams: type: object properties: type: $ref: '#/components/schemas/Snowball' language: $ref: '#/components/schemas/SnowballLanguage' required: - type - language title: SnowballParams StemmingAlgorithm: oneOf: - $ref: '#/components/schemas/SnowballParams' description: Different stemming algorithms with their configs. title: StemmingAlgorithm Bm25ConfigStemmer: oneOf: - $ref: '#/components/schemas/StemmingAlgorithm' - description: Any type description: >- Configuration of the stemmer. Processes tokens to their root form. Default: initialized Snowball stemmer for specified `language` or English if not specified. title: Bm25ConfigStemmer Bm25Config: type: object properties: k: type: number format: double default: 1.2 description: >- Controls term frequency saturation. Higher values mean term frequency has more impact. Default is 1.2 b: type: number format: double default: 0.75 description: >- Controls document length normalization. Ranges from 0 (no normalization) to 1 (full normalization). Higher values mean longer documents have less impact. Default is 0.75. avg_len: type: number format: double default: 256 description: Expected average document length in the collection. Default is 256. tokenizer: $ref: '#/components/schemas/TokenizerType' language: type: - string - 'null' description: >- Defines which language to use for text preprocessing. This parameter is used to construct default stopwords filter and stemmer. To disable language-specific processing, set this to `"language": "none"`. If not specified, English is assumed. lowercase: type: - boolean - 'null' description: Lowercase the text before tokenization. Default is `true`. ascii_folding: type: - boolean - 'null' description: >- If true, normalize tokens by folding accented characters to ASCII (e.g., "ação" -> "acao"). Default is `false`. stopwords: $ref: '#/components/schemas/Bm25ConfigStopwords' description: >- Configuration of the stopwords filter. Supports list of pre-defined languages and custom stopwords. Default: initialized for specified `language` or English if not specified. stemmer: $ref: '#/components/schemas/Bm25ConfigStemmer' description: >- Configuration of the stemmer. Processes tokens to their root form. Default: initialized Snowball stemmer for specified `language` or English if not specified. min_token_len: type: - integer - 'null' description: >- Minimum token length to keep. If token is shorter than this, it will be discarded. Default is `None`, which means no minimum length. max_token_len: type: - integer - 'null' description: >- Maximum token length to keep. If token is longer than this, it will be discarded. Default is `None`, which means no maximum length. description: Configuration of the local bm25 models. title: Bm25Config DocumentOptions: oneOf: - type: object additionalProperties: description: Any type - $ref: '#/components/schemas/Bm25Config' description: >- Option variants for text documents. Ether general-purpose options or BM25-specific options. BM25-specific will only take effect if the `qdrant/bm25` is specified as a model. title: DocumentOptions Document: type: object properties: text: type: string description: >- Text of the document. This field will be used as input for the embedding model. model: type: string description: >- Name of the model used to generate the vector. List of available models depends on a provider. options: $ref: '#/components/schemas/DocumentOptions' description: >- Additional options for the model, will be passed to the inference service as-is. See model cards for available options. required: - text - model description: >- WARN: Work-in-progress, unimplemented Text document for embedding. Requires inference infrastructure, unimplemented. title: Document Image: type: object properties: image: description: 'Image data: base64 encoded image or an URL' model: type: string description: >- Name of the model used to generate the vector. List of available models depends on a provider. options: type: - object - 'null' additionalProperties: description: Any type description: Parameters for the model Values of the parameters are model-specific required: - image - model description: >- WARN: Work-in-progress, unimplemented Image object for embedding. Requires inference infrastructure, unimplemented. title: Image InferenceObject: type: object properties: object: description: >- Arbitrary data, used as input for the embedding model. Used if the model requires more than one input or a custom input. model: type: string description: >- Name of the model used to generate the vector. List of available models depends on a provider. options: type: - object - 'null' additionalProperties: description: Any type description: Parameters for the model Values of the parameters are model-specific required: - object - model description: >- WARN: Work-in-progress, unimplemented Custom object for embedding. Requires inference infrastructure, unimplemented. title: InferenceObject VectorInput: oneOf: - type: array items: type: number format: double - $ref: '#/components/schemas/SparseVector' - type: array items: type: array items: type: number format: double - $ref: '#/components/schemas/ExtendedPointId' - $ref: '#/components/schemas/Document' - $ref: '#/components/schemas/Image' - $ref: '#/components/schemas/InferenceObject' title: VectorInput Mmr: type: object properties: diversity: type: - number - 'null' format: double description: >- Tunable parameter for the MMR algorithm. Determines the balance between diversity and relevance. A higher value favors diversity (dissimilarity to selected results), while a lower value favors relevance (similarity to the query vector). Must be in the range [0, 1]. Default value is 0.5. candidates_limit: type: - integer - 'null' description: |- The maximum number of candidates to consider for re-ranking. If not specified, the `limit` value is used. description: Maximal Marginal Relevance (MMR) algorithm for re-ranking the points. title: Mmr NearestQueryMmr: oneOf: - $ref: '#/components/schemas/Mmr' - description: Any type description: >- Perform MMR (Maximal Marginal Relevance) reranking after search, using the same vector in this query to calculate relevance. title: NearestQueryMmr NearestQuery: type: object properties: nearest: $ref: '#/components/schemas/VectorInput' mmr: $ref: '#/components/schemas/NearestQueryMmr' description: >- Perform MMR (Maximal Marginal Relevance) reranking after search, using the same vector in this query to calculate relevance. required: - nearest title: NearestQuery RecommendStrategy: type: string enum: - average_vector - best_score - sum_scores description: >- How to use positive and negative examples to find the results, default is `average_vector`: * `average_vector` - Average positive and negative vectors and create a single query with the formula `query = avg_pos + avg_pos - avg_neg`. Then performs normal search. * `best_score` - Uses custom search objective. Each candidate is compared against all examples, its score is then chosen from the `max(max_pos_score, max_neg_score)`. If the `max_neg_score` is chosen then it is squared and negated, otherwise it is just the `max_pos_score`. * `sum_scores` - Uses custom search objective. Compares against all inputs, sums all the scores. Scores against positive vectors are added, against negatives are subtracted. title: RecommendStrategy RecommendInputStrategy: oneOf: - $ref: '#/components/schemas/RecommendStrategy' - description: Any type description: How to use the provided vectors to find the results title: RecommendInputStrategy RecommendInput: type: object properties: positive: type: - array - 'null' items: $ref: '#/components/schemas/VectorInput' description: Look for vectors closest to the vectors from these points negative: type: - array - 'null' items: $ref: '#/components/schemas/VectorInput' description: Try to avoid vectors like the vector from these points strategy: $ref: '#/components/schemas/RecommendInputStrategy' description: How to use the provided vectors to find the results title: RecommendInput RecommendQuery: type: object properties: recommend: $ref: '#/components/schemas/RecommendInput' required: - recommend title: RecommendQuery ContextPair: type: object properties: positive: $ref: '#/components/schemas/VectorInput' negative: $ref: '#/components/schemas/VectorInput' required: - positive - negative title: ContextPair DiscoverInputContext1: type: array items: $ref: '#/components/schemas/ContextPair' title: DiscoverInputContext1 DiscoverInputContext: oneOf: - $ref: '#/components/schemas/ContextPair' - $ref: '#/components/schemas/DiscoverInputContext1' - description: Any type description: Search space will be constrained by these pairs of vectors title: DiscoverInputContext DiscoverInput: type: object properties: target: $ref: '#/components/schemas/VectorInput' context: $ref: '#/components/schemas/DiscoverInputContext' description: Search space will be constrained by these pairs of vectors required: - target - context title: DiscoverInput DiscoverQuery: type: object properties: discover: $ref: '#/components/schemas/DiscoverInput' required: - discover title: DiscoverQuery ContextInput1: type: array items: $ref: '#/components/schemas/ContextPair' title: ContextInput1 ContextInput: oneOf: - $ref: '#/components/schemas/ContextPair' - $ref: '#/components/schemas/ContextInput1' - description: Any type title: ContextInput ContextQuery: type: object properties: context: $ref: '#/components/schemas/ContextInput' required: - context title: ContextQuery Direction: type: string enum: - asc - desc title: Direction OrderByDirection: oneOf: - $ref: '#/components/schemas/Direction' - description: Any type description: 'Direction of ordering: `asc` or `desc`. Default is ascending.' title: OrderByDirection StartFrom: oneOf: - type: integer format: int64 - type: number format: double - type: string format: date-time title: StartFrom OrderByStartFrom: oneOf: - $ref: '#/components/schemas/StartFrom' - description: Any type description: >- Which payload value to start scrolling from. Default is the lowest value for `asc` and the highest for `desc` title: OrderByStartFrom OrderBy: type: object properties: key: type: string description: Payload key to order by direction: $ref: '#/components/schemas/OrderByDirection' description: 'Direction of ordering: `asc` or `desc`. Default is ascending.' start_from: $ref: '#/components/schemas/OrderByStartFrom' description: >- Which payload value to start scrolling from. Default is the lowest value for `asc` and the highest for `desc` required: - key title: OrderBy OrderByInterface: oneOf: - type: string - $ref: '#/components/schemas/OrderBy' title: OrderByInterface OrderByQuery: type: object properties: order_by: $ref: '#/components/schemas/OrderByInterface' required: - order_by title: OrderByQuery Fusion: type: string enum: - rrf - dbsf description: >- Fusion algorithm allows to combine results of multiple prefetches. Available fusion algorithms: * `rrf` - Reciprocal Rank Fusion (with default parameters) * `dbsf` - Distribution-Based Score Fusion title: Fusion FusionQuery: type: object properties: fusion: $ref: '#/components/schemas/Fusion' required: - fusion title: FusionQuery Rrf: type: object properties: k: type: - integer - 'null' description: K parameter for reciprocal rank fusion weights: type: - array - 'null' items: type: number format: double description: >- Weights for each prefetch source. Higher weight gives more influence on the final ranking. If not specified, all prefetches are weighted equally. The number of weights should match the number of prefetches. description: Parameters for Reciprocal Rank Fusion title: Rrf RrfQuery: type: object properties: rrf: $ref: '#/components/schemas/Rrf' required: - rrf title: RrfQuery ValueVariants: oneOf: - type: string - type: integer format: int64 - type: boolean title: ValueVariants MatchValue: type: object properties: value: $ref: '#/components/schemas/ValueVariants' required: - value description: Exact match of the given value title: MatchValue MatchText: type: object properties: text: type: string required: - text description: Full-text match of the strings. title: MatchText MatchTextAny: type: object properties: text_any: type: string required: - text_any description: Full-text match of at least one token of the string. title: MatchTextAny MatchPhrase: type: object properties: phrase: type: string required: - phrase description: Full-text phrase match of the string. title: MatchPhrase AnyVariants: oneOf: - type: array items: type: string - type: array items: type: integer format: int64 title: AnyVariants MatchAny: type: object properties: any: $ref: '#/components/schemas/AnyVariants' required: - any description: Exact match on any of the given values title: MatchAny MatchExcept: type: object properties: except: $ref: '#/components/schemas/AnyVariants' required: - except description: Should have at least one value not matching the any given values title: MatchExcept Match: oneOf: - $ref: '#/components/schemas/MatchValue' - $ref: '#/components/schemas/MatchText' - $ref: '#/components/schemas/MatchTextAny' - $ref: '#/components/schemas/MatchPhrase' - $ref: '#/components/schemas/MatchAny' - $ref: '#/components/schemas/MatchExcept' description: Match filter request title: Match FieldConditionMatch: oneOf: - $ref: '#/components/schemas/Match' - description: Any type description: Check if point has field with a given value title: FieldConditionMatch Range: type: object properties: lt: type: - number - 'null' format: double description: point.key < range.lt gt: type: - number - 'null' format: double description: point.key > range.gt gte: type: - number - 'null' format: double description: point.key >= range.gte lte: type: - number - 'null' format: double description: point.key <= range.lte description: Range filter request title: Range DatetimeRange: type: object properties: lt: type: - string - 'null' format: date-time description: point.key < range.lt gt: type: - string - 'null' format: date-time description: point.key > range.gt gte: type: - string - 'null' format: date-time description: point.key >= range.gte lte: type: - string - 'null' format: date-time description: point.key <= range.lte description: Range filter request title: DatetimeRange RangeInterface: oneOf: - $ref: '#/components/schemas/Range' - $ref: '#/components/schemas/DatetimeRange' title: RangeInterface FieldConditionRange: oneOf: - $ref: '#/components/schemas/RangeInterface' - description: Any type description: Check if points value lies in a given range title: FieldConditionRange GeoPoint: type: object properties: lon: type: number format: double lat: type: number format: double required: - lon - lat description: Geo point payload schema title: GeoPoint GeoBoundingBox: type: object properties: top_left: $ref: '#/components/schemas/GeoPoint' bottom_right: $ref: '#/components/schemas/GeoPoint' required: - top_left - bottom_right description: >- Geo filter request Matches coordinates inside the rectangle, described by coordinates of lop-left and bottom-right edges title: GeoBoundingBox FieldConditionGeoBoundingBox: oneOf: - $ref: '#/components/schemas/GeoBoundingBox' - description: Any type description: Check if points geolocation lies in a given area title: FieldConditionGeoBoundingBox GeoRadius: type: object properties: center: $ref: '#/components/schemas/GeoPoint' radius: type: number format: double description: Radius of the area in meters required: - center - radius description: >- Geo filter request Matches coordinates inside the circle of `radius` and center with coordinates `center` title: GeoRadius FieldConditionGeoRadius: oneOf: - $ref: '#/components/schemas/GeoRadius' - description: Any type description: Check if geo point is within a given radius title: FieldConditionGeoRadius GeoLineString: type: object properties: points: type: array items: $ref: '#/components/schemas/GeoPoint' required: - points description: Ordered sequence of GeoPoints representing the line title: GeoLineString GeoPolygon: type: object properties: exterior: $ref: '#/components/schemas/GeoLineString' interiors: type: - array - 'null' items: $ref: '#/components/schemas/GeoLineString' description: >- Interior lines (if present) bound holes within the surface each GeoLineString must consist of a minimum of 4 points, and the first and last points must be the same. required: - exterior description: >- Geo filter request Matches coordinates inside the polygon, defined by `exterior` and `interiors` title: GeoPolygon FieldConditionGeoPolygon: oneOf: - $ref: '#/components/schemas/GeoPolygon' - description: Any type description: Check if geo point is within a given polygon title: FieldConditionGeoPolygon ValuesCount: type: object properties: lt: type: - integer - 'null' description: point.key.length() < values_count.lt gt: type: - integer - 'null' description: point.key.length() > values_count.gt gte: type: - integer - 'null' description: point.key.length() >= values_count.gte lte: type: - integer - 'null' description: point.key.length() <= values_count.lte description: Values count filter request title: ValuesCount FieldConditionValuesCount: oneOf: - $ref: '#/components/schemas/ValuesCount' - description: Any type description: Check number of values of the field title: FieldConditionValuesCount FieldCondition: type: object properties: key: type: string description: Payload key match: $ref: '#/components/schemas/FieldConditionMatch' description: Check if point has field with a given value range: $ref: '#/components/schemas/FieldConditionRange' description: Check if points value lies in a given range geo_bounding_box: $ref: '#/components/schemas/FieldConditionGeoBoundingBox' description: Check if points geolocation lies in a given area geo_radius: $ref: '#/components/schemas/FieldConditionGeoRadius' description: Check if geo point is within a given radius geo_polygon: $ref: '#/components/schemas/FieldConditionGeoPolygon' description: Check if geo point is within a given polygon values_count: $ref: '#/components/schemas/FieldConditionValuesCount' description: Check number of values of the field is_empty: type: - boolean - 'null' description: >- Check that the field is empty, alternative syntax for `is_empty: "field_name"` is_null: type: - boolean - 'null' description: >- Check that the field is null, alternative syntax for `is_null: "field_name"` required: - key description: All possible payload filtering conditions title: FieldCondition PayloadField: type: object properties: key: type: string description: Payload field name required: - key description: Payload field title: PayloadField IsEmptyCondition: type: object properties: is_empty: $ref: '#/components/schemas/PayloadField' required: - is_empty description: Select points with empty payload for a specified field title: IsEmptyCondition IsNullCondition: type: object properties: is_null: $ref: '#/components/schemas/PayloadField' required: - is_null description: Select points with null payload for a specified field title: IsNullCondition HasIdCondition: type: object properties: has_id: type: array items: $ref: '#/components/schemas/ExtendedPointId' required: - has_id description: ID-based filtering condition title: HasIdCondition HasVectorCondition: type: object properties: has_vector: type: string required: - has_vector description: Filter points which have specific vector assigned title: HasVectorCondition FilterShould1: type: array items: $ref: '#/components/schemas/Condition' title: FilterShould1 FilterShould: oneOf: - $ref: '#/components/schemas/Condition' - $ref: '#/components/schemas/FilterShould1' - description: Any type description: At least one of those conditions should match title: FilterShould MinShould: type: object properties: conditions: type: array items: $ref: '#/components/schemas/Condition' min_count: type: integer required: - conditions - min_count title: MinShould FilterMinShould: oneOf: - $ref: '#/components/schemas/MinShould' - description: Any type description: At least minimum amount of given conditions should match title: FilterMinShould FilterMust1: type: array items: $ref: '#/components/schemas/Condition' title: FilterMust1 FilterMust: oneOf: - $ref: '#/components/schemas/Condition' - $ref: '#/components/schemas/FilterMust1' - description: Any type description: All conditions must match title: FilterMust FilterMustNot1: type: array items: $ref: '#/components/schemas/Condition' title: FilterMustNot1 FilterMustNot: oneOf: - $ref: '#/components/schemas/Condition' - $ref: '#/components/schemas/FilterMustNot1' - description: Any type description: All conditions must NOT match title: FilterMustNot Filter: type: object properties: should: $ref: '#/components/schemas/FilterShould' description: At least one of those conditions should match min_should: $ref: '#/components/schemas/FilterMinShould' description: At least minimum amount of given conditions should match must: $ref: '#/components/schemas/FilterMust' description: All conditions must match must_not: $ref: '#/components/schemas/FilterMustNot' description: All conditions must NOT match title: Filter Nested: type: object properties: key: type: string filter: $ref: '#/components/schemas/Filter' required: - key - filter description: Select points with payload for a specified nested field title: Nested NestedCondition: type: object properties: nested: $ref: '#/components/schemas/Nested' required: - nested title: NestedCondition Condition: oneOf: - $ref: '#/components/schemas/FieldCondition' - $ref: '#/components/schemas/IsEmptyCondition' - $ref: '#/components/schemas/IsNullCondition' - $ref: '#/components/schemas/HasIdCondition' - $ref: '#/components/schemas/HasVectorCondition' - $ref: '#/components/schemas/NestedCondition' - $ref: '#/components/schemas/Filter' title: Condition GeoDistanceParams: type: object properties: origin: $ref: '#/components/schemas/GeoPoint' to: type: string description: Payload field with the destination geo point required: - origin - to title: GeoDistanceParams GeoDistance: type: object properties: geo_distance: $ref: '#/components/schemas/GeoDistanceParams' required: - geo_distance title: GeoDistance DatetimeExpression: type: object properties: datetime: type: string required: - datetime title: DatetimeExpression DatetimeKeyExpression: type: object properties: datetime_key: type: string required: - datetime_key title: DatetimeKeyExpression MultExpression: type: object properties: mult: type: array items: $ref: '#/components/schemas/Expression' required: - mult title: MultExpression SumExpression: type: object properties: sum: type: array items: $ref: '#/components/schemas/Expression' required: - sum title: SumExpression NegExpression: type: object properties: neg: $ref: '#/components/schemas/Expression' required: - neg title: NegExpression AbsExpression: type: object properties: abs: $ref: '#/components/schemas/Expression' required: - abs title: AbsExpression DivParams: type: object properties: left: $ref: '#/components/schemas/Expression' right: $ref: '#/components/schemas/Expression' by_zero_default: type: - number - 'null' format: double required: - left - right title: DivParams DivExpression: type: object properties: div: $ref: '#/components/schemas/DivParams' required: - div title: DivExpression SqrtExpression: type: object properties: sqrt: $ref: '#/components/schemas/Expression' required: - sqrt title: SqrtExpression PowParams: type: object properties: base: $ref: '#/components/schemas/Expression' exponent: $ref: '#/components/schemas/Expression' required: - base - exponent title: PowParams PowExpression: type: object properties: pow: $ref: '#/components/schemas/PowParams' required: - pow title: PowExpression ExpExpression: type: object properties: exp: $ref: '#/components/schemas/Expression' required: - exp title: ExpExpression Log10Expression: type: object properties: log10: $ref: '#/components/schemas/Expression' required: - log10 title: Log10Expression LnExpression: type: object properties: ln: $ref: '#/components/schemas/Expression' required: - ln title: LnExpression DecayParamsExpressionTarget: oneOf: - $ref: '#/components/schemas/Expression' - description: Any type description: The target value to start decaying from. Defaults to 0. title: DecayParamsExpressionTarget DecayParamsExpression: type: object properties: x: $ref: '#/components/schemas/Expression' target: $ref: '#/components/schemas/DecayParamsExpressionTarget' description: The target value to start decaying from. Defaults to 0. scale: type: - number - 'null' format: double description: >- The scale factor of the decay, in terms of `x`. Defaults to 1.0. Must be a non-zero positive number. midpoint: type: - number - 'null' format: double description: >- The midpoint of the decay. Should be between 0 and 1.Defaults to 0.5. Output will be this value when `|x - target| == scale`. required: - x title: DecayParamsExpression LinDecayExpression: type: object properties: lin_decay: $ref: '#/components/schemas/DecayParamsExpression' required: - lin_decay title: LinDecayExpression ExpDecayExpression: type: object properties: exp_decay: $ref: '#/components/schemas/DecayParamsExpression' required: - exp_decay title: ExpDecayExpression GaussDecayExpression: type: object properties: gauss_decay: $ref: '#/components/schemas/DecayParamsExpression' required: - gauss_decay title: GaussDecayExpression Expression: oneOf: - type: number format: double - type: string - $ref: '#/components/schemas/Condition' - $ref: '#/components/schemas/GeoDistance' - $ref: '#/components/schemas/DatetimeExpression' - $ref: '#/components/schemas/DatetimeKeyExpression' - $ref: '#/components/schemas/MultExpression' - $ref: '#/components/schemas/SumExpression' - $ref: '#/components/schemas/NegExpression' - $ref: '#/components/schemas/AbsExpression' - $ref: '#/components/schemas/DivExpression' - $ref: '#/components/schemas/SqrtExpression' - $ref: '#/components/schemas/PowExpression' - $ref: '#/components/schemas/ExpExpression' - $ref: '#/components/schemas/Log10Expression' - $ref: '#/components/schemas/LnExpression' - $ref: '#/components/schemas/LinDecayExpression' - $ref: '#/components/schemas/ExpDecayExpression' - $ref: '#/components/schemas/GaussDecayExpression' title: Expression FormulaQuery: type: object properties: formula: $ref: '#/components/schemas/Expression' defaults: type: object additionalProperties: description: Any type required: - formula title: FormulaQuery Sample: type: string enum: - random title: Sample SampleQuery: type: object properties: sample: $ref: '#/components/schemas/Sample' required: - sample title: SampleQuery FeedbackItem: type: object properties: example: $ref: '#/components/schemas/VectorInput' score: type: number format: double required: - example - score title: FeedbackItem NaiveFeedbackStrategyParams: type: object properties: a: type: number format: double b: type: number format: double c: type: number format: double required: - a - b - c title: NaiveFeedbackStrategyParams NaiveFeedbackStrategy: type: object properties: naive: $ref: '#/components/schemas/NaiveFeedbackStrategyParams' required: - naive title: NaiveFeedbackStrategy FeedbackStrategy: oneOf: - $ref: '#/components/schemas/NaiveFeedbackStrategy' title: FeedbackStrategy RelevanceFeedbackInput: type: object properties: target: $ref: '#/components/schemas/VectorInput' feedback: type: array items: $ref: '#/components/schemas/FeedbackItem' strategy: $ref: '#/components/schemas/FeedbackStrategy' required: - target - feedback - strategy title: RelevanceFeedbackInput RelevanceFeedbackQuery: type: object properties: relevance_feedback: $ref: '#/components/schemas/RelevanceFeedbackInput' required: - relevance_feedback title: RelevanceFeedbackQuery Query: oneOf: - $ref: '#/components/schemas/NearestQuery' - $ref: '#/components/schemas/RecommendQuery' - $ref: '#/components/schemas/DiscoverQuery' - $ref: '#/components/schemas/ContextQuery' - $ref: '#/components/schemas/OrderByQuery' - $ref: '#/components/schemas/FusionQuery' - $ref: '#/components/schemas/RrfQuery' - $ref: '#/components/schemas/FormulaQuery' - $ref: '#/components/schemas/SampleQuery' - $ref: '#/components/schemas/RelevanceFeedbackQuery' title: Query QueryInterface: oneOf: - $ref: '#/components/schemas/VectorInput' - $ref: '#/components/schemas/Query' title: QueryInterface PrefetchQuery: oneOf: - $ref: '#/components/schemas/QueryInterface' - description: Any type description: >- Query to perform. If missing without prefetches, returns points ordered by their IDs. title: PrefetchQuery PrefetchFilter: oneOf: - $ref: '#/components/schemas/Filter' - description: Any type description: >- Filter conditions - return only those points that satisfy the specified conditions. title: PrefetchFilter QuantizationSearchParams: type: object properties: ignore: type: boolean default: false description: If true, quantized vectors are ignored. Default is false. rescore: type: - boolean - 'null' description: >- If true, use original vectors to re-score top-k results. Might require more time in case if original vectors are stored on disk. If not set, qdrant decides automatically apply rescoring or not. oversampling: type: - number - 'null' format: double description: >- Oversampling factor for quantization. Default is 1.0. Defines how many extra vectors should be preselected using quantized index, and then re-scored using original vectors. For example, if `oversampling` is 2.4 and `limit` is 100, then 240 vectors will be preselected using quantized index, and then top-100 will be returned after re-scoring. description: Additional parameters of the search title: QuantizationSearchParams SearchParamsQuantization: oneOf: - $ref: '#/components/schemas/QuantizationSearchParams' - description: Any type description: Quantization params title: SearchParamsQuantization AcornSearchParams: type: object properties: enable: type: boolean default: false description: >- If true, then ACORN may be used for the HNSW search based on filters selectivity. Improves search recall for searches with multiple low-selectivity payload filters, at cost of performance. max_selectivity: type: - number - 'null' format: double description: >- Maximum selectivity of filters to enable ACORN. If estimated filters selectivity is higher than this value, ACORN will not be used. Selectivity is estimated as: `estimated number of points satisfying the filters / total number of points`. 0.0 for never, 1.0 for always. Default is 0.4. description: ACORN-related search parameters title: AcornSearchParams SearchParamsAcorn: oneOf: - $ref: '#/components/schemas/AcornSearchParams' - description: Any type description: ACORN search params title: SearchParamsAcorn SearchParams: type: object properties: hnsw_ef: type: - integer - 'null' description: >- Params relevant to HNSW index Size of the beam in a beam-search. Larger the value - more accurate the result, more time required for search. exact: type: boolean default: false description: >- Search without approximation. If set to true, search may run long but with exact results. quantization: $ref: '#/components/schemas/SearchParamsQuantization' description: Quantization params indexed_only: type: boolean default: false description: >- If enabled, the engine will only perform search among indexed or small segments. Using this option prevents slow searches in case of delayed index, but does not guarantee that all uploaded vectors will be included in search results acorn: $ref: '#/components/schemas/SearchParamsAcorn' description: ACORN search params description: Additional parameters of the search title: SearchParams PrefetchParams: oneOf: - $ref: '#/components/schemas/SearchParams' - description: Any type description: Search params for when there is no prefetch title: PrefetchParams LookupLocationShardKey: oneOf: - $ref: '#/components/schemas/ShardKeySelector' - description: Any type description: >- Specify in which shards to look for the points, if not specified - look in all shards title: LookupLocationShardKey LookupLocation: type: object properties: collection: type: string description: Name of the collection used for lookup vector: type: - string - 'null' description: >- Optional name of the vector field within the collection. If not provided, the default vector field will be used. shard_key: $ref: '#/components/schemas/LookupLocationShardKey' description: >- Specify in which shards to look for the points, if not specified - look in all shards required: - collection description: >- Defines a location to use for looking up the vector. Specifies collection and vector field name. title: LookupLocation PrefetchLookupFrom: oneOf: - $ref: '#/components/schemas/LookupLocation' - description: Any type description: >- The location to use for IDs lookup, if not specified - use the current collection and the 'using' vector Note: the other collection vectors should have the same vector size as the 'using' vector in the current collection title: PrefetchLookupFrom Prefetch: type: object properties: prefetch: $ref: '#/components/schemas/PrefetchPrefetch' description: >- Sub-requests to perform first. If present, the query will be performed on the results of the prefetches. query: $ref: '#/components/schemas/PrefetchQuery' description: >- Query to perform. If missing without prefetches, returns points ordered by their IDs. using: type: - string - 'null' description: >- Define which vector name to use for querying. If missing, the default vector is used. filter: $ref: '#/components/schemas/PrefetchFilter' description: >- Filter conditions - return only those points that satisfy the specified conditions. params: $ref: '#/components/schemas/PrefetchParams' description: Search params for when there is no prefetch score_threshold: type: - number - 'null' format: double description: Return points with scores better than this threshold. limit: type: - integer - 'null' description: Max number of points to return. Default is 10. lookup_from: $ref: '#/components/schemas/PrefetchLookupFrom' description: >- The location to use for IDs lookup, if not specified - use the current collection and the 'using' vector Note: the other collection vectors should have the same vector size as the 'using' vector in the current collection title: Prefetch QueryRequestPrefetch1: type: array items: $ref: '#/components/schemas/Prefetch' title: QueryRequestPrefetch1 QueryRequestPrefetch: oneOf: - $ref: '#/components/schemas/Prefetch' - $ref: '#/components/schemas/QueryRequestPrefetch1' - description: Any type description: >- Sub-requests to perform first. If present, the query will be performed on the results of the prefetch(es). title: QueryRequestPrefetch QueryRequestQuery: oneOf: - $ref: '#/components/schemas/QueryInterface' - description: Any type description: >- Query to perform. If missing without prefetches, returns points ordered by their IDs. title: QueryRequestQuery QueryRequestFilter: oneOf: - $ref: '#/components/schemas/Filter' - description: Any type description: >- Filter conditions - return only those points that satisfy the specified conditions. title: QueryRequestFilter QueryRequestParams: oneOf: - $ref: '#/components/schemas/SearchParams' - description: Any type description: Search params for when there is no prefetch title: QueryRequestParams WithVector: oneOf: - type: boolean - type: array items: type: string description: Options for specifying which vector to include title: WithVector QueryRequestWithVector: oneOf: - $ref: '#/components/schemas/WithVector' - description: Any type description: >- Options for specifying which vectors to include into the response. Default is false. title: QueryRequestWithVector PayloadSelectorInclude: type: object properties: include: type: array items: type: string description: Only include this payload keys required: - include title: PayloadSelectorInclude PayloadSelectorExclude: type: object properties: exclude: type: array items: type: string description: Exclude this fields from returning payload required: - exclude title: PayloadSelectorExclude PayloadSelector: oneOf: - $ref: '#/components/schemas/PayloadSelectorInclude' - $ref: '#/components/schemas/PayloadSelectorExclude' description: Specifies how to treat payload selector title: PayloadSelector WithPayloadInterface: oneOf: - type: boolean - type: array items: type: string - $ref: '#/components/schemas/PayloadSelector' description: Options for specifying which payload to include or not title: WithPayloadInterface QueryRequestWithPayload: oneOf: - $ref: '#/components/schemas/WithPayloadInterface' - description: Any type description: >- Options for specifying which payload to include or not. Default is false. title: QueryRequestWithPayload QueryRequestLookupFrom: oneOf: - $ref: '#/components/schemas/LookupLocation' - description: Any type description: >- The location to use for IDs lookup, if not specified - use the current collection and the 'using' vector Note: the other collection vectors should have the same vector size as the 'using' vector in the current collection title: QueryRequestLookupFrom QueryRequest: type: object properties: shard_key: $ref: '#/components/schemas/QueryRequestShardKey' prefetch: $ref: '#/components/schemas/QueryRequestPrefetch' description: >- Sub-requests to perform first. If present, the query will be performed on the results of the prefetch(es). query: $ref: '#/components/schemas/QueryRequestQuery' description: >- Query to perform. If missing without prefetches, returns points ordered by their IDs. using: type: - string - 'null' description: >- Define which vector name to use for querying. If missing, the default vector is used. filter: $ref: '#/components/schemas/QueryRequestFilter' description: >- Filter conditions - return only those points that satisfy the specified conditions. params: $ref: '#/components/schemas/QueryRequestParams' description: Search params for when there is no prefetch score_threshold: type: - number - 'null' format: double description: Return points with scores better than this threshold. limit: type: - integer - 'null' description: Max number of points to return. Default is 10. offset: type: - integer - 'null' description: Offset of the result. Skip this many points. Default is 0 with_vector: $ref: '#/components/schemas/QueryRequestWithVector' description: >- Options for specifying which vectors to include into the response. Default is false. with_payload: $ref: '#/components/schemas/QueryRequestWithPayload' description: >- Options for specifying which payload to include or not. Default is false. lookup_from: $ref: '#/components/schemas/QueryRequestLookupFrom' description: >- The location to use for IDs lookup, if not specified - use the current collection and the 'using' vector Note: the other collection vectors should have the same vector size as the 'using' vector in the current collection title: QueryRequest HardwareUsage: type: object properties: cpu: type: integer payload_io_read: type: integer payload_io_write: type: integer payload_index_io_read: type: integer payload_index_io_write: type: integer vector_io_read: type: integer vector_io_write: type: integer required: - cpu - payload_io_read - payload_io_write - payload_index_io_read - payload_index_io_write - vector_io_read - vector_io_write description: Usage of the hardware resources, spent to process the request title: HardwareUsage UsageHardware: oneOf: - $ref: '#/components/schemas/HardwareUsage' - description: Any type title: UsageHardware ModelUsage: type: object properties: tokens: type: integer format: uint64 required: - tokens title: ModelUsage InferenceUsage: type: object properties: models: type: object additionalProperties: $ref: '#/components/schemas/ModelUsage' required: - models title: InferenceUsage UsageInference: oneOf: - $ref: '#/components/schemas/InferenceUsage' - description: Any type title: UsageInference Usage: type: object properties: hardware: $ref: '#/components/schemas/UsageHardware' inference: $ref: '#/components/schemas/UsageInference' description: Usage of the hardware resources, spent to process the request title: Usage CollectionsCollectionNamePointsQueryPostResponsesContentApplicationJsonSchemaUsage: oneOf: - $ref: '#/components/schemas/Usage' - description: Any type title: >- CollectionsCollectionNamePointsQueryPostResponsesContentApplicationJsonSchemaUsage Payload: type: object additionalProperties: description: Any type title: Payload ScoredPointPayload: oneOf: - $ref: '#/components/schemas/Payload' - description: Any type description: Payload - values assigned to the point title: ScoredPointPayload VectorOutput: oneOf: - type: array items: type: number format: double - $ref: '#/components/schemas/SparseVector' - type: array items: type: array items: type: number format: double description: Vector Data stored in Point title: VectorOutput VectorStructOutput2: type: object additionalProperties: $ref: '#/components/schemas/VectorOutput' title: VectorStructOutput2 VectorStructOutput: oneOf: - type: array items: type: number format: double - type: array items: type: array items: type: number format: double - $ref: '#/components/schemas/VectorStructOutput2' description: Vector data stored in Point title: VectorStructOutput ScoredPointVector: oneOf: - $ref: '#/components/schemas/VectorStructOutput' - description: Any type description: Vector of the point title: ScoredPointVector ScoredPointShardKey: oneOf: - $ref: '#/components/schemas/ShardKey' - description: Any type description: Shard Key title: ScoredPointShardKey OrderValue: oneOf: - type: integer format: int64 - type: number format: double title: OrderValue ScoredPointOrderValue: oneOf: - $ref: '#/components/schemas/OrderValue' - description: Any type description: Order-by value title: ScoredPointOrderValue ScoredPoint: type: object properties: id: $ref: '#/components/schemas/ExtendedPointId' version: type: integer format: uint64 description: Point version score: type: number format: double description: Points vector distance to the query vector payload: $ref: '#/components/schemas/ScoredPointPayload' description: Payload - values assigned to the point vector: $ref: '#/components/schemas/ScoredPointVector' description: Vector of the point shard_key: $ref: '#/components/schemas/ScoredPointShardKey' description: Shard Key order_value: $ref: '#/components/schemas/ScoredPointOrderValue' description: Order-by value required: - id - version - score description: Search result title: ScoredPoint QueryResponse: type: object properties: points: type: array items: $ref: '#/components/schemas/ScoredPoint' required: - points title: QueryResponse Search_query_points_Response_200: type: object properties: usage: $ref: >- #/components/schemas/CollectionsCollectionNamePointsQueryPostResponsesContentApplicationJsonSchemaUsage time: type: number format: double description: Time spent to process this request status: type: string result: $ref: '#/components/schemas/QueryResponse' title: Search_query_points_Response_200 securitySchemes: default: type: apiKey in: header name: api-key ``` ## Examples **Request** ```json {} ``` **Response** ```json { "usage": { "hardware": { "cpu": 1, "payload_io_read": 1, "payload_io_write": 1, "payload_index_io_read": 1, "payload_index_io_write": 1, "vector_io_read": 1, "vector_io_write": 1 }, "inference": { "models": {} } }, "time": 0.002, "status": "ok", "result": { "points": [ { "id": 42, "version": 3, "score": 0.75, "payload": {}, "vector": {}, "shard_key": "region_1", "order_value": 42 } ] } } ``` **SDK Code** ```csharp using Qdrant.Client; using Qdrant.Client.Grpc; var client = new QdrantClient("localhost", 6334); // Query nearest by ID await client.QueryAsync( collectionName: "{collection_name}", query: Guid.Parse("43cf51e2-8777-4f52-bc74-c2cbde0c8b04") ); // Recommend on the average of these vectors await client.QueryAsync( collectionName: "{collection_name}", query: new RecommendInput { Positive = { Guid.Parse("43cf51e2-8777-4f52-bc74-c2cbde0c8b04"), new float[] { 0.11f, 0.35f, 0.6f } }, Negative = { new float[] { 0.01f, 0.45f, 0.67f } } } ); // Fusion query await client.QueryAsync( collectionName: "{collection_name}", prefetch: new List { new() { Query = new (float, uint)[] { (0.22f, 1), (0.8f, 42), }, Using = "sparse", Limit = 20 }, new() { Query = new float[] { 0.01f, 0.45f, 0.67f }, Using = "dense", Limit = 20 } }, query: Fusion.Rrf ); // 2-stage query await client.QueryAsync( collectionName: "{collection_name}", prefetch: new List { new() { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 } }, query: new float[][] { [0.1f, 0.2f], [0.2f, 0.1f], [0.8f, 0.9f] }, usingVector: "colbert", limit: 10 ); // Random sampling (as of 1.11.0) await client.QueryAsync( collectionName: "{collection_name}", query: Sample.Random ); // Score boost depending on payload conditions (as of 1.14.0) await client.QueryAsync( collectionName: "{collection_name}", prefetch: [ new PrefetchQuery { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 }, ], query: new Formula { Expression = new SumExpression { Sum = { "$score", new MultExpression { Mult = { 0.5f, Match("tag", ["h1", "h2", "h3", "h4"]) }, }, new MultExpression { Mult = { 0.25f, Match("tag", ["p", "li"]) } }, }, }, }, limit: 10 ); // Score boost geographically closer points (as of 1.14.0) await client.QueryAsync( collectionName: "{collection_name}", prefetch: [ new PrefetchQuery { Query = new float[] { 0.01f, 0.45f, 0.67f }, Limit = 100 }, ], query: new Formula { Expression = new SumExpression { Sum = { "$score", WithExpDecay( new() { X = new GeoDistance { Origin = new GeoPoint { Lat = 52.504043, Lon = 13.393236 }, To = "geo.location", }, Scale = 5000, } ), }, }, Defaults = { ["geo.location"] = new Dictionary { ["lat"] = 48.137154, ["lon"] = 11.576124, }, }, } ); ``` ```python from qdrant_client import QdrantClient, models client = QdrantClient(url="http://localhost:6333") # Query nearest by ID nearest = client.query_points( collection_name="{collection_name}", query="43cf51e2-8777-4f52-bc74-c2cbde0c8b04", ) # Recommend on the average of these vectors recommended = client.query_points( collection_name="{collection_name}", query=models.RecommendQuery(recommend=models.RecommendInput( positive=["43cf51e2-8777-4f52-bc74-c2cbde0c8b04", [0.11, 0.35, 0.6, ...]], negative=[[0.01, 0.45, 0.67, ...]] )) ) # Fusion query hybrid = client.query_points( collection_name="{collection_name}", prefetch=[ models.Prefetch( query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]), using="sparse", limit=20, ), models.Prefetch( query=[0.01, 0.45, 0.67, ...], # <-- dense vector using="dense", limit=20, ), ], query=models.FusionQuery(fusion=models.Fusion.RRF), ) # 2-stage query refined = client.query_points( collection_name="{collection_name}", prefetch=models.Prefetch( query=[0.01, 0.45, 0.67, ...], # <-- dense vector limit=100, ), query=[ [0.1, 0.2, ...], # <─┐ [0.2, 0.1, ...], # < ├─ multi-vector [0.8, 0.9, ...], # < ┘ ], using="colbert", limit=10, ) # Random sampling (as of 1.11.0) sampled = client.query_points( collection_name="{collection_name}", query=models.SampleQuery(sample=models.Sample.RANDOM) ) # Score boost depending on payload conditions (as of 1.14.0) tag_boosted = client.query_points( collection_name="{collection_name}", prefetch=models.Prefetch( query=[0.2, 0.8, ...], # <-- dense vector limit=50 ), query=models.FormulaQuery( formula=models.SumExpression(sum=[ "$score", models.MultExpression(mult=[0.5, models.FieldCondition(key="tag", match=models.MatchAny(any=["h1", "h2", "h3", "h4"]))]), models.MultExpression(mult=[0.25, models.FieldCondition(key="tag", match=models.MatchAny(any=["p", "li"]))]) ] )) ) # Score boost geographically closer points (as of 1.14.0) geo_boosted = client.query_points( collection_name="{collection_name}", prefetch=models.Prefetch( query=[0.2, 0.8, ...], # <-- dense vector limit=50 ), query=models.FormulaQuery( formula=models.SumExpression(sum=[ "$score", models.GaussDecayExpression( gauss_decay=models.DecayParamsExpression( x=models.GeoDistance( geo_distance=models.GeoDistanceParams( origin=models.GeoPoint( lat=52.504043, lon=13.393236 ), # Berlin to="geo.location" ) ), scale=5000 # 5km ) ) ]), defaults={"geo.location": models.GeoPoint(lat=48.137154, lon=11.576124)} # Munich ) ) ``` ```rust use qdrant_client::qdrant::{ Condition, DecayParamsExpressionBuilder, Expression, FormulaBuilder, Fusion, GeoPoint, PointId, PrefetchQueryBuilder, Query, QueryPointsBuilder, RecommendInputBuilder, Sample, }; use qdrant_client::Qdrant; let client = Qdrant::from_url("http://localhost:6334").build()?; // Query nearest by ID let _nearest = client.query( QueryPointsBuilder::new("{collection_name}") .query(PointId::from("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")) ).await?; // Recommend on the average of these vectors let _recommendations = client.query( QueryPointsBuilder::new("{collection_name}") .query(Query::new_recommend( RecommendInputBuilder::default() .add_positive(vec![0.1; 8]) .add_negative(PointId::from(0)) )) ).await?; // Fusion query let _hybrid = client.query( QueryPointsBuilder::new("{collection_name}") .add_prefetch(PrefetchQueryBuilder::default() .query(vec![(1, 0.22), (42, 0.8)]) .using("sparse") .limit(20u64) ) .add_prefetch(PrefetchQueryBuilder::default() .query(vec![0.01, 0.45, 0.67]) .using("dense") .limit(20u64) ) .query(Fusion::Rrf) ).await?; // 2-stage query let _refined = client.query( QueryPointsBuilder::new("{collection_name}") .add_prefetch(PrefetchQueryBuilder::default() .query(vec![0.01, 0.45, 0.67]) .limit(100u64) ) .query(vec![ vec![0.1, 0.2], vec![0.2, 0.1], vec![0.8, 0.9], ]) .using("colbert") .limit(10u64) ).await?; // Random sampling (as of 1.11.0) let _sampled = client .query( QueryPointsBuilder::new("{collection_name}") .query(Query::new_sample(Sample::Random)) ) .await?; // Score boost depending on payload conditions (as of 1.14.0) let _tag_boosted = client.query( QueryPointsBuilder::new("{collection_name}") .add_prefetch(PrefetchQueryBuilder::default() .query(vec![0.01, 0.45, 0.67]) .limit(100u64) ) .query(FormulaBuilder::new(Expression::sum_with([ Expression::score(), Expression::mult_with([ Expression::constant(0.5), Expression::condition(Condition::matches("tag", ["h1", "h2", "h3", "h4"])), ]), Expression::mult_with([ Expression::constant(0.25), Expression::condition(Condition::matches("tag", ["p", "li"])), ]), ]))) .limit(10) ).await?; // Score boost geographically closer points (as of 1.14.0) let _geo_boosted = client.query( QueryPointsBuilder::new("{collection_name}") .add_prefetch( PrefetchQueryBuilder::default() .query(vec![0.01, 0.45, 0.67]) .limit(100u64), ) .query( FormulaBuilder::new(Expression::sum_with([ Expression::score(), Expression::exp_decay( DecayParamsExpressionBuilder::new(Expression::geo_distance_with( // Berlin GeoPoint { lat: 52.504043, lon: 13.393236 }, "geo.location", )) .scale(5_000.0), ), ])) // Munich .add_default("geo.location", GeoPoint { lat: 48.137154, lon: 11.576124 }), ) .limit(10), ) .await?; ``` ```java import static io.qdrant.client.QueryFactory.fusion; import static io.qdrant.client.QueryFactory.nearest; import static io.qdrant.client.QueryFactory.recommend; import static io.qdrant.client.VectorInputFactory.vectorInput; import java.util.UUID; import io.qdrant.client.QdrantClient; import io.qdrant.client.QdrantGrpcClient; import io.qdrant.client.grpc.Points.Fusion; import io.qdrant.client.grpc.Points.PrefetchQuery; import io.qdrant.client.grpc.Points.QueryPoints; import io.qdrant.client.grpc.Points.RecommendInput; QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); // Query nearest by ID client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .setQuery(nearest(UUID.fromString("43cf51e2-8777-4f52-bc74-c2cbde0c8b04"))) .build()) .get(); // Recommend on the average of these vectors client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .setQuery( recommend( RecommendInput.newBuilder() .addPositive(vectorInput(UUID.fromString("43cf51e2-8777-4f52-bc74-c2cbde0c8b04"))) .addPositive(vectorInput(0.11f, 0.35f, 0.6f)) .addNegative(vectorInput(0.01f, 0.45f, 0.67f)) .build())) .build()) .get(); // Fusion query client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .addPrefetch( PrefetchQuery.newBuilder() .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42))) .setUsing("sparse") .setLimit(20) .build()) .addPrefetch( PrefetchQuery.newBuilder() .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f))) .setUsing("dense") .setLimit(20) .build()) .setQuery(fusion(Fusion.RRF)) .build()) .get(); // 2-stage query client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .addPrefetch( PrefetchQuery.newBuilder() .setQuery(nearest(0.01f, 0.45f, 0.67f)) .setLimit(100) .build()) .setQuery( nearest( new float[][] { {0.1f, 0.2f}, {0.2f, 0.1f}, {0.8f, 0.9f} })) .setUsing("colbert") .setLimit(10) .build()) .get(); // Random sampling (as of 1.11.0) client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .setQuery(sample(Sample.Random)) .build()) .get(); // Score boost depending on payload conditions (as of 1.14.0) client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .addPrefetch( PrefetchQuery.newBuilder() .setQuery(nearest(0.01f, 0.45f, 0.67f)) .setLimit(100) .build()) .setQuery( formula( Formula.newBuilder() .setExpression( sum( SumExpression.newBuilder() .addSum(variable("$score")) .addSum( mult( MultExpression.newBuilder() .addMult(constant(0.5f)) .addMult( condition( matchKeywords( "tag", List.of("h1", "h2", "h3", "h4")))) .build())) .addSum(mult(MultExpression.newBuilder() .addMult(constant(0.25f)) .addMult( condition( matchKeywords( "tag", List.of("p", "li")))) .build())) .build())) .build())) .build()) .get(); // Score boost geographically closer points (as of 1.14.0) client .queryAsync( QueryPoints.newBuilder() .setCollectionName("{collection_name}") .addPrefetch( PrefetchQuery.newBuilder() .setQuery(nearest(0.01f, 0.45f, 0.67f)) .setLimit(100) .build()) .setQuery( formula( Formula.newBuilder() .setExpression( sum( SumExpression.newBuilder() .addSum(variable("$score")) .addSum( expDecay( DecayParamsExpression.newBuilder() .setX( geoDistance( GeoDistance.newBuilder() .setOrigin( GeoPoint.newBuilder() .setLat(52.504043) .setLon(13.393236) .build()) .setTo("geo.location") .build())) .setScale(5000) .build())) .build())) .putDefaults( "geo.location", value( Map.of( "lat", value(48.137154), "lon", value(11.576124)))) .build())) .build()) .get(); ``` ```go package client import ( "context" "fmt" "github.com/qdrant/go-client/qdrant" ) func query() { client, err := qdrant.NewClient(&qdrant.Config{ Host: "localhost", Port: 6334, }) if err != nil { panic(err) } // Query nearest by ID points, err := client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Query: qdrant.NewQueryID(qdrant.NewID("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")), }) if err != nil { panic(err) } fmt.Println("Query results: ", points) // Recommend on the average of these vectors points, err = client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Query: qdrant.NewQueryRecommend(&qdrant.RecommendInput{ Positive: []*qdrant.VectorInput{ qdrant.NewVectorInputID(qdrant.NewID("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")), qdrant.NewVectorInput(0.11, 0.35, 0.6), }, Negative: []*qdrant.VectorInput{ qdrant.NewVectorInput(0.01, 0.45, 0.67), }, }), }) if err != nil { panic(err) } fmt.Println("Query results: ", points) // Fusion query points, err = client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Prefetch: []*qdrant.PrefetchQuery{ { Query: qdrant.NewQuerySparse([]uint32{1, 42}, []float32{0.22, 0.8}), Using: qdrant.PtrOf("sparse"), }, { Query: qdrant.NewQuery(0.01, 0.45, 0.67), Using: qdrant.PtrOf("dense"), }, }, Query: qdrant.NewQueryFusion(qdrant.Fusion_RRF), }) if err != nil { panic(err) } fmt.Println("Query results: ", points) // 2-stage query points, err = client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Prefetch: []*qdrant.PrefetchQuery{ { Query: qdrant.NewQuery(0.01, 0.45, 0.67), }, }, Query: qdrant.NewQueryMulti([][]float32{ {0.1, 0.2}, {0.2, 0.1}, {0.8, 0.9}, }), Using: qdrant.PtrOf("colbert"), }) if err != nil { panic(err) } fmt.Println("Query results: ", points) // Random sampling (as of 1.11.0) points, err = client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Query: qdrant.NewQuerySample(qdrant.Sample_Random), }) if err != nil { panic(err) } fmt.Println("Query results: ", points) // Score boost depending on payload conditions (as of 1.14.0) points, err = client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Prefetch: []*qdrant.PrefetchQuery{ { Query: qdrant.NewQuery(0.01, 0.45, 0.67), }, }, Query: qdrant.NewQueryFormula(&qdrant.Formula{ Expression: qdrant.NewExpressionSum(&qdrant.SumExpression{ Sum: []*qdrant.Expression{ qdrant.NewExpressionVariable("$score"), qdrant.NewExpressionMult(&qdrant.MultExpression{ Mult: []*qdrant.Expression{ qdrant.NewExpressionConstant(0.5), qdrant.NewExpressionCondition(qdrant.NewMatchKeywords("tag", "h1", "h2", "h3", "h4")), }, }), qdrant.NewExpressionMult(&qdrant.MultExpression{ Mult: []*qdrant.Expression{ qdrant.NewExpressionConstant(0.25), qdrant.NewExpressionCondition(qdrant.NewMatchKeywords("tag", "p", "li")), }, }), }, }), }), }) // Score boost geographically closer points (as of 1.14.0) client.Query(context.Background(), &qdrant.QueryPoints{ CollectionName: "{collection_name}", Prefetch: []*qdrant.PrefetchQuery{ { Query: qdrant.NewQuery(0.2, 0.8), }, }, Query: qdrant.NewQueryFormula(&qdrant.Formula{ Expression: qdrant.NewExpressionSum(&qdrant.SumExpression{ Sum: []*qdrant.Expression{ qdrant.NewExpressionVariable("$score"), qdrant.NewExpressionExpDecay(&qdrant.DecayParamsExpression{ X: qdrant.NewExpressionGeoDistance(&qdrant.GeoDistance{ Origin: &qdrant.GeoPoint{ Lat: 52.504043, Lon: 13.393236, }, To: "geo.location", }), }), }, }), Defaults: qdrant.NewValueMap(map[string]any{ "geo.location": map[string]any{ "lat": 48.137154, "lon": 11.576124, }, }), }), }) } ``` ```typescript import { QdrantClient } from "@qdrant/js-client-rest"; const client = new QdrantClient({ host: "localhost", port: 6333 }); // Query nearest by ID let _nearest = client.query("{collection_name", { query: "43cf51e2-8777-4f52-bc74-c2cbde0c8b04" }); // Recommend on the average of these vectors let _recommendations = client.query("{collection_name}", { query: { recommend: { positive: ["43cf51e2-8777-4f52-bc74-c2cbde0c8b04", [0.11, 0.35, 0.6]], negative: [0.01, 0.45, 0.67] } } }); // Fusion query let _hybrid = client.query("{collection_name}", { prefetch: [ { query: { values: [0.22, 0.8], indices: [1, 42], }, using: 'sparse', limit: 20, }, { query: [0.01, 0.45, 0.67], using: 'dense', limit: 20, }, ], query: { fusion: 'rrf', }, }); // 2-stage query let _refined = client.query("{collection_name}", { prefetch: { query: [1, 23, 45, 67], limit: 100, }, query: [ [0.1, 0.2], [0.2, 0.1], [0.8, 0.9], ], using: 'colbert', limit: 10, }); // Random sampling (as of 1.11.0) let _sampled = client.query("{collection_name}", { query: { sample: "random" }, }); // Score boost depending on payload conditions (as of 1.14.0) const tag_boosted = await client.query("{collection_name}", { prefetch: { query: [0.2, 0.8, 0.1, 0.9], limit: 50 }, query: { formula: { sum: [ "$score", { mult: [ 0.5, { key: "tag", match: { any: ["h1", "h2", "h3", "h4"] }} ] }, { mult: [ 0.25, { key: "tag", match: { any: ["p", "li"] }} ] } ] } } }); // Score boost geographically closer points (as of 1.14.0) const distance_boosted = await client.query("{collection_name}", { prefetch: { query: [0.2, 0.8, ...], limit: 50 }, query: { formula: { sum: [ "$score", { gauss_decay: { x: { geo_distance: { origin: { lat: 52.504043, lon: 13.393236 }, // Berlin to: "geo.location" } }, scale: 5000 // 5km } } ] }, defaults: { "geo.location": { lat: 48.137154, lon: 11.576124 } } // Munich } }); ``` ```ruby require 'uri' require 'net/http' url = URI("http://localhost:6333/collections/collection_name/points/query") http = Net::HTTP.new(url.host, url.port) request = Net::HTTP::Post.new(url) request["api-key"] = '' request["Content-Type"] = 'application/json' request.body = "{}" response = http.request(request) puts response.read_body ``` ```php request('POST', 'http://localhost:6333/collections/collection_name/points/query', [ 'body' => '{}', 'headers' => [ 'Content-Type' => 'application/json', 'api-key' => '', ], ]); echo $response->getBody(); ``` ```swift import Foundation let headers = [ "api-key": "", "Content-Type": "application/json" ] let parameters = [] as [String : Any] let postData = JSONSerialization.data(withJSONObject: parameters, options: []) let request = NSMutableURLRequest(url: NSURL(string: "http://localhost:6333/collections/collection_name/points/query")! as URL, cachePolicy: .useProtocolCachePolicy, timeoutInterval: 10.0) request.httpMethod = "POST" request.allHTTPHeaderFields = headers request.httpBody = postData as Data let session = URLSession.shared let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in if (error != nil) { print(error as Any) } else { let httpResponse = response as? HTTPURLResponse print(httpResponse) } }) dataTask.resume() ```