{ "openapi": "3.1.0", "info": { "title": "Chatsax API", "version": "1.1.0", "description": "Agent search infrastructure. Core model request schemas follow Jina AI OpenAPI 2026.07.27.1603. Chatsax authentication, quotas, ownership and gateway routes are independent. Source: https://api.jina.ai/openapi.json" }, "paths": { "/v1/embeddings": { "post": { "tags": [ "Search Foundation Models" ], "summary": "Embeddings", "description": "Generate embeddings for text, images, or documents.\n\nWorld-class multimodal multilingual embeddings for semantic search,\nsimilarity matching, clustering, and classification tasks.", "operationId": "embeddings_v1_embeddings_post", "requestBody": { "content": { "application/json": { "schema": { "oneOf": [ { "$ref": "#/components/schemas/EmbeddingsV2Request" }, { "$ref": "#/components/schemas/EmbeddingsV3Request" }, { "$ref": "#/components/schemas/EmbeddingsV5Request" }, { "$ref": "#/components/schemas/EmbeddingsV4Request" }, { "$ref": "#/components/schemas/CodeEmbeddings500MRequest" }, { "$ref": "#/components/schemas/CodeEmbeddings1500MRequest" }, { "$ref": "#/components/schemas/ClipV1Request" }, { "$ref": "#/components/schemas/ClipV2Request" }, { "$ref": "#/components/schemas/ColbertV1Request" }, { "$ref": "#/components/schemas/ColbertV2Request" }, { "$ref": "#/components/schemas/ELSERV2Request" } ], "title": "Embedding Request", "discriminator": { "propertyName": "model", "mapping": { "jina-embeddings-v2-base-en": "#/components/schemas/EmbeddingsV2Request", "jina-embeddings-v2-base-zh": "#/components/schemas/EmbeddingsV2Request", "jina-embeddings-v2-base-de": "#/components/schemas/EmbeddingsV2Request", "jina-embeddings-v2-base-es": "#/components/schemas/EmbeddingsV2Request", "jina-embeddings-v2-base-code": "#/components/schemas/EmbeddingsV2Request", "jina-embeddings-v3": "#/components/schemas/EmbeddingsV3Request", "jina-embeddings-v5-text-nano": "#/components/schemas/EmbeddingsV5Request", "jina-embeddings-v5-text-small": "#/components/schemas/EmbeddingsV5Request", "jina-embeddings-v5-omni-small": "#/components/schemas/EmbeddingsV5Request", "jina-embeddings-v5-omni-nano": "#/components/schemas/EmbeddingsV5Request", "jina-embeddings-v4": "#/components/schemas/EmbeddingsV4Request", "jina-code-embeddings-0.5b": "#/components/schemas/CodeEmbeddings500MRequest", "jina-code-embeddings-1.5b": "#/components/schemas/CodeEmbeddings1500MRequest", "jina-clip-v1": "#/components/schemas/ClipV1Request", "jina-clip-v2": "#/components/schemas/ClipV2Request", "jina-colbert-v1-en": "#/components/schemas/ColbertV1Request", "jina-colbert-v2": "#/components/schemas/ColbertV2Request", "elser-v2": "#/components/schemas/ELSERV2Request" } } } } }, "required": true }, "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/EmbeddingResponse" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] } }, "/v1/rerank": { "post": { "tags": [ "Search Foundation Models" ], "summary": "Rerank", "description": "Rerank documents by relevance to a query.\n\nWorld-class reranker for maximizing search relevancy and RAG accuracy.", "operationId": "rerank_v1_rerank_post", "requestBody": { "content": { "application/json": { "schema": { "oneOf": [ { "$ref": "#/components/schemas/TextRerankerRequest" }, { "$ref": "#/components/schemas/RerankerM0Request" }, { "$ref": "#/components/schemas/RerankerV3Request" } ], "title": "Reranking Request", "discriminator": { "propertyName": "model", "mapping": { "jina-reranker-v2-base-multilingual": "#/components/schemas/TextRerankerRequest", "jina-reranker-v1-tiny-en": "#/components/schemas/TextRerankerRequest", "jina-reranker-v1-turbo-en": "#/components/schemas/TextRerankerRequest", "jina-reranker-v1-base-en": "#/components/schemas/TextRerankerRequest", "jina-colbert-v1-en": "#/components/schemas/TextRerankerRequest", "jina-colbert-v2": "#/components/schemas/TextRerankerRequest", "jina-reranker-m0": "#/components/schemas/RerankerM0Request", "jina-reranker-v3": "#/components/schemas/RerankerV3Request", "jina-reranker-v3.5": "#/components/schemas/RerankerV3Request" } } } } }, "required": true }, "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/RerankingResponse" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] } }, "/v1/classify": { "post": { "tags": [ "Zero/Few-shot Classification" ], "summary": "Classify", "description": "Classify text or images into categories.\n\nZero-shot and few-shot classification. Zero-shot uses embedding similarity,\nfew-shot uses a trained classifier.", "operationId": "classify_v1_classify_post", "requestBody": { "content": { "application/json": { "schema": { "anyOf": [ { "oneOf": [ { "$ref": "#/components/schemas/EmbeddingsV2ZeroShotClassificationRequest" }, { "$ref": "#/components/schemas/EmbeddingsV3ZeroShotClassificationRequest" }, { "$ref": "#/components/schemas/EmbeddingsV4ZeroShotClassificationRequest" }, { "$ref": "#/components/schemas/EmbeddingsV5ZeroShotClassificationRequest" }, { "$ref": "#/components/schemas/ClipZeroShotClassificationRequest" }, { "$ref": "#/components/schemas/CodeEmbeddingsZeroShotClassificationRequest" } ], "discriminator": { "propertyName": "model", "mapping": { "jina-embeddings-v2-base-en": "#/components/schemas/EmbeddingsV2ZeroShotClassificationRequest", "jina-embeddings-v2-base-zh": "#/components/schemas/EmbeddingsV2ZeroShotClassificationRequest", "jina-embeddings-v2-base-de": "#/components/schemas/EmbeddingsV2ZeroShotClassificationRequest", "jina-embeddings-v2-base-es": "#/components/schemas/EmbeddingsV2ZeroShotClassificationRequest", "jina-embeddings-v2-base-code": "#/components/schemas/EmbeddingsV2ZeroShotClassificationRequest", "jina-embeddings-v3": "#/components/schemas/EmbeddingsV3ZeroShotClassificationRequest", "jina-embeddings-v4": "#/components/schemas/EmbeddingsV4ZeroShotClassificationRequest", "jina-embeddings-v5-text-nano": "#/components/schemas/EmbeddingsV5ZeroShotClassificationRequest", "jina-embeddings-v5-text-small": "#/components/schemas/EmbeddingsV5ZeroShotClassificationRequest", "jina-clip-v1": "#/components/schemas/ClipZeroShotClassificationRequest", "jina-clip-v2": "#/components/schemas/ClipZeroShotClassificationRequest", "jina-code-embeddings-0.5b": "#/components/schemas/CodeEmbeddingsZeroShotClassificationRequest", "jina-code-embeddings-1.5b": "#/components/schemas/CodeEmbeddingsZeroShotClassificationRequest" } } }, { "$ref": "#/components/schemas/FewShotClassificationRequest" } ], "title": "Classification Request" } } }, "required": true }, "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ClassificationResponse" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] } }, "/v1/train": { "post": { "tags": [ "Zero/Few-shot Classification" ], "summary": "Train", "description": "Train or update a few-shot classifier.\n\nCreate a new classifier with labeled examples, or update an existing one\nwith additional training data.", "operationId": "train_v1_train_post", "requestBody": { "content": { "application/json": { "schema": { "anyOf": [ { "oneOf": [ { "$ref": "#/components/schemas/EmbeddingsV2CreateTrainingRequest" }, { "$ref": "#/components/schemas/EmbeddingsV3CreateTrainingRequest" }, { "$ref": "#/components/schemas/EmbeddingsV4CreateTrainingRequest" }, { "$ref": "#/components/schemas/EmbeddingsV5CreateTrainingRequest" }, { "$ref": "#/components/schemas/ClipCreateTrainingRequest" } ], "discriminator": { "propertyName": "model", "mapping": { "jina-embeddings-v2-base-en": "#/components/schemas/EmbeddingsV2CreateTrainingRequest", "jina-embeddings-v2-base-zh": "#/components/schemas/EmbeddingsV2CreateTrainingRequest", "jina-embeddings-v2-base-de": "#/components/schemas/EmbeddingsV2CreateTrainingRequest", "jina-embeddings-v2-base-es": "#/components/schemas/EmbeddingsV2CreateTrainingRequest", "jina-embeddings-v2-base-code": "#/components/schemas/EmbeddingsV2CreateTrainingRequest", "jina-embeddings-v3": "#/components/schemas/EmbeddingsV3CreateTrainingRequest", "jina-embeddings-v4": "#/components/schemas/EmbeddingsV4CreateTrainingRequest", "jina-embeddings-v5-text-nano": "#/components/schemas/EmbeddingsV5CreateTrainingRequest", "jina-embeddings-v5-text-small": "#/components/schemas/EmbeddingsV5CreateTrainingRequest", "jina-clip-v1": "#/components/schemas/ClipCreateTrainingRequest", "jina-clip-v2": "#/components/schemas/ClipCreateTrainingRequest" } } }, { "$ref": "#/components/schemas/UpdateTrainingRequest" } ], "title": "Training Request" } } }, "required": true }, "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/TrainingResponse" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] } }, "/v1/classifiers": { "get": { "tags": [ "Zero/Few-shot Classification" ], "summary": "List Classifiers", "description": "List all classifiers owned by the authenticated user.", "operationId": "list_classifiers_v1_classifiers_post", "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "items": { "$ref": "#/components/schemas/ClassifierSummary" }, "type": "array", "title": "Response List Classifiers V1 Classifiers Post" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] }, "post": { "tags": [ "Zero/Few-shot Classification" ], "summary": "List Classifiers", "description": "List all classifiers owned by the authenticated user.", "operationId": "list_classifiers_v1_classifiers_post", "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "items": { "$ref": "#/components/schemas/ClassifierSummary" }, "type": "array", "title": "Response List Classifiers V1 Classifiers Post" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] } }, "/v1/classifiers/{classifier_id}": { "delete": { "tags": [ "Zero/Few-shot Classification" ], "summary": "Delete Classifier", "description": "Delete a classifier by ID.", "operationId": "delete_classifier_v1_classifiers__classifier_id__delete", "security": [ { "ChatsaxKey": [] } ], "parameters": [ { "name": "classifier_id", "in": "path", "required": true, "schema": { "type": "string", "title": "Classifier Id" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "type": "object", "additionalProperties": true, "title": "Response Delete Classifier V1 Classifiers Classifier Id Delete" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } } } }, "/v1/models": { "get": { "tags": [ "Model List" ], "summary": "List Models", "description": "List all available Jina AI models.\n\nReturns model metadata in OpenRouter-compatible format including\nmodel IDs, input/output modalities, context lengths, and pricing.", "operationId": "list_models_v1_models_get", "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ModelListResponse" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [] } }, "/v1/models/{model_id}": { "get": { "tags": [ "Model List" ], "summary": "Get Model", "description": "Get details for a specific model.\n\nAccepts both full form (`jina-ai/jina-embeddings-v3`) and\nshort form (`jina-embeddings-v3`).", "operationId": "get_model_v1_models__model_id__get", "parameters": [ { "name": "model_id", "in": "path", "required": true, "schema": { "type": "string", "title": "Model Id" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ModelInfo" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [] } }, "/v1/chat/completions": { "post": { "summary": "Chat: jina-vlm or jina-deepsearch-v1, selected by model", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "required": [ "messages" ], "properties": { "model": { "type": "string" }, "messages": { "type": "array", "items": { "type": "object" } }, "stream": { "type": "boolean" }, "reasoning_effort": { "enum": [ "low", "medium", "high" ] }, "budget_tokens": { "type": "integer" }, "max_tokens": { "type": "integer" } }, "additionalProperties": true } } } } } }, "/v1/batch/embeddings": { "post": { "tags": [ "Batch Embeddings" ], "summary": "Create a batch embedding job", "description": "Submit a batch of texts for asynchronous embedding processing.", "operationId": "create_batch_job_v1_batch_embeddings_post", "requestBody": { "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BatchEmbeddingRequest" } } }, "required": true }, "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BatchStatus" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } }, "security": [ { "ChatsaxKey": [] } ] } }, "/v1/batch/{batch_id}": { "get": { "tags": [ "Batch Embeddings" ], "summary": "Get batch job status", "description": "Retrieve the status of a batch embedding job.", "operationId": "get_batch_status_v1_batch__batch_id__get", "security": [ { "ChatsaxKey": [] } ], "parameters": [ { "name": "batch_id", "in": "path", "required": true, "schema": { "type": "string", "title": "Batch Id" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BatchStatus" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } } }, "delete": { "tags": [ "Batch Embeddings" ], "summary": "Cancel a batch job", "description": "Cancel a pending or processing batch job.", "operationId": "cancel_batch_job_v1_batch__batch_id__delete", "security": [ { "ChatsaxKey": [] } ], "parameters": [ { "name": "batch_id", "in": "path", "required": true, "schema": { "type": "string", "title": "Batch Id" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/BatchStatus" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } } } }, "/v1/batches": { "get": { "tags": [ "Batch Embeddings" ], "summary": "List batch jobs", "description": "List recent batch jobs for the authenticated user.", "operationId": "list_batch_jobs_v1_batches_get", "security": [ { "ChatsaxKey": [] } ], "parameters": [ { "name": "limit", "in": "query", "required": false, "schema": { "type": "integer", "default": 20, "title": "Limit" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": { "type": "array", "items": { "$ref": "#/components/schemas/BatchStatus" }, "title": "Response List Batch Jobs V1 Batches Get" } } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } } } }, "/v1/batch/{batch_id}/output": { "get": { "tags": [ "Batch Embeddings" ], "summary": "Download batch job output", "description": "Stream the output JSONL file for a completed batch job.", "operationId": "download_batch_output_v1_batch__batch_id__output_get", "security": [ { "ChatsaxKey": [] } ], "parameters": [ { "name": "batch_id", "in": "path", "required": true, "schema": { "type": "string", "title": "Batch Id" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": {} } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } } } }, "/v1/batch/{batch_id}/errors": { "get": { "tags": [ "Batch Embeddings" ], "summary": "Download batch job error file", "description": "Stream the error JSONL file for a completed batch job.", "operationId": "download_batch_errors_v1_batch__batch_id__errors_get", "security": [ { "ChatsaxKey": [] } ], "parameters": [ { "name": "batch_id", "in": "path", "required": true, "schema": { "type": "string", "title": "Batch Id" } } ], "responses": { "200": { "description": "Successful Response", "content": { "application/json": { "schema": {} } } }, "422": { "description": "Validation Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/HTTPValidationError" } } } }, "429": { "$ref": "#/components/responses/TooManyRequests" }, "400": { "$ref": "#/components/responses/BadRequest" }, "401": { "$ref": "#/components/responses/Unauthorized" }, "403": { "$ref": "#/components/responses/Forbidden" }, "404": { "$ref": "#/components/responses/NotFound" }, "500": { "$ref": "#/components/responses/InternalServerError" }, "503": { "$ref": "#/components/responses/ServiceUnavailable" }, "504": { "$ref": "#/components/responses/GatewayTimeout" }, "409": { "$ref": "#/components/responses/Conflict" } } } }, "/v1/read": { "post": { "summary": "Read a web page", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "url": { "type": "string", "format": "uri" }, "html": { "type": "string" }, "pdf": { "type": "string" }, "viewport": { "type": "object" }, "injectPageScript": { "type": "array", "items": { "type": "string" } }, "instruction": { "type": "string" }, "jsonSchema": { "type": "object" } }, "additionalProperties": true } } } } }, "get": { "summary": "Query-parameter convenience endpoint", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "parameters": [ { "name": "url", "in": "query", "required": true, "schema": { "type": "string", "format": "uri" } } ] } }, "/v1/search": { "post": { "summary": "Search the web", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "q": { "type": "string" }, "num": { "type": "integer" }, "gl": { "type": "string" }, "hl": { "type": "string" }, "location": { "type": "string" }, "page": { "type": "integer" } }, "additionalProperties": true, "required": [ "q" ] } } } } }, "get": { "summary": "Query-parameter convenience endpoint", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "parameters": [ { "name": "q", "in": "query", "required": true, "schema": { "type": "string" } } ] } }, "/v1/segment": { "post": { "summary": "Tokenize and segment text", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "required": [ "content" ], "properties": { "content": { "type": "string" }, "tokenizer": { "type": "string", "default": "cl100k_base" }, "return_tokens": { "type": "boolean" }, "return_chunks": { "type": "boolean" }, "max_chunk_length": { "type": "integer", "description": "Maximum characters per chunk" } } } } } } } }, "/v1/deepsearch": { "post": { "summary": "Research with jina-deepsearch-v1; supports SSE", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "required": [ "messages" ], "properties": { "model": { "type": "string" }, "messages": { "type": "array", "items": { "type": "object" } }, "stream": { "type": "boolean" }, "reasoning_effort": { "enum": [ "low", "medium", "high" ] }, "budget_tokens": { "type": "integer" }, "max_tokens": { "type": "integer" } }, "additionalProperties": true } } } } } }, "/api/health": { "get": { "summary": "Gateway health and configuration", "security": [], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } } } }, "/api/capabilities": { "get": { "summary": "API capability discovery", "security": [], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } } } }, "/api/me": { "get": { "summary": "Current key metadata and 30-day usage", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } } } }, "/api/admin/keys": { "get": { "summary": "List managed API keys (administrator only)", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } } }, "post": { "summary": "Create a scoped API key (administrator only)", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "required": [ "name" ], "properties": { "name": { "type": "string" }, "scopes": { "type": "array", "items": { "type": "string" } }, "rate_limit_per_minute": { "type": "integer", "default": 60 }, "daily_request_limit": { "type": "integer", "default": 10000 } } } } } } } }, "/api/admin/keys/{id}": { "delete": { "summary": "Revoke a managed API key (administrator only)", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "parameters": [ { "name": "id", "in": "path", "required": true, "schema": { "type": "string" } } ] } }, "/api/admin/upstream": { "get": { "summary": "Verified provider token balance (administrator only)", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } } } }, "/mcp": { "post": { "summary": "Stateless MCP Streamable HTTP JSON-RPC", "security": [ { "ChatsaxKey": [] } ], "responses": { "200": { "description": "Success; response follows the provider contract." }, "400": { "description": "Invalid request" }, "401": { "description": "Missing or invalid Chatsax key" }, "403": { "description": "Insufficient scope" }, "429": { "description": "Request limit exceeded; see Retry-After" }, "502": { "description": "Provider failure" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "additionalProperties": true } } } } } }, "/api/auth/config": { "get": { "tags": [ "Account" ], "summary": "Available sign-in providers", "operationId": "chatsax_get_api_auth_config", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } } } }, "/api/auth/session": { "get": { "tags": [ "Account" ], "summary": "Current session", "operationId": "chatsax_get_api_auth_session", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } } } }, "/api/auth/register": { "post": { "tags": [ "Account" ], "summary": "Register and send a verification email", "operationId": "chatsax_post_api_auth_register", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Email links expire after 30 minutes. Successful login/reset sets a secure HttpOnly session cookie. Registration does not grant tokens before verification.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "email": { "type": "string", "format": "email" }, "password": { "type": "string", "format": "password", "minLength": 8 }, "consent": { "const": true }, "name": { "type": "string", "maxLength": 100 } }, "required": [ "email", "password", "consent" ] } } } } } }, "/api/auth/login": { "post": { "tags": [ "Account" ], "summary": "Sign in with email and password", "operationId": "chatsax_post_api_auth_login", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Email links expire after 30 minutes. Successful login/reset sets a secure HttpOnly session cookie. Registration does not grant tokens before verification.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "email": { "type": "string", "format": "email" }, "password": { "type": "string", "format": "password", "minLength": 8 } }, "required": [ "email", "password" ] } } } } } }, "/api/auth/forgot": { "post": { "tags": [ "Account" ], "summary": "Send a password reset link", "operationId": "chatsax_post_api_auth_forgot", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Email links expire after 30 minutes. Successful login/reset sets a secure HttpOnly session cookie. Registration does not grant tokens before verification.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "email": { "type": "string", "format": "email" } }, "required": [ "email" ] } } } } } }, "/api/auth/resend-verification": { "post": { "tags": [ "Account" ], "summary": "Resend an email verification link", "operationId": "chatsax_post_api_auth_resend-verification", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Email links expire after 30 minutes. Successful login/reset sets a secure HttpOnly session cookie. Registration does not grant tokens before verification.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "email": { "type": "string", "format": "email" } }, "required": [ "email" ] } } } } } }, "/api/auth/reset": { "post": { "tags": [ "Account" ], "summary": "Set a new password", "operationId": "chatsax_post_api_auth_reset", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Email links expire after 30 minutes. Successful login/reset sets a secure HttpOnly session cookie. Registration does not grant tokens before verification.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "token": { "type": "string", "minLength": 64, "maxLength": 64 }, "password": { "type": "string", "format": "password", "minLength": 8 } }, "required": [ "token", "password" ] } } } } } }, "/api/auth/logout": { "post": { "tags": [ "Account" ], "summary": "Sign out and revoke the current session", "operationId": "chatsax_post_api_auth_logout", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": {} } } } } } }, "/api/auth/profile": { "post": { "tags": [ "Account" ], "summary": "Update your profile", "operationId": "chatsax_post_api_auth_profile", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "name": { "type": "string", "minLength": 1, "maxLength": 100 } }, "required": [ "name" ] } } } } } }, "/api/auth/verify": { "get": { "tags": [ "Account" ], "summary": "Verify an email link", "operationId": "chatsax_get_api_auth_verify", "security": [], "responses": { "303": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Browser redirect endpoint. Consumes the verification link once, grants a 10M trial once per verified account, sets a session cookie and redirects to Key Manager.", "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string", "minLength": 64, "maxLength": 64 } } ] } }, "/api/auth/oauth/google": { "get": { "tags": [ "Account" ], "summary": "Start Google sign-in", "operationId": "chatsax_get_api_auth_oauth_google", "security": [], "responses": { "303": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Browser navigation endpoint. Redirects to the provider and stores a short-lived state cookie. The callback completes sign-in and redirects to Key Manager." } }, "/api/auth/oauth/github": { "get": { "tags": [ "Account" ], "summary": "Start Github sign-in", "operationId": "chatsax_get_api_auth_oauth_github", "security": [], "responses": { "303": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Browser navigation endpoint. Redirects to the provider and stores a short-lived state cookie. The callback completes sign-in and redirects to Key Manager." } }, "/api/account/overview": { "get": { "tags": [ "Account" ], "summary": "Account balance, recent ledger and purchases", "operationId": "chatsax_get_api_account_overview", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "wallet contains balance_tokens, reserved_tokens and available_tokens. Recent purchases and ledger are limited to 50 rows each." } }, "/api/account/keys": { "get": { "tags": [ "Account" ], "summary": "List your API keys", "operationId": "chatsax_get_api_account_keys", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Only prefixes and metadata are returned. At most 20 active keys per account." }, "post": { "tags": [ "Account" ], "summary": "Create an API key", "operationId": "chatsax_post_api_account_keys", "security": [ { "AccountSession": [] } ], "responses": { "201": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Full plaintext key is returned only once. Keys share the account token balance, with individual input and request budgets.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "name": { "type": "string", "minLength": 1, "maxLength": 100 }, "max_input_bytes": { "type": "integer", "minimum": 1024, "maximum": 8388608, "default": 2097152 }, "max_request_tokens": { "type": "integer", "minimum": 10000, "maximum": 10000000, "default": 10000000 } }, "required": [ "name" ] } } } } } }, "/api/account/keys/{id}": { "patch": { "tags": [ "Account" ], "summary": "Rename a key or change request budgets", "operationId": "chatsax_patch_api_account_keys_id", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "name": { "type": "string", "minLength": 1, "maxLength": 100 }, "max_input_bytes": { "type": "integer", "minimum": 1024, "maximum": 8388608, "default": 2097152 }, "max_request_tokens": { "type": "integer", "minimum": 10000, "maximum": 10000000, "default": 10000000 } } } } } }, "parameters": [ { "name": "id", "in": "path", "required": true, "schema": { "type": "string" } } ] }, "delete": { "tags": [ "Account" ], "summary": "Revoke an API key", "operationId": "chatsax_delete_api_account_keys_id", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "parameters": [ { "name": "id", "in": "path", "required": true, "schema": { "type": "string" } } ] } }, "/api/billing/plans": { "get": { "tags": [ "Billing" ], "summary": "Token packages and available payment methods", "operationId": "chatsax_get_api_billing_plans", "security": [], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } } } }, "/api/billing/checkout": { "post": { "tags": [ "Billing" ], "summary": "Create a token purchase checkout", "operationId": "chatsax_post_api_billing_checkout", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Creates a payable checkout; does not itself charge. Card prices are USD/EUR. Alipay and WeChat settle in CNY using the disclosed fixed merchant rate. Returned fields include order_id, currency and amount_minor, plus a redirect URL and/or QR data. Tokens are credited only after verified payment.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "plan": { "type": "string", "enum": [ "standard", "scale" ] }, "currency": { "type": "string", "enum": [ "usd", "eur" ], "default": "usd" }, "method": { "type": "string", "enum": [ "card", "alipay", "wxpay" ], "default": "card" } }, "required": [ "plan" ] } } } } } }, "/api/billing/order": { "get": { "tags": [ "Billing" ], "summary": "Check one of your payment orders", "operationId": "chatsax_get_api_billing_order", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Retrieves the order and wallet; reconciles Stripe state when necessary. A success return URL alone never credits tokens.", "parameters": [ { "name": "id", "in": "query", "required": true, "schema": { "type": "string" } } ] } }, "/api/billing/portal": { "post": { "tags": [ "Billing" ], "summary": "Open your Stripe billing portal", "operationId": "chatsax_post_api_billing_portal", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Requires a Stripe customer from a prior card purchase or card setup. Returns a short-lived provider URL for billing and invoice management.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": {} } } } } } }, "/api/billing/auto-recharge": { "get": { "tags": [ "Billing" ], "summary": "Automatic recharge settings and saved card summary", "operationId": "chatsax_get_api_billing_auto-recharge", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } } }, "post": { "tags": [ "Billing" ], "summary": "Enable or disable automatic recharge", "operationId": "chatsax_post_api_billing_auto-recharge", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Enable requires a consented saved card, consent=true and all settings. Threshold must not exceed the package size. To stop new automatic charges, send only {\"enabled\":false}. A decline or required card authentication pauses automatic recharge.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "enabled": { "type": "boolean" }, "consent": { "type": "boolean" }, "threshold_tokens": { "type": "integer", "minimum": 1 }, "plan": { "type": "string", "enum": [ "standard", "scale" ] }, "currency": { "type": "string", "enum": [ "usd", "eur" ] }, "max_daily_charges": { "type": "integer", "minimum": 1, "maximum": 3 } }, "required": [ "enabled" ] } } } } } }, "/api/billing/payment-method/setup": { "post": { "tags": [ "Billing" ], "summary": "Start consented card setup", "operationId": "chatsax_post_api_billing_payment-method_setup", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "description": "Returns a Stripe setup checkout URL. Saving a card does not purchase tokens or enable automatic recharge.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "consent": { "const": true } }, "required": [ "consent" ] } } } } }, "get": { "tags": [ "Billing" ], "summary": "Reconcile your card setup", "operationId": "chatsax_get_api_billing_payment-method_setup", "security": [ { "AccountSession": [] } ], "responses": { "200": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" }, "401": { "description": "Sign in with a verified account" } }, "parameters": [ { "name": "id", "in": "query", "required": true, "schema": { "type": "string" } } ] } }, "/api/contact": { "post": { "tags": [ "Support" ], "summary": "Submit a private support request", "operationId": "chatsax_post_api_contact", "security": [], "responses": { "201": { "description": "Success", "content": { "application/json": { "schema": { "type": "object" } } } }, "400": { "description": "Invalid input" }, "429": { "description": "Request limit reached" } }, "description": "Stored privately with a ticket reference. Limited to 5 requests per IP and 3 per email per hour. Do not include payment card details or API secrets.", "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "email": { "type": "string", "format": "email" }, "category": { "type": "string", "enum": [ "integration", "billing", "account", "privacy", "security" ] }, "subject": { "type": "string", "minLength": 3, "maxLength": 200 }, "message": { "type": "string", "minLength": 10, "maxLength": 6000 } }, "required": [ "email", "subject", "message" ] } } } } } } }, "components": { "schemas": { "AudioDoc": { "properties": { "audio": { "type": "string", "title": "Audio", "description": "Audio as a URL or base64-encoded string." } }, "type": "object", "required": [ "audio" ], "title": "AudioDoc" }, "BaseUsage": { "properties": { "total_tokens": { "type": "integer", "title": "Total Tokens", "description": "Total number of tokens processed." } }, "type": "object", "required": [ "total_tokens" ], "title": "BaseUsage" }, "BatchEmbeddingRequest": { "properties": { "model": { "type": "string", "enum": [ "jina-embeddings-v5-text-nano", "jina-embeddings-v5-text-small" ], "title": "Model", "description": "The embedding model to use for batch processing." }, "input_url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Input Url", "description": "URL to input JSONL file (GCS, S3, or HTTP). Either input_url or input must be provided." }, "input": { "anyOf": [ { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ], "title": "Input", "description": "Inline JSONL lines for small batches. Either input_url or input must be provided." }, "task": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Task", "description": "Task optimization: retrieval, text-matching, clustering, or classification.", "default": "text-matching" }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 1024.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for output embeddings (1-1024)." }, "normalized": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Normalized", "description": "If true (default), embeddings are L2-normalized to unit length.", "default": true }, "webhook_url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Webhook Url", "description": "URL to POST notification when job completes." } }, "type": "object", "required": [ "model" ], "title": "BatchEmbeddingRequest", "description": "Request to create a batch embedding job.", "examples": [ { "dimensions": 512, "input_url": "gs://my-bucket/inputs.jsonl", "model": "jina-embeddings-v5-text-small", "task": "retrieval" }, { "input": [ { "body": { "input": "Hello world" }, "custom_id": "req-1" }, { "body": { "input": "Batch embeddings" }, "custom_id": "req-2" } ], "model": "jina-embeddings-v5-text-nano", "task": "text-matching" } ] }, "BatchStats": { "properties": { "total": { "type": "integer", "title": "Total", "description": "Total number of input lines." }, "completed": { "type": "integer", "title": "Completed", "description": "Number of successfully processed lines.", "default": 0 }, "failed": { "type": "integer", "title": "Failed", "description": "Number of failed lines.", "default": 0 }, "total_tokens": { "type": "integer", "title": "Total Tokens", "description": "Total tokens consumed across all processed lines.", "default": 0 } }, "type": "object", "required": [ "total" ], "title": "BatchStats", "description": "Statistics for a batch job." }, "BatchStatus": { "properties": { "batch_id": { "type": "string", "title": "Batch Id", "description": "Unique identifier for the batch job." }, "status": { "type": "string", "enum": [ "pending", "processing", "completed", "failed", "expired", "cancelled" ], "title": "Status", "description": "Current status of the batch job." }, "model": { "type": "string", "title": "Model", "description": "The model used for embedding." }, "created_at": { "type": "string", "title": "Created At", "description": "ISO 8601 timestamp when job was created." }, "completed_at": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Completed At", "description": "ISO 8601 timestamp when job completed (if finished)." }, "expires_at": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Expires At", "description": "ISO 8601 timestamp when output URL expires (created_at + 24h)." }, "output_url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Output Url", "description": "Signed URL to download output JSONL (24h expiry)." }, "error_file_url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Error File Url", "description": "URL to download error JSONL for failed lines." }, "error": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "title": "Error", "description": "Error message if job failed." }, "stats": { "anyOf": [ { "$ref": "#/components/schemas/BatchStats" }, { "type": "null" } ], "description": "Processing statistics." } }, "type": "object", "required": [ "batch_id", "status", "model", "created_at" ], "title": "BatchStatus", "description": "Status of a batch embedding job.", "examples": [ { "batch_id": "batch_abc123", "completed_at": "2024-01-15T10:35:00Z", "created_at": "2024-01-15T10:30:00Z", "expires_at": "2024-01-16T10:30:00Z", "model": "jina-embeddings-v5-text-small", "output_url": "https://storage.googleapis.com/...", "stats": { "completed": 998, "failed": 2, "total": 1000 }, "status": "completed" } ] }, "ClassificationPrediction": { "properties": { "object": { "type": "string", "title": "Object", "description": "Object type, always `classification`.", "default": "classification" }, "index": { "type": "integer", "title": "Index", "description": "Position of this result in the input list." }, "prediction": { "anyOf": [ { "type": "string" }, { "additionalProperties": { "type": "string" }, "type": "object" } ], "title": "Prediction", "description": "Predicted label, or a dict of labels per group." }, "score": { "anyOf": [ { "type": "number" }, { "additionalProperties": { "type": "number" }, "type": "object" } ], "title": "Score", "description": "Confidence score, or a dict of scores per group." }, "predictions": { "anyOf": [ { "items": { "$ref": "#/components/schemas/ClassificationPredictionLabel" }, "type": "array" }, { "additionalProperties": { "items": { "$ref": "#/components/schemas/ClassificationPredictionLabel" }, "type": "array" }, "type": "object" } ], "title": "Predictions", "description": "All candidate labels with scores, or a dict per group." } }, "type": "object", "required": [ "index", "prediction", "score", "predictions" ], "title": "ClassificationPrediction", "description": "Classification result for one input item." }, "ClassificationPredictionLabel": { "properties": { "label": { "type": "string", "title": "Label", "description": "The candidate label." }, "score": { "type": "number", "title": "Score", "description": "Confidence score for this label." } }, "type": "object", "required": [ "label", "score" ], "title": "ClassificationPredictionLabel", "description": "A label with its confidence score." }, "ClassificationResponse": { "properties": { "data": { "items": { "$ref": "#/components/schemas/ClassificationPrediction" }, "type": "array", "title": "Data", "description": "Classification results, one per input item." }, "usage": { "$ref": "#/components/schemas/BaseUsage", "description": "Token usage statistics." } }, "type": "object", "required": [ "data", "usage" ], "title": "ClassificationResponse", "description": "Response containing classification results." }, "ClassifierSummary": { "properties": { "classifier_id": { "type": "string", "format": "uuid", "title": "Classifier Id", "description": "Unique identifier for the classifier" }, "user_id": { "type": "string", "format": "uuid", "title": "User Id", "description": "User ID who owns the classifier" }, "model": { "type": "string", "title": "Model", "description": "Name of the model used" }, "labels": { "items": { "type": "string" }, "type": "array", "title": "Labels", "description": "List of classification labels" }, "access": { "type": "string", "title": "Access", "description": "Access level (e.g., private, public)" }, "updated_number": { "type": "integer", "title": "Updated Number", "description": "Number of times updated" }, "used_number": { "type": "integer", "title": "Used Number", "description": "Number of times used" }, "created_at": { "type": "string", "format": "date-time", "title": "Created At", "description": "Creation timestamp" }, "updated_at": { "type": "string", "format": "date-time", "title": "Updated At", "description": "Last update timestamp" }, "used_at": { "anyOf": [ { "type": "string", "format": "date-time" }, { "type": "null" } ], "title": "Used At", "description": "Last used timestamp" }, "is_active": { "type": "boolean", "title": "Is Active", "description": "Whether the classifier is currently active", "default": true }, "is_latest": { "type": "boolean", "title": "Is Latest", "description": "Whether this is the latest version of the classifier", "default": true }, "metadata_": { "additionalProperties": true, "type": "object", "title": "Metadata", "description": "Additional metadata" } }, "type": "object", "required": [ "classifier_id", "user_id", "model", "labels", "access", "updated_number", "used_number", "created_at", "updated_at" ], "title": "ClassifierSummary" }, "ClipCreateTrainingRequest": { "properties": { "access": { "type": "string", "title": "Access", "description": "Access level for the training data. Can be 'public' or 'private'.", "default": "public" }, "num_iters": { "type": "integer", "title": "Num Iters", "description": "Number of iterations for the training process.", "default": 10 }, "model": { "type": "string", "enum": [ "jina-clip-v1", "jina-clip-v2" ], "title": "Model", "description": "CLIP model for zero-shot classification of images and text." }, "input": { "items": { "anyOf": [ { "$ref": "#/components/schemas/TextTrainingItem" }, { "$ref": "#/components/schemas/ImageTrainingItem" } ] }, "type": "array", "maxItems": 512, "minItems": 2, "title": "Input", "description": "Must be a list of text-label or image-label training items with at least two unique labels. For batch training, provide a list with up to 512 items. Each image can be up to 8MB." } }, "type": "object", "required": [ "model", "input" ], "title": "ClipCreateTrainingRequest", "example": { "access": "public", "input": [ { "image": "https://images.unsplash.com/photo-1518717758536-85ae29035b6d", "label": "Cat" }, { "label": "Art", "text": "A painting of a sunset over the mountains." } ], "model": "jina-clip-v2", "num_iters": 12 } }, "ClipV1Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "normalized": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Normalized", "description": "If true (default), embeddings are L2-normalized to unit length.", "default": true }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "model": { "type": "string", "const": "jina-clip-v1", "title": "Model", "description": "The CLIP model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "$ref": "#/components/schemas/PDFDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Content to embed: a string, `TextDoc`, `ImageDoc`, `PDFDoc`, or a list of items. PDFs must be sent individually. Images up to 8MB." } }, "type": "object", "required": [ "model", "input" ], "title": "ClipV1Request", "description": "CLIP v1 multimodal model for images and text in a shared vector space.", "example": { "embedding_type": "float", "input": [ { "image": "https://i.ibb.co/nQNGqL0/beach1.jpg" }, "A beautiful sunset over the beach" ], "model": "jina-clip-v1", "normalized": true } }, "ClipV2Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "normalized": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Normalized", "description": "If true (default), embeddings are L2-normalized to unit length.", "default": true }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "model": { "type": "string", "const": "jina-clip-v2", "title": "Model", "description": "The CLIP model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "$ref": "#/components/schemas/PDFDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Content to embed: a string, `TextDoc`, `ImageDoc`, `PDFDoc`, or a list of items. PDFs must be sent individually. Images up to 8MB." }, "task": { "anyOf": [ { "type": "string", "const": "retrieval.query" }, { "type": "null" } ], "title": "Task", "description": "Set to `retrieval.query` to optimize for search queries. Leave unset for documents." }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 1024.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for the output embedding. Range: 1-1024." } }, "type": "object", "required": [ "model", "input" ], "title": "ClipV2Request", "description": "CLIP v2 with Matryoshka representation learning for flexible dimensions.", "example": { "dimensions": 512, "embedding_type": "float", "input": [ { "image": "https://i.ibb.co/nQNGqL0/beach1.jpg" }, "Jina AI - Your Search Foundation - Supercharged" ], "model": "jina-clip-v2", "normalized": true, "task": "retrieval.query" } }, "ClipZeroShotClassificationRequest": { "properties": { "labels": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "type": "object" } ], "title": "Labels", "description": "Candidate labels: a list of up to 512 labels, or a dict of up to 8 groups with max 64 labels each." }, "model": { "type": "string", "enum": [ "jina-clip-v1", "jina-clip-v2" ], "title": "Model", "description": "The CLIP model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Content to classify: a string, `TextDoc`, `ImageDoc`, or list of up to 512 items. Images up to 8MB." } }, "type": "object", "required": [ "labels", "model", "input" ], "title": "ClipZeroShotClassificationRequest", "description": "Zero-shot classification using CLIP for text and images.", "example": { "input": [ "A photo of a cat sitting on a windowsill.", { "image": "https://images.unsplash.com/photo-1518717758536-85ae29035b6d" } ], "labels": [ "Animal", "Object", "Scene" ], "model": "jina-clip-v2" } }, "CodeEmbeddings1500MRequest": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "model": { "type": "string", "const": "jina-code-embeddings-1.5b", "title": "Model", "description": "The code embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Code or text to embed: a string, `TextDoc`, or a list of items." }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 1536.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for the output embedding. Range: 1-1536." }, "task": { "anyOf": [ { "type": "string", "enum": [ "nl2code.query", "nl2code.passage", "qa.query", "qa.passage", "code2code.query", "code2code.passage", "code2nl.query", "code2nl.passage", "code2completion.query", "code2completion.passage" ] }, { "type": "null" } ], "title": "Task", "description": "Task type: `nl2code`, `qa`, `code2code`, `code2nl`, or `code2completion`. Use `.query` for queries, `.passage` for documents.", "default": "nl2code.query" } }, "type": "object", "required": [ "model", "input" ], "title": "CodeEmbeddings1500MRequest", "description": "Code embedding model (1.5B) for code search and understanding.", "examples": [ { "dimensions": 512, "input": [ "import * as ElementPlusIconsVue from '@element-plus/icons-vue'\nconst app = createApp(App)\nfor (const [key, component] of Object.entries(ElementPlusIconsVue)) {\n app.component(key, component)\n}" ], "model": "jina-code-embeddings-1.5b", "task": "nl2code.passage" } ] }, "CodeEmbeddings500MRequest": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "model": { "type": "string", "const": "jina-code-embeddings-0.5b", "title": "Model", "description": "The code embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Code or text to embed: a string, `TextDoc`, or a list of items." }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 896.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for the output embedding. Range: 1-896." }, "task": { "anyOf": [ { "type": "string", "enum": [ "nl2code.query", "nl2code.passage", "qa.query", "qa.passage", "code2code.query", "code2code.passage", "code2nl.query", "code2nl.passage", "code2completion.query", "code2completion.passage" ] }, { "type": "null" } ], "title": "Task", "description": "Task type: `nl2code`, `qa`, `code2code`, `code2nl`, or `code2completion`. Use `.query` for queries, `.passage` for documents.", "default": "nl2code.query" } }, "type": "object", "required": [ "model", "input" ], "title": "CodeEmbeddings500MRequest", "description": "Code embedding model (500M) for code search and understanding.", "examples": [ { "dimensions": 512, "embedding_type": "float", "input": [ "import * as ElementPlusIconsVue from '@element-plus/icons-vue'\nconst app = createApp(App)\nfor (const [key, component] of Object.entries(ElementPlusIconsVue)) {\n app.component(key, component)\n}" ], "model": "jina-code-embeddings-0.5b", "task": "nl2code.passage" } ] }, "CodeEmbeddingsZeroShotClassificationRequest": { "properties": { "labels": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "type": "object" } ], "title": "Labels", "description": "Candidate labels: a list of up to 512 labels, or a dict of up to 8 groups with max 64 labels each." }, "model": { "type": "string", "enum": [ "jina-code-embeddings-0.5b", "jina-code-embeddings-1.5b" ], "title": "Model", "description": "The code embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Code to classify: a string, `TextDoc`, or list of up to 512 items." } }, "type": "object", "required": [ "labels", "model", "input" ], "title": "CodeEmbeddingsZeroShotClassificationRequest", "description": "Zero-shot classification using code embedding models.", "example": { "input": [ "def hello_world():\n print('Hello, World!')", "SELECT * FROM users WHERE id = 1;" ], "labels": [ "Python", "JavaScript", "SQL", "Rust" ], "model": "jina-code-embeddings-0.5b" } }, "ColbertV1Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "model": { "type": "string", "const": "jina-colbert-v1-en", "title": "Model", "description": "The ColBERT model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to embed: a string, `TextDoc`, or a list of items." }, "input_type": { "anyOf": [ { "type": "string", "enum": [ "query", "document" ] }, { "type": "null" } ], "title": "Input Type", "description": "Role of the input: `query` for search queries, `document` for passages.", "default": "document" } }, "type": "object", "required": [ "model", "input" ], "title": "ColbertV1Request", "description": "ColBERT v1 for token-level late interaction retrieval.", "example": { "embedding_type": "float", "input": [ "A beautiful sunset over the beach", "Jina AI - Your Search Foundation - Supercharged" ], "input_type": "document", "model": "jina-colbert-v1-en" } }, "ColbertV2Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "model": { "type": "string", "const": "jina-colbert-v2", "title": "Model", "description": "The ColBERT model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to embed: a string, `TextDoc`, or a list of items." }, "input_type": { "anyOf": [ { "type": "string", "enum": [ "query", "document" ] }, { "type": "null" } ], "title": "Input Type", "description": "Role of the input: `query` for search queries, `document` for passages.", "default": "document" }, "dimensions": { "anyOf": [ { "type": "integer", "enum": [ 64, 96, 128 ] }, { "type": "null" } ], "title": "Dimensions", "description": "Dimensions per token: `64`, `96`, or `128`. Defaults to 128." } }, "type": "object", "required": [ "model", "input" ], "title": "ColbertV2Request", "description": "ColBERT v2 with Matryoshka representation learning for flexible dimensions.", "example": { "dimensions": 128, "embedding_type": "float", "input": [ "A beautiful sunset over the beach" ], "input_type": "query", "model": "jina-colbert-v2" } }, "ELSERV2EmbeddingUsage": { "properties": { "total_tokens": { "type": "integer", "title": "Total Tokens", "description": "Total number of tokens processed." }, "input_tokens": { "type": "integer", "title": "Input Tokens", "description": "Number of tokens in the input." }, "output_tokens": { "type": "integer", "title": "Output Tokens", "description": "Number of tokens in the output." } }, "type": "object", "required": [ "total_tokens", "input_tokens", "output_tokens" ], "title": "ELSERV2EmbeddingUsage", "description": "Token usage statistics for Elser v2 embedding requests." }, "ELSERV2Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "model": { "type": "string", "const": "elser-v2", "title": "Model", "description": "The sparse embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to embed: a string, `TextDoc`, or a list of items." }, "task": { "anyOf": [ { "type": "string", "enum": [ "retrieval.query", "retrieval.passage" ] }, { "type": "null" } ], "title": "Task", "description": "Task optimization: `retrieval.query` for queries, `retrieval.passage` for documents." } }, "type": "object", "required": [ "model", "input" ], "title": "ELSERV2Request", "description": "ELSER v2 request schema.", "example": { "input": [ "What is machine learning?", "How does AI work?" ], "model": "elser-v2", "task": "retrieval.query" } }, "EmbeddingResponse": { "properties": { "model": { "type": "string", "title": "Model", "description": "The model used to generate the embeddings." }, "object": { "type": "string", "title": "Object", "description": "Object type, always `list`.", "default": "list" }, "usage": { "anyOf": [ { "$ref": "#/components/schemas/EmbeddingUsage" }, { "$ref": "#/components/schemas/ELSERV2EmbeddingUsage" }, { "$ref": "#/components/schemas/BaseUsage" } ], "title": "Usage", "description": "Token usage statistics." }, "data": { "anyOf": [ { "items": { "$ref": "#/components/schemas/SingleEmbeddingData" }, "type": "array" }, { "items": { "$ref": "#/components/schemas/MultiEmbeddingData" }, "type": "array" }, { "items": { "additionalProperties": { "type": "number" }, "type": "object" }, "type": "array" } ], "title": "Data", "description": "List of embeddings, one per input item. For sparse embeddings (elser-v2), this is a list of dicts mapping tokens to scores." } }, "type": "object", "required": [ "model", "usage", "data" ], "title": "EmbeddingResponse", "description": "Response containing embeddings for all input items." }, "EmbeddingUsage": { "properties": { "total_tokens": { "type": "integer", "title": "Total Tokens", "description": "Total number of tokens processed." }, "prompt_tokens": { "type": "integer", "title": "Prompt Tokens", "description": "Number of text input tokens. For multimodal responses the per-modality fields below account for non-text tokens; for text-only responses this equals `total_tokens`." }, "image_tokens": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "title": "Image Tokens", "description": "Number of tokens from image inputs." }, "audio_tokens": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "title": "Audio Tokens", "description": "Number of tokens from audio inputs." }, "video_tokens": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "title": "Video Tokens", "description": "Number of tokens from video inputs." } }, "type": "object", "required": [ "total_tokens", "prompt_tokens" ], "title": "EmbeddingUsage", "description": "Token usage statistics for embedding requests.\n\n`prompt_tokens` counts text input tokens (including the task prefix and\nany structural/special framing). For text-only models \u2014 and any response\nthat doesn't populate a per-modality breakdown \u2014 it equals `total_tokens`.\nFor multimodal v5-omni responses, `total_tokens` is the aggregate across\nall modalities and the per-modality fields (`image_tokens` /\n`audio_tokens` / `video_tokens`) are sub-counts, so the invariant holds\nexactly (absent per-modality fields count as 0):\n\n total_tokens == prompt_tokens + image_tokens + audio_tokens + video_tokens\n\nPDF pages are rendered to images and counted in `image_tokens`.\n\nAll MM fields are Optional so text-only responses don't have to populate\nthem, and serializers should drop unset fields (`exclude_none=True`)." }, "EmbeddingsV2CreateTrainingRequest": { "properties": { "access": { "type": "string", "title": "Access", "description": "Access level for the training data. Can be 'public' or 'private'.", "default": "public" }, "num_iters": { "type": "integer", "title": "Num Iters", "description": "Number of iterations for the training process.", "default": 10 }, "model": { "type": "string", "enum": [ "jina-embeddings-v2-base-en", "jina-embeddings-v2-base-zh", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-es", "jina-embeddings-v2-base-code" ], "title": "Model", "description": "Text embedding model for zero-shot classification." }, "input": { "items": { "$ref": "#/components/schemas/TextTrainingItem" }, "type": "array", "maxItems": 512, "minItems": 2, "title": "Input", "description": "Must be a list of text-label training items with at least two unique labels. For batch training, provide a list with up to 512 items." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV2CreateTrainingRequest", "example": { "access": "private", "input": [ { "label": "Biology", "text": "Explain the process of photosynthesis." }, { "label": "Geography", "text": "What is the capital of France?" } ], "model": "jina-embeddings-v2-base-en", "num_iters": 15 } }, "EmbeddingsV2Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "normalized": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Normalized", "description": "If true (default), embeddings are L2-normalized to unit length.", "default": true }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "model": { "type": "string", "enum": [ "jina-embeddings-v2-base-en", "jina-embeddings-v2-base-zh", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-es", "jina-embeddings-v2-base-code" ], "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to embed: a string, `TextDoc`, or a list of items." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV2Request", "description": "Jina Embeddings v2 text embedding models.", "example": { "embedding_type": "float", "input": [ "A beautiful sunset over the beach", "Jina AI - Your Search Foundation - Supercharged" ], "model": "jina-embeddings-v2-base-en", "normalized": true, "truncate": false } }, "EmbeddingsV2ZeroShotClassificationRequest": { "properties": { "labels": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "type": "object" } ], "title": "Labels", "description": "Candidate labels: a list of up to 512 labels, or a dict of up to 8 groups with max 64 labels each." }, "model": { "type": "string", "enum": [ "jina-embeddings-v2-base-en", "jina-embeddings-v2-base-zh", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-es", "jina-embeddings-v2-base-code" ], "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to classify: a string, `TextDoc`, or list of up to 512 items." } }, "type": "object", "required": [ "labels", "model", "input" ], "title": "EmbeddingsV2ZeroShotClassificationRequest", "description": "Zero-shot classification using Embeddings v2.", "example": { "input": [ "Write a Python function to reverse a string.", "Summarize the main findings of the research paper." ], "labels": [ "Simple task", "Complex reasoning", "Creative writing" ], "model": "jina-embeddings-v2-base-en" } }, "EmbeddingsV3CreateTrainingRequest": { "properties": { "access": { "type": "string", "title": "Access", "description": "Access level for the training data. Can be 'public' or 'private'.", "default": "public" }, "num_iters": { "type": "integer", "title": "Num Iters", "description": "Number of iterations for the training process.", "default": 10 }, "model": { "type": "string", "const": "jina-embeddings-v3", "title": "Model", "description": "Text embedding model for zero-shot classification." }, "input": { "items": { "$ref": "#/components/schemas/TextTrainingItem" }, "type": "array", "maxItems": 512, "minItems": 2, "title": "Input", "description": "Must be a list of text-label training items with at least two unique labels. For batch training, provide a list with up to 512 items." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV3CreateTrainingRequest", "example": { "access": "public", "input": [ { "label": "Biology", "text": "Describe the structure of a plant cell." }, { "label": "Creative writing", "text": "Write a poem about the ocean." } ], "model": "jina-embeddings-v3", "num_iters": 20 } }, "EmbeddingsV3Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "normalized": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Normalized", "description": "If true (default), embeddings are L2-normalized to unit length.", "default": true }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "model": { "type": "string", "const": "jina-embeddings-v3", "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to embed: a string, `TextDoc`, or a list of items." }, "task": { "anyOf": [ { "type": "string", "enum": [ "retrieval.query", "retrieval.passage", "text-matching", "classification", "separation" ] }, { "type": "null" } ], "title": "Task", "description": "Task optimization: `retrieval.query` for queries, `retrieval.passage` for documents, `text-matching` for similarity, `classification`, or `separation` for clustering." }, "late_chunking": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Late Chunking", "description": "If true, concatenates all inputs and processes as one sequence before splitting. Useful for context across chunks." }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 1024.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for the output embedding. Range: 1-1024." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV3Request", "description": "Jina Embeddings v3 with task-specific optimization and flexible dimensions.", "example": { "dimensions": 512, "embedding_type": "float", "input": [ "A beautiful sunset over the beach" ], "late_chunking": false, "model": "jina-embeddings-v3", "normalized": true, "task": "retrieval.query" } }, "EmbeddingsV3ZeroShotClassificationRequest": { "properties": { "labels": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "type": "object" } ], "title": "Labels", "description": "Candidate labels: a list of up to 512 labels, or a dict of up to 8 groups with max 64 labels each." }, "model": { "type": "string", "const": "jina-embeddings-v3", "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to classify: a string, `TextDoc`, or list of up to 512 items." } }, "type": "object", "required": [ "labels", "model", "input" ], "title": "EmbeddingsV3ZeroShotClassificationRequest", "description": "Zero-shot classification using Embeddings v3.", "example": { "input": [ "Explain the theory of relativity in simple terms.", "Design a database schema for an e-commerce platform." ], "labels": [ "Technical explanation", "Creative writing", "Business solution" ], "model": "jina-embeddings-v3" } }, "EmbeddingsV4CreateTrainingRequest": { "properties": { "access": { "type": "string", "title": "Access", "description": "Access level for the training data. Can be 'public' or 'private'.", "default": "public" }, "num_iters": { "type": "integer", "title": "Num Iters", "description": "Number of iterations for the training process.", "default": 10 }, "model": { "type": "string", "const": "jina-embeddings-v4", "title": "Model", "description": "Multimodal embedding model for zero-shot classification." }, "input": { "items": { "anyOf": [ { "$ref": "#/components/schemas/TextTrainingItem" }, { "$ref": "#/components/schemas/ImageTrainingItem" } ] }, "type": "array", "maxItems": 512, "minItems": 2, "title": "Input", "description": "Must be a list of text-label or image-label training items with at least two unique labels. For batch training, provide a list with up to 512 items." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV4CreateTrainingRequest", "example": { "access": "private", "input": [ { "label": "Dog", "text": "A dog playing in the park." }, { "image": "https://images.unsplash.com/photo-1465101046530-73398c7f28ca", "label": "Rocket" } ], "model": "jina-embeddings-v4", "num_iters": 25 } }, "EmbeddingsV4Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "model": { "type": "string", "const": "jina-embeddings-v4", "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "$ref": "#/components/schemas/PDFDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Content to embed: a string, `TextDoc`, `ImageDoc`, `PDFDoc`, or a list of items. PDFs must be sent individually. Images up to 8MB; PDFs up to 8MB." }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "late_chunking": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Late Chunking", "description": "If true, concatenates all text inputs and processes as one sequence before splitting. Only works with text." }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 2048.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for the output embedding. Range: 1-2048." }, "task": { "anyOf": [ { "type": "string", "enum": [ "text-matching", "retrieval.query", "retrieval.passage", "code.query", "code.passage" ] }, { "type": "null" } ], "title": "Task", "description": "Task optimization: `text-matching` (default), `retrieval.query`, `retrieval.passage`, `code.query`, or `code.passage`.", "default": "text-matching" }, "return_multivector": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Return Multivector", "description": "If true, returns one embedding per token. Cannot be used with `dimensions`.", "default": false }, "return_tokenized_input": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Return Tokenized Input", "description": "If true, returns tokens alongside multi-vector embeddings. Requires `return_multivector=true`.", "default": false } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV4Request", "description": "Jina Embeddings v4 multimodal model for text, images, and PDFs.", "examples": [ { "dimensions": 512, "embedding_type": "float", "input": [ { "image": "https://i.ibb.co/nQNGqL0/beach1.jpg" }, { "text": "A beautiful sunset over the beach" } ], "model": "jina-embeddings-v4" }, { "embedding_type": "float", "input": [ "Jina AI - Your Search Foundation - Supercharged" ], "late_chunking": true, "model": "jina-embeddings-v4", "task": "retrieval.passage" }, { "dimensions": 512, "embedding_type": "float", "input": { "pdf": "https://example.com/sample.pdf" }, "model": "jina-embeddings-v4" } ] }, "EmbeddingsV4ZeroShotClassificationRequest": { "properties": { "labels": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "type": "object" } ], "title": "Labels", "description": "Candidate labels: a list of up to 512 labels, or a dict of up to 8 groups with max 64 labels each." }, "model": { "type": "string", "const": "jina-embeddings-v4", "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to classify: a string, `TextDoc`, or list of up to 512 items." } }, "type": "object", "required": [ "labels", "model", "input" ], "title": "EmbeddingsV4ZeroShotClassificationRequest", "description": "Zero-shot classification using Embeddings v4.", "example": { "input": [ "Describe the process of photosynthesis.", "Analyze the sentiment of the following review: 'The product exceeded my expectations.'" ], "labels": [ "Factual answer", "Sentiment analysis", "Marketing content" ], "model": "jina-embeddings-v4" } }, "EmbeddingsV5CreateTrainingRequest": { "properties": { "access": { "type": "string", "title": "Access", "description": "Access level for the training data. Can be 'public' or 'private'.", "default": "public" }, "num_iters": { "type": "integer", "title": "Num Iters", "description": "Number of iterations for the training process.", "default": 10 }, "model": { "type": "string", "enum": [ "jina-embeddings-v5-text-nano", "jina-embeddings-v5-text-small" ], "title": "Model", "description": "Text embedding model for zero-shot classification." }, "input": { "items": { "$ref": "#/components/schemas/TextTrainingItem" }, "type": "array", "maxItems": 512, "minItems": 2, "title": "Input", "description": "Must be a list of text-label training items with at least two unique labels. For batch training, provide a list with up to 512 items." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV5CreateTrainingRequest", "example": { "access": "public", "input": [ { "label": "Biology", "text": "Describe the structure of a plant cell." }, { "label": "Creative writing", "text": "Write a poem about the ocean." } ], "model": "jina-embeddings-v5-text-small", "num_iters": 20 } }, "EmbeddingsV5Request": { "properties": { "embedding_type": { "anyOf": [ { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, { "items": { "type": "string", "enum": [ "float", "base64", "binary", "ubinary" ] }, "type": "array" }, { "type": "null" } ], "title": "Embedding Type", "description": "Output encoding format: `float`, `base64`, `binary`, `ubinary`, or a list of these." }, "normalized": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Normalized", "description": "If true (default), embeddings are L2-normalized to unit length.", "default": true }, "truncate": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Truncate", "description": "If true, truncates input exceeding the model's max token limit instead of returning an error.", "default": false }, "model": { "type": "string", "enum": [ "jina-embeddings-v5-text-nano", "jina-embeddings-v5-text-small", "jina-embeddings-v5-omni-small", "jina-embeddings-v5-omni-nano" ], "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "$ref": "#/components/schemas/VideoDoc" }, { "$ref": "#/components/schemas/AudioDoc" }, { "$ref": "#/components/schemas/PDFDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "$ref": "#/components/schemas/VideoDoc" }, { "$ref": "#/components/schemas/AudioDoc" }, { "$ref": "#/components/schemas/MergedContentGroup" } ] }, "type": "array" } ], "title": "Input", "description": "Content to embed: a string, `TextDoc`, `ImageDoc`, `VideoDoc`, `AudioDoc`, `PDFDoc`, or a list of items. List items may also be `{content: [...]}` groups \u2014 mixed-modality chunks fused into ONE embedding per group. PDFs must be sent as single inputs, not in a list. Text-only model variants reject non-text items." }, "task": { "anyOf": [ { "type": "string", "enum": [ "retrieval.query", "retrieval.passage", "text-matching", "clustering", "classification" ] }, { "type": "null" } ], "title": "Task", "description": "Task optimization: `retrieval.query` for queries, `retrieval.passage` for documents, `text-matching` for similarity, `clustering`, or `classification`.", "default": "text-matching" }, "dimensions": { "anyOf": [ { "type": "integer", "maximum": 1024.0, "exclusiveMinimum": 0.0 }, { "type": "null" } ], "title": "Dimensions", "description": "Number of dimensions for the output embedding. Range: 1-1024." } }, "type": "object", "required": [ "model", "input" ], "title": "EmbeddingsV5Request", "description": "Jina Embeddings v5 model with task-specific LoRA adapters and flexible\ndimensions. Small/nano sizes support text-only; the omni variant also\naccepts images, videos, audio, and PDFs in a single shared vector space.\nEach top-level list item is a single modality (one Doc class), with one\nexception: a `MergedContentGroup` (`{\"content\": [...]}`) fuses multiple\nchunks (text + image/video/audio) into a single embedding via one model\nforward pass.", "examples": [ { "dimensions": 512, "embedding_type": "float", "input": [ "A beautiful sunset over the beach" ], "model": "jina-embeddings-v5-text-small", "normalized": true, "task": "retrieval.query" }, { "embedding_type": "float", "input": [ { "image": "https://i.ibb.co/nQNGqL0/beach1.jpg" }, { "text": "A beautiful sunset over the beach" } ], "model": "jina-embeddings-v5-text-small", "task": "retrieval.passage" } ] }, "EmbeddingsV5ZeroShotClassificationRequest": { "properties": { "labels": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "additionalProperties": { "items": { "type": "string" }, "type": "array" }, "type": "object" } ], "title": "Labels", "description": "Candidate labels: a list of up to 512 labels, or a dict of up to 8 groups with max 64 labels each." }, "model": { "type": "string", "enum": [ "jina-embeddings-v5-text-nano", "jina-embeddings-v5-text-small" ], "title": "Model", "description": "The embedding model to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to classify: a string, `TextDoc`, or list of up to 512 items." } }, "type": "object", "required": [ "labels", "model", "input" ], "title": "EmbeddingsV5ZeroShotClassificationRequest", "description": "Zero-shot classification using Embeddings v5 text models.", "example": { "input": [ "Explain the theory of relativity in simple terms.", "Design a database schema for an e-commerce platform." ], "labels": [ "Technical explanation", "Creative writing", "Business solution" ], "model": "jina-embeddings-v5-text-small" } }, "FewShotClassificationRequest": { "properties": { "classifier_id": { "type": "string", "title": "Classifier Id", "description": "ID of the trained classifier to use." }, "input": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array" } ], "title": "Input", "description": "Text to classify: a string, `TextDoc`, or list of up to 512 items." } }, "type": "object", "required": [ "classifier_id", "input" ], "title": "FewShotClassificationRequest", "description": "Few-shot classification using a trained classifier.", "example": { "classifier_id": "cls-2024-07-14-xyz", "input": [ "Summarize the main points of the article.", "Classify the following statement: 'The service was prompt and friendly.'" ] } }, "HTTPValidationError": { "title": "HTTPValidationError", "type": "object", "properties": { "detail": { "title": "Detail", "type": "string" }, "errors": { "title": "Errors", "type": "array", "items": { "type": "object", "properties": { "field": { "title": "Field", "type": "string" }, "message": { "title": "Message", "type": "string" }, "type": { "title": "Type", "type": "string" }, "input": { "title": "Input" } } } } } }, "ImageDoc": { "properties": { "image": { "type": "string", "title": "Image", "description": "Image as a URL or base64-encoded string." } }, "type": "object", "required": [ "image" ], "title": "ImageDoc" }, "ImageTrainingItem": { "properties": { "image": { "type": "string", "title": "Image", "description": "Input image URL or base64 for training." }, "label": { "type": "string", "title": "Label", "description": "Label for the input image." } }, "type": "object", "required": [ "image", "label" ], "title": "ImageTrainingItem" }, "MergedContentGroup": { "properties": { "content": { "items": { "anyOf": [ { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "$ref": "#/components/schemas/VideoDoc" }, { "$ref": "#/components/schemas/AudioDoc" } ] }, "type": "array", "minItems": 1, "title": "Content", "description": "Ordered list of modality chunks (text / image / video / audio). Must contain at least one chunk." } }, "type": "object", "required": [ "content" ], "title": "MergedContentGroup", "description": "Mixed-modality chunks fused into ONE embedding per group.\n\nThe executor sends every chunk to the model in a single forward pass\n(interleaved per the model's special-token convention) and returns one\nvector per `MergedContentGroup`. Order within `content` is semantically\nmeaningful \u2014 re-ordering may change the resulting embedding.\n\nPDFs are intentionally not allowed inside `content`: they're rasterized\nto N per-page image prompts, which doesn't compose with single-pass\nfusion. Send PDFs as standalone single-input requests instead." }, "ModelDatacenter": { "properties": { "country_code": { "type": "string", "title": "Country Code", "description": "ISO country code of the datacenter." } }, "type": "object", "required": [ "country_code" ], "title": "ModelDatacenter", "description": "Datacenter location for a model." }, "ModelInfo": { "properties": { "id": { "type": "string", "title": "Id", "description": "Unique model identifier (e.g., `jina-ai/jina-embeddings-v3`)." }, "hugging_face_id": { "type": "string", "title": "Hugging Face Id", "description": "HuggingFace model ID." }, "name": { "type": "string", "title": "Name", "description": "Human-readable model name." }, "created": { "type": "integer", "title": "Created", "description": "Unix timestamp when the model was released." }, "input_modalities": { "items": { "type": "string" }, "type": "array", "title": "Input Modalities", "description": "Supported input modalities: `text`, `image`, `video`, `audio`." }, "output_modalities": { "items": { "type": "string" }, "type": "array", "title": "Output Modalities", "description": "Output types: `embeddings`, `text`." }, "quantization": { "type": "string", "title": "Quantization", "description": "Quantization level, if any." }, "context_length": { "type": "integer", "title": "Context Length", "description": "Maximum context length in tokens." }, "max_output_length": { "type": "integer", "title": "Max Output Length", "description": "Maximum output dimensions." }, "pricing": { "$ref": "#/components/schemas/ModelPricing", "description": "Pricing information." }, "supported_sampling_parameters": { "items": { "type": "string" }, "type": "array", "title": "Supported Sampling Parameters", "description": "Supported sampling parameters." }, "supported_features": { "items": { "type": "string" }, "type": "array", "title": "Supported Features", "description": "Supported features." }, "description": { "type": "string", "title": "Description", "description": "Model description." }, "datacenters": { "items": { "$ref": "#/components/schemas/ModelDatacenter" }, "type": "array", "title": "Datacenters", "description": "Available datacenter locations." } }, "type": "object", "required": [ "id", "hugging_face_id", "name", "created", "input_modalities", "output_modalities", "quantization", "context_length", "max_output_length", "pricing", "supported_sampling_parameters", "supported_features", "description", "datacenters" ], "title": "ModelInfo", "description": "Information about a single model." }, "ModelListResponse": { "properties": { "data": { "items": { "$ref": "#/components/schemas/ModelInfo" }, "type": "array", "title": "Data", "description": "List of available models." } }, "type": "object", "required": [ "data" ], "title": "ModelListResponse", "description": "Response containing list of available models.", "example": { "data": [ { "context_length": 8192, "created": 1725840000, "datacenters": [ { "country_code": "US" } ], "description": "jina-embeddings-v3 is a multilingual embedding model...", "hugging_face_id": "jinaai/jina-embeddings-v3", "id": "jina-ai/jina-embeddings-v3", "input_modalities": [ "text" ], "max_output_length": 1024, "name": "Jina AI: Jina Embeddings v3", "output_modalities": [ "embeddings" ], "pricing": { "completion": "0", "image": "0", "input_cache_read": "0", "input_cache_write": "0", "prompt": "0.00000005", "request": "0" }, "quantization": "", "supported_features": [], "supported_sampling_parameters": [] } ] } }, "ModelPricing": { "properties": { "prompt": { "type": "string", "title": "Prompt", "description": "Price per prompt token." }, "completion": { "type": "string", "title": "Completion", "description": "Price per completion token." }, "image": { "type": "string", "title": "Image", "description": "Price per image." }, "request": { "type": "string", "title": "Request", "description": "Price per request." }, "input_cache_read": { "type": "string", "title": "Input Cache Read", "description": "Price for input cache read." }, "input_cache_write": { "type": "string", "title": "Input Cache Write", "description": "Price for input cache write." } }, "type": "object", "required": [ "prompt", "completion", "image", "request", "input_cache_read", "input_cache_write" ], "title": "ModelPricing", "description": "Pricing information for a model." }, "MultiEmbeddingData": { "properties": { "object": { "type": "string", "title": "Object", "description": "Object type, always `embeddings` for multi-vector output.", "default": "embeddings" }, "index": { "type": "integer", "title": "Index", "description": "Position of this result in the input list." }, "embeddings": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "items": { "items": { "type": "number" }, "type": "array" }, "type": "array" } ], "title": "Embeddings", "description": "Per-token embedding vectors. Each is a list of floats or base64 string." }, "tokenized_input": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "title": "Tokenized Input", "description": "Tokens corresponding to each embedding, if `return_tokenized_input=true`." } }, "type": "object", "required": [ "index", "embeddings" ], "title": "MultiEmbeddingData", "description": "Multi-vector embedding result (one vector per token)." }, "PDFDoc": { "properties": { "pdf": { "type": "string", "title": "Pdf", "description": "PDF as a URL or base64-encoded string." } }, "type": "object", "required": [ "pdf" ], "title": "PDFDoc" }, "RerankerM0Request": { "properties": { "query": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/ImageDoc" } ], "title": "Query", "description": "The query to rank documents against: a text string, or an image object (`{\"image\": }`) for a visual query." }, "top_n": { "anyOf": [ { "type": "integer", "minimum": 1.0 }, { "type": "null" } ], "title": "Top N", "description": "Number of top results to return. If not set, returns all documents." }, "return_documents": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Return Documents", "description": "If true (default), includes document content in each result.", "default": true }, "model": { "type": "string", "const": "jina-reranker-m0", "title": "Model", "description": "The multimodal reranking model to use." }, "documents": { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" } ] }, "type": "array", "minItems": 1, "title": "Documents", "description": "Documents to rank: strings, `TextDoc`, or `ImageDoc` objects." } }, "type": "object", "required": [ "query", "model", "documents" ], "title": "RerankerM0Request", "description": "Multimodal reranking for text and images.", "example": { "documents": [ { "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png" }, { "text": "Attention Is All You Need: This groundbreaking paper introduced the Transformer architecture, revolutionizing natural language processing and becoming the foundation for modern large language models like GPT and BERT." }, "Recent advances in computer vision have led to the development of vision transformers (ViTs) that achieve state-of-the-art performance on image classification tasks by treating images as sequences of patches.", { "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png" }, { "text": "Graph Neural Networks: A Review of Methods and Applications. This comprehensive survey covers the latest developments in graph-based machine learning, including graph convolutional networks and graph attention mechanisms." }, "Traditional database systems struggle with the volume and variety of modern data workloads, leading to the adoption of distributed computing frameworks like Apache Spark and Hadoop for big data processing." ], "model": "jina-reranker-m0", "query": "innovative artificial intelligence research papers and visual data analysis", "return_documents": false, "top_n": 4 } }, "RerankerV3Request": { "properties": { "query": { "type": "string", "title": "Query", "description": "The search query to rank documents against." }, "top_n": { "anyOf": [ { "type": "integer", "minimum": 1.0 }, { "type": "null" } ], "title": "Top N", "description": "Number of top results to return. If not set, returns all documents." }, "return_documents": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Return Documents", "description": "If true (default), includes document content in each result.", "default": true }, "model": { "type": "string", "enum": [ "jina-reranker-v3", "jina-reranker-v3.5" ], "title": "Model", "description": "The reranking model to use." }, "documents": { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array", "minItems": 1, "title": "Documents", "description": "Documents to rank: strings or `TextDoc` objects." }, "max_doc_length": { "anyOf": [ { "type": "integer", "maximum": 8192.0, "minimum": 1.0 }, { "type": "null" } ], "title": "Max Doc Length", "description": "Maximum tokens per document (1-8192). Omit to use the model's default (2048 for jina-reranker-v3, 8192 for jina-reranker-v3.5)." }, "return_embeddings": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Return Embeddings", "description": "If true, returns the document embedding alongside the relevance score." } }, "type": "object", "required": [ "query", "model", "documents" ], "title": "RerankerV3Request", "description": "Listwise reranker for high-quality multilingual retrieval. `max_doc_length`\nis an optional override; when omitted it stays `None` and the serving executor\napplies its configured per-model default, keeping that default in one place.", "example": { "documents": [ "Climate Change and Renewable Energy: A Global Perspective. Rising temperatures and extreme weather events are accelerating the transition to solar and wind power worldwide.", "Le changement climatique et l'\u00e9nergie renouvelable : une perspective europ\u00e9enne. L'Union europ\u00e9enne investit massivement dans les technologies vertes pour atteindre la neutralit\u00e9 carbone d'ici 2050.", "Klimawandel und erneuerbare Energien: Deutsche Energiewende. Deutschland f\u00fchrt die Transformation des Energiesektors durch innovative Technologien und politische Ma\u00dfnahmen an.", "\u6c14\u5019\u53d8\u5316\u5bf9\u53ef\u518d\u751f\u80fd\u6e90\u7684\u5f71\u54cd\uff1a\u4e2d\u56fd\u7684\u7eff\u8272\u53d1\u5c55\u6218\u7565\u3002\u4e2d\u56fd\u6b63\u5728\u5927\u529b\u63a8\u8fdb\u592a\u9633\u80fd\u548c\u98ce\u80fd\u9879\u76ee\uff0c\u4ee5\u5b9e\u73b0\u78b3\u4e2d\u548c\u76ee\u6807\u3002", "Artificial Intelligence in Healthcare: Revolutionizing Medical Diagnosis. AI-powered systems are improving accuracy in disease detection and treatment recommendations.", "Blockchain Technology: Transforming Financial Services. Decentralized finance applications are reshaping traditional banking and payment systems." ], "model": "jina-reranker-v3", "query": "Climate change impact on renewable energy adoption", "return_documents": false, "return_embeddings": false, "top_n": 4 } }, "RerankingResponse": { "properties": { "model": { "type": "string", "title": "Model", "description": "The model used for reranking." }, "object": { "type": "string", "title": "Object", "description": "Object type, always `list`.", "default": "list" }, "usage": { "$ref": "#/components/schemas/BaseUsage", "description": "Token usage statistics." }, "results": { "items": { "$ref": "#/components/schemas/RerankingResult" }, "type": "array", "title": "Results", "description": "Ranked documents, sorted by relevance score descending." } }, "type": "object", "required": [ "model", "usage", "results" ], "title": "RerankingResponse", "description": "Response containing ranked documents." }, "RerankingResult": { "properties": { "index": { "type": "integer", "title": "Index", "description": "Position of this document in the original input list." }, "relevance_score": { "type": "number", "title": "Relevance Score", "description": "Relevance score. Higher is more relevant." }, "document": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" }, { "$ref": "#/components/schemas/ImageDoc" }, { "type": "null" } ], "title": "Document", "description": "Document content, if `return_documents=true`." }, "embedding": { "anyOf": [ { "items": { "type": "number" }, "type": "array" }, { "type": "null" } ], "title": "Embedding", "description": "Document embedding, if `return_embeddings=true`." } }, "type": "object", "required": [ "index", "relevance_score" ], "title": "RerankingResult", "description": "A single reranking result." }, "SingleEmbeddingData": { "properties": { "object": { "type": "string", "title": "Object", "description": "Object type, always `embedding`.", "default": "embedding" }, "index": { "type": "integer", "title": "Index", "description": "Position of this embedding in the input list." }, "embedding": { "anyOf": [ { "type": "string" }, { "items": { "type": "number" }, "type": "array" }, { "additionalProperties": { "type": "number" }, "type": "object" } ], "title": "Embedding", "description": "The embedding vector: a list of floats for dense embeddings, a dict of token->score for sparse embeddings, or a base64 string for other encodings." } }, "type": "object", "required": [ "index", "embedding" ], "title": "SingleEmbeddingData", "description": "Single embedding result for one input item." }, "TextDoc": { "properties": { "text": { "type": "string", "title": "Text", "description": "Text content to process." } }, "type": "object", "required": [ "text" ], "title": "TextDoc" }, "TextRerankerRequest": { "properties": { "query": { "type": "string", "title": "Query", "description": "The search query to rank documents against." }, "top_n": { "anyOf": [ { "type": "integer", "minimum": 1.0 }, { "type": "null" } ], "title": "Top N", "description": "Number of top results to return. If not set, returns all documents." }, "return_documents": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "title": "Return Documents", "description": "If true (default), includes document content in each result.", "default": true }, "model": { "type": "string", "enum": [ "jina-reranker-v2-base-multilingual", "jina-reranker-v1-tiny-en", "jina-reranker-v1-turbo-en", "jina-reranker-v1-base-en", "jina-colbert-v1-en", "jina-colbert-v2" ], "title": "Model", "description": "The reranking model to use." }, "documents": { "items": { "anyOf": [ { "type": "string" }, { "$ref": "#/components/schemas/TextDoc" } ] }, "type": "array", "minItems": 1, "title": "Documents", "description": "Documents to rank: strings or `TextDoc` objects." } }, "type": "object", "required": [ "query", "model", "documents" ], "title": "TextRerankerRequest", "description": "Text reranking request for v1/v2 models.", "example": { "documents": [ "Climate Change and Renewable Energy: A Global Perspective. Rising temperatures and extreme weather events are accelerating the transition to solar and wind power worldwide.", "Le changement climatique et l'\u00e9nergie renouvelable : une perspective europ\u00e9enne. L'Union europ\u00e9enne investit massivement dans les technologies vertes pour atteindre la neutralit\u00e9 carbone d'ici 2050.", "Klimawandel und erneuerbare Energien: Deutsche Energiewende. Deutschland f\u00fchrt die Transformation des Energiesektors durch innovative Technologien und politische Ma\u00dfnahmen an.", "\u6c14\u5019\u53d8\u5316\u5bf9\u53ef\u518d\u751f\u80fd\u6e90\u7684\u5f71\u54cd\uff1a\u4e2d\u56fd\u7684\u7eff\u8272\u53d1\u5c55\u6218\u7565\u3002\u4e2d\u56fd\u6b63\u5728\u5927\u529b\u63a8\u8fdb\u592a\u9633\u80fd\u548c\u98ce\u80fd\u9879\u76ee\uff0c\u4ee5\u5b9e\u73b0\u78b3\u4e2d\u548c\u76ee\u6807\u3002", "Artificial Intelligence in Healthcare: Revolutionizing Medical Diagnosis. AI-powered systems are improving accuracy in disease detection and treatment recommendations.", "Blockchain Technology: Transforming Financial Services. Decentralized finance applications are reshaping traditional banking and payment systems." ], "model": "jina-reranker-v2-base-multilingual", "query": "Climate change impact on renewable energy adoption", "return_documents": false, "top_n": 4 } }, "TextTrainingItem": { "properties": { "text": { "type": "string", "title": "Text", "description": "Input text for training." }, "label": { "type": "string", "title": "Label", "description": "Label for the input text." } }, "type": "object", "required": [ "text", "label" ], "title": "TextTrainingItem" }, "TrainingResponse": { "properties": { "classifier_id": { "type": "string", "title": "Classifier Id", "description": "ID of the classifier that was trained or updated." }, "num_samples": { "type": "integer", "title": "Num Samples", "description": "Number of training samples processed in this request." }, "usage": { "$ref": "#/components/schemas/BaseUsage", "description": "Token usage statistics for the training request." } }, "type": "object", "required": [ "classifier_id", "num_samples" ], "title": "TrainingResponse", "example": { "classifier_id": "00000000-0000-0000-0000-000000000000", "num_samples": 6, "usage": { "total_tokens": 1830 } } }, "UpdateTrainingRequest": { "properties": { "classifier_id": { "type": "string", "title": "Classifier Id", "description": "ID of the classifier to use for few-shot classification." }, "input": { "anyOf": [ { "$ref": "#/components/schemas/TextTrainingItem" }, { "$ref": "#/components/schemas/ImageTrainingItem" }, { "items": { "anyOf": [ { "$ref": "#/components/schemas/TextTrainingItem" }, { "$ref": "#/components/schemas/ImageTrainingItem" } ] }, "type": "array" } ], "title": "Input", "description": "Input(s) for updating training. Accepts a `TextTrainingItem`, `ImageTrainingItem`, or a list of them. For batch update, provide a list with up to 512 items." }, "num_iters": { "type": "integer", "title": "Num Iters", "description": "Number of iterations for the training process.", "default": 10 } }, "type": "object", "required": [ "classifier_id", "input" ], "title": "UpdateTrainingRequest", "example": { "classifier_id": "cls-2025-07-15-xyz", "input": [ { "label": "Physics", "text": "Summarize the theory of relativity." }, { "image": "https://images.unsplash.com/photo-1506744038136-46273834b3fb", "label": "Landscape" }, { "label": "Biology", "text": "Explain the process of photosynthesis." }, { "image": "https://images.unsplash.com/photo-1465101046530-73398c7f28ca", "label": "Rocket" } ], "num_iters": 20 } }, "VideoDoc": { "properties": { "video": { "type": "string", "title": "Video", "description": "Video as a URL or base64-encoded string." } }, "type": "object", "required": [ "video" ], "title": "VideoDoc" }, "ErrorResponse": { "title": "ErrorResponse", "type": "object", "required": [ "detail" ], "properties": { "detail": { "title": "Detail", "type": "string", "description": "Human-readable error message", "example": "Invalid API key. Verify your API key at https://chatsax.com/api-dashboard/key-manager or generate a new one." }, "code": { "title": "Code", "type": "string", "description": "Machine-readable error code for programmatic handling", "example": "AUTH_INVALID_API_KEY" }, "request_id": { "title": "Request ID", "type": "string", "description": "Unique request identifier for tracing and support", "example": "03f58cd9da22fabf" } } } }, "securitySchemes": { "ChatsaxKey": { "type": "http", "scheme": "bearer", "bearerFormat": "Chatsax API key" }, "AccountSession": { "type": "apiKey", "in": "cookie", "name": "__Host-chatsax_session", "description": "HttpOnly login session. Register and sign in on chatsax.com; browser requests include the cookie. API keys do not authorize account administration or purchases." } }, "responses": { "BadRequest": { "description": "- **INPUT_MODEL_NOT_FOUND**: Model 'model_name' not found. Available models: ....\n- **INPUT_INVALID_LABELS**: Invalid training labels. At least {min_labels} unique labels are required for training.\n- **INPUT_LABEL_LIMIT_EXCEEDED**: Label limit exceeded: {current} labels provided, maximum N allowed for your plan. Reduce the number of labels or upgrade your plan at https://chatsax.com/api-dashboard/key-manager.\n- **INPUT_TOKEN_LIMIT_EXCEEDED**: Input text exceeds the model's maximum of {max_tokens} tokens. Use 'truncate: true' to automatically truncate, or split into smaller chunks.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "Unauthorized": { "description": "- **AUTH_MISSING_API_KEY**: Authentication required. Provide your API key via the Authorization header: 'Authorization: Bearer '. Get your API key at https://chatsax.com/api-dashboard/key-manager.\n- **AUTH_INVALID_API_KEY**: Invalid API key. Verify your API key at https://chatsax.com/api-dashboard/key-manager or generate a new one.\n- **AUTH_INVALID_FORMAT**: Invalid authorization format. Use 'Authorization: Bearer ' with a valid Chatsax API key.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "Forbidden": { "description": "- **AUTHZ_INSUFFICIENT_BALANCE**: Insufficient account balance. Top up your account at https://chatsax.com/api-dashboard/key-manager.\n- **AUTHZ_RESOURCE_LIMIT_EXCEEDED**: Resource limit exceeded for your plan. Current: {current}, limit: N. Delete existing resources or upgrade your plan at https://chatsax.com/api-dashboard/key-manager.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "NotFound": { "description": "- **RESOURCE_NOT_FOUND**: {resource_type} '{resource_id}' not found or access denied. Verify the ID and your permissions.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "Conflict": { "description": "- **CONFLICT_RESOURCE_BUSY**: {resource_type} '{resource_id}' is currently being modified. Please retry after a few moments.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "TooManyRequests": { "description": "- **RATE_REQUEST_LIMIT_EXCEEDED**: Request rate limit exceeded: {current}/N requests per minute. Reduce request frequency or upgrade your plan at https://chatsax.com/api-dashboard/key-manager.\n- **RATE_TOKEN_LIMIT_EXCEEDED**: Token rate limit exceeded: {current:,}/{limit:,} tokens per minute. Reduce batch sizes or upgrade your plan at https://chatsax.com/api-dashboard/key-manager.\n- **RATE_CONCURRENCY_LIMIT_EXCEEDED**: Concurrency limit exceeded: {current}/N concurrent requests. Wait for pending requests to complete before sending new ones.\n- **RATE_IP_LIMIT_EXCEEDED**: IP rate limit exceeded. Too many requests from this IP address. Reduce request frequency.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "InternalServerError": { "description": "- **INTERNAL_ERROR**: An unexpected error occurred. If this persists, contact support with the request_id from this response.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "ServiceUnavailable": { "description": "- **SERVICE_UNAVAILABLE**: Service temporarily unavailable. Please retry with exponential backoff.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } }, "GatewayTimeout": { "description": "- **SERVICE_TIMEOUT**: Service request timed out. Try with smaller inputs or retry later.", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorResponse" } } } } } }, "tags": [ { "name": "Model List", "description": "List available Jina AI models and their capabilities.\n\nReturns model metadata in OpenRouter-compatible format including model IDs, input/output modalities, context lengths, and pricing information. Use this endpoint to discover available models before making API calls.\n" }, { "name": "Search Foundation Models", "description": "Generate embeddings and rerank documents using state-of-the-art models.\n\n**Embeddings** convert text, images, and documents into dense vector representations for semantic search, RAG, and similarity matching. Available models include `jina-embeddings-v3` (multilingual, 8K context), `jina-embeddings-v4` (multimodal, 32K context), `jina-embeddings-v5-text-small` (multilingual, 32K context, 1024-dim), `jina-embeddings-v5-text-nano` (multilingual, 8K context, 768-dim), `jina-embeddings-v5-omni-small` (multilingual multimodal, 32K context, 1024-dim), `jina-embeddings-v5-omni-nano` (multilingual multimodal, 8K context, 768-dim), and `jina-clip-v2` (text-image, 89 languages).\n\n**Reranking** refines search results by scoring query-document relevance. Models include `jina-reranker-v3.5` and `jina-reranker-v3` (0.6B, 131K context, listwise reranking), `jina-reranker-m0` (multimodal, 29 languages), `jina-reranker-v2-base-multilingual` (100+ languages, function calling support), and `jina-colbert-v2` (late interaction for high precision).\n" }, { "name": "Zero/Few-shot Classification", "description": "Categorize text and images using embedding-based classification.\n\n**Zero-shot**: Classify inputs into semantic labels without training data. Supports up to 512 labels, or 8 label groups of up to 64 labels each. Best for flexible, immediate classification with descriptive labels.\n\n**Few-shot**: Train custom classifiers with labeled examples (200-400 samples recommended). Supports incremental updates and handles domain-specific or time-sensitive data. Limited to 16 classes and 16 classifiers per API key.\n\nSupports multilingual text via `jina-embeddings-v3`, `jina-embeddings-v5-text-small`, `jina-embeddings-v5-text-nano`, and multimodal (text/image) via `jina-clip-v2` or `jina-embeddings-v4`.\n" }, { "name": "Batch Embeddings", "description": "Asynchronous batch embedding processing for large-scale workloads.\n\nSubmit embedding jobs with up to 50,000 inputs via GCS file URL or up to 10,000 inputs inline. Jobs are processed asynchronously - poll for status, then download output as JSONL when complete.\n\n**Workflow**: 1) Submit job via `POST /v1/batch/embeddings` with input data and model selection. 2) Poll `GET /v1/batch/{batch_id}` until status is `completed`. 3) Download results from `GET /v1/batch/{batch_id}/output`.\n\n**Input format**: OpenAI-compatible JSONL with `custom_id` and `body.input` fields. Supports both inline JSON arrays and GCS-hosted JSONL files for larger batches.\n\n**Supported models**: `jina-embeddings-v5-text-small` (1024-dim, 32K context) and `jina-embeddings-v5-text-nano` (768-dim, 8K context). All task types supported: retrieval, text-matching, clustering, classification.\n\nOutput files expire after 24 hours. Optional webhook notifications on job completion.\n" }, { "name": "Health Check", "description": "Liveness and readiness probes for service health monitoring. For internal use only." }, { "name": "Account" }, { "name": "Billing" }, { "name": "Support" } ], "servers": [ { "url": "https://chatsax.com" }, { "url": "https://api.chatsax.com" } ], "security": [ { "ChatsaxKey": [] } ] }