{ "openapi": "3.0.1", "info": { "title": "RAG Pipeline", "description": "One-click RAG pipeline: chunks text, generates embeddings, and stores vectors in Pinecone or Qdrant. Provide your content and API keys -- the orchestrator handles the rest.", "version": "0.0", "x-build-id": "jqy70JcaYv4FWqh7h" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/labrat011~rag-pipeline/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-labrat011-rag-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/labrat011~rag-pipeline/runs": { "post": { "operationId": "runs-sync-labrat011-rag-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/labrat011~rag-pipeline/run-sync": { "post": { "operationId": "run-sync-labrat011-rag-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "embedding_api_key", "vector_db_api_key", "index_name" ], "properties": { "text": { "title": "Text to Process", "type": "string", "description": "Plain text or Markdown content to chunk, embed, and store. For bulk processing from a crawler, use source_dataset_id instead." }, "source_dataset_id": { "title": "Source Dataset ID (from Crawler)", "type": "string", "description": "Apify dataset ID from a previous actor run (e.g., Website Content Crawler). Each item's text field will be chunked, embedded, and stored. Takes priority over the text input." }, "file_url": { "title": "File URL (Apify Storage)", "type": "string", "description": "HTTPS URL to a single file in Apify Storage. Must use HTTPS. For multiple files, use file_urls instead." }, "file_urls": { "title": "File URLs — Bulk (Apify Storage)", "type": "array", "description": "List of HTTPS URLs to files in Apify Storage. Up to 20 URLs per run. Contents are fetched and concatenated before chunking.", "items": { "type": "string" } }, "source_dataset_field": { "title": "Source Dataset Text Field", "type": "string", "description": "Which field to read from source dataset items. Common values: 'text', 'markdown', 'content'.", "default": "text" }, "chunking_strategy": { "title": "Chunking Strategy", "enum": [ "recursive", "markdown", "sentence" ], "type": "string", "description": "How to split content. 'recursive' (default): paragraphs, then sentences, then words. 'markdown': splits on headers. 'sentence': sentence boundaries only.", "default": "recursive" }, "chunk_size": { "title": "Chunk Size (tokens)", "minimum": 64, "maximum": 8192, "type": "integer", "description": "Target size for each chunk in tokens. Recommended: 256-1024 for most RAG use cases.", "default": 512 }, "chunk_overlap": { "title": "Chunk Overlap (tokens)", "minimum": 0, "maximum": 2048, "type": "integer", "description": "Overlapping tokens between consecutive chunks. Helps preserve context across boundaries.", "default": 64 }, "embedding_api_key": { "title": "Embedding API Key (OpenAI or Cohere)", "type": "string", "description": "Your OpenAI or Cohere API key for generating embeddings. Never logged or stored beyond this run." }, "embedding_provider": { "title": "Embedding Provider", "enum": [ "openai", "cohere" ], "type": "string", "description": "Which embedding provider to use.", "default": "openai" }, "embedding_model": { "title": "Embedding Model", "type": "string", "description": "Which embedding model to use. OpenAI: 'text-embedding-3-small' (1536d), 'text-embedding-3-large' (3072d). Cohere: 'embed-english-v3.0' (1024d), 'embed-multilingual-v3.0' (1024d).", "default": "text-embedding-3-small" }, "embedding_batch_size": { "title": "Embedding Batch Size", "minimum": 1, "maximum": 2048, "type": "integer", "description": "Texts per embedding API request. Higher = faster but more memory. OpenAI max: 2048, Cohere max: 96.", "default": 128 }, "vector_db_api_key": { "title": "Vector DB API Key", "type": "string", "description": "Your Pinecone or Qdrant API key. Never logged or stored beyond this run." }, "vector_db_provider": { "title": "Vector Database Provider", "enum": [ "pinecone", "qdrant" ], "type": "string", "description": "Which vector database to write to.", "default": "pinecone" }, "index_name": { "title": "Index / Collection Name", "type": "string", "description": "Pinecone: the index name. Qdrant: the collection name (auto-created if it doesn't exist)." }, "qdrant_url": { "title": "Qdrant Cluster URL", "type": "string", "description": "Qdrant Cloud only: your full cluster URL (e.g., 'https://xyz.us-west-1.aws.cloud.qdrant.io:6333'). Not needed for Pinecone." }, "pinecone_namespace": { "title": "Namespace (Pinecone only)", "type": "string", "description": "Pinecone namespace. Leave empty for the default namespace. Ignored for Qdrant.", "default": "" }, "qdrant_distance_metric": { "title": "Distance Metric (Qdrant only)", "enum": [ "Cosine", "Dot", "Euclid" ], "type": "string", "description": "Distance metric for Qdrant collection creation. Only used when auto-creating a collection.", "default": "Cosine" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } }