# Perplexity Ultra - Grounded JSON Extraction (`chriskelch77/perplexity-ultra`) Actor Turn grounded web research into validated JSON with schema enforcement, source merging, confidence scoring, and batch processing. - **URL**: https://apify.com/chriskelch77/perplexity-ultra.md - **Developed by:** [Chris](https://apify.com/chriskelch77) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 research requests This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## ๐Ÿ“ฆ Perplexity Ultra V1.0 ### ๐Ÿš€ Turn Web Research Into Validated JSON Most AI tools give you text you still have to parse. **Perplexity Ultra gives you structured, validated JSON you can use directly in your application.** It combines grounded search, schema validation, JSON repair, and confidence scoring into a single API. --- ### ๐Ÿง  What this is Perplexity Ultra is a **production-ready API for grounded research and structured data extraction using Perplexity**. It is not just a wrapper. It adds: * query planning * validation * repair * observability So you can safely use grounded AI in real applications. --- ### ๐Ÿš€ What this solves Working with grounded LLMs in production is hard: * responses are inconsistent * JSON often breaks * citations are messy or missing * costs can spike unexpectedly * debugging failures is painful Perplexity Ultra handles these problems for you. --- ### ๐Ÿค” Why not just use Perplexity directly? Perplexity is powerful, but raw responses are not production-ready: * JSON often breaks * outputs are inconsistent * citations are messy * retries and failures are hard to handle * costs can spike without control Perplexity Ultra adds a reliability layer: * multi-query planning * structured extraction with schema validation * automatic JSON repair * source merging and deduplication * confidence scoring and metadata * batch execution on Apify --- ### โญ Core Feature: Structured Extraction The core capability of Perplexity Ultra is: #### `POST /v1/extract` It turns grounded web research into structured JSON: * runs multiple search queries * merges and filters sources * extracts structured data using your schema * validates the output * repairs broken JSON when needed * returns confidence and metadata #### This is ideal for: * competitor datasets * market research pipelines * enrichment workflows * structured AI backends --- ### ๐Ÿงช Example Use Case **Input:** ```` Find 10 competitors of Notion for mid-market teams ```` **Output:** Structured JSON containing: * company names * websites * categorized data * sources and confidence Instead of parsing messy text, you get clean, validated data ready to store, filter, or display in your application. --- ### ๐Ÿงฉ Core capabilities #### ๐Ÿ” Grounded research * Multi-query execution (not just one search) * Source merging and deduplication * Citation-aware responses * Works across multiple Perplexity models --- #### ๐Ÿงพ Structured extraction * Convert web-grounded data into JSON * JSON Schema validation (AJV) * Automatic cleanup of: * markdown wrappers * extra prose * malformed JSON * Optional secondary repair pass for hard failures --- #### ๐Ÿ›ก Reliability layer * Deterministic JSON repair (fast, regex-based) * Retry + fallback across models * Explicit failure responses (no silent corruption) --- #### ๐Ÿ’ฐ Cost & control * Per-request and per-run budget limits * Query count limits * Concurrency control (rate-safe execution) * Cost estimation per request --- #### โšก Performance * Exact response caching (100% reuse) * Prefix caching for repeated prompts * Multi-tenant cache isolation --- #### ๐Ÿ” Security & privacy (basic guardrails) * Optional PII masking (emails, phone numbers) * Log redaction for sensitive payloads * BYOK (bring your own API key) --- #### ๐Ÿงช Observability Every response includes metadata: * latency * estimated cost * model used * validation result * confidence score Optional debug mode stores: * raw upstream responses * repaired JSON * validation errors --- #### ๐Ÿ“Š Batch processing (Apify native) * Process datasets row-by-row * Automatic retries * Dead-letter dataset for failures * Webhook on completion --- ### ๐Ÿ“ก API Overview #### Endpoints | Endpoint | Description | | ---------------------- | ---------------------------------------- | | `POST /v1/research` | Grounded research + synthesis | | `POST /v1/extract` | Structured JSON extraction (recommended) | | `POST /v1/verify` | Claim verification | | `POST /v1/compare` | Entity comparison | | `POST /v1/search-plan` | Preview query plan (no cost) | | `POST /v1/batch` | Dataset processing | | `GET /v1/health` | Health check | --- ### โš™๏ธ Presets Presets define execution behavior. | Preset | Behavior | | ---------------------- | --------------------------------- | | `ultra-fast-research` | Low latency, minimal queries | | `ultra-smart-research` | Balanced depth + cost | | `ultra-extract` | Optimized for structured output | | `ultra-verify` | Evidence-focused validation | | `ultra-deep` | High-depth research (higher cost) | | `ultra-batch` | Stable batch processing | | `custom` | Full manual control | --- ### โœ๏ธ Example: Structured Extraction #### Request ```json POST /v1/extract { "query": "Find 10 competitors of Notion for mid-market teams", "preset": "ultra-extract", "schema": { "type": "object", "properties": { "companies": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "website": { "type": "string" } }, "required": ["name"] } } }, "required": ["companies"] } } ```` *** #### Response ```json { "data": { "structured": { "companies": [ { "name": "ClickUp", "website": "https://clickup.com" } ] }, "sources": [ { "url": "https://example.com", "domain": "example.com" } ], "validation": { "valid": true, "errors": [] }, "confidence": { "confidence": 0.82, "grade": "high" } }, "meta": { "requestId": "req_123", "preset": "ultra-extract", "queryCount": 4, "sourceCount": 12, "latencyMs": 3201, "repairCount": 1, "validationPassed": true } } ``` *** ### โš ๏ธ Important notes - This API **reduces hallucinations** by grounding responses in search results, but does not guarantee perfect factual accuracy - Structured output is **validated against your schema**, but may fail if the data cannot be reliably extracted - Confidence scores are **heuristics**, not guarantees *** ### ๐Ÿงญ When to use this vs raw Perplexity #### Use Perplexity Ultra when you need: - structured JSON output - reliable, repeatable results - production-ready pipelines - cost control - debugging visibility - batch processing #### Use raw Perplexity when you need: - quick, ad-hoc queries - interactive exploration *** ### ๐Ÿงฑ Architecture (simplified) ```text Request โ†’ Normalizer โ†’ Preset Resolver โ†’ Query Planner โ†’ Perplexity Adapters โ†’ Source Normalization โ†’ Extraction / Synthesis โ†’ Validation + Repair โ†’ Confidence Scoring โ†’ Response Envelope ``` *** ### ๐Ÿงช Best use cases - competitor research - market analysis - vendor comparison - data enrichment pipelines - claim verification - structured dataset generation *** ### ๐Ÿงฉ Deployment Runs as: - Apify Actor (batch + server) - Standby API (Express) Supports: - BYOK (Perplexity API key) - dataset-based workflows - webhook integrations *** ### ๐Ÿ”Œ Works with Perplexity and OpenRouter Perplexity Ultra supports both: - native Perplexity API - Perplexity models through OpenRouter This gives you: - provider flexibility - redundancy and fallback options - easier integration into existing stacks All while keeping a consistent interface for grounded research and structured extraction. *** ### ๐Ÿ Summary Perplexity Ultra turns search-based AI into structured, application-ready data. Instead of handling: - broken JSON - inconsistent outputs - retries - cost spikes you get: - validated results - predictable structure - observability - control *** # Actor input Schema ## `mode` (type: `string`): Choose 'api' to run a Standby server, or 'batch' to process a dataset. ## `perplexityApiKey` (type: `string`): Your Perplexity (or OpenRouter) API Key. This is stored securely. ## `modelChain` (type: `array`): If the first model fails, the Actor will automatically retry with the next in the list. ## `policyPreset` (type: `string`): Apply a predefined configuration profile. ultra-fast minimizes latency, ultra-smart enables repair + debug, ultra-private enforces PII masking and zero data retention, ultra-json enables strict JSON mode. ## `taskClass` (type: `string`): Hint for the smart routing engine. 'auto' infers the task from the request shape. ## `route` (type: `string`): Which API route to target (usually chat for Perplexity models). ## `maxCostPerRunUsd` (type: `number`): Hard budget limit. The Actor will stop processing if this cost is exceeded. ## `maxCostPerRequestUsd` (type: `number`): Maximum allowed cost for a single API request. ## `timeoutMs` (type: `integer`): Per-model timeout in milliseconds. ## `latencyTargetMs` (type: `integer`): Target latency hint for the routing engine. Lower values prefer faster models. ## `maxRetries` (type: `integer`): Maximum number of retries per request. ## `enableExactCache` (type: `boolean`): If enabled, identical requests will be served from the internal cache (SHA-256 hash), saving tokens and money. ## `cacheTtlSeconds` (type: `integer`): How long cached responses remain valid. ## `cacheNamespace` (type: `string`): Isolate cache entries by namespace. Useful for multi-tenant setups. ## `enablePrefixCache` (type: `boolean`): Enable advanced prefix-based caching for long prompts. ## `userScopedCache` (type: `boolean`): Isolate cache by user ID (if provided in request headers). ## `jsonStrictMode` (type: `boolean`): Automatically strip markdown and repair broken JSON syntax from the AI's output. ## `jsonSchema` (type: `object`): Validate LLM output against this JSON Schema. Used with JSON strict mode. ## `enableDeterministicRepair` (type: `boolean`): Try regex-based JSON repair before falling back to model-based repair. ## `maxRepairAttempts` (type: `integer`): How many times to attempt repair before failing. ## `enableRepairModel` (type: `boolean`): Use a specialized model to fix JSON that deterministic repair cannot handle. ## `repairModel` (type: `string`): Model to use for JSON self-healing if deterministic repair fails. ## `enableDebugArtifacts` (type: `boolean`): Store JSON parse/validation failure details for post-run inspection. ## `piiMasking` (type: `boolean`): Scrub emails and phone numbers before sending data to the AI provider. ## `zdrOnly` (type: `boolean`): Only route to providers that support zero data retention policies. ## `redactLogs` (type: `boolean`): Redact sensitive data from Actor logs. ## `datasetId` (type: `string`): The Apify Dataset ID containing the rows you want to process. ## `requestTemplate` (type: `object`): The Perplexity payload template. Use {{fieldName}} to inject data from your dataset. ## `inputMapping` (type: `object`): Advanced mapping of dataset fields to template variables. ## `sampleSize` (type: `integer`): Process only the first N rows (useful for testing). ## `deadLetterDatasetId` (type: `string`): Failed batch rows are pushed to this dataset for inspection. ## `webhookUrl` (type: `string`): URL to POST a summary to when batch processing completes. ## Actor input object example ```json { "mode": "api", "modelChain": [ "perplexity/sonar", "perplexity/sonar-reasoning" ], "policyPreset": "custom", "taskClass": "auto", "route": "chat", "maxCostPerRunUsd": 5, "timeoutMs": 30000, "maxRetries": 2, "enableExactCache": true, "cacheTtlSeconds": 86400, "cacheNamespace": "default", "enablePrefixCache": true, "userScopedCache": true, "jsonStrictMode": true, "enableDeterministicRepair": true, "maxRepairAttempts": 1, "enableRepairModel": false, "repairModel": "perplexity/sonar", "enableDebugArtifacts": true, "piiMasking": false, "zdrOnly": false, "redactLogs": true, "sampleSize": 10 } ``` # Actor output Schema ## `results` (type: `string`): When running in Batch mode, all extracted rows and metadata are saved here. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("chriskelch77/perplexity-ultra").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("chriskelch77/perplexity-ultra").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call chriskelch77/perplexity-ultra --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=chriskelch77/perplexity-ultra", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Perplexity Ultra - Grounded JSON Extraction", "description": "Turn grounded web research into validated JSON with schema enforcement, source merging, confidence scoring, and batch processing.", "version": "1.0", "x-build-id": "RdJB2cecKrgf93hct" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/chriskelch77~perplexity-ultra/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-chriskelch77-perplexity-ultra", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/chriskelch77~perplexity-ultra/runs": { "post": { "operationId": "runs-sync-chriskelch77-perplexity-ultra", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/chriskelch77~perplexity-ultra/run-sync": { "post": { "operationId": "run-sync-chriskelch77-perplexity-ultra", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Execution Mode", "enum": [ "api", "batch" ], "type": "string", "description": "Choose 'api' to run a Standby server, or 'batch' to process a dataset.", "default": "api" }, "perplexityApiKey": { "title": "Perplexity API Key (BYOK)", "type": "string", "description": "Your Perplexity (or OpenRouter) API Key. This is stored securely." }, "modelChain": { "title": "Model Fallback Chain", "type": "array", "description": "If the first model fails, the Actor will automatically retry with the next in the list.", "items": { "type": "string" }, "default": [ "perplexity/sonar", "perplexity/sonar-reasoning" ] }, "policyPreset": { "title": "Policy Preset", "enum": [ "ultra-fast", "ultra-smart", "ultra-private", "ultra-json", "ultra-batch", "ultra-extract", "ultra-debug", "custom" ], "type": "string", "description": "Apply a predefined configuration profile. ultra-fast minimizes latency, ultra-smart enables repair + debug, ultra-private enforces PII masking and zero data retention, ultra-json enables strict JSON mode.", "default": "custom" }, "taskClass": { "title": "Task Class", "enum": [ "auto", "chat", "extraction", "classification", "reasoning", "embedding" ], "type": "string", "description": "Hint for the smart routing engine. 'auto' infers the task from the request shape.", "default": "auto" }, "route": { "title": "Target API Route", "enum": [ "chat", "responses", "embeddings" ], "type": "string", "description": "Which API route to target (usually chat for Perplexity models).", "default": "chat" }, "maxCostPerRunUsd": { "title": "Max Cost Per Run (USD)", "minimum": 0, "type": "number", "description": "Hard budget limit. The Actor will stop processing if this cost is exceeded.", "default": 5 }, "maxCostPerRequestUsd": { "title": "Max Cost Per Request (USD)", "minimum": 0, "type": "number", "description": "Maximum allowed cost for a single API request." }, "timeoutMs": { "title": "Timeout (ms)", "minimum": 1000, "type": "integer", "description": "Per-model timeout in milliseconds.", "default": 30000 }, "latencyTargetMs": { "title": "Latency Target (ms)", "minimum": 100, "type": "integer", "description": "Target latency hint for the routing engine. Lower values prefer faster models." }, "maxRetries": { "title": "Max Retries", "minimum": 0, "type": "integer", "description": "Maximum number of retries per request.", "default": 2 }, "enableExactCache": { "title": "Enable Exact Response Cache", "type": "boolean", "description": "If enabled, identical requests will be served from the internal cache (SHA-256 hash), saving tokens and money.", "default": true }, "cacheTtlSeconds": { "title": "Cache TTL (seconds)", "minimum": 1, "type": "integer", "description": "How long cached responses remain valid.", "default": 86400 }, "cacheNamespace": { "title": "Cache Namespace", "type": "string", "description": "Isolate cache entries by namespace. Useful for multi-tenant setups.", "default": "default" }, "enablePrefixCache": { "title": "Enable Prefix Cache", "type": "boolean", "description": "Enable advanced prefix-based caching for long prompts.", "default": true }, "userScopedCache": { "title": "User Scoped Cache", "type": "boolean", "description": "Isolate cache by user ID (if provided in request headers).", "default": true }, "jsonStrictMode": { "title": "Enable JSON Self-Healing", "type": "boolean", "description": "Automatically strip markdown and repair broken JSON syntax from the AI's output.", "default": true }, "jsonSchema": { "title": "JSON Schema", "type": "object", "description": "Validate LLM output against this JSON Schema. Used with JSON strict mode." }, "enableDeterministicRepair": { "title": "Enable Deterministic Repair", "type": "boolean", "description": "Try regex-based JSON repair before falling back to model-based repair.", "default": true }, "maxRepairAttempts": { "title": "Max Repair Attempts", "minimum": 0, "type": "integer", "description": "How many times to attempt repair before failing.", "default": 1 }, "enableRepairModel": { "title": "Enable Repair Model", "type": "boolean", "description": "Use a specialized model to fix JSON that deterministic repair cannot handle.", "default": false }, "repairModel": { "title": "Repair Model Name", "type": "string", "description": "Model to use for JSON self-healing if deterministic repair fails.", "default": "perplexity/sonar" }, "enableDebugArtifacts": { "title": "Enable Debug Artifacts", "type": "boolean", "description": "Store JSON parse/validation failure details for post-run inspection.", "default": true }, "piiMasking": { "title": "Enable PII Masking", "type": "boolean", "description": "Scrub emails and phone numbers before sending data to the AI provider.", "default": false }, "zdrOnly": { "title": "Zero Data Retention Only", "type": "boolean", "description": "Only route to providers that support zero data retention policies.", "default": false }, "redactLogs": { "title": "Redact Logs", "type": "boolean", "description": "Redact sensitive data from Actor logs.", "default": true }, "datasetId": { "title": "Target Dataset ID (Batch Mode Only)", "type": "string", "description": "The Apify Dataset ID containing the rows you want to process." }, "requestTemplate": { "title": "Request Template (Batch Mode Only)", "type": "object", "description": "The Perplexity payload template. Use {{fieldName}} to inject data from your dataset." }, "inputMapping": { "title": "Input Field Mapping", "type": "object", "description": "Advanced mapping of dataset fields to template variables." }, "sampleSize": { "title": "Batch Sample Size", "minimum": 1, "type": "integer", "description": "Process only the first N rows (useful for testing).", "default": 10 }, "deadLetterDatasetId": { "title": "Dead Letter Dataset ID", "type": "string", "description": "Failed batch rows are pushed to this dataset for inspection." }, "webhookUrl": { "title": "Completion Webhook URL", "type": "string", "description": "URL to POST a summary to when batch processing completes." } } }, "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 } } } } } } } } } } ```