openapi: 3.0.0
info:
title: Portkey Analytics > Graphs Logs API
description: The Portkey REST API. Please see https://portkey.ai/docs/api-reference for more details.
version: 2.0.0
termsOfService: https://portkey.ai/terms
contact:
name: Portkey Developer Forum
url: https://portkey.wiki/community
license:
name: MIT
url: https://github.com/Portkey-AI/portkey-openapi/blob/master/LICENSE
servers:
- url: https://api.portkey.ai/v1
description: Portkey API Public Endpoint
security:
- Portkey-Key: []
tags:
- name: Logs
description: Custom Logger to add external logs to Portkey.
paths:
/logs:
servers:
- url: https://api.portkey.ai/v1
description: Portkey API Public Endpoint
- url: SELF_HOSTED_GATEWAY_URL
description: Self-Hosted Gateway URL
post:
summary: Insert New logs
tags:
- Logs
description: Submit one or more log entries
requestBody:
required: true
content:
application/json:
schema:
oneOf:
- $ref: '#/components/schemas/CustomLog'
- type: array
items:
$ref: '#/components/schemas/CustomLog'
responses:
'200':
description: Successful response
x-code-samples:
- lang: python
label: Default
source: "from portkey_ai import Portkey\n\nportkey = Portkey(\n api_key=\"PORTKEY_API_KEY\",\n)\n\nrequest = {\n \"url\": \"https://api.someprovider.com/model/generate\",\n \"method\": \"POST\",\n \"headers\": {\"Content-Type\": \"application/json\"},\n \"body\": {\"prompt\": \"What is AI?\"},\n}\nresponse = {\n \"status\": 200,\n \"headers\": {\"Content-Type\": \"application/json\"},\n \"body\": {\"response\": \"AI stands for Artificial Intelligence...\"},\n \"response_time\": 123,\n}\nmetadata = {\n \"user_id\": \"123\",\n \"user_name\": \"John Doe\",\n}\n\nresult = portkey.logs.create(request=request, response=response, metadata=metadata)\n\nprint(result)\n"
- lang: javascript
label: Default
source: "import Portkey from \"portkey-ai\";\n\nconst portkey = new Portkey({\n apiKey:\"PORTKEY_API_KEY\"\n})\n\nasync function main() {\n const request = {\n url: \"https://api.someprovider.com/model/generate\",\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: { prompt: \"What is AI?\" },\n };\n const response = {\n status: 200,\n headers: { \"Content-Type\": \"application/json\" },\n body: { response: \"AI stands for Artificial Intelligence...\" },\n response_time: 123,\n };\n const metadata = {\n user_id: \"123\",\n user_name: \"John Doe\",\n };\n const result = await portkey.logs.create({\n request: request,\n response: response,\n metadata: metadata,\n });\n console.log(result);\n}\n\nmain();\n"
- lang: curl
label: Default
source: "curl -X POST \"https://api.portkey.ai/v1/logs\" \\\n-H \"x-portkey-api-key: PORTKEY_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n \"request\": {\n \"url\": \"https://api.someprovider.com/model/generate\",\n \"method\": \"POST\",\n \"headers\": { \"Content-Type\": \"application/json\" },\n \"body\": { \"prompt\": \"What is AI?\" }\n },\n \"response\": {\n \"status\": 200,\n \"headers\": { \"Content-Type\": \"application/json\" },\n \"body\": { \"response\": \"AI stands for Artificial Intelligence...\" },\n \"response_time\": 123\n },\n \"metadata\": {\n \"user_id\": \"123\",\n \"user_name\": \"John Doe\"\n }\n}'\n"
- lang: curl
label: Self-Hosted
source: "curl -X POST \"SELF_HOSTED_GATEWAY_URL/logs\" \\\n-H \"x-portkey-api-key: PORTKEY_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n \"request\": {\n \"url\": \"https://api.someprovider.com/model/generate\",\n \"method\": \"POST\",\n \"headers\": { \"Content-Type\": \"application/json\" },\n \"body\": { \"prompt\": \"What is AI?\" }\n },\n \"response\": {\n \"status\": 200,\n \"headers\": { \"Content-Type\": \"application/json\" },\n \"body\": { \"response\": \"AI stands for Artificial Intelligence...\" },\n \"response_time\": 123\n },\n \"metadata\": {\n \"user_id\": \"123\",\n \"user_name\": \"John Doe\"\n }\n}'\n"
- lang: python
label: Self-Hosted
source: "from portkey_ai import Portkey\n\nportkey = Portkey(\n api_key=\"PORTKEY_API_KEY\",\n base_url=\"SELF_HOSTED_GATEWAY_URL\"\n)\n\nrequest = {\n \"url\": \"https://api.someprovider.com/model/generate\",\n \"method\": \"POST\",\n \"headers\": {\"Content-Type\": \"application/json\"},\n \"body\": {\"prompt\": \"What is AI?\"},\n}\nresponse = {\n \"status\": 200,\n \"headers\": {\"Content-Type\": \"application/json\"},\n \"body\": {\"response\": \"AI stands for Artificial Intelligence...\"},\n \"response_time\": 123,\n}\nmetadata = {\n \"user_id\": \"123\",\n \"user_name\": \"John Doe\",\n}\n\nresult = portkey.logs.create(request=request, response=response, metadata=metadata)\n\nprint(result)\n"
- lang: javascript
label: Self-Hosted
source: "import Portkey from \"portkey-ai\";\n\nconst portkey = new Portkey({\n apiKey:\"PORTKEY_API_KEY\",\n baseUrl: \"SELF_HOSTED_GATEWAY_URL\"\n})\n\nasync function main() {\n const request = {\n url: \"https://api.someprovider.com/model/generate\",\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: { prompt: \"What is AI?\" },\n };\n const response = {\n status: 200,\n headers: { \"Content-Type\": \"application/json\" },\n body: { response: \"AI stands for Artificial Intelligence...\" },\n response_time: 123,\n };\n const metadata = {\n user_id: \"123\",\n user_name: \"John Doe\",\n };\n const result = await portkey.logs.create({\n request: request,\n response: response,\n metadata: metadata,\n });\n console.log(result);\n}\n\nmain();\n"
/logs/{logId}:
servers:
- url: https://api.portkey.ai/v1
description: Portkey API Public Endpoint
- url: SELF_HOSTED_GATEWAY_URL
description: Self-Hosted Gateway URL
get:
tags:
- Logs
summary: Get a specific log
parameters:
- name: logId
in: path
required: true
schema:
type: string
responses:
'200':
description: Successful response
content:
application/json:
schema:
$ref: '#/components/schemas/LogObject'
components:
schemas:
AnalyticsMetrics:
type: object
description: Analytics metrics extracted from the log entry
properties:
id:
type: string
format: uuid
description: Unique identifier for the log entry
example: 550e8400-e29b-41d4-a716-446655440000
organisation_id:
type: string
description: Organization identifier
example: org-123
organisation_name:
type: string
description: Organization name
example: Acme Corp
prompt_id:
type: string
description: Prompt identifier
example: prompt-789
prompt_version_id:
type: string
description: Prompt version identifier
example: prompt-v1
config_id:
type: string
description: Configuration identifier
example: config-123
created_at:
type: string
format: date-time
description: Timestamp when the log was created
example: '2024-01-15T10:30:00.000'
is_success:
type: boolean
description: Whether the request was successful (status code 200-299)
example: true
ai_org:
type: string
description: AI provider organization (e.g., openai, anthropic)
example: openai
ai_model:
type: string
description: AI model used
example: gpt-4o
req_units:
type: number
format: float
description: Request token units
example: 100
res_units:
type: number
format: float
description: Response token units
example: 50
total_units:
type: number
format: float
description: Total token units (req_units + res_units)
example: 150
cost:
type: number
format: float
description: Cost in the specified currency
example: 0.002
cost_currency:
type: string
description: Currency code for the cost
default: USD
example: USD
request_url:
type: string
format: uri
description: Sanitized request URL
example: https://api.openai.com/v1/chat/completions
request_method:
type: string
description: HTTP method
example: POST
response_status_code:
type: integer
format: int32
description: HTTP response status code
example: 200
response_time:
type: integer
format: int64
description: Response time in milliseconds
example: 1234
is_proxy_call:
type: boolean
description: Whether this was a proxy call
example: true
cache_status:
type: string
nullable: true
description: Cache status (e.g., HIT, MISS, DISABLED, SEMANTIC HIT)
example: MISS
cache_type:
type: string
nullable: true
description: Type of cache used
example: semantic
stream_mode:
type: integer
nullable: true
description: Whether streaming was enabled (1) or not (0)
example: 1
retry_success_count:
type: integer
description: Number of successful retries
example: 0
trace_id:
type: string
description: Distributed tracing trace ID
example: trace-123
span_id:
type: string
description: Distributed tracing span ID
example: span-456
span_name:
type: string
description: Name of the span
example: llm
parent_span_id:
type: string
description: Parent span ID in distributed tracing
example: span-789
mode:
type: string
description: Request mode (e.g., single, loadbalance, fallback)
example: single
virtual_key:
type: string
description: Virtual key identifier
example: vk-123
source:
type: string
description: Source of the request (e.g., rubeus, proxy)
example: rubeus
runtime:
type: string
description: Runtime environment
example: node
runtime_version:
type: string
description: Runtime version
example: 18.0.0
sdk_version:
type: string
description: SDK version
example: 1.0.0
config:
type: string
description: Configuration slug or ID
example: pc-config-123
internal_trace_id:
type: string
description: Internal trace ID for gateway tracking
example: internal-trace-123
last_used_option_index:
type: integer
description: Index of the last used option in the config
example: 0
config_version_id:
type: string
description: Configuration version identifier
example: config-v1
prompt_slug:
type: string
description: Prompt slug
example: my-prompt
workspace_slug:
type: string
nullable: true
description: Workspace slug
example: my-workspace
log_store_file_path_format:
type: string
description: Path format version for log storage
example: v1
metadata.key:
type: array
items:
type: string
nullable: true
description: Array of metadata keys
example:
- key1
- key2
metadata.value:
type: array
items:
type: string
nullable: true
description: Array of metadata values
example:
- value1
- value2
api_key_id:
type: string
description: API key identifier
example: api-key-123
request_parsing_time:
type: integer
format: int64
description: Time taken to parse the request in milliseconds
example: 5
pre_processing_time:
type: integer
format: int64
description: Time taken for pre-processing in milliseconds
example: 10
cache_processing_time:
type: integer
format: int64
description: Time taken for cache processing in milliseconds
example: 2
response_parsing_time:
type: integer
format: int64
description: Time taken to parse the response in milliseconds
example: 8
gateway_processing_time:
type: integer
format: int64
description: Total gateway processing time in milliseconds
example: 50
upstream_response_time:
type: integer
format: int64
description: Upstream provider response time in milliseconds
example: 1200
LogRequest:
type: object
required:
- url
- method
- portkeyHeaders
properties:
url:
type: string
format: uri
description: Sanitized request URL
example: https://api.openai.com/v1/chat/completions
method:
type: string
description: HTTP method
enum:
- GET
- POST
- PUT
- DELETE
- PATCH
- OPTIONS
- HEAD
example: POST
headers:
type: object
additionalProperties:
type: string
description: Request headers (only present when debug logging is enabled)
example:
Content-Type: application/json
Authorization: Bearer hashed_value
body:
type: object
additionalProperties: true
description: Request body (only present when debug logging is enabled)
portkeyHeaders:
type: object
additionalProperties:
type: string
description: Portkey-specific headers
example:
x-portkey-trace-id: trace-123
x-portkey-span-id: span-456
x-portkey-metadata: '{"key":"value"}'
CustomLog:
type: object
properties:
request:
type: object
properties:
url:
type: string
method:
type: string
headers:
type: object
additionalProperties:
type: string
body:
type: object
required:
- url
- body
response:
type: object
properties:
status:
type: integer
headers:
type: object
additionalProperties:
type: string
body:
type: object
response_time:
type: integer
required:
- body
metadata:
type: object
properties:
trace_id:
type: string
span_id:
type: string
span_name:
type: string
additionalProperties:
type: string
required:
- request
- response
LogResponse:
type: object
required:
- status
- responseTime
- lastUsedOptionJsonPath
properties:
status:
type: integer
format: int32
description: HTTP response status code
example: 200
headers:
type: object
additionalProperties:
type: string
description: Response headers (only present when debug logging is enabled or request failed)
example:
Content-Type: application/json
body:
type: object
additionalProperties: true
description: Response body (only present when debug logging is enabled or request failed). May be redacted for certain embedding models.
example:
id: chatcmpl-123
object: chat.completion
created: 1677652288
responseTime:
type: integer
format: int64
description: Response time in milliseconds
example: 1234
lastUsedOptionJsonPath:
type: string
description: JSON path to the last used option in the config
example: $.config.options[0]
RequestResponseObject:
type: object
properties:
body:
type: object
additionalProperties: true
description: The body content
headers:
type: object
additionalProperties:
type: string
description: Headers if present
url:
type: string
format: uri
description: URL if present
method:
type: string
description: HTTP method if present
LogObject:
type: object
required:
- _id
- request
- response
- organisation_id
- created_at
properties:
_id:
type: string
format: uuid
description: Unique identifier for the log entry
nullable: true
example: 550e8400-e29b-41d4-a716-446655440000
request:
$ref: '#/components/schemas/LogRequest'
response:
$ref: '#/components/schemas/LogResponse'
organisation_id:
type: string
description: Organization identifier
nullable: true
example: org-123
created_at:
type: string
format: date-time
description: Timestamp when the log was created
nullable: true
example: '2024-01-15T10:30:00.000Z'
metrics:
$ref: '#/components/schemas/AnalyticsMetrics'
description: Analytics metrics object containing detailed metrics about the request
finalUntransformedRequest:
$ref: '#/components/schemas/RequestResponseObject'
description: The original request before any transformations (only present when debug logging is enabled)
originalResponse:
$ref: '#/components/schemas/RequestResponseObject'
description: The original response from the provider (only present when debug logging is enabled or request failed)
transformedRequest:
$ref: '#/components/schemas/RequestResponseObject'
description: The request after transformations (only present when debug logging is enabled)
securitySchemes:
Portkey-Key:
type: apiKey
in: header
name: x-portkey-api-key
Virtual-Key:
type: apiKey
in: header
name: x-portkey-virtual-key
Provider-Auth:
type: http
scheme: bearer
Provider-Name:
type: apiKey
in: header
name: x-portkey-provider
Config:
type: apiKey
in: header
name: x-portkey-config
Custom-Host:
type: apiKey
in: header
name: x-portkey-custom-host
x-server-groups:
ControlPlaneServers:
- url: https://api.portkey.ai/v1
description: Portkey API Public Endpoint
- url: SELF_HOSTED_CONTROL_PLANE_URL
description: Self-Hosted Control Plane URL
DataPlaneServers:
- url: https://api.portkey.ai/v1
description: Portkey API Public Endpoint
- url: SELF_HOSTED_GATEWAY_URL
description: Self-Hosted Gateway URL
PublicServers:
- url: https://api.portkey.ai
description: Portkey Public API (no auth required)
x-mint:
mcp:
enabled: true
name: Portkey MCP
description: Official MCP Server for Portkey Docs & APIs
x-code-samples:
navigationGroups:
- id: endpoints
title: Endpoints
- id: assistants
title: Assistants
- id: legacy
title: Legacy
groups:
- id: audio
title: Audio
description: 'Learn how to turn audio into text or text into audio.
Related guide: [Speech to text](https://platform.openai.com/docs/guides/speech-to-text)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createSpeech
path: createSpeech
- type: endpoint
key: createTranscription
path: createTranscription
- type: endpoint
key: createTranslation
path: createTranslation
- type: object
key: CreateTranscriptionResponseJson
path: json-object
- type: object
key: CreateTranscriptionResponseVerboseJson
path: verbose-json-object
- id: chat
title: Chat
description: 'Given a list of messages comprising a conversation, the model will return a response.
Related guide: [Chat Completions](https://platform.openai.com/docs/guides/text-generation)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createChatCompletion
path: create
- type: object
key: CreateChatCompletionResponse
path: object
- type: object
key: CreateChatCompletionStreamResponse
path: streaming
- id: realtime
title: Realtime
description: 'WebSocket proxy for provider Realtime APIs (`GET` upgrade). Use `wss://` with the same `/v1` data-plane base as other gateway routes.
Related guide: [OpenAI Realtime API](https://platform.openai.com/docs/guides/realtime)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: connectRealtime
path: connect
- id: embeddings
title: Embeddings
description: 'Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
Related guide: [Embeddings](https://platform.openai.com/docs/guides/embeddings)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createEmbedding
path: create
- type: object
key: Embedding
path: object
- id: rerank
title: Rerank
description: 'Rerank a list of documents based on their relevance to a query. Reranking improves search results by scoring documents based on semantic relevance rather than keyword matching.
Supported providers: Cohere, Voyage, Jina, Pinecone, Bedrock, Azure AI.
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createRerank
path: create
- type: object
key: CreateRerankResponse
path: object
- id: fine-tuning
title: Fine-tuning
description: 'Manage fine-tuning jobs to tailor a model to your specific training data.
Related guide: [Fine-tune models](https://platform.openai.com/docs/guides/fine-tuning)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createFineTuningJob
path: create
- type: endpoint
key: listPaginatedFineTuningJobs
path: list
- type: endpoint
key: listFineTuningEvents
path: list-events
- type: endpoint
key: listFineTuningJobCheckpoints
path: list-checkpoints
- type: endpoint
key: retrieveFineTuningJob
path: retrieve
- type: endpoint
key: cancelFineTuningJob
path: cancel
- type: object
key: FinetuneChatRequestInput
path: chat-input
- type: object
key: FinetuneCompletionRequestInput
path: completions-input
- type: object
key: FineTuningJob
path: object
- type: object
key: FineTuningJobEvent
path: event-object
- type: object
key: FineTuningJobCheckpoint
path: checkpoint-object
- id: batch
title: Batch
description: 'Create large batches of API requests for asynchronous processing. The Batch API returns completions within 24 hours for a 50% discount.
Related guide: [Batch](https://platform.openai.com/docs/guides/batch)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createBatch
path: create
- type: endpoint
key: retrieveBatch
path: retrieve
- type: endpoint
key: cancelBatch
path: cancel
- type: endpoint
key: listBatches
path: list
- type: object
key: Batch
path: object
- type: object
key: BatchRequestInput
path: request-input
- type: object
key: BatchRequestOutput
path: request-output
- id: files
title: Files
description: 'Files are used to upload documents that can be used with features like [Assistants](https://platform.openai.com/docs/api-reference/assistants), [Fine-tuning](https://platform.openai.com/docs/api-reference/fine-tuning), and [Batch API](https://platform.openai.com/docs/guides/batch).
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createFile
path: create
- type: endpoint
key: listFiles
path: list
- type: endpoint
key: retrieveFile
path: retrieve
- type: endpoint
key: deleteFile
path: delete
- type: endpoint
key: downloadFile
path: retrieve-contents
- type: object
key: OpenAIFile
path: object
- id: images
title: Images
description: 'Given a prompt and/or an input image, the model will generate a new image.
Related guide: [Image generation](https://platform.openai.com/docs/guides/images)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createImage
path: create
- type: endpoint
key: createImageEdit
path: createEdit
- type: endpoint
key: createImageVariation
path: createVariation
- type: object
key: Image
path: object
- id: models
title: Models
description: 'List and describe the various models available in the API. You can refer to the [Models](https://platform.openai.com/docs/models) documentation to understand what models are available and the differences between them.
'
navigationGroup: endpoints
sections:
- type: endpoint
key: listModels
path: list
- type: endpoint
key: retrieveModel
path: retrieve
- type: endpoint
key: deleteModel
path: delete
- type: object
key: Model
path: object
- id: moderations
title: Moderations
description: 'Given some input text, outputs if the model classifies it as potentially harmful across several categories.
Related guide: [Moderations](https://platform.openai.com/docs/guides/moderation)
'
navigationGroup: endpoints
sections:
- type: endpoint
key: createModeration
path: create
- type: object
key: CreateModerationResponse
path: object
- id: assistants
title: Assistants
beta: true
description: 'Build assistants that can call models and use tools to perform tasks.
[Get started with the Assistants API](https://platform.openai.com/docs/assistants)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createAssistant
path: createAssistant
- type: endpoint
key: listAssistants
path: listAssistants
- type: endpoint
key: getAssistant
path: getAssistant
- type: endpoint
key: modifyAssistant
path: modifyAssistant
- type: endpoint
key: deleteAssistant
path: deleteAssistant
- type: object
key: AssistantObject
path: object
- id: threads
title: Threads
beta: true
description: 'Create threads that assistants can interact with.
Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createThread
path: createThread
- type: endpoint
key: getThread
path: getThread
- type: endpoint
key: modifyThread
path: modifyThread
- type: endpoint
key: deleteThread
path: deleteThread
- type: object
key: ThreadObject
path: object
- id: messages
title: Messages
beta: true
description: 'Create messages within threads
Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createMessage
path: createMessage
- type: endpoint
key: listMessages
path: listMessages
- type: endpoint
key: getMessage
path: getMessage
- type: endpoint
key: modifyMessage
path: modifyMessage
- type: endpoint
key: deleteMessage
path: deleteMessage
- type: object
key: MessageObject
path: object
- id: runs
title: Runs
beta: true
description: 'Represents an execution run on a thread.
Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createRun
path: createRun
- type: endpoint
key: createThreadAndRun
path: createThreadAndRun
- type: endpoint
key: listRuns
path: listRuns
- type: endpoint
key: getRun
path: getRun
- type: endpoint
key: modifyRun
path: modifyRun
- type: endpoint
key: submitToolOuputsToRun
path: submitToolOutputs
- type: endpoint
key: cancelRun
path: cancelRun
- type: object
key: RunObject
path: object
- id: run-steps
title: Run Steps
beta: true
description: 'Represents the steps (model and tool calls) taken during the run.
Related guide: [Assistants](https://platform.openai.com/docs/assistants/overview)
'
navigationGroup: assistants
sections:
- type: endpoint
key: listRunSteps
path: listRunSteps
- type: endpoint
key: getRunStep
path: getRunStep
- type: object
key: RunStepObject
path: step-object
- id: vector-stores
title: Vector Stores
beta: true
description: 'Vector stores are used to store files for use by the `file_search` tool.
Related guide: [File Search](https://platform.openai.com/docs/assistants/tools/file-search)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createVectorStore
path: create
- type: endpoint
key: listVectorStores
path: list
- type: endpoint
key: getVectorStore
path: retrieve
- type: endpoint
key: modifyVectorStore
path: modify
- type: endpoint
key: deleteVectorStore
path: delete
- type: object
key: VectorStoreObject
path: object
- id: vector-stores-files
title: Vector Store Files
beta: true
description: 'Vector store files represent files inside a vector store.
Related guide: [File Search](https://platform.openai.com/docs/assistants/tools/file-search)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createVectorStoreFile
path: createFile
- type: endpoint
key: listVectorStoreFiles
path: listFiles
- type: endpoint
key: getVectorStoreFile
path: getFile
- type: endpoint
key: deleteVectorStoreFile
path: deleteFile
- type: object
key: VectorStoreFileObject
path: file-object
- id: vector-stores-file-batches
title: Vector Store File Batches
beta: true
description: 'Vector store file batches represent operations to add multiple files to a vector store.
Related guide: [File Search](https://platform.openai.com/docs/assistants/tools/file-search)
'
navigationGroup: assistants
sections:
- type: endpoint
key: createVectorStoreFileBatch
path: createBatch
- type: endpoint
key: getVectorStoreFileBatch
path: getBatch
- type: endpoint
key: cancelVectorStoreFileBatch
path: cancelBatch
- type:
# --- truncated at 32 KB (33 KB total) ---
# Full source: https://raw.githubusercontent.com/api-evangelist/portkey/refs/heads/main/openapi/portkey-logs-api-openapi.yml