h2oGPTe
h2oGPTe is an AI-powered search assistant for your internal teams to answer questions gleaned from large volumes of documents, websites and workplace content.
Actions198
- Agent Actions
- API Key Actions
- Chat Actions
- Asks Question in a Given Chat Session. If Stream Is Enabled, the Server Sends Stream of Delta Messages. The Stream Is te...
- Changes the Vote Value of a Chat Message
- Creates Chat Session
- Creates Job to Delete Chat Sessions
- Deletes Agent Server Directories
- Deletes Agent Server Files
- Deletes Collection
- Deletes Specific Chat Messages
- Fetches Chat Message and Metadata for Messages in a Chat Session
- Fetches Chat Message Meta Information
- Fetches Metadata for References of a Chat Message
- Finds a Chat Session by ID
- Gets Stats of a Agent Server Directory
- List Chat Sessions
- List Suggested Questions for a Given Chat Session
- Lists Agent Server Files
- Lists Stats of Agent Server Directories
- Lists User's Questions and Answers that Have a Feedback
- Removes a Collection Reference From the Chat Session
- Removes a Prompt Template Reference From the Chat Session
- Update Feedback for a Specific Answer to a Question
- Updates a Collection Reference of a Chat Session
- Updates a Prompt Template Reference of a Chat Session
- Updates the Name of a Chat Session
- Collection Actions
- Archives a Collection Along with Its Associated Data
- Counts a Number of Chat Sessions with the Collection
- Counts a Number of Documents in the Collection
- Create a Collection
- Creates a Job to Delete Collection Thumbnail
- Creates a Job to Delete Collections
- Creates a Job to Update Collection Thumbnail
- Creates Job to Import Collection to the Collection
- Creates Job to Insert Document to the Collection
- Creates Job to Remove Documents From the Collection
- Deletes Collection
- Deletes Collection Thumbnail
- Fetches All Users' Collection Metadata Sorted by Last Update Time by Default
- Fetches Collection Chat Settings
- Fetches Collection Metadata
- Fetches Collection Settings
- Finds Chunks Related to a Message Using Lexical Search
- Finds Chunks Related to a Message Using Semantic Search
- Get a Collection
- Import an Already Stored Document to an Existing Collection
- List a Collection's Documents
- List Chat Sessions for a Given Collection
- List Collections
- List Suggested Questions for a Given Collection
- Removes a Prompt Template Reference From the Collection
- Removes a Size Limit for a Collection
- Removes an Expiry Date From a Collection
- Removes an Inactivity Interval From the Collection
- Removes Sharing of a Collection to a User
- Removes Sharing of a Collection to All Other Users Except the Original Owner
- Removes the Document From the Collection
- Updates Collection Settings
- Resets the Prompt Settings for a Given Collection
- Restores an Archived Collection to an Active Status
- Returns a List of Access Permissions for a Given Collection
- Returns a List of Group Access Permissions for a Given Collection
- Returns Specific Chunks in a Collection
- Sets a Maximum Limit on the Total Size of Documents (Sum) Added to a Collection
- Shares a Collection to a Group
- Shares a Collection to a User
- Updates a Flag Specifying Whether a Collection Is Private or Public
- Updates a Prompt Template Reference of a Collection
- Updates an Expiry Date of a Collection
- Updates an Inactivity Interval of a Collection
- Updates Attributes of an Existing Collection
- Updates Collection Chat Settings
- Updates Collection Metadata
- Updates Collection Thumbnail
- Configuration Actions
- Deletes Global Configuration Items
- Deletes Role Configuration Items
- Deletes User Configuration Items
- Gets Configurations for a Given Role
- Gets Global Configurations
- Gets User Configurations
- Resets User Configuration Item
- Sets Configuration Item for a Given Role
- Sets Global Configuration Item
- Sets User Configuration Item
- Document Actions
- Assigns a Tag to the Document
- Counts a Number of Chat Sessions with the Document
- Counts a Number of Documents
- Creates Job to Delete Documents
- Creates Job to Process Document
- Deletes a Document
- Deletes Document Summaries
- Fetches Document Guardrails Settings
- Fetches Document Internal Metadata
- Fetches Document Metadata
- Fetches Document Page Layout
- Fetches Document Page Ocr Model
- Fetches Document Summary
- Fetches Document User Source File
- Fetches Recent Document Summaries/extractions/transformations
- Finds a Document by ID
- List Chat Sessions for a Given Document
- List Documents
- Lists Collections for Containing a Given Document
- Removes a Tag From a Document
- Returns All Chunks for a Specific Document
- Updates Attributes of an Existing Document
- Updates Document Metadata
- Document Ingestion Actions
- Adds Files From the AWS S3 Storage Into a Collection
- Adds Files From the Azure Blob Storage Into a Collection
- Adds Files From the Google Cloud Storage Into a Collection
- Adds Files From the Local System Into a Collection
- Adds Plain Text to a Collection
- Converts Files Uploaded in "Agent_only" Ingest Mode to PDF and Parses Them
- Crawls and Ingest a URL Into a Collection
- Creates a Job to Add Files From the AWS S3 Storage Into a Collection
- Creates a Job to Add Files From the Azure Blob Storage Into a Collection
- Creates a Job to Add Files From the Google Cloud Storage Into a Collection
- Creates a Job to Add Files From the Local System Into a Collection
- Creates a Job to Add Plain Text to a Collection
- Creates a Job to Crawl and Ingest a URL Into a Collection
- Creates a Job to Ingest Uploaded Document
- Creates a Job to Parse Files Uploaded in "Agent_only" Ingest Mode
- Ingest Uploaded Document
- Uploads File to H2OGPTe Instance
- Job Actions
- Model Actions
- Creates a Topic Model on the Collection
- Creates Job for Creation of a Topic Model
- Encode Texts for Semantic Searching
- Extract Information From One or More Contexts Using an LLM
- Helper to Get Reasonable (Easy to Use) Defaults for Guardrails/PII Settings
- Returns Performance Statistics Grouped by Models
- Returns Usage Statistics for All Models
- Returns Usage Statistics Grouped by Models
- Returns Usage Statistics Grouped by Models and Users
- Returns Usage Statistics Grouped by Users
- Runs a Self-Test for a Given Model
- Send a Message and Get a Response From an LLM
- Summarize One or More Contexts Using an LLM
- Permission Actions
- Deletes Roles for Given Unique Identifiers
- Assigns Permission to a Given Role
- Assigns Roles to a Given Group
- Assigns Roles to a Given User
- Associates a User with a Document They Have Permission On
- Checks if Collection Permission Is Granted for a Given User
- Checks if Permission Is Granted for a Given User
- Creates a Role
- Creates a User Group
- Deletes Groups for Given Group Names
- Deletes Groups for Given Unique Identifiers
- Deletes Roles for Given Role Names
- Finds Role for a Given Unique Identifier
- Finds Roles Associated with a Given Group
- Finds Roles Associated with a Given User
- Finds User for a Given Unique Identifier
- Lists Permissions of a Given Group
- Lists Permissions of a Given Role
- Lists Permissions of a Given User
- Removes Permission From a Given Role
- Removes Roles From a Given Group
- Removes Roles From a Given User
- Resets Group Roles
- Resets User Roles
- Returns a List of All Registered Users for the System
- Returns System Permissions
- Sets a New Set of Permissions for a Given Role
- Sets Priority for a Given Role
- Prompt Template Actions
- Creates a New Prompt Template
- Deletes a Prompt Template
- Finds a Prompt Template by ID
- List Prompt Templates
- Lists Prompt Templates, Including Hidden Default Templates
- Removes Access to a Prompt Template for a Group
- Removes Sharing of a Prompt Template to a User
- Removes Sharing of a Prompt Template to All Other Users Except the Original Owner
- Resets and Shares a Prompt Template to a New List of Groups
- Resets and Shares a Prompt Template to a New List of Users
- Returns a List of Access Permissions for a Given Prompt Template
- Returns a List of Group Access Permissions for a Given Prompt Template
- Shares a Prompt Template to a User
- Shares a Prompt Template with a Group
- Updates a Flag Specifying Whether a Default Prompt Template Is Visible or Hidden to Users
- Updates Attributes of a Given Prompt Template
- Tag Actions
Overview
This node operation creates a job to add files from an Azure Blob Storage container into a specified document collection. It is designed for scenarios where you want to ingest documents stored in Azure Blob Storage into a collection for further processing, searching, or analysis within the system.
Typical use cases include:
- Automating the ingestion of large volumes of documents stored in Azure Blob Storage into a centralized collection.
- Preparing documents for AI-powered search, summarization, or question-answering workflows.
- Managing document collections by importing external data sources seamlessly.
For example, a user might have a set of PDF reports stored in an Azure Blob Storage container and wants to ingest them into a collection to enable semantic search and generate summaries automatically.
Properties
| Name | Meaning |
|---|---|
| Collection ID | String ID of the collection to add the ingested documents into (required). |
| Container | Name of the Azure Blob Storage container where the files are located (required). |
| Paths | Path or list of paths to files or directories within the Azure Blob Storage container (required). |
| Account Name | Name of the Azure storage account (required). |
| Additional Options | A collection of optional parameters to customize the ingestion job: |
| - Audio Input Language | Language of audio files; default is "auto" for automatic detection. |
| - Chunk By Page | Boolean indicating whether each page should be treated as a separate chunk. If true, keep_tables_as_one_chunk is ignored. |
| - Credentials | JSON object containing Azure credentials; if the container is private, either an account key or SAS token must be provided here. |
| - Gen Doc Questions | Boolean to auto-generate sample questions for each document using a large language model (LLM). |
| - Gen Doc Summaries | Boolean to auto-generate document summaries using LLM. |
| - Handwriting Check | Boolean to check pages for handwriting; specialized models will be used if handwriting is detected. |
| - Ingest Mode | Option to select the ingest mode: "standard" (files ingested for retrieval-augmented generation) or "agent_only" (bypasses standard ingestion). |
| - Keep Tables As One Chunk | Boolean indicating whether tables identified by the parser should be kept as a single chunk. |
| - Metadata | JSON object with metadata to associate with the ingested documents. |
| - Ocr Model | Method to extract text from images using AI-enabled OCR models; default is "auto". |
| - Tesseract Lang | Language code to use when OCR model is set to "tesseract". |
| - Timeout | Timeout in seconds for the ingestion job; default is 0 (no timeout). |
Output
The node outputs the response from the API call that creates the ingestion job. The output includes details about the created job such as its unique identifier, status, and any relevant metadata returned by the server.
The main output field is:
json: Contains the full response data from the job creation request, including job ID and status.
There is no binary data output for this operation.
Dependencies
- Requires access to an API endpoint that manages document ingestion jobs.
- Requires an API authentication token or API key credential configured in n8n to authorize requests.
- For private Azure Blob Storage containers, valid Azure credentials (account key or SAS token) must be provided in the
credentialsproperty. - Network connectivity to Azure Blob Storage and the ingestion API service is necessary.
Troubleshooting
- Authentication errors: Ensure that the API key credential is correctly configured and has permissions to create ingestion jobs.
- Azure credentials issues: If the container is private, verify that the provided Azure credentials (
account_keyorsas_token) are valid and have access to the specified container. - Invalid paths or container names: Double-check the container name and file paths; incorrect values will cause the ingestion job to fail.
- Timeouts: If the ingestion process takes too long, consider increasing the
timeoutvalue or checking network stability. - Unsupported file formats: Ensure that the files in the specified paths are supported by the ingestion system.
Links and References
- Azure Blob Storage Documentation
- Optical Character Recognition (OCR) Models
- Retrieval-Augmented Generation (RAG)
This summary covers the logic and configuration of the "Creates a Job to Add Files From the Azure Blob Storage Into a Collection" operation under the Document Ingestion resource.