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 provides an interface to encode text chunks using a specified embedding model for semantic searching purposes. It is designed to convert textual data into vector embeddings that can be used in semantic search systems, enabling more meaningful and context-aware search results beyond simple keyword matching.
Typical use cases include:
- Preparing document chunks or text snippets for semantic similarity searches.
- Enhancing search relevance in knowledge bases, FAQs, or document repositories.
- Supporting AI-powered search assistants by encoding user queries and documents into comparable vector spaces.
For example, you might input multiple paragraphs of text (chunks) and receive their corresponding embeddings, which can then be stored or compared against query embeddings to find semantically similar content.
Properties
| Name | Meaning |
|---|---|
| Model ID | ID of the embedding model that will be used to encode the text chunks. |
| Chunks | The text chunks (strings) to be encoded into embeddings for semantic retrieval purposes. |
Output
The output contains a JSON field with the response from the embedding model API. This typically includes the vector embeddings corresponding to each input chunk. These embeddings are numerical arrays representing the semantic meaning of the input texts.
If the node supports binary data output, it would represent encoded vectors or related binary payloads, but based on the provided code and properties, the output is JSON-based embeddings.
Dependencies
- Requires access to an external embedding model API endpoint.
- Needs an API key credential configured in n8n to authenticate requests to the embedding service.
- The base URL for the API is constructed from credentials and used to send POST requests to the
/embedding_models/{model_id}/encode_chunks_for_retrievalendpoint.
Troubleshooting
- Invalid Model ID: If the provided model ID does not exist or is incorrect, the API may return an error. Verify the model ID is correct and available.
- Empty or Malformed Chunks: Ensure the "Chunks" property contains valid text data. Empty strings or improperly formatted input may cause errors or empty embeddings.
- Authentication Errors: Missing or invalid API key credentials will prevent successful API calls. Confirm that the API key is correctly set up in n8n.
- Timeouts or Network Issues: Large inputs or network problems may cause request timeouts. Adjust timeout settings or check network connectivity if issues arise.
Links and References
- Refer to your embedding model provider's API documentation for details on supported models and encoding formats.
- For semantic search concepts and embedding usage, see resources on vector search and natural language processing embeddings.