Reference
- Sessions
- Apps
- Agents
- Tasks
- Tools
- Models
- Datasources
- All Objects
Get App
Retrieve a JSON object of the retrieved app. Will also return a list of all tasks, tools, agents, models and datasources as a front end helper
Path Parameters
The resourceSlug is a url parameter of the teamId associated with the user. Anywhere the resourceSlug is used can be interpreted as a teamId
The appId of the app to be retrieved
Response
Unique Mongodb identifier for the object
True or false check to determine if the agent is allowed to delegate tasks to other agents in the context of an app
A detailed description of what the LLM will be doing
The goal of the agent is fed into the LLM, this allows the LLM to know it's role in the RAG pipeline
The linked ObjectId of the model being used by this agent (this links to a Model object)
Name of the agent
Fed into the LLM to help it provide a more detailed and correct response
True or false check to determine if custom verbosity is used in agent, higher verbosity requires agent to include more of the retrieved documents at the expense of longer answers, lower verbosity can result in shorter answers but can also ommit crucial details
A secondary model used to execute function calls (this links to a Model object), set to null if unused automatically by the API
IconAttachment object used to hold the attached icon used for the agent (this links to an IconAttachment object);
Mongodb Object id, unique identifier, length of 24 characters fitting the following regex; [a-f0-9]{24}
Organisation the agent is linked to (generally the org of the user that created the agent)
Team the agent is linked to (generally the team of the user that created the agent)
Array of the tools the agent can access to improve performance and abstract tool functionality from agent usage
An agent object used for tasks or apps
Unique Mongodb identifier for the object
True or false check to determine if the agent is allowed to delegate tasks to other agents in the context of an app
A detailed description of what the LLM will be doing
The goal of the agent is fed into the LLM, this allows the LLM to know it's role in the RAG pipeline
The linked ObjectId of the model being used by this agent (this links to a Model object)
Name of the agent
Fed into the LLM to help it provide a more detailed and correct response
True or false check to determine if custom verbosity is used in agent, higher verbosity requires agent to include more of the retrieved documents at the expense of longer answers, lower verbosity can result in shorter answers but can also ommit crucial details
A secondary model used to execute function calls (this links to a Model object), set to null if unused automatically by the API
IconAttachment object used to hold the attached icon used for the agent (this links to an IconAttachment object);
Mongodb Object id, unique identifier, length of 24 characters fitting the following regex; [a-f0-9]{24}
Organisation the agent is linked to (generally the org of the user that created the agent)
Team the agent is linked to (generally the team of the user that created the agent)
Array of the tools the agent can access to improve performance and abstract tool functionality from agent usage
cross site token
The identifier of the connection associated with the datasource.
The date and time when the datasource was created.
The identifier of the data destination.
The name of the datasource.
The original name of the datasource.
The identifier of the data source.
The type of source for the datasource.
The identifier of the workspace associated with the datasource.
Unique identifier for the datasource.
Configuration settings for chunking unstructured data, including partitioning and chunking strategies, character limits, and similarity thresholds.
The maximum number of characters allowed per chunk.
The number of characters after which a new chunk is created.
The number of characters to overlap between chunks.
Indicates whether to apply overlap to all chunks or only between adjacent chunks.
The partitioning strategy used for unstructured data.
auto, fast, hi_res, ocr_only Threshold for similarity when chunking by similarity, with a value between 0.0 and 1.0.
0 < x < 1The chunking strategy used for unstructured data.
basic, by_title, by_page, by_similarity Configuration settings for the datasource connection.
Configuration settings for the datasource connection. Structure is dependent on the datasource type.
The identifier of the data destination.
The name of the datasource connection.
Specifies the behavior for handling non-breaking schema updates.
Optional prefix to be added to the destination's namespace. Can be null.
The identifier of the data source.
The status of the datasource connection. This should match the enum values defined by the Airbyte API and should allow creation in a paused state.
Specifies where the data should be stored geographically.
Defines how the namespace should be determined for the data.
The format of the namespace, can be null if not applicable.
Scheduling information for the datasource connection.
Optional description of the datasource.
Schema discovered during the data source connection. The structure depends on the source type.
The field used for embedding within the datasource.
The name of the file associated with the datasource, if applicable.
Indicates whether the datasource is hidden from standard views.
The date and time when the datasource was last synced. Null indicates it has never been synced.
Identifier of the embedding model used, if applicable.
Identifier of the organization to which the datasource belongs.
The record count details for the datasource, including total, successful, and failed records.
The current status of the datasource.
draft, processing, embedding, ready Configuration settings for processing streams of data, breaking them into smaller chunks for more manageable processing.
Configuration settings for a specific stream, used to break down large volumes of data into smaller, manageable chunks for processing.
List of child stream identifiers that are checked for inclusion in the sync.
List of fields that act as the cursor for incremental syncs.
A map of field names to their descriptions.
Provides details about a specific field, including its description and type.
List of fields that make up the primary key for the stream.
The synchronization mode used for the stream.
Identifier of the team to which the datasource belongs.
A temporary field to limit CRON frequency based on the plan. This will be replaced with a more robust solution in the future.
The field used to apply time weighting within the datasource.
Unique identifier for the model.
Configuration settings for the model.
API key for accessing the model.
The base URL for the model's API.
API key for accessing Cohere services.
API key for accessing Groq services.
The model configuration setting.
The length of the embeddings generated by the model.
The specific AI model used.
The type of the model.
The name of the model.
Identifier of the organization to which the model belongs.
Identifier of the team to which the model belongs.
The general type of the model (e.g., embedding, language model).
A detailed description of the task.
The name of the task.
Unique identifier for the task.
Identifier of the agent associated with the task.
Indicates if the task is executed asynchronously.
Contextual information related to the task.
Indicates if only the final output should be displayed.
The expected output of the task.
Array of form field configurations associated with the task.
The label displayed for the form field.
The name attribute of the form field.
The position of the form field within the form layout.
The data type of the form field.
string, number, radio, checkbox, select, multiselect, date An optional description for the form field.
Options available for fields like radio, select, or multiselect.
Indicates if the form field is required.
A tooltip providing additional information about the form field.
Indicates if the task is hidden from standard views.
Icon associated with the task, either an attachment or an object containing the icon details.
Filename of the attachment at the point of upload
Indicates if the output of the task is structured.
Identifier of the organization to which the task belongs.
The file output of the task.
The JSON output of the task.
The Pydantic output of the task.
Indicates if the task requires human input.
Identifier of the team to which the task belongs.
List of tool identifiers associated with the task.
Data related to the tool, including runtime, environment variables, and more.
The name of the data.
API key associated with the tool.
Indicates if the tool is a built-in feature.
Code associated with the tool.
A description of the data.
Environment variables required by the tool.
Key used to match OpenAPI specifications.
Requirements needed by the tool.
The runtime environment for the tool.
A detailed description of the tool.
The name of the tool.
The type of tool.
function, rag Mongodb Object id, unique identifier, length of 24 characters fitting the following regex; [a-f0-9]{24}
The schema associated with the tool.
Identifier of the datasource associated with the tool.
Identifier of the function associated with the tool.
Logs related to the function's execution.
Indicates whether the tool is hidden.
Icon associated with the tool.
Filename of the attachment at the point of upload
Identifier of the organization to which the tool belongs.
Configuration settings for the retriever.
Array of metadata field information objects.
A description of the metadata field.
The name of the metadata field.
The data type of the metadata field.
string, integer, float Number of results to retrieve.
The type of retriever used by the tool.
raw, self_query, time_weighted, multi_query Identifier of the tool's revision.
The current state of the tool.
pending, ready, error Identifier of the team to which the tool belongs.