Embeddings
Embeddings
Converts input data (like text) into numerical vectors that represent the semantic meaning of the data, enabling downstream AI tasks such as similarity searches or natural language understanding.
Parameters:
Models: Specifies the model ID to be used for text generation.
Input: Defines the input data that will be processed to generate embeddings. This can include text, images, or other data types that need to be converted into embeddings for further use in AI workflows.
Fallback Outputs: Specifies the default output to be returned if the model is unable to generate results based on the input.
See this node in example Agent: Customer service agent
Vector Search
Input: Defines the input data that will be processed to generate embeddings. This can include text, images, or other data types that need to be converted into embeddings for further use in AI workflows.
Limit: This value controls the maximum number of search results returned.
Min Relevance Score: Specifies the minimum similarity score required for a result to be considered relevant and returned. For instance, if set to 0.5, any result scoring below 0.5 is filtered out as less relevant.
Most Vector Search implementations use a similarity measure such as cosine similarity. In this method, the angle between two embedding vectors (the query and a candidate result) is measured. A score of 1 means the vectors align perfectly (highly similar), while a score of 0 means they are orthogonal (unrelated):
0.3 Score Example: Suggests only a loose relationship to your query. The content may share a broad theme but lacks direct or strong alignment.
0.6 Score Example: Indicates a more meaningful match, showing noticeable alignment with the query’s intent or context.
Vector Memory Store: The storage location where embeddings are saved and retrieved during the search. This is the dataset or vector database that the search queries for relevant information. Read more in Vector Memory Management.
Fallback Outputs: Specifies the default output to be returned if the model is unable to generate results based on the input.
Save to Vector Memory
Used to store embeddings (numerical representations of data) into a specified memory set for future searches and retrieval. Here’s an explanation of its parameters:
Input to Embed: The data you want to convert into embeddings (such as text or images) and store in the vector memory. This input is transformed into a numerical vector for later use in searches.
Input Full Text: A full, unprocessed version of the input data (e.g., the original text). This field ensures that while the embeddings are stored, the raw input is also saved for reference during retrieval or debugging.
Name of the Memory Set: Specifies the target memory set where embeddings will be stored. Read more in Vector Memory Management.
Fallback Outputs: Defines what the system should return if it fails to process the input or store the data successfully, ensuring the workflow remains operational even if errors occur.
This node is crucial for storing embeddings, enabling effective retrieval during later vector searches.
Save Session
Used to store specific pieces of text with a unique identifier, making it easy to retrieve later. Here’s a breakdown of the parameters:
Text to Save: This is the actual content or data you want to save during the session. It could be any information you plan to use or reference later in the workflow.
Unique Name for this Item: This serves as a unique identifier or label for the saved text. Each item is stored with its own unique name, ensuring it can be easily retrieved when needed.
These fields allow you to save session data in a structured and retrievable format, supporting the continuity of workflows by preserving key information.
Read Session
The Read Session node retrieves previously saved session data using a unique identifier. Here’s what the parameters represent:
Item Name: This specifies the unique name of the item you want to retrieve from the session. It should match the name that was used to save the item in the Save Session node.
Fallback Outputs: Defines what the node should return if the requested item cannot be found or if an error occurs, ensuring the workflow can continue without interruptions.
This node allows you to pull specific stored data from a session for further processing or use in your workflow.
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