📄️ LLM Text Complete
The LLM Text Complete task is used to generate a natural language response based on the provided context.
📄️ LLM Generate Embeddings
The LLM Generate Embeddings task is used to convert input text into a sequence of vectors, also known as embeddings. These embeddings are processed versions of the input text and can be stored in a vector database for later retrieval. This task utilizes a previously integrated language model (LLM) to generate the embeddings.
📄️ LLM Store Embeddings
The LLM Store Embeddings task is used to store the generated embeddings produced by the LLM Generate Embeddings task in a vector database. The stored embeddings serve as a repository of information that can be later accessed by the LLM Get Embeddings task for efficient and quick retrieval of related data.
📄️ LLM Get Embeddings
The LLM Get Embeddings task retrieves numerical vector representations of words, phrases, sentences, or documents that have been previously generated or learned by the model. Unlike the LLM Generate Embeddings task, which creates vector representations from input data, this task focuses on efficiently accessing pre-existing embeddings. This is useful for utilizing embeddings that have already been computed and stored without regenerating them.
📄️ LLM Index Document
The LLM Index Document task is used to index a document into a vector database for efficient search, retrieval, and processing at a later stage.
📄️ LLM Get Document
The LLM Get Document task is used to retrieve the content of a specified document for further data processing using AI tasks. It supports a wide range of media types and allows integration with various file formats to facilitate comprehensive data handling and processing.
📄️ LLM Index Text
The LLM Index Text task is designed to index the provided text into a vector space for efficient search, retrieval, and processing at a later stage.
📄️ LLM Search Index
The LLM Search Index task is used to search a vector database or repository of vector embeddings of already processed and indexed documents to get the closest match. This task is typically used in scenarios where you need to retrieve or manipulate data stored in a database using a natural language query.
📄️ LLM Chat Complete
The LLM Chat Complete task is used to complete a chat query based on additional instructions. It can be used to govern the model's behavior to minimize deviation from the intended objective.