How to solve the challenge of combining real-time retrieval of external data with AI-generated results?

2025-08-22

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Retrieval Augmented Generation (RAG) System Application

TaskingAI's built-in RAG system enables data integration through the following processes:

Creating a Data Collection: Specify embedding_model_id to define the vectorization method (e.g. text-embedding-3-small)
Data preprocessing: control the chunking strategy via the text_splitter parameter (suggested token mode, chunk_size=200)
on-line search: call retrieval.query() interface, the system will automatically match the most relevant text fragment
Generation Enhancement: Inject the search results into the prompt template as context, for example:
'Answer based on the following information: {context} n Question: {query}'

Best practice: update the dataset on a regular basis with the Google Search plugin for dynamic data updates.