Data ingestion into Pinecone was optimized by preprocessing markdown files to enhance readability. This involved removing unwanted elements such as images and dividers. A function utilizing regular expressions was created for this task. Additionally, articles were split into fixed-length chunks, allowing individual concepts to be indexed separately. This method simplifies the relevance calculation of data in response to user queries, although it has its limitations in chunking content without considering inherent meaning.
To improve data quality for ingestion into Pinecone, markdown was preprocessed to remove images, dividers, and excess whitespace, enhancing readability and relevance.
Splitting articles into fixed-length chunks can enhance data pertinence, allowing for clearer relevance calculations between user queries and concept-focused entries.
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