Supercharge your Gaianet node by generating a vector knowledge base from any API. Demo slides: https://hackmd.io/@santteegt/ByoykY4nC#/ Link to Docs below
Connect outputs from the custom parser module to the next stage in the pipeline. The parsed and structured data needs to be properly linked to the embedding system to ensure a smooth transition from raw data to vector embeddings. This connection is crucial for maintaining data integrity and ensuring that all relevant information is accurately processed in subsequent steps.
Transfer normalized data chunks to the embedding system for vectorization. This step involves feeding the processed data into the embedding model to generate vector representations, which are essential for creating a searchable knowledge base. Ensuring the accurate and efficient transfer of data chunks is crucial for the performance of the embedding system.
[X] Implement transform function to extract text from the chunking stage
Connect outputs from the custom parser module to the next stage in the pipeline. The parsed and structured data needs to be properly linked to the embedding system to ensure a smooth transition from raw data to vector embeddings. This connection is crucial for maintaining data integrity and ensuring that all relevant information is accurately processed in subsequent steps.
Transfer normalized data chunks to the embedding system for vectorization. This step involves feeding the processed data into the embedding model to generate vector representations, which are essential for creating a searchable knowledge base. Ensuring the accurate and efficient transfer of data chunks is crucial for the performance of the embedding system.