Conclusion

In summary, the provided code outlines a robust process for semantic search using Pinecone and MiniLM-L6. It loads a dataset, generates embeddings, and upserts them into Pinecone indexes. With semantic queries, it efficiently retrieves relevant records. The system showcases adaptability by handling modified queries effectively. This guide underscores the importance of efficient vector storage and powerful language models in semantic search systems.

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