is this correct "you can customize VAPI's RAG (Retrieval-Augmented Generation) implementation to suit your specific needs and optimize for lower latency. Integrating your own vector database for semantic and keyword search is a great way to maintain control over the search performance and relevance. For document processing, using Unstructured can help you extract and process information efficiently. Lastly, implementing reranking with Cohere can enhance the quality of the results by ensuring the most relevant information is presented first. To achieve this, you would typically need to: Set up your own vector database that is optimized for fast retrieval. This could be something like Elasticsearch, FAISS, or any other efficient vector search solution. Integrate Unstructured into your data processing pipeline to handle the extraction and processing of information from documents. Use Cohere's reranking capabilities to reorder the results based on relevance, which can be done by scoring the results and sorting them accordingly. By handling these components on your own infrastructure or choosing cloud services with low-latency characteristics, you can minimize the latency that might be introduced by external platforms like make.com. For detailed guidance on how to implement these customizations with VAPI, you might want to check out the Advanced Concepts section in the documentation or reach out to the support team for more specific instructions. If you need further assistance or have any more questions, please let me know!"

@kishan Yes, that's a great summary of how you can customize VAPI's RAG implementation to enhance performance and relevance for your specific needs. Your approach to setting up a vector database, integrating Unstructured for document processing, and using Cohere for reranking is spot on. These steps can indeed help in maintaining control over search performance and ensuring the most relevant information is prioritized. For more detailed guidance on implementing these customizations with VAPI, the Advanced Concepts section in the documentation would be the right place to start. If you have any further questions or require additional assistance, feel free to reach out to the support team or ask me, and I'll be happy to help!

how to create a custom tool call to take details

@kishan 1) VAPI does support Retrieval-Augmented Generation (RAG). For more detailed information and documentation on RAG within VAPI, you can visit the Advanced Concepts section of the VAPI documentation. Unfortunately, I cannot provide a direct link, but you can easily find this section by navigating to the VAPI documentation homepage and looking for Advanced Concepts in the navigation menu. 2) To develop tool calls to get information from a vector database, you would typically need to: - Define the API endpoints for your vector database that will handle search queries. - Create a custom tool that makes HTTP requests to these endpoints, passing the necessary parameters for the search. - Parse the response from the vector database to extract the relevant information. - Integrate this tool into your VAPI workflow where you need to retrieve information from the vector database. If you need specific code examples or further assistance, please refer to the API Reference section in the VAPI documentation or reach out to the support team for more detailed guidance. If there's anything else I can help you with, let me know!

@Vapi @Shubham Bajaj Help please to create a tool call from vapi .We are not aware on how we can set up this.

@kishan It's basically using custom tool for external KB and your make[dot]com has nothing do with the latency here. It all depends upon your knowledge base setup.