Summary

In this chapter, we covered several key concepts and techniques:

• We learned how to utilize the CSVLoader to efficiently load data from CSV files, enhancing the performance of our chatbot by ensuring that selected information is communicated effectively with the retriever.
• We continued to adhere to the Retrieval Augmented Generation (RAG) process, which involves retrieving relevant information from the context to generate responses to questions.
• We used HuggingFaceEndpoint to manage and orchestrate the Mistral LLM.
• To further improve the quality and accuracy of the context, we integrated a ContextualCompressionRetriever alongside the base retriever. This step is crucial for filtering out unnecessary data and extracting only the relevant information from the dataset, thereby enhancing the efficiency and speed of our application.
• Before sending the prompt to the Large Language Model (LLM), we applied these enhancements to ensure that the context is both accurate and contextually rich, thereby improving the overall performance of our response.