๐ This video introduces the use of embeddings and ChromaDB for the multi doc retriever.
โ๏ธ Having a GPU is recommended for faster processing, but it can also be run on a CPU.
๐ The video demonstrates how to work with multiple PDF files instead of text files.
๐ There are two ways of doing embeddings: using Hugging Face embeddings or using instructor embeddings.
๐ The instructor embeddings are custom embeddings that can be used for specific purposes.
๐ป LangChain is used to locally run the embeddings.
๐ฅ The model and necessary files are downloaded for usage.
๐ป The embeddings are set up for vector storage.
๐ ChromaDB is used to set up the vector store.
๐ The video introduces the use of instructor embeddings in a retriever for LangChain retrieval QA.
๐ The retriever utilizes the instructor embeddings to find contexts that match a given query.
๐ The top documents selected by the embeddings in the retriever provide relevant information for specific queries.
๐ก LangChain Retrieval QA is able to find answers from the same paper and can provide information about ToolFormer.
๐ By asking questions about ToolFormer, we can learn about its functionalities and the tools that can be used with it.
๐ LangChain Retrieval QA is useful for extracting specific information from papers and can even provide insights from related survey papers.
๐ Using embedding system for instructing better without relying on OpenAI for language models.
๐ The system is able to retrieve information and answer specific questions about retrieval augmentation and differences between REALM and RAG models.
๐ More privacy in data processing by not sending all data to the large language model for embeddings.
๐ก Using an actual language model for replying and embedding.
๐ป Deleting and bringing back the ChromaDB database.
๐ง Exploring custom models for various tasks.
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