Day 2 - RAG - What is Vector DB ?
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Learn about Vector DB and its role in RAG, enabling efficient storage and querying of vector embeddings for private documents
Action Steps
- Break down private documents into chunks based on specific criteria
- Feed these chunks into an embedding model to generate vector embeddings
- Store the generated vector embeddings in a Vector DB for efficient querying
- Use the Vector DB to retrieve relevant documents based on similarity scores
- Integrate the Vector DB with an LLM to enable RAG functionality
Who Needs to Know This
Data scientists and AI engineers can benefit from understanding Vector DB to improve their RAG pipelines and integrate private documents with LLMs
Key Insight
💡 Vector DB enables efficient storage and querying of vector embeddings, making it a crucial component in RAG pipelines
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Discover Vector DB and its role in RAG! Store and query vector embeddings for private documents to supercharge your LLM integrations
Key Takeaways
Learn about Vector DB and its role in RAG, enabling efficient storage and querying of vector embeddings for private documents
Full Article
To recall, Integrating our private documents with LLM is called RAG. Lets assume that, we have some pdfs containing our data. That data in the pdf will be broken down into chunks based on some criteria. That chunk will be fed as input to the model. More specifically embedding model. This model will generate a point. How the point is generated ? Lets take a simple example: Today is Wednesday Tomorrow is Thursday I am travelling today Wedne
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