How can I use LangChain and Google Gemini Pro to extract and store embeddings of PDF content in Pinecone

Question

Can i know How can I use LangChain and Google Gemini Pro to extract and store embeddings of PDF content in Pinecone?

Answer 1

You can use LangChain with Google Gemini Pro for text embedding and store those embeddings in Pinecone for efficient semantic search over PDF content.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Uses LangChain’s PyPDFLoader to parse PDF content efficiently.
• Google Gemini Pro generates high-quality text embeddings.
• Pinecone provides fast, scalable vector storage for embeddings.
• Ensures end-to-end pipeline from PDF extraction to vector storage.

Hence, this solution enables semantic search on PDF content by combining LangChain, Gemini Pro embeddings, and Pinecone, making information retrieval efficient and scalable.