learn how to query pdf using langchain open ai in 5 min

certainly! in this tutorial, we'll learn how to query pdf documents using langchain and openai's api. langchain is a powerful library that simplifies working with language models and allows you to access and process data from various sources, including pdfs.

prerequisites
before we start, ensure you have the following:

1. **python** installed (preferably version 3.7 or higher).
2. **pip** to install packages.

step 1: install required packages
you need to install the necessary packages. open your terminal and run:

- `langchain`: the main library we’ll be using.
- `openai`: the openai api client.
- `pypdf2`: a library for reading pdf files.

step 2: set up your environment

step 3: load the pdf document

step 4: create embeddings and vector store
we will use openai embeddings and faiss for efficient document retrieval.

step 5: set up the retrievalqa chain
now, we will set up the retrievalqa chain, which allows us to query the pdf:

step 6: query the pdf
finally, we can query the pdf with a question:

complete code example
here’s the complete code for your reference:

conclusion
you have now successfully set up a simple system to query pdf documents using langchain and openai. you can modify the `question` variable to ask different queries based on the content of your pdf.

additional notes
- make sure your pdf is text-based; scanned images may require ocr processing.
- experiment with different questions to get the most relevant information from your documents.

happy coding!

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