This notebook guides you through using Constitutional AI chain in LangChain for the purpose of trying to protect your LLM App from malicious hackers and malicious prompt engineerings.
The implimentation of unified objectives is based on this paper:
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This notebook guides you through the basics of loading multiple PDF file externally into Pinecone as embeddings(vectors).
It also guides you on the basics of querying your custom PDF files data to get answers back (semantic search) from the Pinecone vector database, via the OpenAI LLM API. We walk through 2 approaches, first using the RetrievalQA chain and the second using VectorStoreAgent
Using LLMs to query your own data is a powerful application to become operationally efficient for various tasks requiring looking up large documents.
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As promised, these are the links to the Generative AI Whitepapers from KPMG, Accenture, McKinsey
Explore OpenAI's Function Calling API using LangChain. Simple Weather Bot using LangChain and OpenAI API. This feature is big because it opens up the portal to be able to call vendor tools and custom tools from your LLM app/bots in a more reliable manner.
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This notebook guides you through the basics of loading a custom TXT and a PDF file externally into Pinecone as embeddings(vectors).
It also guides you on the basics of querying your custom TXT/PDF file to get answers back (semantic search) from the Pinecone vector database, via the OpenAI LLM API.
Using LLMs to query your own data is a powerful application to become operationally efficient for various tasks requiring looking up large documents.
Watch the YouTube Tutorial Video
Here is the link to the Scale AI Readiness Report : https://go.scale.com/hubfs/Scale-Zeitgeist-AI-Readiness-Report-2023.pdf Link to public domain tex of The Wonderful Wizard of Oz: https://www.gutenberg.org/ebooks/55