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A Hybrid Search and Augmented Generation prompting solution using Python [OpenAI API Embeddings](https://platform.openai.com/docs/guides/embeddings) persisted to a [Pinecone](https://docs.pinecone.io/docs/python-client) vector database index and managed by [LangChain](https://www.langchain.com/). Implements the following:
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-**System Prompting**. How do use the system prompt to modify LLM text completion behavior.
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-**Templates**. How to create templates in order keep your prompt DRY.
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-**LangChain**. How to setup a project using LangChain as an alternative to vendor specific LLM PyPi packages.
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-**PDF Loader**. a command-line pdf loader program that extracts text, vectorizes, and
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loads into a Pinecone dot product vector database that is dimensioned to match OpenAI embeddings.
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-**Retrieval Augmented Generation**. A chatGPT prompt based on a hybrid search retriever that locates relevant documents from the vector database and includes these in OpenAI prompts.
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-**Retrieval Augmented Generation (RAG)**. A chatGPT prompt based on a hybrid search retriever that locates relevant documents from the vector database and includes these in OpenAI prompts.
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Secondarily, I also use this repo for demonstrating how to setup [Pydantic](https://docs.pydantic.dev/latest/) to manage your project settings and how to safely work with sensitive credentials data inside your project.
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