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docs: fix up summary README
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README.md

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A Python [LangChain](https://www.langchain.com/) - [Pinecone](https://docs.pinecone.io/docs/python-client) proof of concept Retrieval Augmented Generation (RAG) models using sales support PDF documents.
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Implements the following:
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- 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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- a hybrid search retriever that locates relevant documents from the vector database and includes these in OpenAI prompts.
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See:
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- [LangChain RAG](https://python.langchain.com/docs/use_cases/question_answering/)

models/hybrid_search_retreiver.py

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# -*- coding: utf-8 -*-
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# pylint: disable=too-few-public-methods
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"""
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Sales Support Model (SSM) for the LangChain project.
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Hybrid Search Retriever. A class that combines the following:
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- OpenAI prompting and ChatModel
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- PromptingWrapper
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- Vector embedding with Pinecone
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- Hybrid Retriever to combine vector embeddings with text search
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Provides a pdf loader program that extracts text, vectorizes, and
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loads into a Pinecone dot product vector database that is dimensioned
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to match OpenAI embeddings.
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See: https://python.langchain.com/docs/modules/model_io/llms/llm_caching
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https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf
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https://python.langchain.com/docs/integrations/retrievers/pinecone_hybrid_search

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