Lecture on phython a gate way to excel your online work

Lecture on phython a gate way to excel your online work

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  Hope to Skills Lecture#32 Irfan Malik, Dr. Sheraz Naseer
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  Agenda ● LangChain ● Chains ●Retrieval Chains ● QA Chains ● QA with Source Document ● Qdrant 2
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  Lang Chain Introduction 3 Langchainis an open-source framework for building large language model (LLM) applications. It provides a set of tools and libraries that make it easy to develop and deploy LLM applications, such as chatbots, question answering systems, and text summarization tools.
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  What are Chains? 4 Chainsare the core building block of Langchain applications. A chain is a sequence of steps that are performed on a piece of text. These steps can include: ● Retrieval ● Generation ● Post-processing
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  What are RetrievalChains? 5 Retrieval chains are chains that specifically focus on retrieving relevant documents from a corpus/data. This is useful for tasks such as question answering and document summarization.
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  What are QAChains? 6 QA chains are specifically designed for question answering. They typically include a retrieval step to retrieve relevant documents from a corpus/data, as well as a generation step to generate an answer to the question using the retrieved documents.
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  QA with SourceDocument? 7 QA with Source Document chain is a specific type of QA chain that includes a step to return the source documents that were used to generate the answer. This is useful for tasks where the user needs to be able to verify the answer or learn more about the source of the information.
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  Qdrant Introduction 8 Qdrant isan open-source vector database that allows you to store, index, and search high-dimensional vectors at scale. It is a highly performant and scalable solution for vector search applications, such ● Question answering ● Recommendation systems ● Natural language processing (NLP)
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  Benefits of usingQdrant 9 There are several benefits to using Qdrant for vector search applications: ● Performance: It is highly performant, even for large datasets. ● Scalability: It is scalable to handle large amounts of traffic and data. ● Ease of use: It is easy to use and provides a simple API for storing, indexing, and searching vectors. ● Free to use: It is free to use and modify.
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  Ready for Coding 10